Journal articles: 'Forest CPRs' – Grafiati (2024)

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The impacts of forest management on habitat characteristics, species richness, and population dynamics of small mammals remain ambiguous. We studied the response of small-mammal populations, including snowshoe hares, to clear-cutting with protection of advanced regeneration and soils (CPRS) and precommercial thinning (PCT). We compared stands recently treated by CPRS or PCT with established stands dominated by deciduous or coniferous trees in two large blocks of mixed forests. We measured habitat components and abundance of small mammals in the four stand types. Trees (DBH ≥ 9 cm) became very rare in CPRS stands and remained at low density in PCT stands, which stimulated the growth of herbs and seedlings, resulting in increased lateral cover. Tree harvest also generated coarse woody debris in CPRS stands, which did not persist in PCT stands. Small mammals responded to these disturbances in a species-specific manner but, overall, relative abundance and species richness of small mammals were lower in PCT stands than in CPRS and closed stands. Our results suggest that forest managers should exclude some stands from PCT following CPRS or natural perturbations, to maintain ecosystem diversity at the landscape level.

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The ownership of common property resources was always in conflict within community and community vs government. Definition about common property resources are advanced on the mainly upon their priority, visibility, accessibility and understanding. Further, its broad classification includes harvesting, extraction and natural production. The nature of dependency among Common Property Resources(CPRs) and Private Property Resources(PPRs) follows higher exploitation with initial compared to the later. The present study is carried out in Tribal dominated district of Odisha, India i.e. Mayurbhanj through stratified and multi-stage random sampling to answer how important are CPRs for tribal across Mayurbhanj and their dependency upon types of resources with utilisation and limitation. The assessment of LDI (Livelihood Dependency Index) reveals higher dependency of sample respondents of Mayurbhanj upon CPRs for livelihood continuation even after having an average membership in Self Help Group programme from last seven years. There exist massive need of making diversified occupation adjustment especially for population residing nearby forest like engagement in transport of forest items, maintenance, local auction, daily wage based leaf collection and storage for government auction, maintenance and engaging women members in manufacturing of CPR based items.

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During the present study it was observed that the Forest Development Agencies and Village Forest Committees play an important role in National Afforestration Programs by protection and conservation of natural resources through their active involvement. The FDA’s also helps in securing people’s participation in planning and regeneration efforts to ensure sustainability and equitable distribution of forest products from the regenerated lands, and to promote the partnership concept in the management and administration of forest and common property resources (CPRs) in the Forest Division. 1500 ha (Natural regeneration 270 ha, Artificial regeneration 960 ha and Pasture regeneration 270 ha) of afforestration works was done by the Village Forest Committees (VFC’s) under Forest Development Agency (FDA) from the year 2003 to 2007 in the forest division.

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Abstract Resource management characterized by Common Pool Resources (CPRs) requires collective action for its sustainable management. CPRs with “rivalry” and “non-excludable” features face overexploitation problems because unlike the nature of pure public goods, the use of CPRs by one user will reduce the chances of other users taking advantage of it. This study aims to analyze the most appropriate local institutions and tenure arrangements for sustainable mangrove management in Eastern coast area of South Sulawesi, Indonesia. Data and information were collected through in-depth interviews involving key informants selected by using snow-ball method and continued by Focus Group Discussion. Design principles for sustainable management of common pool resources of Ostrom has been used in this study as an analytical framework which illustrate the structure of rules established and imposed by the local CPR’s institutions. The findings show that collective action in sustainable mangrove management can be achieved through accepted rules and agreements that are participatory formulated. The agreed norms and rules have enacted as a benchmark for collective action in maintaining mangrove and to enforce sanctions for violators. The success of the community in sustainable mangrove resources management is mainly supported by the existence of several prerequisites that facilitate collective action. It is necessary to strengthen regulations both at the local and higher level through socialization to the community along with the development of incentive and disincentives system.

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Sustainable forest management involves, among other things, maintaining diversity of ecosystems and preserving their natural processes. The main objective of this paper is to consider the validity of current harvesting techniques applied to the mixed forest of Québec. The old-growth mixed forest naturally regenerates by gap dynamics. However, the harvesting technique most commonly used in the mixed forest is clearcutting, with the result that succession must start over from the beginning. A general forest succession model applicable after such large disturbances is proposed. The most positive estimate indicates that the mixed forest will not be re-established earlier than 250 years after clearcutting. To ensure sustainable development in the mixed forest, harvesting techniques should imitate natural regeneration processes. Key words: balsam fir-yellow birch stands, CPRS, gap dynamics, succession model, sustainability, sustainable development

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Community-based conservation is advocated as an idea that long-term conservation success requires engaging with, providing benefits for, and establishing institutions representing local communities. However, community-based conservation’s efficacy and impact in sustainable resource management varies depending on national natural resource policies and implications for local institutional arrangements. This paper analyses the significance of natural resource management policies and institutional design on the management of common pool resources (CPRs), by comparing Namibian conservancies and community forests. To meet this aim, we reviewed key national policies pertinent to natural resource governance and conducted 28 semi-structured interviews between 2012 and 2013. Key informants included conservancy and community forest staff and committee members, village headmen, NGO coordinators, regional foresters, wildlife officials (wardens), and senior government officials in the Ministry of Environment and Tourism and the Ministry of Agriculture, Water and Forestry. We explored the following questions: how do national natural resource management policies affect the operations of local common pool resource institutions? and how do external factors affect local institutions and community participation in CPRs decision-making? Our results show that a diversity of national policies significantly influenced local institutional arrangements. Formation of conservancies and community forests by communities is not only directly linked with state policies designed to increase wildlife numbers and promote forest growth or improve condition, but also formulated primarily for benefits from and control over natural resources. The often-assumed direct relationship between national policies and local institutional arrangements does not always hold in practice, resulting in institutional mismatch. We aim to advance theoretical and applied discourse on common pool resource governance in social-ecological systems, with implications for sustainable land management policies in Namibia and other landscapes across sub-Saharan Africa.

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Out of the 104 islands of Indian Sundarbans, 54 are inhabited. These islands have mud embankments which protect them from intrusion of river and sea water and have made human settlement possible in these islands. Once the embankments were protected from tidal and storm surges by a layer of mangrove forests. But now the mangrove cover has vanished and consequently the embankments are frequently eroded or develop breaches being directly exposed to tidal and storm surges. One way to protect these mud embankments is to recreate mangrove plantations along the toe-line on the outer side of the embankments. This article uses a framed lab-in-field experiment to measure inclination towards community participation in regenerating common pool resources (CPRs), specifically regeneration of mangrove forest on the outer side of the embankments. The 320 subjects who participated in the experiment were villagers from different islands in the Indian Sundarbans. The experiment is a hybrid of the simple Public Goods Game with Voluntary Contributions Mechanism (VCM) and the Trust Game. The first is used to measure inclination towards contributing resources and effort for generation of CPRs and the second is to see if the participants trust others not to extract from the regenerated CPR and if such trust is reciprocated. The results show that voluntary contributions, contrary to theoretical prediction of free riding, are significantly high, though less than when there is no extraction. Trust levels are also quite high and a third of such trust is reciprocated. JEL Codes: C92, H40, Q23

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This study aims to identify the causes of illegal access for the community in carrying out mangrove utilization activities as raw material for charcoal in Batu Ampar District, Kubu Raya, West Kalimantan. This research was conducted in 3 villages in Batu Ampar Subdistrict, namely Batu Ampar Village, Nipah Panjang Village and Teluk Nibung Village. Data collection was done through field observations, in-depth interviews, documentation studies and literature studies. Data analysis used descriptive analysis method with the Theory of Access approach according to Ribot and Peluso (2003). The results showed that the causes of illegal access in the use of mangroves as a raw material for charcoal by the community in Batu Ampar Subdistrict were the characteristics of protected forest resources as Common Pool Resources (CPRs) and high exclusion costs and the obmission of local government. The communities get the power of access to protected forests in Batu Ampar mainly through access to capital provided by investors (cukong), both collectors and buyers.

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Some regenerating stands of the boreal forest exhibit low juvenile growth after major disturbances, which compromises sustainable forest management objectives. In black spruce – feather moss stands of eastern Canada subject to paludification, careful logging methods could decrease stand productivity with time by preventing a beneficial reduction in organic soil thickness. The aim of this project was to confirm decreases in juvenile growth between stands originating from careful logging and the former stands originating from old fires on the same sites. Stem analyses showed that stands originating from CPRS had significantly better juvenile height growth than the former stands but significantly lower growth than stands originating from recent fire in the study region. If organic matter thickness apparently played a role in the growth differences observed between fire and harvesting, it was not the only factor determining stand productivity. According to our results, cohort status, climatic regime, and quality of the residual organic matter are other factors that seem to drive productivity. Our results show that postharvest management approaches (e.g., site preparation) should be used to increase yields after harvest for the sites to express their full growth potential.

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Forest resources will suffer damage and sustainability if they are used excessively. The characteristics of common-pool resources (CPRs) are forms of natural resources that involve many people so that they are vulnerable to damage due to the inability of the owner or manager to restrict other parties from taking part in utilizing them (non-excludable/non-trivial), and the use made by one party will reduce the benefits for the other party (subtractable). The actors in the network have made a good contribution in solving the challenges of the tasks and functions of the FMU in implementing community empowerment policies so that FMU development programs can be accepted by all stakeholders interests, especially local leaders, business people, relevant technical agencies, community leaders, non-governmental organizations, and village communities as beneficiaries. This research is qualitative-verificative with a case study approach that aims to understand the empirical phenomenon of the implementation process of forest community-based forest management policies. Stakeholder data was collected in implementing community empowerment policies in the Yogyakarta Model KPHP stakeholders. This qualitative analysis is historical-comparative, based on historical chronology with inductive and generative methods. The recommended empirical model analyzes the role of leadership in the FMU and the influence of the symbolic figure (Ngarso Dalem) in forming a network of actors and successfully implementing community empowerment policies in the Yogyakarta Model KPHP. Stakeholders must collaborate to solve the challenges of working on FMUs so that FMU development programs can be accepted by all stakeholders, especially local leaders, business people, relevant technical agencies, community leaders, Non-Governmental Organizations, and the community itself as beneficiaries.

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Introduction Attention Deficit / Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition characterized by inattention, motor hyperactivity and impulsivity. ADHD cognitive and behavioral presentation is characterized by a high heterogeneity (APA, 2013). Indeed, a complex diagnostic process, that considers several validated tools, is, to date, necessary. Objectives The main aim is to develop supervised machine learning (ML) algorithms that could be used to support the diagnostic process for ADHD, by identifying the most relevant features in discriminating between the presence or absence of the ADHD diagnosis in children. Methods We analyzed data from 342 children (Mean age: 8y 8m ± 1y; 61 F) referred for possible ADHD symptomatology. Assessments were performed by an expert clinician and through questionnaires: Social Responsiveness Scale (SRS), Child Behavior Checklist (CBCL), Conners Rating Scale for Parents (CPRS) and for Teachers (CTRS). Data were analyzed using a decision tree classifier and random forest algorithms. Results The decision tree model performed an accuracy of 0.71. The random forest model that was identified as the best tested, performed an accuracy of 0.77 (Figure 1) and it identified as most informative parent- and teacher-rated DSM-oriented ADHD symptoms (Figure 2). Figure 1: Random forest confusion matrix and statistics. Figure 2: Ranking of variables importance. Conclusions A random forest classifier could represent an effective algorithm to support the identification of ADHD children and to simplify the diagnostic process as an initial step. The use of supervised machine learning algorithms could be useful in helping the diagnostic process, highlighting the importance of a personalized medicine approach. Disclosure No significant relationships.

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For millennia, societies have tried to find ways to sustain people’s livelihoods by setting rules to equitably and sustainably access, harvest and manage common pools of resources (CPR) that are productive and rich in species. But what are the elements that explain historical successes and failures? Elinor Ostrom suggested that it depends on at least eight axiomatic principles of good governance, whereas empirical results suggest that these principles are not sufficient to describe them, especially when applied to CPRs that possess great social and ecological diversity. The aim of this article is to explore the behavior of a mathematical model of multi-species forest dynamics that respects the foundations of ecology and Ostrom’s governance theory, in order to detect possible constraints inherent to the functioning of these complex systems. The model reveals that fundamental structural laws of compatibilities between species life-history traits are indeed constraining the level of co-existence (average and variance) between a diversity of co-vulnerable timber resource users (RU) and of competing tree species. These structural constraints can also lead to unexpected outcomes. For instance in wetter forest commons, opening up the access to as many diverse RUs as there are competing tree species, produces a diversity of independently-controlled disturbances on species, collectively improving the chances of coexistence between species with different life-history traits. Similar benefits are observed on forest carbon and on profits from timber harvesting. However in drier forest commons, the same benefits cannot be observed, as predicted on the basis of the constraining laws. The results show that the successes and failures of certain management strategies can be reasonably explained by simple mechanistic theories from ecology and the social-ecological sciences, which are themselves constrained by fundamental ecological invariants. If corroborated, the results could be used, in conjunction with Ostrom’s CPR theory, to understand and solve various human-nature coexistence dilemmas in complex social-ecological systems.

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Strong local institutions are important for the successful governance of common-pool resources (CPRs), but why do such institutions emerge in the first place and why do they sometimes not emerge at all? We argue that voluntary local leaders play an important role in the initiation of self-governance institutions because such leaders can directly affect local users’ perceived costs and benefits associated with self-rule. Drawing on recent work on leadership in organizational behavior, we propose that voluntary leaders can facilitate a cooperative process of local rule creation by exhibiting unselfish behavior and leading by example. We posit that such forms of leadership are particularly important when resource users are weakly motivated to act collectively, such as when confronted with “creeping” environmental problems. We test these ideas by using observations from a laboratory-in-the-field experiment with 128 users of forest commons in Bolivia and Uganda. We find that participants’ agreement to create new rules was significantly stronger in group rounds where voluntary, unselfish leaders were present. We show that unselfish leadership actions make the biggest difference for rule creation under high levels of uncertainty, such as when the resource is in subtle decline and intragroup communication sparse.

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Abstract Endophytic fungi are one of the forest microbes that can be solutions for increasing the resistance of forest plants to environmental stress. Exploration and collection of endophytic fungi from the forest is important because the condition of the forest as a natural habitat for microbes has been disturbed due to degradation and deforestation. A potential plant species for forest and land rehabilitation is white jabon (Neolamarckia cadamba (Roxb.) Bosser). The research objective was to isolate and characterize endophytic fungi and to obtain the dark septate endhophyte from the roots of the white jabon which have the potential to increase plant growth. The fungus was isolated by surface sterilization method and then characterized and identified morphologically. Pathogenicity test was carried out to determine the pathogenicity of the fungus. The results of the study found ten isolates. Most of the isolated fungi showed sterile mycelia with septate hyphae, so they were not identified morphologically. The isolate of CPGS 3 isolates probably belonged to the genus Aureobasidium sp. CPGS 3, CPKR 6, and CPPS 11 isolates are candidates for dark septate endophyte fungi which have the potential to promote plant growth and not become pathogens in chinese cabbage (Brassica rapa).

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We studied the short-term impact of cutting with the protection of regeneration (CPR) on ruffed grouse (Bonasa umbellus) and its habitat. Drumming males were surveyed in the boreal forest of western Quebec between 1990 and 1994 in 5 control stands and 10 stands that were cut in 1992. Cutting caused a 50% decline in total density and basal area of trees. In harvested stands, shrubs were less abundant than in control stands. Lateral cover between 1 and 2 m and canopy closure were also lower in cut stands. In the harvested stands, the density varied between 14.1 and 22.6 drummers/km2 between 1990 and 1992 before the CPR. In 1993, density declined to 10.1 but was reestablished at 21.5 in 1994. Densities did not differ significantly before or after the CPR nor between cut and uncut stands (p > 0.05). After the CPR, the grouse generally established their drumming sites in the periphery of cut areas. There they found lateral cover and canopy closure similar to those measured in control stands by choosing sites where deciduous shrubs were more abundant than in the remainder of the stand. However, the characteristics of the tree stratum were not optimal; their overall density, that of Betulaceae, and that of Salicaceae were higher near drumming sites located in control stands than those in harvested stands. In harvested stands, drumming sites had a lower total basal area of trees, and namely that of deciduous trees, than in control sites. To maintain ruffed grouse populations immediately after cutting in mixed and deciduous stands of public lands, we suggest that CPRs be in 15-ha blocks while maintaining a basal area of at least 7.5 m2/ha, half of which should be in trembling aspen (Populus tremuloides Michx.) and paper birch (Betula papyrifera Marsh.).

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En pessière noire, le procédé de récolte habituel est celui de la coupe avec protection de la régénération et des sols (CPRS), un procédé de coupe totale. Socialement, ce type de pratique est mal perçu principalement pour ses impacts visuels. Toutefois, il existe pour les écosystèmes boréaux, d’autres procédés de récolte adaptés, issus de l’aménagement écosystémique. Cette étude présente une comparaison de l’acceptabilité visuelle des traitements sylvicoles permettant une plus grande rétention végétale par rapport à la CPRS (coupe avec protection de la haute régénération (CPHRS), coupe avec protection des petites tiges marchandes (CPPTM) et CPRS avec rétention de bouquets). Elle vérifie aussi si des différences dans les jugements d’acceptabilité existent selon l’appartenance à une région ressource forestière et à certains groupes d’intérêts, tels les environnementalistes, les amateurs de plein-air et d’intérêt général. Il en ressort que la qualité et la quantité de la régénération laissée après coupe influencent le jugement et que les traitements permettant de conserver une structure résiduelle parviennent à mitiger, partiellement, les impacts visuels des méthodes de coupe rase traditionnelles. Finalement, l’appartenance à une région forestière n’influence pas le jugement en ce qui a trait à l’acceptabilité visuelle des coupes. Les différences entre les groupes d’intérêts laissent plutôt croire que ce sont les connaissances reliées au milieu forestier qui influencent le plus le jugement.

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Background: Air pollution nowadays has seriously threatened the health of the Chinese population, especially in the vulnerable groups of fetuses, infants and toddlers. In particular, the effects of air pollution on children’s neurobehavioral development have attracted widespread attention. Moreover, the early detection of a sensitive period is very important for the precise intervention of the disease. However, such studies focusing on hyperactive behaviors and susceptible window identification are currently lacking in China. Objectives: The study aims to explore the correlation between air pollution exposure and hyperactive behaviors during the early life stage and attempt to identify whether a susceptible exposure window exists that is crucial for further precise intervention. Methods: Based on the Longhua Child Cohort Study, we collected the basic information and hyperactivity index of 26,052 children using a questionnaire conducted from 2015 to 2017, and the Conners’ Parent Rating Scale-revised (CPRS-48) was used to assess hyperactive behaviors. Moreover, the data of air pollution concentration (PM10, PM2.5, NO2, CO, O3 and SO2) were collected from the monitoring station between 2011 to 2017, and a land-use random forest model was used to evaluate the exposure level of each subject. Furthermore, Distributed lag non-linear models (DLNMs) were applied for statistic analysis. Results: The risk of child hyperactivity was found to be positively associated with early life exposure to PM10, PM2.5 and NO2. In particular, for an increase of per 10 µg/m3 in PM10, PM2.5 and NO2 exposure concentration during early life, the risk of child hyperactivity increased significantly during the seventh month of pregnancy to the fourth month after birth, with the strongest association in the ninth month of pregnancy (PM10: OR = 1.043, 95% CI: 1.016–1.071; PM2.5: OR = 1.062, 95% CI: 1.024–1.102; NO2: OR = 1.043, 95% CI: 1.016–1.071). However, no significant associations among early life exposure to CO, O3 and SO2 and child hyperactive behaviors were observed. Conclusions: Early life exposure to PM10, PM2.5 and NO2 is associated with an increased risk of child ADHD-like behaviors at the age around 3 years, and the late-prenatal and early postnatal periods might be the susceptible exposure windows.

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Abstract Background Infectious diarrheal illness is a significant contributor to healthcare costs in the US pediatric population. New multi-pathogen PCR-based panels have shown increased sensitivity over previous methods; however, they are costly and clinical utility may be limited in many cases. Clinical Prediction Rules (CPRs) may help optimize the appropriate use of these tests. Furthermore, Natural Language Processing (NLP) is an emerging tool to extract clinical history for decision support. Here, we examine NLP for the validation of a CPR for pediatric diarrhea. Methods Using data from a prospective clinical trial at 5 US pediatric hospitals, 961 diarrheal cases were assessed for etiology and relevant clinical variables. Of 65 variables collected in that study, 42 were excluded in our models based on a scarcity of documentation in reviewed clinical charts. The remaining 23 variables were ranked by random forest (RF) variable importance and utilized in both an RF and stepwise logistic regression (LR) model for viral-only etiology. We investigated whether NLP could accurately extract data from clinical notes comparable to study questionnaires. We used the eHOST abstraction software to abstract 6 clinical variables from patient charts that were useful in our CPR. These data will be used to train an NLP algorithm to extract the same variables from additional charts, and be combined with data from 2 other variables coded in the EMR to externally validate our model. Results Both RF and LR models achieved cross-validated area under the receiver operating characteristic curves of 0.74 using the top 5 variables (season, age, bloody diarrhea, vomiting/nausea, and fever), which did not improve significantly with the addition of more variables. Of 270 charts abstracted for NLP training, there were 41 occurrences of bloody diarrhea annotated, 339 occurrences of vomiting, and 145 occurrences of fever. Inter-annotator agreement over 9 training sets ranged between 0.63 and 0.83. Conclusion We have constructed a parsimonious CPR involving only 5 inputs for the prediction of a viral-only etiology for pediatric diarrheal illness using prospectively collected data. With the training of an NLP algorithm for automated chart abstraction we will validate the CPR. NLP could allow a CPR to run without manual data entry to improve care. Disclosures All authors: No reported disclosures.

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Since the 1990s, Newfoundland’s woodland caribou (Rangifer tarandus caribou) population has declined by an estimated 66%. Low calf recruitment has been associated to the decline, possibly triggered by increasing calf predation and/or decreasing resources. To investigate the role of landscape composition in this system, we studied the yearly (2005-2008) calving/post-calving range (CPCR) of 104 satellite-collared females belonging to six herds. We mapped nine disturbance factors (e.g. roads, logging, etc), as well as vegetation cover types (e.g. coniferous, deciduous forests, etc), and determined the total area they occupied within CPCRs yearly for each herd. Using an information theoretic approach, we assessed the model that best explained variation in recruitment using these components. Based on corrected Akaike Information Criterion, the model that best explained variation in calf recruitment included total disturbance and deciduous forest area, both showing the expected negative relationship with calf recruitment. Other landscape variables among the models with ΔAICc < 2 were mixed forest, also with a suggested negative relationship, and barrens and wetlands with a significant positive trend. This study highlights the need to minimize total disturbance footprint and account for resulting changes in forest composition within CPCRs during land use planning. Expanding forestry operations and road infrastructure in critical woodland caribou habitat across Canada may additionally contribute to habitat loss via fragmentation. This in turn, may lead to range recession beyond the initial local avoidance footprint. We see the possibility of using calf recruitment models based on landscape parameters, among others, to predict the impact of new industrial developments on calf recruitment.

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The article analyzes the administrative and legal powers of forest protection and hunting service within the system of state management bodies in the field of forest protection and hunting. The relevance of the research topic is due to the processes of general reform of the forestry industry of Ukraine and the need to clarify the law enforcement powers of officials in this industry. The purpose of the study is to highlight the problems of law enforcement in the forestry industry; clarifying the powers of law enforcement entities, directions for reforming forestry law enforcement agencies, and prospects for improving their activities. The object of the study is the legal regulation of security activities aimed at ensuring law and order and legality in the forestry industry. The research is based on the use of general scientific and special methods. With the help of the dialectical method, internal contradictions in the activities of law enforcement agencies in the forestry industry were revealed. The analysis method is used to identify gaps in legislation regulating the powers of supervisory bodies, etc. Formulated conclusions are aimed at practical use. Thus, it is proposed: definition of the concept of «huntsman»; separation of the management function and state control in forestry enterprises; transfer of the control function to the level of territorial bodies of the State Forestry Agency. Thus, at the enterprises of the State Forestry Agency system and other permanent forest users, the functioning of forest protection will remain without the status of a law enforcement body, but with the right to draw up protocols and the limited right to consider cases of administrative offenses in the field of forest protection.

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The charcoal industry, specifically charcoal production, is tremendously valuable to Kenya for its contribution to economic, social and environmental nexus. Considering the degradation of ecosystems and charcoal production’s critical role, the government established the Forest (charcoal) rules of 2009, assigning commercial charcoal production under Charcoal Producer Associations (CPAs). Identifying numerous bans in the recent past, this paper sets out to understand CPAs’ potentials and challenges in attaining sustainability within the sector. Using focus group discussions with CPA members from Tana River and Kitui counties, the paper outlines analysed data within the functionality, governance and policy implications parameters of operation. The findings show high economic value for the members and an in-depth environmental significance to the communities within which these CPAs exist. Thus, we propose a schematic to enhance charcoal production processes to achieve sustainable ecosystems and livelihoods. There is high potential within the CPAs for the sector’s sustainability through monitoring platforms, restoration plans, adopting sustainable practices, knowledge dissemination and societal advancement. To advance this untapped potential of these associations, we recommend building their technical, business and governance skills, exploring various restoration schemes, financial and regulatory support in implementation, and policy support.

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The pine wood nematode (PWN), Bursaphelenchus xylophilus, is the pathogen of pine wilt disease (PWD), resulting in huge losses in pine forests. However, its pathogenic mechanism remains unclear. The cathepsin L-like cysteine proteinase (CPL) genes are multifunctional genes related to the parasitic abilities of plant-parasitic nematodes, but their functions in PWN remain unclear. We cloned three cpl genes of PWN (Bx-cpls) by rapid amplification of cDNA ends (RACE) and analyzed their characteristics using bioinformatic methods. The tissue specificity of cpl gene of PWN (Bx-cpl) was studied using in situ mRNA hybridization (ISH). The functions of Bx-cpls in development and pathogenicity were investigated using real-time quantitative PCR (qPCR) and RNA interference (RNAi). The results showed that the full-length cDNAs of Bx-cpl-1, Bx-cpl-2, and Bx-cpl-3 were 1163 bp, 1305 bp, and 1302 bp, respectively. Bx-cpls could accumulate specifically in the egg, intestine, and genital system of PWN. During different developmental stages of PWN, the expression of Bx-cpls in the egg stage was highest. After infection, the expression levels of Bx-cpls increased and reached their highest at the initial stage of PWD, then declined gradually. The silencing of Bx-cpl could reduce the feeding, reproduction, and pathogenicity of PWN. These results revealed that Bx-cpls play multiple roles in the development and pathogenic processes of PWN.

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New urbanization often leads to land-use and land-cover change (LUCC), which inevitably affects ecosystem services (ESs). Although it is traditionally believed that urbanization reduces ecosystem services, some studies have shown that reasonable urban development facilitates ecosystem conservation. Previous studies have focused on the impacts of urbanization on either LUCC or ESs, with fewer dynamic assessments of the coordination of the three. Taking China’s contiguous poor areas (CPAs) as an example, this study applied coupling coordination, path analysis, and a multiscale geographically weighted regression (MGWR) model to identify the dynamic relationship among urbanization, land use, and the environment and then predicted their coupling coordination under shared socioeconomic pathways (SSP-RCP) in 2035 using the Patch Generation Land Use Simulation (PLUS) and a random forest model. The results of the study show that (1) urbanization, land-use change, and environmental loads in China’s CPAs showed an inconsistent upward trend. There was a slight overall decrease in ESs before 2013, which was consistent with the early stage of the Environmental Kuznets Curve (EKC); after that time, they showed different characteristics. (2) From 2000 to 2018, the coupling coordination degree of CPAs decreased slightly due to urbanization, geographic factors, and grassland and unused land. LUCC was essential to maintaining the system balance. The SN (southern contiguous poverty area) was at a basic level of coordination, while the other regions showed a moderate imbalance. (3) According to scenario projections, the degree of coupling coordination in all regions will increase by 2035. Environmental prioritization and sustainable routes are the best options for CPAs’ development. The SN is more stable, while the WN (western contiguous poverty area) has the lowest coupling coordination. (4) Environmentally friendly urbanization should be carried out with land management tailored to local conditions. Measures that could be recommended include establishing ecological pilot zones in SN areas, prioritizing the protection of grassland ecosystems in WN areas, and promoting intensive land use in the NN (northern contiguous poverty area). The present study offers a novel perspective on the interplay between the economy and the environment at the county level and achieves predictive coupling coordination through the integration of PLUS and random forest models. This investigation into coordinated urbanization–LUCC–ES development in CPAs yields valuable insights for enhancing environmental and economic well-being in similar regions within China, as well as globally.

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Many semi-arid coniferous forests in western North America have reached historically unprecedented densities over the past 150 years and are dominated by shade-tolerant trees. Silvicultural treatments generally open the canopy but may not restore shade-intolerant species. We determined crossover-point irradiance (CPI) (light at which the height growth rank of pairs of species changes) for seedlings in Sierra Nevada mixed-conifer forest and used these to interpret light environments produced by fuels-reduction thinning and group selection with reserved large trees. Nine of 21 species pairs had well-defined CPIs. The CPI of the most common shade-tolerant and intolerant species (white fir ( Abies concolor (Gordon & Glendl.) Lindl. ex Hildebr.) and ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson)) was 22.5 mol·m–2·day–1 or 41% of full sun. Median understory irradiance increased from 9.2 mol·m–2·day–1 (17% full sun) in pretreatment forest to 13 mol·m–2·day–1 (24% full sun) in lightly and 15.5 mol·m–2·day–1 (28% full sun) in moderately thinned stands and 37 mol·m–2·day–1 (67% full sun) in group-selection openings. We estimate that 5%–20% of ground area in lightly to moderately thinned stands would have enough light to favor shade-intolerant over shade-tolerant growth compared with 89% of ground area in group-selection openings. The CPI provides a tool to assess regeneration implications of treatment modification such as increasing heterogeneity of thinning to enhance regeneration or reserving large trees in group-selection openings to maintain wildlife habitat.

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This aim of this study was to develop and assess the viability of a leadership scale that measures leadership from the perspective of the leader. A criterion sample was used of firefighters across USA federal land management agencies who are qualified crew bosses. The Supervisor Perceived Leadership Scale (SPLS) consists of perceptions of items that have measured the most essential leadership skills – competent decision making, integrity and personal genuineness from the perspective of the subordinate in the previously developed Crewmember Perceived Leadership Scale (CPLS). Through confirmatory factor analysis, an 18-item, 2-factor structure was supported. However, the items that did not load were highly related and loaded well on the CPLS. The SPLS had a low to moderate relationship with authentic leadership and global competence. The gap between the SPLS and CPLS provides insight about the potential differences between the average fire leader and the exceptional fire leader. These results and implications for future research are discussed.

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BackgroundThere has been a sharp rise in opioid use for non-cancer pain globally, including for rheumatic and musculoskeletal conditions. Despite increased awareness of adverse effects, they remain commonly prescribed in most countries. Clinical prediction models (CPMs) offer the possibility of assessing individual risk allowing better allocation of resources towards those at risk. Machine learning (ML) approaches can address nonlinear relationships and complex interactions between variables and are increasingly used to develop CPMs.ObjectivesTo develop, validate, and compare the performance of three CPMs based on regression and ML, which leverage primary care data to estimate the risk of opioid-related death in patients prescribed opioids for non-cancer pain.MethodsPatients ≥18 years old without prior cancer who were prescribed any opioid between 01/01/2006 and 31/12/2017 were identified in the Clinical Practice Research Datalink (CPRD), representative of national patient data from UK primary care. Only new opioid users were included. Index date was date of first prescription, with censoring at withdrawal from the CPRD or after not having an opioid prescription for two years. Baseline data were extracted from each patient’s records, including demographic information, comorbidities, concomitant medications, and the opioid type being prescribed. 49 candidate predictors were used to train three competing risk models: a Fine&Gray regression model with LASSO regularisation, a survival random forest (RF), and a neural network (DeepHit). The outcome was opioid-related mortality and other cause mortality the competing event, defined using a curated ICD-10 codelist. Predictive performance of the models, like area under the receiver characteristic operator curve (AUCROC) were calculated using 5-fold cross validation.ResultsWe included a total of 1,029,681 patients, of which 1,240 experienced an opioid-related death, and 52,833 experienced a competing death.The Fine&Gray, RF and DeepHit models achieved average AUCROC values of 0.83(95% CI: 0.81-0.85), 0.78(0.77-0.79) and 0.81(0.80-0.82) respectively (Figure 1). At optimum risk cut point, as per Youden’s index, the models achieved sensitivities of 0.82(0.78-0.85), 0.75(0.67-0.82) and 0.80(0.78-0.83), and specificities of 0.78(0.73-0.82), 0.75(0.68-0.83) and 0.78(0.75-0.8) when predicting 12-month risk, respectively.In the Fine&Gray model, factors associated with increased risk were history of substance use disorder (hazards ratio [HR]: 3.40, 95% CI:3.12-3.69) and alcohol abuse (HR:3.07, 95% CI:2.93-3.22). COPD (HR:1.53, 95% CI:1.48-1.58) and moderate liver disease (HR:1.31, 95% CI:0.99-1.63) were the comorbidities associated with highest risk. Morphine (HR:2.39, 95% CI:2.08-2.69) and oxycodone (HR:1.10, 95% CI:1.00-1.20) at initiation and concomitant gabapentinoids (HR:1.99, 95% CI:1.80-2.18) and benzodiazepines (HR:1.30, 95% CI:1.24-1.36) were associated with an increased risk. HR for rheumatologic diseases was 1.08 (95% CI:1.01-1.14).Figure 1.AUCROC of the three models vs. prediction horizon of the model. 95% CI of mean performance shaded.ConclusionThe Fine&Gray and DeepHit models showed comparable discriminative performance. Substance abuse, lung and liver comorbidities, morphine or oxycodone at initiation and co-prescription of gabapentinoids and benzodiazepine, were some of the factors associated with a higher risk of opioid-related mortality.AcknowledgementsJBA acknowledges the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science (Grant Ref: 218529/Z/19/Z).MJ is funded through an NIHR Advanced Fellowship (NIHR301413).Disclosure of InterestsNone Declared.

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IntroductionAtrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies.Methods and analysisWe will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states.Ethics and disseminationPermission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.Trial registration numberA systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).

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The cyber-physical power system (CPPS) is a modern infrastructure utilising information and communication technology that has become more vulnerable to cyberattacks in recent years. The attack magnitude injected by the adversary is stealthier and it cannot be detected using conventional bad data detection techniques. Protecting sensitive data from data integrity attacks (DIA) is essential for ensuring system security and reliability. A tragic event will occur if the attack goes unreported. Therefore, DIA detection is highly vital for the operator in the control centre to make important decisions. This paper addresses the attack impact on WAC applications and attack detection using the model-based method and data-driven-based methods. On the basis of the validation of performance indicators, various detection approaches are simulated and compared to determine the best detection strategy. Simulation results show that in the model-based anomaly detection method, the recursive polynomial model estimator (RPME) has better detection performance than the recursive least square estimator (RLSE). The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). On the WSCC 3 machine 9-bus system, the efficacy of the suggested methods is evaluated.

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How we use, or do not use our natural resources is a question that has been debated for millennia. Still an answer remains out of reach. It is a complex issue that often involves a social dilemma known as “the tragedy of the commons”. Many common pool resources, from fish stocks, to forests, to natural tourism destinations have an associated marketing system that may place pressure on the resource. If poorly managed, the resources sustainable future is questionable. This paper explains how commons and strategic action field theory can enhance a macromarketing analysis of marketing systems that involve a common pool resource (CPR) pointing to potential responses and solutions. The unique challenges faced are discussed, particularly in the interdependent areas of: property rights, power and equitable distribution. The findings from an empirical application confirm that CPRs destabilise a marketing system. Solutions may be found in the way the issue is negotiated, how the rules are structured and the perspective from which the issue is considered.

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Introduction: The apparent heterogeneity of multiple myeloma (MM) constitutes a key challenge in the clinical management and the design of effective therapeutic interventions, while it entails the identification of biomarkers with a strong prognostic value. In this context and taking into account patients' inconvenience to invasive bone marrow (BM) aspiration, the assessment of circulating plasma cells (CPCs) in liquid biopsies, at the time of diagnosis, has been proposed as a useful assay with prognostic value. Different methodologies have been applied for the detection of CPCs; the most common is the use of multicolor flow cytometry (MFC), mainly of 2-, 4-, or 6-color combination panels, which however yielded heterogeneous results due to variations in the detection efficacy of each approach. In the present study, we applied the standardized and highly sensitive Next Generation Flow Cytometry (NGF) approach, to detect CPCs in diagnostic MM peripheral blood (PB) samples, we compared their phenotypic characteristics with the aberrant clonal cells of BM matched samples and we correlated their presence with disease characteristics. Patients and Methods: PB and BM matched samples from 182 consecutive MM patients, at diagnosis, were evaluated for the presence of aberrant plasma cells (APCs) following the standard operating procedures (SOP) of NGF, according to EuroFlow guidelines. All these patients were diagnosed and treated in a single center (Department of Clinical Therapeutics, N.K. University of Athens, Greece). Samples were collected in EDTA-anticoagulated tubes and treated with the bulk-lysis procedure. Recovered cells were stained with antibodies against surface CD19-PEC7, CD27-BV510, CD38-FITC, CD45-PERCP, CD56-PE and CD138-BV421 and the intracellular CyIgκ-APC and CyIgλ-APCC750 to verify clonality. Six to ten million cells were acquired per sample, thus reaching a median Limit of Detection (LOD) of 3.5x10-6. Optimal PMT voltages were set according to the EuroFlow SOP for instrument set-up and daily performance status of FACSCANTOII was monitored with both CS&T (BD) and Rainbow beads (Spherotech Inc, Lake Forest, IL). Results: CPCs were detected in 158/182 (86.8%) MM diagnostic samples within a range of 0.0002% to 63.8% of total PB nucleated cells (PBNCs). The CPCs showed the same aberrant phenotype as the one detected in the BM for all cases, although with a significantly reduced intensity for the markers CD27, CD38, CD138 and CD56. When more than one phenotypically distinct subgroups were detectable in the BM, the same phenotypic subsets were present in the PB with the same relative frequency for >90% of bi/multi-phenotypic cases. The higher number of CPCs (>0.1% of all PBNCs) strongly correlated with an increased BM infiltration rate by myeloma cells (p<0.0001), with ISS-3 disease stage (p<0.0001) and with the presence of high-risk cytogenetics [t(4;14), t(14;16) and/or del(17p53); p<0.0001]. There was also weaker correlation between high number of CPCs and high serum creatinine levels (p=0.015). Inversely, the absence of CPCs or the presence of CPCs at numbers ≤0.001% correlated with lower serum β2-microglobulin (p<0.0001), with higher hemoglobin levels (p<0.0001) and with the presence of an elevated normal plasma cell compartment within the BM (i.e. ≥5% of all PCs; r2=0.84, p<0.0001). There was no association between the CPC number and the therapeutic response to induction treatment (IMWG criteria). Despite the short follow-up period (median of 16 months), there is a trend for inferior PFS in patients with high CPCs (p=0.16). Conclusions: The NGF approach using the EuroFlow protocol enables the detection of even rare CPCs in diagnostic MM PB samples, due to the high number of cells acquired and the elegantly elaborated 8-color marker combinations which allows for the detection of CPCs with even a non-typical phenotype. Our matched PB and BM analysis revealed that BM APCs and CPCs share very similar characteristics suggesting that liquid biopsy offers a representative alternative for the phenotypic characterization of BM APCs. The correlation of high CPCs with adverse disease characteristics suggests that the quantification of CPCs by standardized NGF may emerge as a valuable surrogate prognostic biomarker which could replace other invasive methods or other less informative assays. Disclosures Terpos: Janssen: Honoraria, Other: Travel expenses, Research Funding; Medison: Honoraria; Amgen: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses, Research Funding; Genesis: Honoraria, Other: Travel expenses, Research Funding; Celgene: Honoraria. Gavriatopoulou:Amgen: Honoraria; Janssen: Honoraria, Other: Travel expenses; Genesis: Honoraria, Other: Travel expenses; Takeda: Honoraria, Other: Travel expenses. Kastritis:Genesis: Honoraria; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Honoraria; Prothena: Honoraria. Dimopoulos:Sanofi Oncology: Research Funding.

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Urologische Erkrankungen betreffen ein großes Klientel auch außerhalb der fachärztlichen Versorgung. Das chronische Beckenschmerzsyndrom (CPPS) ist eine der häufigsten Erkrankungen in der Urologie und Gynäkologie und spielt auch in der allgemeinmedizinischen und schmerztherapeutischen Praxis eine große Rolle. Die chronische Prostatitis als Unterform des CPPS stellt die häufigste urologische Diagnose beim unter 50-jährigen Mann dar und zeigt sich häufig therapierefraktär. Hier kann die Akupunktur für sich und im Zusammenspiel mit anderen Therapieverfahren nachweislich und neurophysiologisch gut erklärbar zur Beschwerdelinderung beitragen.

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Headaches account for up to 4.5% of emergency department visits, where they present a significant diagnostic challenge. While primary headaches are benign, secondary headaches can be life-threatening. It is essential to rapidly differentiate between primary and secondary headaches as the latter require immediate diagnostic work-up. Current assessment relies on subjective measures; time constraints can result in overuse of diagnostic neuroimaging, prolonging diagnosis, and adding to economic burden. There is therefore an unmet need for a time- and cost-efficient, quantitative triaging tool to guide further diagnostic testing. Routine blood tests may provide important diagnostic and prognostic biomarkers indicating underlying headache causes. In this retrospective study (approved by the UK Medicines and Healthcare products Regulatory Agency Independent Scientific Advisory Committee for Clinical Practice Research Datalink (CPRD) research [20_000173]), UK CPRD real-world data from patients (n = 121,241) presenting with headache from 1993–2021 were used to generate a predictive model based on a machine learning (ML) approach for primary versus secondary headaches. A ML-based predictive model was constructed using two different methods (logistic regression and random forest) and the following predictors were evaluated: ten standard measurements of complete blood count (CBC) test, 19 ratios of the ten CBC test parameters, and patient demographic and clinical characteristics. The model’s predictive performance was assessed using a set of cross-validated model performance metrics. The final predictive model showed modest predictive accuracy using the random forest method (balanced accuracy: 0.7405). The sensitivity, specificity, false negative rate (incorrect prediction of secondary headache as primary headache), and false positive rate (incorrect prediction of primary headache as secondary headache) were 58%, 90%, 10%, and 42%, respectively. The ML-based prediction model developed could provide a useful time- and cost-effective quantitative clinical tool to facilitate the triaging of patients presenting to the clinic with headache.

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Aim: To explore the mechanism of cancer by employing a comprehensive analysis of DNA methylation patterns and variations among pan-cancer cohorts. Materials & methods: This research focused on the discovery of universally specific or common biomarkers by mathematical statistics and machine learning methods in The Cancer Genome Atlas. Results: We found 138 differently methylated CpGs (DMCs) with a common methylation trend and eight common differently methylated regions in different cancer cohorts. Additionally, we found 99 DMCs to distinguish 32 different cancer cohorts in random forest analysis because of the specificity mechanism, but each DMC still had high instability. Conclusion: Our results could facilitate the development of biomarkers that are universally specific and common features across pan-cancer cohorts.

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Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5′-C-phosphate-G-3′ (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a “dominant” region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.

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Every clinical decision relies on the doctor's expertise and comprehension.This standard procedure may, despite appearances, lead to errors, biases, and increased costs that compromise the patients' Quality of Service (QoS).There is a pressing need for adaptable equipment for critical patient care in developing nations like India.The majority of Indian hospitals are unable to provide their patients with adequate medical care due to a lack of suitable, simple, and expandable intelligent systems.The development of a comprehensive system that will enable hospitals to provide vital patients with a real-time feedback system is the objective of this project.Using IBM cloud computing as a service platform and machine learning, we propose a standard architecture, language, and classification scheme for analyzing vital patient health data (PaaS).The development of a machine learning (ML) method for predicting a patient's fitness is the primary goal of this study.Our models and data are stored and managed by IBM Watson Studio and IBM Cloud.The Base Predictors for our ml models are Nave Bayes, Logistic Regression, the KNeighbors Classifier, the Decision Tree Classifier, the Random Forest Classifier, the Gradient Boosting Classifier, and the MLP Classifier.The precision of the model has been increased by employing the ensemble learning bagging strategy.We use a variety of machine learning algorithms for ensemble learning.The Critical Patient Management System, or CPMS, is a mobile application we developed that allows for real-time data and record viewing.Data that is relevant to ML model training and deployment can be fetched in real time from IBM Cloud and made available through CPMS because of the way the system is built.Doctors can use Ml tendencies to predict a patient's health status.The CPMS will send an SMS notification to the duty physician and nurse to provide immediate care if the situation worsens as anticipated.Hospitals might get a smart healthcare solution if the mission, milliliter models, and mobile application are combined.

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Background Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. Objective The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. Methods To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. Results We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. Conclusions For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. International Registered Report Identifier (IRRID) DERR1-10.2196/48892

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Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieved an area under the ROC curve (AUC) of 0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC = 0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.

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Every day CPFs are registered on the stock exchange. People seeking greater profitability, exposing themselves to great risks without even knowing how to analyze the best opportunities. Whenever you start to learn something, it is normal to have many difficulties and challenges, because the act of knowing something “new” is challenging, even more so when it involves money. Therefore, a comparative analysis was carried out between some of the Artificial Intelligence methods, applied in standards on the stock exchange, aiming to improve the assertiveness of the operations carried out and seeking their statistically proven efficiency. In this way, increasing the chances of the operations being winners. The algorithms were trained separately from historical data of five stocks, namely: Petrobras, Itaú, Bradesco, Vale and Ambev. And the algorithms of Linear Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Random Forest and Decision Trees were used.

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Background. Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN). Methods. Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model. Results. The methylation analysis tool (ChAMP) considered DECs that were up-regulated ( n =121) and down-regulated ( n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model. Conclusion. Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.

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BackgroundThe impact of the opioid epidemic in North America has caused concern in the United Kingdom and across Europe. Long-term opioid use is associated with several opioid-related harms and adverse outcomes. Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Understanding what factors are associated with long-term opioid use in such patients is the first step in helping to develop targeted interventions for deprescribing.ObjectivesThe objective of this study was to (i) quantify the proportion of new opioid users with fibromyalgia who become long-term opioid users (ii) identify risk factors associated with long-term opioid use in fibromyalgia.MethodsA retrospective cohort study was conducted using data from the Clinical Practice Research Datalink (CPRD). CPRD is a national database of electronic health records within primary care, representation of the UK general population. Fibromyalgia patients without prior cancer who were new opioid users between 01/Jan/2006 and 31/Aug/2021 were included. Long-term opioid use was defined as at least 3 opioid prescriptions issued within a 90-day period, or ≥1 opioid prescription lasting at least 90 days, in the first year of follow-up. Logistic regression analysis and a random forest model were used to identify risk factors related to long-term opioid use. Compared to logistic regression, random forest variable importance measures covered both the effects of individual predictor variables as well as multivariate interactions with other predictor variables. In the random forest model, the mean decrease accuracy (MDA) index to measure the variables’ importance was used; the larger the value of the mean decrease accuracy, the more important the variable is.ResultsOf the 28,552 fibromyalgia patients who were new opioids users, 7,375 (26%) became long-term opioid users in the first year. In the fully adjusted logistic regression model, suicide and self-harm (adjusted odds ratio [aOR]: 1.98, 95% confidence interval (CI): 1.72-2.27, p<0.001), substance use disorder (aOR: 2.07, 95%CI: 1.62-2.66, p<0.001), most deprived (Index of Multiple Deprivation (IMD) quintile 5/most deprived, aOR: 1.98, 95%CI: 1.69-2.33, p<0.001, compared with quintile 1/least deprived), obesity (aOR: very severely obese, OR: 2.35, 95%CI: 1.76-3.18, p<0.001) and mean daily morphine milligram equivalents (MME) at the initiation (High (120-199 MME/day): aOR: 34.81, 95%CI: 18- 78.05, p<0.001 compared to mean daily MME: Low (<50 MME/day)) were associated with higher risk of long-term opioid use. The three most important variables using the random forest model were mean MME/day at initiation (MDA 140), history of suicide and self-harm (MDA 40) and IMD (MDA 30).ConclusionAlmost 1 in 4 patients with fibromyalgia starting opioids became a long-term opioid user in this nationally representative dataset across the UK within the first year. The dose of initial opioid prescription, high deprivation score, history of suicide and self-harm, substance use disorder and obesity were associated with long-term opioid use.AcknowledgementsFunded by a FOREUM Career Research Grant and NIHR. MJ is supported by an NIHR Advanced Fellowship [NIHR301413]. The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care.Disclosure of InterestsNone Declared.

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Bull fertility is an important economic trait, and the use of subfertile sem*n for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of sem*n functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile sem*n. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and sem*n parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 sem*n parameters and a balanced proportion of each type of–omics data. The results showed that unlike the sem*n parameters studied the–omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.

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AbstractThe Illumina Infinium HumanMethylation27 (Illumina 27K) BeadChip assay is a relatively recent high-throughput technology that allows over 27,000 CpGs to be assayed. The Illumina 27K methylation data is less commonly used in comparison to gene expression in bioinformatics. It provides a critical need to find the optimal feature ranking (FR) method for handling the high dimensional data. The optimal FR method on the classifier is not well known, and choosing the best performing FR method becomes more challenging in high dimensional data setting. Therefore, identifying the statistical methods which boost the inference is of crucial importance in this context. This paper describes the detailed performances of FR methods such as fisher score, information gain, chi-square, and minimum redundancy and maximum relevance on different classification methods such as Adaboost, Random Forest, Naive Bayes, and Support Vector Machines. Through simulation study and real data applications, we show that the fisher score as an FR method, when applied on all the classifiers, achieved best prediction accuracy with significantly small number of ranked features.

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Diagnosing urothelial cancer (UCa) via invasive cystoscopy is painful, specifically in men, and can cause infection and bleeding. Because the UCa risk is higher for male patients, urinary non-invasive UCa biomarkers are highly desired to stratify men for invasive cystoscopy. We previously identified multiple DNA methylation sites in urine samples that detect UCa with a high sensitivity and specificity in men. Here, we identified the most relevant markers by employing multiple statistical approaches and machine learning (random forest, boosted trees, LASSO) using a dataset of 251 male UCa patients and 111 controls. Three CpG sites located in ALOX5, TRPS1 and an intergenic region on chromosome 16 have been concordantly selected by all approaches, and their combination in a single decision matrix for clinical use was tested based on their respective thresholds of the individual CpGs. The combination of ALOX5 and TRPS1 yielded the best overall sensitivity (61%) at a pre-set specificity of 95%. This combination exceeded both the diagnostic performance of the most sensitive bioinformatic approach and that of the best single CpG. In summary, we showed that overlap analysis of multiple statistical approaches identifies the most reliable biomarkers for UCa in a male collective. The results may assist in stratifying men for cystoscopy.

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Abstract Purpose: Intratumoral heterogeneity emerges from accumulating genetic and epigenetic changes during tumorigenesis, which may contribute to therapeutic failure and drug resistance. We aimed to develop a tool that can evaluate the intratumoral epigenetic heterogeneity using the DNA methylation profiles from bulk tumors. Methods: Ggenomic DNA of three laser micro-dissected tumor regions, including digestive tract surface (DTS), central bulk (CB), and invasive margin (IM), was extracted from formalin-fixed paraffin-embedded (FFPE) sections of 98 colorectal cancer patients. The genome-wide methylation profiles were generated with methylation array. The most variable methylated probes were selected to construct a DNA methylation-based heterogeneity (MeHEG) estimation tool that can deconvolve the proportion of each reference tumor region with the support vector machine model-based method. A PCR-based assay for quantitative analysis of DNA methylation (QASM) was developed to specifically determine the methylation status of each CpG in MeHEG assay at single-base resolution to realize fast evaluation of epigenetic heterogeneity. Results: In the discovery set with 79 patients, the differentially methylated CpGs among the three tumor regions were found. The 7 most representative CpGs were identified by random forest analysis and subsequently selected to develop the MeHEG algorithm. We validated its performance of deconvolution of tumor regions in an independent cohort with 19 patients. In addition, we showed the significant association of MeHEG-based epigenetic heterogeneity with the genomic heterogeneity in mutation and copy number variation in our in-house and TCGA cohorts. Finally, we found that the patients with higher MeHEG score had worse disease-free and overall survival outcomes. Conclusion: By constructing a 7-loci panel with a machine learning approach and QASM assay to facilitate its use in a PCR manner, we developed a valuable method to evaluate the intratumoral epigenetic heterogeneity. The MeHEG algorithm provides innovative insights into the intratumoral heterogeneity from the epigenetic perspective that may add valuable sources to current knowledge about tumor heterogeneity. Citation Format: Yumo Xie, Yu Zhang, Guannan Tang, Xiaolin Wang, Meijin Huang, Yanxin Luo, Huichuan Yu. Spatial deconvolution from bulk DNA methylation profiles determines intratumoral epigenetic heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6079.

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Abstract BACKGROUND Uncontrolled studies have suggested that methylation-based epigenetic subtypes can be used for prognostication of glioma. We used the prospective randomized CATNON trial to validate the clinical relevance of these epigenetic subtypes. METHODS The phase III CATNON trial randomized 751 adult patients with newly diagnosed 1p/19q non-codeleted anaplastic glioma to 59.4 Gy radiotherapy +/- concurrent and/or adjuvant TMZ. CNV data and methylation data were derived from Infinium MethylationEPIC arrays. Epigenetic subtyping and risk of progression to G-CIMP low were determined from random forest models and 7 specific CpGs (PMID: 29642018). IDH1/2 status was determined with a glioma-tailored NGS panel. Overall survival (OS) was measured from date of randomization. RESULTS Methylation analysis was performed on 654 tumors: 440 were IDH1/2mt, 204 IDH1/2wt and of 10 IDH1/2 status was unknown; 8 IDH1/2mt were 1p/19q codeleted. Based on methylation, tumors were classified as G-CIMP high (n=409), G-CIMP low (n=19), codel-like (n=18), mesenchymal-like (n=107), classic-like (n=48), and PA-like tumors (n=53). Median OS between these epigenetic subtypes varied considerably: codel-like 9.1 yrs, G-CIMP high 9.5 yrs, G-CIMP low 2.8 yrs, mesenchymal-like 1.3 yrs, classic-like 1.6 yrs, and PA-like 2.8 yrs. The difference in OS of the IDH1/2mt astrocytoma subgroup patients was prominent [G-CIMP low vs G-CIMP high: HR 4.12, 95% CI 2.37-7.19, p &lt; 0.001]. Within the IDH1/2mt G-CIMP high astrocytoma patients, 115 tumors were predicted to have risk of progression to G-CIMP low and patients with such tumors indeed had poorer survival [risk vs no-risk: HR 1.59, 95% CI 1.10-2.31, p = 0.02]. Median OS in G-CIMP high tumors with (n=37) and without (n=366) CDKN2A/B HD was 3.3 yrs versus not reached [p&lt; 0.001], in G-CIMP low tumors it was 1.2 yrs (n=6) versus 4.4 yrs (n=12) [p=0.008]. CONCLUSIONS In IDH1/2mt anaplastic astrocytoma, G-CIMP status and CDKN2A/B HD are of independent prognostic value.

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The high-throughput correlated DNA methylation (DNAmeth) dataset generated from Illumina Infinium Human Methylation 27 (IIHM 27K) BeadChip assay. In the DNAmeth data, there are several CpG sites for every gene, and these grouped CpG sites are highly correlated. Most of the current filtering-based ranking (FBR) methods do not consider the group correlation structures. Obtaining the significant features with the FBR methods and applying these features to the classifiers to attain the best classification accuracy in highly correlated DNAmeth data is a challenging task. In this research, we introduce a resampling of group least absolute shrinkage and selection operator (glasso) FBR method capable of ignoring the unrelated features in the data considering the group correlation among the features. The various classifiers, such as random forests (RF), Naive Bayes (NB), and support vector machines (SVM) with the significant CpGs obtained from the proposed resampling of group lasso-based ranking (RGLR) method helped to boost the classification accuracy. Through simulated and experimental prostate DNAmeth data, we showed that higher performance of accuracy, sensitivity, specificity, and geometric mean is achieved by ignoring the unimportant CpG sites through the RGLR method.

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Background Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets. Method The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA. Results Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction. Conclusion The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

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Diabetic retinopathy (DR), a diabetic microangiopathy caused by diabetes, affects approximately 93 million people, worldwide. However, the drugs used to treat DR have limited efficacy and the variety of side effects. This is possibly because the complicated pathogenesis of DR is associated with multiple proteins. In this work, we attempted to identify potential drugs against DR-associated proteins and predict potential targets for drugs using in silico prediction of chemical-protein interactions (CPI) based on multitarget quantitative structure-activity relationship (mt-QSAR) method. Therefore, we developed 128 binary classifiers to predict the CPI for 15 DR targets using random forest (RF), k -nearest neighbours (KNN), support vector machine (SVM), and neural network (NN) algorithms with MACCS, extended connectivity fingerprints (ECFP6) fingerprints, and protein descriptors. In order to facilitate discovery of the novel drugs and target identification using the 128 binary classifiers, a free web server (DRDB) was developed. Compound Danshen Dripping Pills (CDDP), composed of Salvia miltiorrhiza, Panax notoginseng, and borneol, is commonly used in the treatment of cardiovascular diseases. To explore the applicability of DRDB, the potential CPIs of CDDP in treatment of DR were investigated based on DRDB. In vitro experimental validation demonstrated that cryptotanshinone and protocatechuic acid, two key components of CDDP, are capable of targeting ICAM-1 which is one of the key target of DR. We hope that this work can facilitate development of more effective clinical strategies for the treatment of DR.

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Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

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This investigation explores the potential of plasma lipidomic signatures for aiding in the diagnosis of Multiple Sclerosis (MS) and evaluating the clinical course and disease activity of diseased patients. Plasma samples from 60 patients with MS (PwMS) were clinically stratified to either a relapsing-remitting (RRMS) or a chronic progressive MS course and 60 age-matched controls were analyzed using state-of-the-art direct infusion quantitative shotgun lipidomics. To account for potential confounders, data were filtered for age and BMI correlations. The statistical analysis employed supervised and unsupervised multivariate data analysis techniques, including a principal component analysis (PCA), a partial least squares discriminant analysis (oPLS-DA) and a random forest (RF). To determine whether the significant absolute differences in the lipid subspecies have a relevant effect on the overall composition of the respective lipid classes, we introduce a class composition visualization (CCV). We identified 670 lipids across 16 classes. PwMS showed a significant increase in diacylglycerols (DAG), with DAG 16:0;0_18:1;0 being proven to be the lipid with the highest predictive ability for MS as determined by RF. The alterations in the phosphatidylethanolamines (PE) were mainly linked to RRMS while the alterations in the ether-bound PEs (PE O-) were found in chronic progressive MS. The amount of CE species was reduced in the CPMS cohort whereas TAG species were reduced in the RRMS patients, both lipid classes being relevant in lipid storage. Combining the above mentioned data analyses, distinct lipidomic signatures were isolated and shown to be correlated with clinical phenotypes. Our study suggests that specific plasma lipid profiles are not merely associated with the diagnosis of MS but instead point toward distinct clinical features in the individual patient paving the way for personalized therapy and an enhanced understanding of MS pathology.