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The objective of this research was to examine the Love-Hate and Identification Relationship of Individuals Participating in Euroleague Match for Recreational Purposes. The study was conducted using a relational survey methodology. The study's population comprises persons who watching recreational purpose part in the Euroleague match held in Istanbul in 2023–2024 season, while the sample consists of 178 voluntary participants selected through convenience sampling. The participants completed the Fan Love-Hate Scale and Fan Identification Scale, in addition to being asked about their gender, marital status, age, educational status, and frequency of attending football matches per week. The data collected from the personal information form and scales was entered into the IBM SPSS 24.0 software package for analysis. Statistical analyses were conducted using the Independent Sample T test and One-way Anova methods. The LSD test was employed to ascertain the dissimilarity between the groups. The Pearson correlation analysis was utilized to ascertain the association between the variables of love-hate and identity. In summary, it is evident that demographic factors, including gender and age, significantly influence fan perceptions and sports identification. In contrast, there is no substantial correlation observed between attributes such as level of education achieved and the frequency of engaging in sports activities, and the aforementioned outcomes. The significant associations identified between the Fan Love-Hate Scale and the Sports Fan Identification Scale underscore the complex relationship between fans' emotional experiences and their connection to sports. Further investigations could be conducted to go deeper into the underlying causes that contribute to these relationships and inequalities, so resulting in a more thorough understanding of fan psychology. [ABSTRACT FROM AUTHOR]

Objective: Acute myocardial infarction (AMI) is a severe condition with high morbidity and mortality rates. This study aimed to identify hub genes potentially associated with AMI and assess their clinical utility in predicting AMI occurrence. Methods: Gene microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted on samples from patients with AMI and control samples to identify modules significantly associated with AMI. GO and KEGG analyses were applied to investigate the potential functions of these hub genes. Lastly, the mendelian randomization (MR) method was applied to analyze the causal relationship between the hub gene TNF and AMI. Results: 285 differentially expressed genes (DEGs) were identified through WCGNA and were clustered into 6 modules. The yellow module appeared most relevant to AMI. Further exploration through GO and KEGG pathway enrichment showed that key hub genes in the yellow module were linked to positive regulation of cytokine production, cytokine receptor binding, NF—kappa B signaling pathway, IL−17 signaling pathway, and TNF signaling pathway. The top 10 genes identified through Cytoscape software analysis were IL1B, TNF, TLR4, TLR2, FCGR3B, MMP9, CXCL8, TLR8, ICAM1, and JUK. Utilizing inverse variance weighting (IVW) analysis, we discovered a significant association between TNF and AMI risk, with an OR of 0.946 (95% CI = 0.911–0.984, p = 0.005). Conclusions: The result of this study indicated that TNF, TLR2, TLR4, IL1B and FCGR3B may be potential biodiagnostic markers for AMI. TNF can inhibit inflammatory and oxidative stress responses in AMI, exerting a protective role in the heart. [ABSTRACT FROM AUTHOR]

Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.gov). Interpretations can now be done at a fraction of the time, cost, and effort it would typically take using a standard chemical ecology data analysis pipeline. The package was tested in two experimental contexts: (1) A dataset of purified internal standards, which showed our algorithms correctly identified the known compounds with R2 values ranging from 0.827–0.999 along concentrations ranging from 1 × 10−5 to 1 × 103 ng/μl, (2) A large, previously published dataset, where the number and types of compounds identified were comparable (or identical) to those identified with the traditional manual peak annotation process, and NMDS analysis of the compounds produced the same pattern of significance as in the original study. Both the speed and accuracy of GC -MS data processing are drastically improved with uafR because it allows users to fluidly interact with their experiment following tentative library identifications [i.e. after the m/z spectra have been matched against an installed chemical fragmentation database (e.g. NIST)]. Use of uafR will allow larger datasets to be collected and systematically interpreted quickly. Furthermore, the functions of uafR could allow backlogs of previously collected and annotated data to be processed by new personnel or students as they are being trained. This is critical as we enter the era of exposomics, metabolomics, volatilomes, and landscape level, high-throughput chemotyping. This package was developed to advance collective understanding of chemical data and is applicable to any research that benefits from GC -MS analysis. It can be downloaded for free along with sample datasets from Github at github.org/castratton/uafR or installed directly from R or RStudio using the developer tools: 'devtools::install_github("castratton/uafR")'. [ABSTRACT FROM AUTHOR]

Observers can discriminate between correct versus incorrect perceptual decisions with feelings of confidence. The centro-parietal positivity build-up rate (CPP slope) has been suggested as a likely neural signature of accumulated evidence, which may guide both perceptual performance and confidence. However, CPP slope also covaries with reaction time, which also covaries with confidence in previous studies, and performance and confidence typically covary; thus, CPP slope may index signatures of perceptual performance rather than confidence per se. Moreover, perceptual metacognition—including neural correlates—has largely been studied in vision, with few exceptions. Thus, we lack understanding of domain-general neural signatures of perceptual metacognition outside vision. Here we designed a novel auditory pitch identification task and collected behavior with simultaneous 32-channel EEG in healthy adults. Participants saw two tone labels which varied in tonal distance on each trial (e.g., C vs D, C vs F), then heard a single auditory tone; they identified which label was correct and rated confidence. We found that pitch identification confidence varied with tonal distance, but performance, metacognitive sensitivity (trial-by-trial covariation of confidence with accuracy), and reaction time did not. Interestingly, however, while CPP slope covaried with performance and reaction time, it did not significantly covary with confidence. We interpret these results to mean that CPP slope is likely a signature of first-order perceptual processing and not confidence-specific signals or computations in auditory tasks. Our novel pitch identification task offers a valuable method to examine the neural correlates of auditory and domain-general perceptual confidence. [ABSTRACT FROM AUTHOR]

Based on 908 consumer questionnaire data from 15 urban areas in Shanghai, we use the binary logit model to empirically analyze the impact of traceability label trust on consumers' traceable pork purchase behavior and the moderating effect of food safety identification. After constructing the theoretical analysis framework, this paper verifies it from the two aspects of statistical analysis and econometric analysis and tests the robustness of the final results. The results show that: first, traceability label trust has a significant positive impact on consumers' traceability pork behavior. Second, food safety identification can significantly strengthen and promote this process. Third, consumers' purchasing behavior is significantly positively affected by traceable pork consumption scenarios and price labels, but the permanent elderly in the composition of family members significantly negative impact on it. Therefore, we put forward relevant policy suggestions, such as strengthening the knowledge popularization and publicity based on the advantageous commodity attributes of traceable pork, carrying out food safety knowledge popularization education, and enhancing consumers' risk perception and food safety identification ability. [ABSTRACT FROM AUTHOR]

In almost every country, patents need to be renewed multiple times after they are granted. A patentee assesses the value of the patent and then pays a renewal fee to keep it active for another stipulated period. The factors that characterize the value of a patent is subjective. This paper aims to address the research gap of building an accurate model for predicting the renewal life (often considered as a substitute for the patent value) of Indian patents, and identification of significant factors that influence the renewal life. This study uses an extensive data set collected from the Indian Patent Office for all granted patents filed between 1995 and 2005. The popular statistical and machine learning algorithms do not result in accurate predictive models, because the patent renewal life distribution (at least for the Indian patents) shows unusual spikes at the two extreme values, which makes the modeling task more challenging. We propose a new two-stage hybrid model by combining an efficient multi-class classifier and a binomial regression model for predicting the complex renewal data distribution. We conducted a comparative analysis of the proposed model with several state-of-the-art machine learning and statistical models. The results show that the proposed hybrid model gives 90% accuracy as compared to the best competitor which gives only 40% accuracy. [ABSTRACT FROM AUTHOR]

China's "the Belt and Road Initiative" (BRI) is a top-level cooperation initiative among countries proposed by China, which has promoted China's cooperation with relevant countries in various aspects and fields. Research reports from think tanks and experts on the evaluation, analysis, and research conclusions of the BRI can reflect the stance, opinions, and demands of various countries abroad regarding the initiative. This paper takes the BRI reports of important think tanks in the " Global Go To Think Tank Index Report 2020" as the subject of its research, and analyzes the key points and development trends of foreign think tank research on the BRI by using text mining, topic evolution, and social network analysis. It provides reasonable suggestions and ideas for promoting the construction of the BRI and deepening related cooperation in China. Research shows that the thematic distribution of research reports on the BRI by think tanks is mainly focused on the fields of politics, economy, and military. The research areas are relatively stable, and there is not a strong trend of thematic evolution. The evolution paths are also mainly distributed in the fields of politics, economy, and military. There are not many expansions in the thematic evolution directions over the years, and there is a strong inheritance of themes. The connection between research themes and the main purpose of the BRI is somewhat inadequate, indicating a certain limitation in the understanding of the BRI. [ABSTRACT FROM AUTHOR]

Background: For young adults and adolescents, excessive internet use has become a serious public health concern due to its negative impact on their health. It has been associated with detrimental effects on both physical and mental health. Negative academic outcomes were observed in the students, including missing classes, lower grades, and academic dismissal. Therefore, the purpose of the current study was to identify factors associated with PIU among undergraduate students at the University of Gondar. Method: A cross-sectional study was conducted at the University of Gondar among 1514 undergraduate students from June 1–20, 2022. The study participants were selected using a stratified simple random selection procedure. Using structural equation modeling, the degree of relationship was ascertained. A p-value of less than 0.05 and an adjusted regression coefficient with a 95% confidence interval (CI) were used to interpret the data. Results: In our study, being from non-health departments [β = 0.11, 95% CI: 0.037, 0.181], current alcohol use [β = 0.12, 95% CI: 0.061, 0.187], depressive symptoms [β = 0.23, 95% CI: 0.175, 0.291], insomnia symptoms [β = 0.12, 95% CI: 0.060, 0.196], and ADHD symptoms [β = 0.11, 95% CI: 0.049, 0.166] had a significant positive effect on PIU, while having a history of head injury had a significant negative effect [β = -0.12, 95% CI: -0.226, -0.021] on PIU. Conclusion and recommendation: Factors such as current alcohol use, non-health department type, depressive symptoms, insomnia, and ADHD symptoms were positively associated with PIU. However, a history of head injuries was negatively associated with PIU. Therefore, strategies aimed at the early identification of PIU may lead to an improvement in the psychosocial health of university students. [ABSTRACT FROM AUTHOR]

With the continuous development of education level and the downturn of economic situation, employment competition is intensifying, more and more high-quality talents appear, and the misfit between people and posts has become a common phenomenon. However, there is no consensus on the relationship between perceived overqualification and employee creativity. Based on the conservation of resource theory, this study reveals the micro mechanism and boundary conditions of the influence of excessive qualification on employee creativity. This study analyzed 487 valid samples obtained in three stages. The results show that: (1) Job crafting has a positive mediating effect on perceived overqualification and creativity, and the path of the two halves is positive; (2) Work withdrawal behavior plays a negative mediating role between the perceived overqualification and creativity. The path in the first half is positive, and the path in the second half is negative; (3) Organizational identity moderates the effect of perceived overqualification on job crafting and work withdrawal behavior. Specifically, the higher the sense of organizational identification, the stronger the positive effect of perceived overqualification on job crafting and the weaker the positive effect on work withdrawal behavior; (4) Organizational identification moderates the mediating role of job crafting and work withdrawal behavior in the relationship between overqualification and creativity. Specifically, the higher the organizational identity, the stronger the indirect positive effect of perceived overqualification on creativity through job crafting, and the weaker the indirect negative impact of perceived overqualification on creativity through work withdrawal behavior. The study conclusion deepens the research on the mechanism of the influence of the perceived overqualification on employees' work behavior, and provides practical enlightenment for the organization and management of employees with excess qualification. [ABSTRACT FROM AUTHOR]

Model-based assessment of the potential impacts of variables on the Sustainable Development Goals (SDGs) can bring great additional information about possible policy intervention points. In the context of sustainability planning, machine learning techniques can provide data-driven solutions throughout the modeling life cycle. In a changing environment, existing models must be continuously reviewed and developed for effective decision support. Thus, we propose to use the Machine Learning Operations (MLOps) life cycle framework. A novel approach for model identification and development is introduced, which involves utilizing the Shapley value to determine the individual direct and indirect contributions of each variable towards the output, as well as network analysis to identify key drivers and support the identification and validation of possible policy intervention points. The applicability of the methods is demonstrated through a case study of the Hungarian water model developed by the Global Green Growth Institute. Based on the model exploration of the case of water efficiency and water stress (in the examined period for the SDG 6.4.1 & 6.4.2) SDG indicators, water reuse and water circularity offer a more effective intervention option than pricing and the use of internal or external renewable water resources. [ABSTRACT FROM AUTHOR]

In the investigation of stratigraphic reservoirs, a significant discrepancy frequently exists between the delineation of the formation pinch-out line as traced using the characteristics of seismic wave reflections and the actual location of the formation pinch-out line. This has been the main problem restricting further hydrocarbon exploration and development. In this study, Hala'alate Mountain on the northwestern margin of the Junggar Basin is taken as an example for carrying out the study of stratigraphic reservoirs by integrating logging, drilling, and 3D seismic data. On the one hand, in studies based on the identification of formation pinch-out points using seismic data, the identification error of reservoir pinch-out lines is reduced by the improved included angle extrapolation method by utilizing the half energy attribute. On the other hand, the Poisson's ratio curve is reconstructed using acoustic curves and oil-gas sensitive logging, then the reservoir oil-bearing facies zone is predicted using Poisson's ratio post-stack genetic inversion to comprehensively analyze the controlling factors of stratigraphic reservoirs. The study area mainly features structural lithologic reservoirs, structural stratigraphic reservoirs and stratigraphic overlaps that pinch out reservoirs. The boundary of a stratigraphic reservoir is affected by the dip angle of the unconformity surface, the formation dip angle, and other factors. The improved included angle extrapolation method improves the identification accuracy of stratigraphic overlap pinch-out reservoirs. The reservoir distribution then is calculated according to Poisson's ratio inversion, improving the prediction accuracy for the reservoir. This method improves the predictive effect for stratigraphic reservoirs and provides a new idea for the exploration and development of similar reservoirs. [ABSTRACT FROM AUTHOR]

3D stratum roaming can visualize the complex geomorphology, underground structure and stratum distribution of geotechnical engineering. Conventional 3D stratum roaming technology is generally aimed at roaming outside the 3D stratum model but rarely roams inside the 3D stratum model, and the efficiency of switching between external and internal roaming is low. In practical engineering, especially in geological and geotechnical engineering, the underground structure and stratum situation are critical. Therefore, focusing on this problem, this paper adopts a three-dimensional roaming engine to connect the inside and outside of a three-dimensional model. Based on an internal and external boundary identification method, the combination of external roaming and internal roaming of the three-dimensional stratum model is implemented by using a stratum virtual surface, and lightweight loading is carried out by controlling the stratum virtual surface to establish the internal and external roaming of the 3D stratum model, and to give full play to the advantages of lightweight loading to provide more intuitive and comprehensive geological information for the project. [ABSTRACT FROM AUTHOR]

Background: The cardiac-brain connection has been identified as the basis for multiple cardio-cerebral diseases. However, effective therapeutic targets for these diseases are still limited. Therefore, this study aimed to identify pleiotropic and specific therapeutic targets for cardio-cerebral diseases using Mendelian randomization (MR) and colocalization analyses. Methods: This study included two large protein quantitative trait loci studies with over 4,000 plasma proteins were included in the discovery and replication cohorts, respectively. We initially used MR to estimate the associations between protein and 20 cardio-cerebral diseases. Subsequently, Colocalization analysis was employed to enhance the credibility of the results. Protein target prioritization was based solely on including highly robust significant results from both the discovery and replication phases. Lastly, the Drug-Gene Interaction Database was utilized to investigate protein-gene-drug interactions further. Results: A total of 46 target proteins for cardio-cerebral diseases were identified as robust in the discovery and replication phases by MR, comprising 7 pleiotropic therapeutic proteins and 39 specific target proteins. Followed by colocalization analysis and prioritization of evidence grades for target protein, 6 of these protein-disease pairs have achieved the highly recommended level. For instance, the PILRA protein presents a pleiotropic effect on sick sinus syndrome and Alzheimer's disease, whereas GRN exerts specific effects on the latter. APOL3, LRP4, and F11, on the other hand, have specific effects on cardiomyopathy and ischemic stroke, respectively. Conclusions: This study successfully identified important therapeutic targets for cardio-cerebral diseases, which benefits the development of preventive or therapeutic drugs. [ABSTRACT FROM AUTHOR]

In a time of increasing threats to bumble bees (Hymenoptera: Apidae: Bombus), it is important to understand their ecology and distribution. As experts are limited in resources to conduct field surveys, there is potential for community scientists to help. The Bumble Bee Watch (BBW) community science program involves volunteers taking photos of bumble bees in Canada and the USA and submitting them, along with geographic and optional plant information, to a website or through an app. Taxon experts then verify the bee species identification. The Bumble Bees of North America database (BBNA) stores data (no photographs) collected and identified by more traditional scientific methods over the same range. Here we compared BBW data to BBNA data over all years and just 2010–2020 to understand the scientific contribution of community scientists to the state of the knowledge about native bumble bees. We found that BBW had similar geographic and species coverage as BBNA. It had records from all 63 provinces, states, and territories where bumble bees occur (including four more than BBNA in 2010–2020), and represented 41 of the 48 species in BBNA (with ten more species than BBNA in 2010–2020). While BBW contributed only 8.50% of records overall, it contributed 25.06% of all records over 2010–2020. BBW confirmed the persistence of species and identified new locations of species, both inside and outside of the previously known extent of occurrences. BBW also contributed a wealth of ecological information, such as unique plant genera and species data for almost all the bee species. Thus, while BBW had fewer bee records than the BBNA database overall, it helped to fill in data gaps and provided novel information, complementing the traditional methods. This community science program is valuable in helping to inform conservation management for bumble bee species. [ABSTRACT FROM AUTHOR]

Misophonia, a heightened aversion to certain sounds, turns common cognitive and social exercises (e.g., paying attention during a lecture near a pen-clicking classmate, coexisting at the dinner table with a food-chomping relative) into challenging endeavors. How does exposure to triggering sounds impact cognitive and social judgments? We investigated this question in a sample of 65 participants (26 misophonia, 39 control) from the general population. In Phase 1, participants saw faces paired with auditory stimuli while completing a gender judgment task, then reported sound discomfort and identification. In Phase 2, participants saw these same faces with novel ones and reported face likeability and memory. For both oral and non-oral triggers, misophonic participants gave higher discomfort ratings than controls did–especially when identification was correct–and performed slower on the gender judgment. Misophonic participants rated lower likeability than controls did for faces they remembered with high discomfort sounds, and face memory was worse overall for faces originally paired with high discomfort sounds. Altogether, these results suggest that misophonic individuals show impairments on social and cognitive judgments if they must endure discomforting sounds. This experiment helps us better understand the day-to-day impact of misophonia and encourages usage of individualized triggers in future studies. [ABSTRACT FROM AUTHOR]

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players. [ABSTRACT FROM AUTHOR]

The automation of pollen identification has seen vast improvements in the past years, with Convolutional Neural Networks coming out as the preferred tool to train models. Still, only a small portion of works published on the matter address the identification of fossil pollen. Fossil pollen is commonly extracted from organic sediment cores and are used by paleoecologists to reconstruct past environments, flora, vegetation, and their evolution through time. The automation of fossil pollen identification would allow paleoecologists to save both time and money while reducing bias and uncertainty. However, Convolutional Neural Networks require a large amount of data for training and databases of fossilized pollen are rare and often incomplete. Since machine learning models are usually trained using labelled fresh pollen associated with many different species, there exists a gap between the training data and target data. We propose a method for a large-scale fossil pollen identification workflow. Our proposed method employs an accelerated fossil pollen extraction protocol and Convolutional Neural Networks trained on the labelled fresh pollen of the species most commonly found in Northeastern American organic sediments. We first test our model on fresh pollen and then on a full fossil pollen sequence totalling 196,526 images. Our model achieved an average per class accuracy of 91.2% when tested against fresh pollen. However, we find that our model does not perform as well when tested on fossil data. While our model is overconfident in its predictions, the general abundance patterns remain consistent with the traditional palynologist IDs. Although not yet capable of accurately classifying a whole fossil pollen sequence, our model serves as a proof of concept towards creating a full large-scale identification workflow. [ABSTRACT FROM AUTHOR]

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples. [ABSTRACT FROM AUTHOR]

Introduction: The rise of new technologies in the field of health is yielding promising results. In certain chronic conditions such as type 2 diabetes mellitus, which ranks among the top five causes of global mortality, it could be useful in supporting patient management. Materials and methods: A systematic review will be conducted on scientific publications from the last 5 years (January 2019 to October 2023) to describe the effect of mobile app usage on glycated hemoglobin for the management of adult patients with type 2 diabetes mellitus who participated in randomized controlled clinical trials. The search will be carried out in the databases of MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), CENTRAL, WoS, Scopus, Epistemonikos, and LILACS. The search strategy will be constructed using both controlled and natural language. Additionally, the Cochrane filter will be applied to identify randomized controlled trials. The review will include scientific articles reporting studies that present results from randomized controlled trials, with texts in Spanish, English, or French, utilizing mobile applications for the management of adult individuals (over 18 years) with type 2 diabetes mellitus, and whose outcomes report the effects on glycated hemoglobin. The Cochrane Risk of Bias Tool will be used to assess the quality of the studies, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology will be implemented to evaluate the certainty of the evidence. Results: The analysis will be conducted by observing the value of the glycated hemoglobin levels of the participants. Given that this data is a quantitative and continuous value, it facilitates the identification of the effects of the mobile applications used for the management of type 2 diabetes mellitus (T2DM) in adults. Furthermore, if sufficient data are available, a meta-analysis will be conducted using IBM-SPSS. The effect of the intervention will be estimated by the mean difference. All point estimates will be accompanied by 95% confidence intervals. A random effects model will be used. The heterogeneity of the results will be assessed using Cochrane's Q and I2 statistics. Discussion: Considering that the quality of content and functionality of certain applications in the healthcare field is highly variable, it is necessary to evaluate the scientific evidence reported on the effect of the use of this type of technology in people with T2DM. [ABSTRACT FROM AUTHOR]

Objective: The objectives of this study are to clarify the scientific definition of comfort food, identify which methodologies are being used in research on this topic and which factors are associated with the consumption of comfort food. Introduction: The consumption of comfort foods is subjective and influenced by individual experiences, as they are known and appreciated by the person. However, divergences about the definition of comfort food in the scientific literature reflect the heterogeneity of the methods used in the research, and consequently identification of possible factors associated with the consumption of this type of food, which can influence the knowledge about the consumption of these foods and their potential effects on the health of those who consume them. Inclusion criteria: Works with a qualitative and quantitative approach published in full in indexed sources or in gray literature, available online in the databases consulted, without restriction on language or year of publication will be included. Methods: The protocol was built based on the methodological recommendations of the Joanna Briggs Institute (JBI) for scoping reviews and the recommendations of the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The Participants, Concept and Context (PCC) mnemonic strategy was built: general population, comfort food concept and world context. Based on this, search strategies were developed for different databases. Instruments were also developed for recording documents, extracting data, justifying the exclusion of documents and not obtaining access to content. A Pilot Study was conducted to test the developed methodology and instruments. The protocol has been registered with the Open Science Framework (OSF) (https://osf.io/gnza4/). The results will be presented in the review resulting from this protocol in three ways: accounting of the documents will be recorded in a PRISMA Flow Diagram, the main information of the studies and their frequencies will be presented in a table, and the union of these outcomes will be presented visually in a Graphical Abstract. [ABSTRACT FROM AUTHOR]

Purpose: To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions. Methods: We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models. Results: We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%. Conclusions: The optimal model of this study is expected to predict the causality accurately. [ABSTRACT FROM AUTHOR]

Threatened shark species are caught in large numbers by artisanal and commercial fisheries and traded globally. Monitoring both which shark species are caught and sold in fisheries, and the export of CITES-restricted products, are essential in reducing illegal fishing. Current methods for species identification rely on visual examination by experts or DNA barcoding techniques requiring specialist laboratory facilities and trained personnel. The need for specialist equipment and/or input from experts means many markets are currently not monitored. We have developed a paper-based Lab-on-a-Chip (LOC) to facilitate identification of three threatened and CITES-listed sharks, bigeye thresher (Alopias superciliosus), pelagic thresher (A. pelagicus) and shortfin mako shark (Isurus oxyrinchus) at market source. DNA was successfully extracted from shark meat and fin samples and combined with DNA amplification and visualisation using Loop Mediated Isothermal Amplification (LAMP) on the LOC. This resulted in the successful identification of the target species of sharks in under an hour, with a working positive and negative control. The LOC provided a simple "yes" or "no" result via a colour change from pink to yellow when one of the target species was present. The LOC serves as proof-of-concept (PoC) for field-based species identification as it does not require specialist facilities. It can be used by non-scientifically trained personnel, especially in areas where there are suspected high frequencies of mislabelling or for the identification of dried shark fins in seizures. [ABSTRACT FROM AUTHOR]

For many species, population sizes are unknown despite their importance for conservation. For population size estimation, capture-mark-recapture (CMR) studies are often used, which include the necessity to identify each individual, mostly through individual markings or genetic characters. Invasive marking techniques, however, can negatively affect the individual fitness. Alternatives are low-impact techniques such as the use of photos for individual identification, for species with stable distinctive phenotypic traits. For the individual identification of photos, a variety of different software, with different requirements, is available. The European fire salamander (Salamandra salamandra) is a species in which individuals, both at the larval stage and as adults, have individual specific patterns that allow for individual identification. In this study, we compared the performance of five different software for the use of photographic identification for the European fire salamander: Amphibian & Reptile Wildbook (ARW), AmphIdent, I3S pattern+, ManderMatcher and Wild-ID. While adults can be identified by all five software, European fire salamander larvae can currently only be identified by two of the five (ARW and Wild-ID). We used one dataset of European fire salamander larval pictures taken in the laboratory and tested this dataset in two of the five software (ARW and Wild-ID). We used another dataset of European fire salamander adult pictures taken in the field and tested this using all five software. We compared the requirements of all software on the pictures used and calculated the False Rejection Rate (FRR) and the Recognition Rate (RR). For the larval dataset (421 pictures) we found that the ARW and Wild-ID performed equally well for individual identification (99.6% and 100% Recognition Rate, respectively). For the adult dataset (377 pictures), we found the best False Rejection Rate in ManderMatcher and the highest Recognition Rate in the ARW. Additionally, the ARW is the only program that requires no image pre-processing. In times of amphibian declines, non-invasive photo identification software allowing capture-mark-recapture studies help to gain knowledge on population sizes, distribution, movement and demography of a population and can thus help to support species conservation. [ABSTRACT FROM AUTHOR]

With the decline of bee populations worldwide, studies determining current wild bee distributions and diversity are increasingly important. Wild bee identification is often completed by experienced taxonomists or by genetic analysis. The current study was designed to compare two methods of identification including: (1) morphological identification by experienced taxonomists using images of field-collected wild bees and (2) genetic analysis of composite bee legs (multiple taxa) using metabarcoding. Bees were collected from conservation grasslands in eastern Iowa in summer 2019 and identified to the lowest taxonomic unit using both methods. Sanger sequencing of individual wild bee legs was used as a positive control for metabarcoding. Morphological identification of bees using images resulted in 36 unique taxa among 22 genera, and >80% of Bombus specimens were identified to species. Metabarcoding was limited to genus-level assignments among 18 genera but resolved some morphologically similar genera. Metabarcoding did not consistently detect all genera in the composite samples, including kleptoparasitic bees. Sanger sequencing showed similar presence or absence detection results as metabarcoding but provided species-level identifications for cryptic species (i.e., Lasioglossum). Genus-specific detections were more frequent with morphological identification than metabarcoding, but certain genera such as Ceratina and Halictus were identified equally well with metabarcoding and morphology. Genera with proportionately less tissue in a composite sample were less likely to be detected using metabarcoding. Image-based methods were limited by image quality and visible morphological features, while genetic methods were limited by databases, primers, and amplification at target loci. This study shows how an image-based identification method compares with genetic techniques, and how in combination, the methods provide valuable genus- and species-level information for wild bees while preserving tissue for other analyses. These methods could be improved and transferred to a field setting to advance our understanding of wild bee distributions and to expedite conservation research. [ABSTRACT FROM AUTHOR]

Parchment, the skins of animals prepared for use as writing surfaces, offers a valuable source of genetic information. Many have clearly defined provenance, allowing for the genetic findings to be evaluated in temporal and spatial context. While these documents can yield evidence of the animal sources, the DNA contained within these aged skins is often damaged and fragmented. Previously, genetic studies targeting parchment have used destructive sampling techniques and so the development and validation of non-destructive sampling methods would expand opportunities and facilitate testing of more precious documents, especially those with historical significance. Here we present genetic data obtained by non-destructive sampling of eight parchments spanning the 15th century to the modern day. We define a workflow for enriching the mitochondrial genome (mtGenome), generating next-generation sequencing reads to permit species identification, and providing interpretation guidance. Using sample replication, comparisons to destructively sampled controls, and by establishing authentication criteria, we were able to confidently assign full/near full mtGenome sequences to 56.3% of non-destructively sampled parchments, each with greater than 90% of the mtGenome reference covered. Six of eight parchments passed all four established thresholds with at least one non-destructive sample, highlighting promise for future studies. [ABSTRACT FROM AUTHOR]

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical. [ABSTRACT FROM AUTHOR]

Since its initial discovery in the Hunan province of China, genomic DNA of porcine circovirus 4 (PCV4) has been detected in pigs across multiple provinces in China, as well as in South Korea. However, the prevalence of porcine circovirus type 4 in Gansu Province, China, remains unknown. To address this gap, we undertook an extensive study where we gathered 121 clinical samples displaying diverse clinical manifestations from pig farms in Gansu Province between 2022 and 2023. Employing a real-time fluorescence quantification method, we identified the presence of PCV4 genome. Out of the 121 clinical samples analyzed, 13 samples tested positive for PCV4, resulting in a positive rate of 10.74% (13/121). This finding confirms the presence of PCV4 in pig farms within Gansu Province, China. Furthermore, we successfully sequenced and analyzed the complete genomes of two distinct PCV4 strains, comparing them with 60 reference sequences archived in the GenBank database. The results revealed a high nucleotide homology (98.2–98.8%) between the strains obtained in this study and the PCV4 reference strains, indicating a relatively low evolutionary rate of the PCV4 genome. Phylogenetic analysis revealed that two strains in this study belong to PCV4a and PCV4c. As far as we know, this study marks the inaugural report on the molecular identification and genomic attributes of PCV4 in Gansu Province, China, offering valuable insights for devising preventive and control strategies against this emerging virus. [ABSTRACT FROM AUTHOR]

Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. However, automation of functional clustering based on phylogenetic trees has been challenging and most of it is done manually. Clustering phylogenetic trees usually requires the delineation of tree-based thresholds (e.g., distances), leading to an ad hoc problem. We propose a new phylogenetic clustering approach that identifies clusters without using ad hoc distances or other pre-defined values. Our workflow combines uniform manifold approximation and projection (UMAP) with Gaussian mixture models as a k-means like procedure to automatically group sequences into clusters. We then apply a "second pass" clade identification algorithm to resolve non-monophyletic groups. We tested our approach with several well-curated protein families (outer membrane porins, acyltransferase, and nuclear receptors) and showed our automated methods recapitulated known subfamilies. We also applied our methods to a broad range of different protein families from multiple databases, including Pfam, PANTHER, and UniProt, and to alignments of RNA viral genomes. Our results showed that AutoPhy rapidly generated monophyletic clusters (subfamilies) within phylogenetic trees evolving at very different rates both within and among phylogenies. The phylogenetic clusters generated by AutoPhy resolved misannotations and identified new protein functional groups and novel viral strains. [ABSTRACT FROM AUTHOR]

Personal identification in forensics is possible with gender determination using DNA (deoxyribonucleic acid) analysis. DNA isolation from teeth samples subjected to extreme temperatures has been shown to predict the gender of the deceased. However, the literature lacks studies on DNA extracted from tooth samples exposed to freezing temperatures. This study aimed to isolate the SRY gene from the extirpated pulp of teeth that were subjected to varying temperatures for gender identification. Thirty teeth with vital pulps, divided into 3 groups were included in the study. Each group consisted of 5 male and 5 female tooth samples. The groups were exposed to diverse environmental factors for three weeks. Group 1: room temperature (R group); Group 2: high temperature (H group) and Group 3: freezing temperature (F group). Later, DNA was isolated from the pulp tissue, and the SRY gene was amplified using PCR (Polymerase Chain Reaction). The Sensitivity and Specificity of the results were analyzed. SRY gene detected in the study samples identified accurate gender with a 46.70% Sensitivity and 93.30% Specificity. Significant difference was found in the correlation between gene expression and gender among the three groups (p = 1.000). The study validates that dental pulp tissue can be a reliable source for DNA extraction. And SRY gene amplification from teeth exposed to diverse environmental conditions. Further investigations are required to validate its application in forensics. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (Re-ID) is a challenging task that aims to recognize the same vehicle across different non-overlapping cameras. Existing attention mechanism-based methods for vehicle Re-ID often suffer from significant intra-class variation and inter-class variation due to various factors such as illumination, occlusion, viewpoint, etc. In this paper, we propose a novel network architecture for vehicle Re-ID, named Dimensional Decoupling Strategy and Non-local Relationship Network (DMNR-Net), which uses three modules to extract complementary features: global feature extraction module, non-local relationship capture module(NRCM), and dimensional decoupling module (DDS). The global feature extraction module captures complete and coarse-grained features from the whole image; the NRCM module extracts saliency information from feature maps in both spatial and channel dimensions; and the DDS decouples spatial and channel features into two branches to extract fine-grained features and focus on specific subspaces. We conduct extensive experiments on two popular publicly datasets, VeRi-776 and VehicleID, to evaluate the effectiveness of our method. The experimental results show that our DMNR-Net outperforms state-of-the-art methods by a large margin on both datasets. [ABSTRACT FROM AUTHOR]

Musculoskeletal conditions are a major source of disability worldwide, and its burden have been rising in the last decades. Rural areas, in particular, are associated with higher prevalence of these conditions as well as higher levels of disability, which is likely related to other determinants that affect these communities. Although digital health has been identified as a potential solution to mitigate the impact of these determinants, it is also known that these populations may face barriers that limit the implementation of these interventions. Therefore, the aim of this scoping review is to comprehensively map the evidence regarding the implementation of digital health interventions in rural populations with chronic musculoskeletal conditions. We will include studies published from the year 2000; that report the use of digital interventions that promote prevention, treatment or monitoring of any chronic musculoskeletal condition or chronic pain from musculoskeletal origin, in patients that live in rural areas. This protocol follows the methodological framework for scoping reviews proposed by Arksey and O'Malley, as well as the Joana Briggs Institute (JBI) approach. We will conduct the search on Medline (PubMed), EMBASE, Web of Science and Scopus, as well as grey literature databases. Two independent reviewers will screen titles and abstracts followed by a full-text review to assess the eligibility of the articles. Data extracted will include the identification of the digital interventions used, barriers and enablers identified by the patients or healthcare providers, the patient-level outcomes measured, and the implementation strategies and outcomes reported. By mapping the evidence on the implementation of digital health interventions in rural communities with musculoskeletal conditions, this scoping review will enhance our understanding of their applicability in real-world settings. [ABSTRACT FROM AUTHOR]

Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model's complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines. [ABSTRACT FROM AUTHOR]

Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pan-cancer samples were concatenated and non-linearly projected to a lower dimension using an ML algorithm. This data was then clustered to identify multi-omics-based novel subgroups. The clinical characterization of these ML subgroups indicated significant differences in overall survival (OS) and disease-free survival (DFS) (p-value<0.0001). The subgroups formed by the patients from different tumors shared similar molecular alterations in terms of immune microenvironment, mutation profile, and enriched pathways. Further, decision-level and feature-level fused classification models were built to identify the novel subgroups for unseen samples. Additionally, the classification models were used to obtain the class labels for the validation samples, and the molecular characteristics were verified. To summarize, this work identified novel ML subgroups using multi-omics data and showed that the patients with different tumor types could be similar molecularly. We also proposed and validated the classification models for subgroup identification. The proposed classification models can be used to identify the novel multi-omics subgroups, and the molecular characteristics of each subgroup can be used to design appropriate treatment regimen. [ABSTRACT FROM AUTHOR]

The emergence of Chinese opera animation allows a wider audience, especially a younger audience, to access and embrace the art of opera heritage. This study used a two-way mixed-design ANOVA to explore the effect of Chinese opera animation on schoolchildren's viewing motivation; the independent variables were the children's grade level and the opera genre of the animation. Grade level was divided into three groups: lower, middle, and upper (grades 2, 4, and 6, respectively). Opera genre consisted of Peking, Yue, and Henan opera. The dependent variable, viewing motivation, comprised six dimensions: entertainment and relaxation, learning knowledge, escapist pastime, aesthetic appreciation, empathic identification, and socializing and sharing. After statistically analyzing the evaluations of 457 participants, the results showed the following: (1) Peking and Yue opera animation had a better entertainment and relaxation effect on the lower and middle groups. Henan opera had a better escapist pastime effect on the upper group but less effect on the lower group. (2) In terms of learning knowledge, empathic identification, aesthetic appreciation, and overall performance, Yue and Henan opera animations were more effective for enhancing viewing motivation compared with Peking opera animation. (3) The middle and lower groups showed higher viewing motivation than the upper group in the learning knowledge, empathic identification, and socializing and sharing dimensions. Overall, grades 2 and 4 were appropriate stages for schoolchildren to engage with opera animation. Our findings can provide a reference for promoting cultural heritage sustainability and support follow-up research on integrating opera animation into children's education. [ABSTRACT FROM AUTHOR]

Background and purpose: In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients. Data sources: The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately. Results: In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis. Conclusions: The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems. Trial registration: Systematic review registration. Prospero registration number: CRD42022307403. [ABSTRACT FROM AUTHOR]

Tens of millions of dried seahorses (genus Hippocampus) are traded annually, and the pressure from this trade along with their life history traits (involved parental care and small migration distances and home ranges) has led to near global population declines. This and other forms of overexploitation have led to all seahorse species being listed in Appendix II under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The signatory nations of CITES recommended a 10-cm size limit of seahorses to ensure harvested individuals have reached reproductive maturity, and have thus had the chance to produce offspring, to maintain a more sustainable global seahorse fishery. We assessed adherence to CITES recommendations using DNA barcoding and size measurements to compare two prominent U.S. dried seahorse markets: (1) traditional Chinese medicine (TCM), and (2) non-medicinal ecommerce and coastal curio (ECC). We also estimated U.S. import abundance from CITES records. Of the nine species identified among all samples (n = 532), eight were found in the TCM trade (n = 168); composed mostly (75%) of the Indo-Pacific species Hippocampus trimaculatus, and Hippocampus spinosissimus, and the Latin American Hippocampus ingens. In contrast, ECC samples (n = 344) included 5 species, primarily juvenile Indo-Pacific Hippocampus kuda (51.5%) and the western Atlantic Hippocampus zosterae (40.7). The majority of TCM samples (85.7%) met the CITES size recommendation, in contrast to 4.8% of ECC samples. These results suggest non-size discriminatory bycatch is the most likely source of imported ECC specimens. In addition, CITES records indicate that approximately 602,275 dried specimens were imported into the U.S. from 2004–2020, but the exact species composition remains unknown as many U.S. imports records list one species or Hippocampus spp. from confiscated shipments due to difficulties in morphological identification and large numbers of individuals per shipment. Molecular identification was used to identify the species composition of confiscated shipment imports containing undesignated species, and similar to TCM, found H. trimaculatus and H. spinosissimus the most abundant. By combining DNA barcoding, size comparisons, and CITES database records, these results provide an important glimpse into the two primary dried U.S. seahorse end-markets, and may further inform the conservation status of several Hippocampus species. [ABSTRACT FROM AUTHOR]

We investigated the impact of flanking stimuli that are orthographic neighbors of central target words in the reading version of the flankers task. Experiment 1 provided a replication of the finding that flanking words that are orthographic neighbors of central target words (e.g., BLUE BLUR BLUE) facilitate lexical decisions relative to unrelated word flankers (e.g., STEP BLUR STEP). Experiment 2 tested the hypothesis that this facilitatory effect might be due to the task that was used in Experiment 1 and in prior research–the lexical decision task. In Experiment 2 the task was perceptual identification, and here we observed that orthographic neighbor flankers interfered with target word identification. Experiment 2 also included a bigram flanker condition (e.g., BL BLUR UE), and here the related bigram flankers facilitated target word identification. We conclude that when the task requires identification of a specific word, effects of lexical competition emerge over and above the facilitatory effects driven by the sublexical spatial pooling of orthographic information across target and flankers, and that the inhibitory influence of lexical competition has an even stronger impact when flankers are whole words. [ABSTRACT FROM AUTHOR]

The assessment of the resilience of Urban Rail Transit Networks (URTNs) and the analysis of their evolutionary characteristics during network growth can help in the design of efficient, safe, and sustainable networks. However, there have been few studies regarding the change of resilience in long-term network development. As for the existing resilience studies, they rarely consider the entire cycle of accident occurrence and repair; furthermore, they ignore the changes in network transportation performance during emergencies. Moreover, the measurement metrics of the important nodes have not been comprehensively considered. Therefore, to remedy these deficiencies, this paper proposes a URTN dynamic resilience assessment model that integrates the entire cycle of incident occurrence and repair, and introduces the network transport effectiveness index E(Gw) to quantitatively assess the network resilience. In addition, a weighted comprehensive identification method of the important nodes (the WH method) is proposed. The application considers the Xi'an urban rail transit network (XURTN) during 2011–2021. The obtained results identify the resilience evolutionary characteristics during network growth. And longer peripheral lines negatively affect the resilience of XURTN during both the attack and the repair processes. The central city network improves the damage index Rdam and the recovery index Rrec by up to 123.46% and 11.65%, respectively, over the overall network. In addition, the WH method can comprehensively and accurately identify the important nodes in the network and their evolutionary characteristics. Compared to the single-factor and topological strategies, the Rdam is 1.17%~178.89% smaller and the Rrec is 1.68%~84.81% larger under the WH strategy. Therefore, this method improves the accuracy of the important node identification. Overall, the insights from this study can provide practical and scientific references for the synergistic development of URTN and urban space, the enhancement of network resilience, and the protection and restoration of important nodes. [ABSTRACT FROM AUTHOR]

Symptom severity on differential host lines is currently used to characterize and identify pathotypes of Plasmodiophora brassicae, which is an obligate, soil-borne chromist pathogen that causes clubroot disease on canola (Brassica napus) and other brassica crops. This process is slow, variable and resource intensive; development of molecular markers could make identification of important pathotypes faster and more consistent for deployment of cultivars with pathotype-specific resistance. In the current study, a variant of gene 9171 was identified in the whole-genome sequences of only the highly virulent pathotypes of P. brassicae from around the world, including the new cohort of virulent pathotypes in Canada; its presence was confirmed using three KASP marker pairs. The gene was not present in the initial cohort of pathotypes identified in Canada. The putative structure, domains, and gene ontogeny of the protein product of gene 9171 were assessed using on-line software resources. Structural analysis of the putative protein produced by gene 9171 indicated that it was localized in the cytosol, and likely involved in cellular processes and catalytic activity. Identification of gene 9171 represents a potentially useful step toward molecular identification of the pathotypes of P. brassicae. [ABSTRACT FROM AUTHOR]

Background: World-wide, health service providers are moving towards innovative models of clinical home-based care services as a key strategy to improve equity of access and quality of care. To optimise existing and new clinical home-based care programs, evidence informed approaches are needed that consider the complexity of the health care system across different contexts. Methods: We present a protocol for working with health services and their partners to perform rapid identification, prioritisation, and co-design of content-appropriate strategies to optimise the delivery of healthcare at home for older people in rural and regional areas. The protocol combines Systems Thinking and Implementation Science using a Consensus Mapping and Co-design (CMC) process delivered over five workshops. Discussion: The protocol will be implemented with rural and regional healthcare providers to identify digital and non-digital solutions that have the potential to inform models of service delivery, improve patient experience, and optimise health outcomes. The combination of system and implementation science is a unique approach for optimising healthcare at home for older populations, especially in the rural context where need is high. This is the first protocol to integrate the use of systems and implementation science into one process and articulating these methods will help with replicating this in future practice. Results of the design phase will translate into practice through standard health service planning methods to enhance implementation and sustainability. The delivery of the protocol will include building capacity of health service workers to embed the design, implementation, and evaluation approach into normal practice. This protocol forms part of the DELIVER (Delivering Enhanced heaLthcare at home through optImising Virtual tools for oldEr people in Rural and regional Australia) Project. Funded by Australia's Medical Research Future Fund, DELIVER involves a collaboration with public health services of Western Victoria, Australia. [ABSTRACT FROM AUTHOR]

In the present research we tested the differential effects of anger versus shame as emotional predictors of ingroup disidentification in one rather collectivistic (Japan) and two rather individualistic societies (Germany, Canada). We tested the idea that individuals cope with socially undesired emotions by disidentifying from their group. Specifically, we predicted that after a group conflict, anger, an undesired emotion in Japan, would elicit disidentification in Japan, whereas shame, an undesired emotion in Canada and Germany, would elicit disidentification in Germany and Canada. Study 1 (N = 378) found that anger, but not shame, was related to disidentification in Japan, whereas shame, but not anger, was related to disidentification in Canada and Germany. Study 2 (N = 171) shows that, after group conflict, Japanese disidentified more when imagining to feel angry, whereas Germans disidentified more when imagining to feel ashamed. Implications for these findings are discussed. [ABSTRACT FROM AUTHOR]

Decades of research in social identity have shown that people instinctively hold positive attitudes towards ingroup members and negative attitudes towards outgroup members. However, it remains unclear how people respond to individuals explicitly identified with both one's ingroup and outgroup. We propose that when people are exposed to dual-identified individuals and groups (e.g., Muslim-Americans explicitly identifying with both their Muslim and American identities), intergroup attitudes will improve, driven more by the ingroup component (American), despite the presence of the outgroup component (Muslim). Moreover, we suggest exposure to dual-identification can also improve attitudes toward the broader outgroup (Muslims more generally), a phenomenon called the gateway-group effect. To test these hypotheses, we created a new measure of dual-identification and conducted three studies involving both Muslim-Americans and Mexican-Americans. Results confirmed that exposure to explicitly dual-identified groups improved attitudes towards the dual-identified group (e.g., Mexican-Americans) as well as toward the respective outgroup (e.g., Mexicans). [ABSTRACT FROM AUTHOR]

Background: Despite physical activity (PA) health benefits, people with dementia (PwD) continue to report low levels of PA engagement compared with healthy older adults. Evidencing that PA initiatives still not reflect effective practice and outcomes. Previous studies have shown that several factors can mediate PA initiatives implementation in this population. However, most prior research have not use implementation science frameworks to outline in-depth barriers and facilitators that enables improved PA strategies in PwD. Therefore, a more holistic understanding of mediating factors is still needed. Objective: To identify multilevel barriers and facilitator factors, applying the Consolidated Framework for Implementation Research (CFIR) to orient a systematic evaluation of one PA project in PwD and provide evidence-based evaluation results to enhance PA implementation efforts for PwD. Method: A qualitative study implemented in 4 German sports associations that applied a PA project for PwD. A total of 13 semi-structured interviews were conducted with 21 participants, project leaders (PLs) and sports trainers (STs). The Consolidated Framework for Implementation Research (CFIR) was used as an evaluation framework to orient both the data collection and analysis. Results: A total of 13 interviews were conducted with 21 participants. The CFIR guided the identification of barriers and facilitating factors that need to be targeted at different levels for successful implementation. Barriers were identified, especially in the external level, as more solid networks and funding for sustainable proposals are still needed. Other barriers were low participation rates, stigma around the disease and the COVID 19 pandemic. On an individual and structural level facilitators were found like motivated appointed leaders, established planning process, and external organizations supporting sports associations in the implementation. Conclusion: Sports projects for PwD can benefit from structuring their interventions based on the CFIR framework as it helps identify multilevel factors that may influence their success and promote PA among PwD. Future efforts should continue working on implementing frameworks that facilitate and reduce the complexity of implementing sustainable PA projects for PwD. [ABSTRACT FROM AUTHOR]

Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant challenges to accurate and complete labeling using mainstream algorithms like YOLOv5. In this study, we established the first artificial dataset of plums and used deep learning to improve target detection. Our improved YOLOv5 algorithm achieved more accurate and rapid batch identification of immature plums, resulting in improved quality and economic benefits. The YOLOv5-plum algorithm showed 91.65% recognition accuracy for immature plums after our algorithmic improvements. Currently, the YOLOv5-plum algorithm has demonstrated significant advantages in detecting unripe plums and can potentially be applied to other unripe fruits in the future. [ABSTRACT FROM AUTHOR]

Reinforcing weak cyberspace assets is an urgent requirement to defend national cybersecurity. Cyberspace key terrain (CKT) is a theory recently proposed for sensing cyberspace posture. Identifying CKT in the asset layer is essential for supporting cyberspace defense decisions. Existing methods ignore the influence of the multi-attribute correlation of cyberspace nodes and cyber attack mission (CAM) diversity, which restricts the recognition accuracy of CKT. To improve the accuracy of CKT identification and explore the relationship between CKT and CAM, we propose an improved cosine similarity technique for order of preference by similarity to the ideal solution (CosS-TOPSIS) method to model CKT and construct a CAM based on the MITRE adversarial tactics, techniques, and common knowledge (ATT&CK) framework to examine the influence of different weighted CAM on modeling CKT. Based on the vulnerability value calculation method of the cyber system in the common vulnerability scoring system version 3.1 (CVSS 3.1), we evaluated the effectiveness of CosS-TOPSIS in identifying CKT using three metrics: correlation coefficient, root mean square error, and mean absolute error. Our experiments showed that, in comparison with the TOPSIS method, the accuracy of the proposed method for identifying CKT improved by 8.9%, and the root mean square error reduced by 16%; simultaneously, CAM was proven to be an essential factor in identifying CKT. The feasibility and reliability of CosS-TOPSIS in identifying CKT and the close relationship between CAM and CKT identification were demonstrated experimentally. In our work, we utilized cosine similarity and FAHP to improve the baseline method. We also introduced three indicators to evaluate the method's reliability. Drawing from ATT&CK, we recommend CAM as a tool for sensing changes in the cyberspace environment and explore its relationship with CKT. Our work has great application potential for identifying cyberspace vulnerabilities, supporting cyberspace defense, and securing national cyberspace facilities. [ABSTRACT FROM AUTHOR]

There is an emergent literature highlighting the positive role of social support and social identification in buffering against the deleterious effects of psychological stressors. Yet, we have limited understanding of how exactly these social factors fit within contemporary stress and coping theory. To advance and gain a greater understanding of these social factors, we explore the associations of social support and social identification on individuals' challenge and threat cognitive appraisals and how this then relates to perceived stress, life satisfaction, turnover intentions, and job performance. A total of 412 workplace employees from private and public sector occupations completed state measures around a recent most stressful experience at work. Results revealed atemporal associations between cognitive resource appraisals with both social support and social identification. Specifically, greater identification with colleagues and lower threat were related to less perceived stress, while having greater social identification (with colleagues and organisation), social support, and lower threat, were related to greater life satisfaction. Greater perceived stress, and lower social identification and life satisfaction, were also related to greater turnover intentions. While greater identification with the organisation and life satisfaction, along with lower perceived stress were related to greater job performance. Taken together, this research provides evidence that social support and social identification play a positive role when trying to promote more adaptive responses to stressful situations. [ABSTRACT FROM AUTHOR]

Background: Pharmacotherapy is necessary for many people with psychiatric disorders and polypharmacy is common. The psychotropic drug-drug interaction (DDI) should be concerned and efficiently monitored by a proper instrument. Objectives: This study aimed to investigate the prevalence and associated factors of psychotropic DDI and to compare the identification utility from three databases: Drugs.com®, Lexicomp®, and Epocrates®. Methods: This was a retrospective cohort design. We collected demographic and clinical data of all patients hospitalised in the psychiatric inpatient unit in 2020. Psychotropic DDI profiles were examined through three databases. Descriptive statistics were used to report comprehensiveness of each database and prevalence of psychotropic DDI. The Fleiss' kappa index would be analysed to indicate agreement strength of DDI severity classification among three databases. Results: From 149 total admissions, the psychotropic DDIs were found in 148 admissions (99.3%). Thorough the study, there were 182 of both psychotropic and other agents prescribed under 1,357 prescriptions. In total, 2,825 psychotropic DDIs were identified by using Drugs.com® 2,500 times, Epocrates® 2,269 times, and Lexicomp® 2,265 times. Interactions with clonazepam was the three most frequent agents when co-administrated with quetiapine (n = 56), risperidone (n = 36), and valproic acid and derivatives (n = 36). Serious DDIs were comparatively lower in incidence and there was no evidence of its association with reported clinical adverse consequences. The study revealed slight and fair agreement regarding severity classification among the three databases was found. DDI events detected by Drugs.com® were greatest in number, but Lexicomp® provided the broadest list of medications prescribed in our study. Conclusion: Among three databases, interactions detected by Drugs.com® were greatest in number, whereas Lexicomp® provided the broadest list of medications. Development of such databases, based on both theoretical and clinical conceptions, should be focused to balance safety of patients and weariness of healthcare providers. [ABSTRACT FROM AUTHOR]

The social identity approach to leadership argues that leaders' capacity to influence and inspire others is grounded in a shared sense of social identity (or 'us-ness') that those leaders create, advance, represent, and embed for the groups they lead. The approach therefore argues that a key task for leaders is to develop insights and skills of (social) identity leadership that allow them to motivate and mobilize groups and transform them into a potent social and organizational force. In contrast to other approaches and programs which focus on leaders' leader identity (their 'I-ness'), the 5R leadership development program supports the development of leaders' social identity by raising awareness of the importance of social identity ('we-ness') for leadership and taking leaders through structured activities that help them build engaged and inclusive teams. The present research assessed the benefits of facilitated and learner self-directed versions of the 5R program (Ns = 27, 22 respectively) relative to a no-treatment control (N = 27). Results (including those of an intention-to-treat analysis; N = 76) indicated that, relative to leaders in the control condition, those who participated in both forms of 5R reported large increases in identity leadership knowledge, as well as medium-sized increases in both team engagement (a compound factor comprised of team identification, team OCB, team efficacy, and work engagement) and 'teamfulness' (comprised of team reflexivity, team psychological safety, team goal clarity, and inclusive team climate). We reflect on the importance of teamfulness for leadership and team functioning and on the value of programs that help leaders develop this. [ABSTRACT FROM AUTHOR]

Guidance on contextually tailored implementation strategies for the prevention, treatment, and control of hypertension is limited in lower-middle income countries (Lower-MIC). To address this limitation, we compiled implementation strategies and accompanying outcomes of evidence-based hypertension interventions currently being implemented in five Lower-MIC. The Global Research on Implementation and Translation Science (GRIT) Coordinating Center (CC) (GRIT-CC) engaged its global network sites at Ghana, Guatemala, India, Kenya, and Vietnam. Purposively sampled implementation science experts completed an electronic survey assessing implementation outcomes, in addition to implementation strategies used in their ongoing hypertension interventions from among 73 strategies within the Expert Recommendations for Implementing Change (ERIC). Experts rated the strategies based on highest priority to their interventions. We analyzed the data by sorting implementation strategies utilized by sites into one of the nine domains in ERIC and summarized the data using frequencies, proportions, and means. Seventeen implementation experts (52.9% men) participated in the exercise. Of Proctor's implementation outcomes identified across sites, all outcomes except for appropriateness were broadly assessed by three or more countries. Overall, 59 out of 73 (81%) strategies were being utilized in the five countries. The highest priority implementation strategies utilized across all five countries focused on evaluative and iterative strategies (e.g., identification of context specific barriers and facilitators) to delivery of patient- and community-level interventions, while the lowest priority was use of financial and infrastructure change strategies. More capacity building strategies (developing stakeholder interrelationships, training and educating stakeholders, and supporting clinicians) were incorporated into interventions implemented in India and Vietnam than Ghana, Kenya, and Guatemala. Although robust implementation strategies are being used in Lower -MICs, there is minimum use of financial and infrastructure change strategies. Our study contributes to the growing literature that demonstrates the use of Expert Recommendations for Implementing Change (ERIC) implementation strategies to deliver evidence-based hypertension interventions in Lower-MICs and will inform future cross-country data harmonization activities in resource-constrained settings. [ABSTRACT FROM AUTHOR]

Orbital and nasal parameters among modern humans show considerable variation, which affects facial shape, and these characteristics vary according to race, region, and period in evolution. The aim of the study was to ascertain whether there are sex differences in the orbital and/or nasal indexes and/or the single measurements used to calculate these in a Kosovar population. The following parameters were taken into consideration: orbital height (OH), orbital width (OW), nasal height (NH), and nasal width (NW). The ratios between orbital index/nasal index (RONI) were calculated. All measurements were obtained from a population sample comprising 408 individuals. The accuracy in sex prediction was 52.86% (CI95% = 45.05%–60.67%) for NW and 64.96% for NH (CI95% = 57.50%– 72.42%). The difference between male and female indexes was statistically significant (P < 0.05). The anthropometric study revealed that only NW and NH are configured as predictors of sexual dimorphism. It could be useful to increase the number of samples to test the discriminant function in other population groups. [ABSTRACT FROM AUTHOR]