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AbstractMorels (Morchella sp. pl.) are valuable mushrooms, and their applications in the pharmaceutical and food industries has steadily increased in China.White mold disease is a common and severe disease in the culturing of morels, which is mainly manifested as white villous mycelial infection on the fruiting bodies of these mushrooms. However, the causal agent is not yet known. Therefore, this study aims to identify, at the species level, the causal pathogen of this disease via morphological and molecular methods and then to confirm its pathogenicity through Koch’s postulates. The pathogen was identified as Neonectria candida. Healthy ascocarps were inoculated with N. candida, which produced the initial disease symptoms with white mold appearing at the inoculation sites. The same pathogen was then isolated again from diseased ascocarps. To our knowledge, this is the first report of Morchella importuna ascocarps with white mold disease caused by N. candida in China. Moreover, this report will inform the establishment of sustainable management strategies for white mold disease in morels. [ABSTRACT FROM AUTHOR]

Objective: To use a standardised reporting tool to identify potential eligible candidates for cochlear implant (CI) referral and quantify the proportion of adults who had a CI referral discussion after presenting with an audiogram within United Kingdom (UK) audiometric criteria. Design: Retrospective multicentre 6-month audit of Audiology clinic databases. Study sample: A total of 810 adults from five geographically diverse UK Audiology sites. Results: Data were collected in late 2019 after UK CI audiometric candidacy criteria changed; one site collected only 3 months of data. The proportion of potential eligible adults (based only on audiometry) considered for CI referral was 64% (521 out of 810) and varied by site (from 50% to 83%). About 24% of patients (123 out of 521) declined CI referral; this also varied across sites (12–45%). The median age of patients where CI referral was not considered was 80 years – significantly higher than the group where CI referral was considered (73 years). Conclusions: CI referral is dependent on where adults live, and how old they are. Older adults are significantly less likely to be considered for CI referral by Audiologists. Audiology clinics need more support to empower staff to talk to patients about CI referral. [ABSTRACT FROM AUTHOR]

In this article, I propose a tractable approach to Bayesian inference in a simple linear regression model for which the standard exogeneity assumption does not hold. By specifying a beta prior for the squared correlation between an error term and regressor, I demonstrate that the implied prior for a bias parameter is t-distributed. If the posterior distribution for the identified regression coefficient is normal, this implies that the posterior distribution for the unidentified treatment effect is the convolution of a normal distribution and a t-distribution. This result is closely related to the literatures on unidentified regression models, imperfect instrumental variables, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

AbstractThis paper develops permutation versions of identification-robust tests in linear instrumental variables regression. Unlike the existing randomization and rank-based tests in which independence between the instruments and the error terms is assumed, the permutation Anderson-Rubin (AR), Lagrange Multiplier (LM) and Conditional Likelihood Ratio (CLR) tests are asymptotically similar and robust to conditional heteroskedasticity under standard exclusion restriction i.e. the orthogonality between the instruments and the error terms. Moreover, when the instruments are independent of the structural error term, the permutation AR tests are exact, hence robust to heavy tails. As such, these tests share the strengths of the rank-based tests and the wild bootstrap AR tests. Numerical illustrations corroborate the theoretical results. [ABSTRACT FROM AUTHOR]

Detection of underground natural gas pipeline micro-leakage based on unmanned aerial vehicle (UAV) hyperspectral remote sensing and GIS. Hyperspectral images can indirectly detect underground natural gas pipeline micro-leakage through spectral and spatial variation characteristics of surface vegetation. However, most of existing studies were based on ground-mounted platforms, which could only perform small-range single-point detection and might occur misidentifications. UAV hyperspectral remote sensing can allow wide-range detection of surface vegetation. Moreover, underground pipeline distribution GIS data can provide prior knowledge about leakage points to exclude misidentifications. Therefore, a natural gas pipeline micro-leakage experiment was set up. UAV hyperspectral images of grasslands and pipeline distribution vector data were obtained, which led to a proposed new wide-range multi-points detection methodology of underground natural gas micro-leakage. Firstly, the vegetation identification index (WVI) $\left({{R_{470}} + {R_{674}}} \right)/\left({{R_{555}} + {R_{750}}} \right)$R470+R674/R555+R750 was designed based on WOA–VMD (whale optimization algorithm – variational modal decomposition) to segment and extract the vegetation stress zones. Then, UAV- and GIS-based natural gas micro-leakage vegetation stress identification model was constructed to obtain the leak points of natural gas micro-leakage. Finally, the recognition ability was evaluated by comparing with three stress vegetation identification indices proposed in previous studies. The result showed that there was no missing or false detection in identification results; the identification and positioning effect was better than other indices, which could meet the practical application requirements. [ABSTRACT FROM AUTHOR]

Identifying unknown deceased individuals, especially those in advanced stages of decomposition, dismemberment, or burning, is a major medico-legal challenge. Forensic anthropologists play a crucial role in these cases due to the numerous characteristics that can be derived from bone remains. Their methods help evaluate the biological profile and individual characteristics, narrowing potential matches. This research investigates the identification methods used by forensic anthropologists in Medicolegal Institutes and Medical Examiner’s Offices in Brazil and the USA. Case reports from forensic anthropology institutes in Vitória, Rio de Janeiro, Porto Velho (Brazil), and New York City (USA) were analysed. In resolved cases, details such as the biological profile, trauma, decomposition extent, and individualization markers were documented. Statistical tests evaluated the association between these variables and identification outcomes. Conventional identification methods included dental analysis, DNA testing, and fingerprint analysis. Additionally, Bayesian methods, visual identification, prosthetics, and X-ray comparison proved useful. This study highlights that forensic anthropologists can employ a variety of methodologies beyond standard practices, enhancing the identification process in complex cases. [ABSTRACT FROM AUTHOR]

I discuss the practical unbiased predictor (PUP; Gonçalves and Ng) from the viewpoint of the literature on identification in event studies. The PUP can be seen as the prediction based on a generalized estimand that encompasses both the difference-in-differences (DID) and the lagged dependent variable (LDV). This feature of the PUP allows for a doubly robust property that the identification is achieved when either the parallel trend assumption or the LDV assumption holds at the expense of richer data. Furthermore, in this case, the bracketing property implies that the PUP identifying the true causal effect is bounded below by the LDV and above by the DID. [ABSTRACT FROM AUTHOR]

Terpenes and terpenoids are the primary bioactive substances present in essential volatile oils, condensed liquids extracted from diverse plant parts. These substances demonstrate remarkable biological activity and are frequently used as medicines, food additives, and scent molecules. Terpenoids have a wide range of pharmacological effects on the human body, including the treatment, prevent, and reduce the discomfort associated with a number of chronic illnesses. Therefore, these bioactive substances are crucial to our everyday existence. As most terpenoids are present in complex form, coupled with many other raw plant elements, it is important to identify and characterize these molecules. This article addresses various classes of terpenoids, their biochemical processes, and their biological functions. Additionally, it includes a comprehensive description of several hyphenated procedures and recently popular analytical approaches used for isolation, identification, and absolute characterization. It also includes a discussion of the various advantages, drawbacks, and challenges encountered during the collection of the sample and throughout the entire research process. [ABSTRACT FROM AUTHOR]

This study examines some teacher sociodemographic factors and school characteristics that may influence teachers' identification of gifted students. Teachers initially nominated a sample of 505 students aged 7–17 years (M = 11.51, SD = 2.07), but only 359 completed a general intelligence test and 225 reached the minimum percentile to be considered potentially gifted. Only 20 of these had a previous diagnosis of giftedness. Results show that the teachers' identification had a sensitivity of 63% and that the teacher's sex and the type of school significantly influenced the identification of giftedness. Female teachers and teachers in private schools made more accurate identifications. There was no evidence for the influence of school setting or the educational stage. [ABSTRACT FROM AUTHOR]

Background: According to the UN Committee Against Torture, all state parties to the Torture Convention have a responsibility to meet the rehabilitation needs of torture victims who have sought asylum within their borders. General practitioners (GPs) can play a crucial role in identifying torture victims and securing rehabilitation when needed. There is a pressing knowledge gap on the knowledge and practices of GPs vis-à-vis potentially tortured patients, and an urgent need for research that investigates GPs' practices of identification, referral, and rehabilitation – in Norway and beyond. This article presents an exploratory qualitative pilot study that investigates the experiences of GPs in Oslo vis-à-vis this patient group. Methods: Semi-structured interviews with five experienced GPs in the greater Oslo area. Interview data was analyzed through thematic analysis and discussed within a theoretical framework seeing GPs as street-level bureaucrats. Results: Issues that emerged in the pilot involve a consistent professional confidence and a particular concern for victims of sexual violence and sexualized torture. The pilot also found a troubling commonsensical reasoning about identification in the asylum process. The GPs asked for the re-establishment of specialist rehabilitation centers for refugees and torture victims to consult in case of need. Alas, the study also confirmed that GPs are a difficult profession to recruit for research. Conclusions: This study indicates that GPs are important actors in terms of identifying torture victims after resettlement, but that there are shortcomings in their training and knowledge, in the overall organization of the healthcare system, and in specialized healthcare, that limit prospects for rehabilitation. KEY POINTS: To what extent are torture victims met or identified by GPs in resettling contexts? This paper presents a pilot study designed to address this knowledge gap and prepare future studies. We found: GPs were confident in their abilities to identify and meet torture victims in a good way – and in the value that is patient-doctor continuity in general medicine. Troubling commonsensical reasoning about identification in the asylum process and in consultations. A lack of and need for qualified and available expertise to consult with and/or refer tortured patients to. [ABSTRACT FROM AUTHOR]

Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring. [ABSTRACT FROM AUTHOR]

The aim of this article is to analyse the subjectivity of medical practice with seriously ill people. The frequency of mortality in certain hospital wards can have traumatic effects on the physician, whose suffering results from the divide between the desire to heal and the fantasy of being an executioner in the event of repeated therapeutic failures. In this context, the practitioner’s experience can oscillate between two extreme subjective positions: one, defensive, of desubjectivation of the patient; the other, emotional, of identification. These two mechanisms seem to be interwoven, as one of the functions of desubjectivation is to resist identification with the patient, which can be a source of distress. Through the testimonies of doctors in specialities such as intensive care, the author illustrates the need to recognise their emotional involvement in the relationship with patients, guaranteeing their personal and professional well-being. [ABSTRACT FROM AUTHOR]

In June 2020, branch canker and brown spot of leaves on walnut were observed in Qingyang, China. A total of five strains were isolated from symptomatic samples and verified for pathogenicity based on Koch's Postulates, which showed that strains Hz4–1, Hz5–2, and Hy3–1 were the causative agents, and preliminarily identified them as members of the genus Nothophoma based on morphological characteristics. Subsequently, multi-locus phylogenetic analysis of the partial sequences of internal transcribed spacer (ITS), nuclear large ribosomal subunit (LSU), partial β-tubulin (TUB2), and RNA polymerase II second largest subunit (RPB2) showed that the three strains were clustered into two groups on the phylogenetic tree: strain Hz4–1 and Hz5–2 and Nothophoma quercina (CBS 6633.92) clustered into one branch with 84% support, while Hy3–1 and Nothophoma spiraeae (CFCC 53 929) clustered within a well-supported clade with a bootstrap value of 99%. As a result, combining morphological characteristics and multi-locus systematic analysis, the strains Hz4–1 and Hz5–2 were identified as N. quercina, and Hy3–1 was identified as N. spiraeae, which is the first report of N. spiraeae causing walnut disease in China. [ABSTRACT FROM AUTHOR]

The genus Hypoxys Amyot & Serville, 1843 was recently revalidated and reviewed. Hypoxys is the second most numerous genus in Edessinae, with around 50 species described so far. Here, 12 new species of Hypoxys, distributed in three species groups, are described: H. quadridens group (H. afasciatus sp. nov. H. ambiguus sp. nov. H. variegatus sp. nov.), H. oxyacanthus group (H. bimaculatus sp. nov. H. stenospilus sp. nov.) and H. balteatus group (H. caienensis sp. nov. H. colombianus sp. nov. H. montanus sp. nov. H. oblongus sp. nov. H. splendidus sp. nov. H. tapirapensis sp. nov. H. tucumensis sp. nov.). Diagnoses for the species, distributional maps, and updated identification keys for the species groups of Hypoxys are provided. The female paratypes of H. breddini Mendonça, Nunes & Fernandes, 2021 are transferred to H. venustus Mendonça, Nunes & Fernandes, 2021. The geographical distribution of H. venustus is expanded. [ABSTRACT FROM AUTHOR]

The advantages demonstrated by infrared spectroscopy (IR) in forensic science and entomology have aroused the interest of forensic entomologists and it has become a powerful analytical tool in the field of forensic entomology (FE) as it yields reliable results in insect identification, gut content analysis, distribution analyses, and developmental time estimations. In this paper, we summarize the current challenges and future perspectives of IR in three main research areas of FE, namely postmortem interval, carrion, and identification. With data from the past three decades (1998-2023), we report on the potential of IR to improve the efficiency of litigation cases and the scientific validity of FE laboratory research. Results show that although IR still has a long way to go and its accessibility needs to be improved, it possesses the potential to become a reliable research technique in FE. [ABSTRACT FROM AUTHOR]

Quantisation is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantised observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantised realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a $ \ell _{p},(0 \lt p \lt 1), $ ℓ p , (0 < p < 1) , quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the $ \ell _{1} $ ℓ 1 minimisation in terms of the sparsity of the induced solution. [ABSTRACT FROM AUTHOR]

As a vital component of mining hoisting equipment, mining wire rope (MWR) is critical in the operation of the mine. Once damaged, it is very easy to cause the loss of life and property. Therefore, it is meaningful to identify the damage to MWR, especially the weak damage identification. This paper proposes a method based on continuous wavelet transform (CWT) and convolutional neural network (CNN) model for MWR damage degree identification. Firstly, the MWR signals with different damage degrees were acquired. Secondly, CWT and data augmentation were performed on the original signals to obtain a time-frequency image dataset of MWR damage degree. Then, a deep learning (DL) model is built for identification and compared with traditional machine learning and some classical CNN models. The results show that the model proposed in this paper is better than the traditional machine learning models. It has the highest accuracy of 91.58% in MWR damage degree identification with classical CNN models. The method focuses on the weak damage of MWR and combines CWT with CNN model to meet the requirements of accuracy and efficiency, detecting the early damage as soon as possible and ensuring intelligent, safe and stable operation in the mining industry. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach called Stack-GA2M to identify fraudulent reviewers in an inherently interpretable manner by fusing both target and non-target features. Specifically, for local interpretability, we adopt GA2M (Standard Generalized Additive Model plus interactions) as the basic classifier to produce three subordinate models trained by using the target features and the non-target features as review textual features and reviewer behavioral features. For global interpretability, we adopt LR (Logistic Regression) as the meta classifier to stack the outputs of three subordinate models to identify the fraudulent reviewers. The white-box model of LR enables us to understand the global interpretability of the target features and the non-target features in identifying fraudulent reviewers. With GA2M, the local interpretability of each subordinate model is derived by using feature importance, spline shape functions for individual features, and heatmaps for interaction terms. Extensive experiments on Yelp dataset demonstrate that the proposed Stack-GA2M approach is superior to state-of-the-art techniques in identifying fraudulent reviewers and exhibits favorable inherent interpretability. [ABSTRACT FROM AUTHOR]

The advent of single-cell RNA sequencing (scRNA-seq) technology enables researchers to gain deep insights into cellular heterogeneity. However, the high dimensionality and noise of scRNA-seq data pose significant challenges to clustering. Therefore, we propose a new single-cell type identification method, called CHLSPCA, to address these challenges. In this model, we innovatively combine correntropy with PCA to address the noise and outliers inherent in scRNA-seq data. Meanwhile, we integrate the hypergraph into the model to extract more valuable information from the local structure of the original data. Subsequently, to capture crucial similarity information not considered by the PCA model, we employ the Gaussian kernel function and the Euclidean metric to mine the similarity information between cells, and incorporate this information into the model as the similarity constraint. Furthermore, the principal components (PCs) of PCA are very dense. A new sparse constraint is introduced into the model to gain sparse PCs. Finally, based on the principal direction matrix learned from CHLSPCA, we conduct extensive downstream analyses on real scRNA-seq datasets. The experimental results show that CHLSPCA performs better than many popular clustering methods and is expected to promote the understanding of cellular heterogeneity in scRNA-seq data analysis and support biomedical research. [ABSTRACT FROM AUTHOR]

AbstractZhachong-13 pills (ZC-13), as a traditional prescription of Mongolian medicine, are often used in the clinical practice of Mongolian hospitals for the treatment of stroke and rheumatic arthritis. In this experiment, UHPLC-Q-Exactive Orbitrap MS was used to explore the chemical composition of ZC-13. The results showed that 315 compounds were identified or inferred, including 56 alkaloids, 77 2-(2-phenylethyl)chromones, 61 flavonoids, 31 tannins, 8 coumarins, 16 lignans, 21 terpenoids, 5 amino acids, 19 organic acids, and 21 other components. In addition, the pharmacological activities related to anti-cerebral ischemia of these components were summarized. This result laid a foundation for further study on the pharmacodynamic material basis of ZC-13 and provided a scientific basis for the formulation of ZC-13 quality specifications. [ABSTRACT FROM AUTHOR]

In the era of personalized medicine, there is an increasing interest in the identification of patients who may benefit from or be sensitive to a specific type of treatment. Recently a threshold linear mixed model was proposed to identify treatment-sensitive subgroups based on a continuous covariate when longitudinal measurements are the outcomes of the study. This model assumes, however, a normal distribution for these measurements. In some studies, the longitudinal measurements are restricted in an interval and subject to floor and ceiling effects caused by a portion of subjects with measurements on the boundaries of the interval, which would violate the normality assumption. In this paper, a threshold mixed-effects Tobit model is introduced to overcome this problem. The proposed models and inference procedures are assessed through simulation studies, as well as an application to the analysis of data from a randomized clinical trial. [ABSTRACT FROM AUTHOR]

In Industrial Control Systems (ICS), where complex interdependencies abound, cyber incidents can have far-reaching consequences. Dependency modelling, a valuable technique for assessing cyber risks, aims to decipher relationships among variables. However, its effectiveness is often hampered by limited data exposure, hindering the analysis of direct and indirect impacts. We present a unique method that transforms dependency modelling data into a Bayesian Network (BN) structure and leverages causality and reasoning to extract inferences from seemingly unrelated events. Using operational ICS data, we confirm our method enables stakeholders to make better decisions about system security, stability, and reliability. [ABSTRACT FROM AUTHOR]

Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system’s efficiency of detecting cancer and help the patient to get the appropriate treatment. [ABSTRACT FROM AUTHOR]

In small-scale multilingual societies, an individual uses a large number of languages and usually identifies one of them as the primary language. Although empirically observable, primary language has been significantly underexplored, particularly the spatiality of its identification. It is not clear whether and how place is related to why one language might be seen as being more significant than other languages. This study predicts the primary language used by multilingual individuals in an indigenous rural region of Cameroon based on two types of place: the residence of multilingual individuals and the birthplace of their family members. The research goals are supported by a random forest model using spatial-sociolinguistic data of the region. Results show that primary language can be predicted at a high accuracy. The residence of multilingual individuals and the birthplace of their family members capture the spatiality behind primary language identification. This work adds to the current discussion of the space–language connection and is valuable for maintaining the endangered languages in small-scale rural societies. [ABSTRACT FROM AUTHOR]

Social network strategy (SNS) testing uses network connections to refer individuals at high risk to HIV testing services (HTS). In Tanzania, SNS testing is offered in communities and health facilities. In communities, SNS testing targets key and vulnerable populations (KVP), while in health facilities it complements index testing by reaching unelicited index contacts. Routine data were used to assess performance and trends over time in PEPFAR-supported sites between October 2021 and March 2023. Key indicators included SNS social contacts tested, and new HIV-positives individuals identified. Descriptive and statistical analysis were conducted. Univariable and multivariable analysis were applied, and variables with P-values <0.2 at univariable analysis were considered for multivariable analysis. Overall, 121,739 SNS contacts were tested, and 7731 (6.4%) previously undiagnosed individuals living with HIV were identified. Tested contacts and identified HIV-positives were mostly aged ≥15 years (>99.7%) and females (80.6% of tests, 79.4% of HIV-positives). Most SNS contacts were tested (78,363; 64.7%) and diagnosed (6376; 82.5%) in communities. SNS tests and HIV-positives grew 11.5 and 6.1-fold respectively, from October-December 2021 to January-March 2023, with majority of clients reached in communities vs. facilities (78,763 vs. 42,976). These results indicate that SNS testing is a promising HIV case-finding approach in Tanzania. [ABSTRACT FROM AUTHOR]

Legal and policy categorizations of group belonging play an important role in analysing lived experiences of discrimination, since the scope of minority protection requires individuals to prove their belonging to a minority group. This article maps the existing classifiers of minority identification as they are used in the Framework Convention for the Protection of National Minorities, Europe's most comprehensive treaty designed to protect the rights of national minorities. I engage with the concept of ethnicity as a "knot of distinction", looking at which minorities qualify as "national" in different countries. When is ethnicity used as a proxy for religion, when for race, and when for language? What categories are omitted? By inductively analysing the rationales presented by different EU countries of which minorities are "national", and based on which grounds, this article reveals a messy, historically and politically driven picture, but one that can help us understand some regional patterns. [ABSTRACT FROM AUTHOR]

Networks consist of interconnected units, known as nodes, and allow to formally describe interactions within a system. Specifically, bipartite networks depict relationships between two distinct sets of nodes, designated as sending and receiving nodes. An integral aspect of bipartite network analysis often involves identifying clusters of nodes with similar behaviors. The computational complexity of models for large bipartite networks poses a challenge. To mitigate this challenge, we employ a Mixture of Latent Trait Analyzers (MLTA) for node clustering. Our approach extends the MLTA to include covariates and introduces a double EM algorithm for estimation. Applying our method to COVID-19 data, with sending nodes representing patients and receiving nodes representing preventive measures, enables dimensionality reduction and the identification of meaningful groups. We present simulation results demonstrating the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Prior research demonstrates that a relationship exists between fan identification and psychological well-being. In the present research, we attempted to both conceptually replicate this finding, testing its generalizability in different fan groups and measured in different contexts, as well as extend it by testing whether fan friendships (Study 1) and fan activities (Studies 2 and 3) mediate the association. Across the three studies, fandom—the social component of fan identity—was found to be a significant predictor of psychological well-being while fanship—the more individualistic component of fan identity—was not. In Study 1, we found that having more friends who share one's fan interest mediates the relationship between fandom identification and well-being. In Studies 2 and 3, attending fan events—a social activity—but not consuming media or online engagement likewise mediated the association between fandom identification and psychological well-being. [ABSTRACT FROM AUTHOR]

Face-swap models have increasingly gained popularity in recent years because of their improvement in generation quality and applications in privacy protection and entertainment. The study primarily examines underlying factors that influence individuals' intention to purchase Face swap apps. A research framework drawing upon the social comparison theory was proposed to explain the relationship between four categories of social comparison (upward identification, upward contrast, downward identification, downward contrast) on self-esteem, appearance dissatisfaction, curiosity, perceived enjoyment, privacy concern, and purchase intention toward paid apps. The PLS-SEM technique was employed to analyze data from a survey of 315 respondents. The results indicated that upon the aforementioned variables, curiosity and privacy concerns were two key factors driving users a higher intention to purchase Face-swap apps. Meanwhile, perceived enjoyment had no significant impact on purchase intention. These exploratory findings contributed a number of theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Major depression is a severe psychological disorder typically diagnosed using scale tests and through the subjective assessment of medical professionals. Along with the continuous development of machine learning techniques, computer technology has been increasingly employed to identify depression in recent years. Traditional methods of automatic depression recognition rely on using the patient's physiological data, such as facial expressions, voice, electroencephalography (EEG), and magnetic resonance imaging (MRI) as input. However, the acquisition cost of these data is relatively high, making it unsuitable for large-scale depression screening. Thus, we explore the possibility of utilizing a house-tree-person (HTP) drawing to automatically detect major depression without requiring the patient's physiological data. The dataset we used for this study consisted of 309 drawings depicting individuals at risk of major depression and 290 drawings depicting individuals without depression risk. We classified the eight features extracted from HTP sketches using four machine-learning models and used multiple cross-validations to calculate recognition rates. The best classification accuracy rate among these models reached 97.2%. Additionally, we conducted ablation experiments to analyze the association between features and information on depression pathology. The results of Wilcoxon rank-sum tests showed that seven of the eight features significantly differed between the major depression group and the regular group. We demonstrated significant differences in HTP drawings between patients with severe depression and everyday individuals, and using HTP sketches to identify depression automatically is feasible, providing a new approach for automatic identification and large-scale screening of depression. [ABSTRACT FROM AUTHOR]

In this study, we investigated autonomous vessel obstacle avoidance using advanced techniques within the Guidance, Navigation, and Control (GNC) framework. We propose a Mixed Integer Linear Programming (MILP) based Guidance system for robust path planning avoiding static and dynamic obstacles. For Navigation, we suggest a multi-modal neural network for perception, demonstrating the identification of obstacle type, position, and orientation using imaging sensors. Additionally, the paper compares an error-based PID control strategy and a Model Predictive Control (MPC) scheme as well. This evaluation aids in better evaluating their performance and determining their applicability within the GNC scheme. We detail the implementation of these systems, present simulation results, and offer a performance evaluation using an experimental dataset. Our findings, analysed through qualitative discussion and quantitative performance indicators, contribute to advancements in autonomous navigation and the control strategies to achieve it. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases lead to a gradual decline in cognitive and motor functions due to the progressive loss of neurons in the central nervous system. The role of dual leucine zipper kinase (DLK) in regulating stress responses and neuronal death pathways highlights its significance as a target against neurodegenerative diseases. The non-availability of FDA-approved drugs emphasizes a need to identify novel DLK-inhibitors. We screened NPAtlas (Natural products) and MedChemExpress (FDA-approved) libraries to identify potent ATP-competitive DLK inhibitors. ADMET analyses identified four compounds (two natural products and two FDA-approved) with favourable features. Subsequently, we performed molecular dynamics simulations to examine the binding-stability and ligand-induced conformational dynamics. Molecular mechanics Poisson Boltzmann surface area (MM-PBSA) calculations demonstrated CID139591660, dithranol, and danthron having greater affinity, while CID156581477 showed lower affinity than control sunitinib. PCA and network analysis results indicated structural and network alteration post-ligand binding. Furthermore, we identified an analogue of CID156581477 using the deep learning-based web server DeLA Drug which demonstrated a higher affinity than its parent compound and the control and identified several crucial interacting residues. Overall, our study provides significant theoretical guidance for designing potent novel DLK inhibitors and compounds that could emerge as promising drug candidates against DLK following laboratory validation. [ABSTRACT FROM AUTHOR]

Unquestioned vicious identifications with dubious (e.g. political) figures seem central in today's multiple crises. As in former dark times, when M. Klein described the dynamics triggered by an 'inner Hitler', the approach offered in this contribution is to explore present forms, which we meet in the consulting room. As will be shown, a crucial step in this process lies in working through those challenges in the countertranserence. [ABSTRACT FROM AUTHOR]

We used a two-phased mixed methods study with an explanatory sequential design to understand how frequently paramedics attend patients who, on paramedic assessment with the Gold Standards Framework Proactive Identification Guidance, are end-of-life and have advance care planning. We subsequently explored paramedic views on paramedic screening of patients to assess if they are end-of-life and onward referral to their General Practitioner for advance care planning. Paramedics screened and recorded 14.9% of patients as end-of-life and 44.3% of these patients were assessed to have no advance care plan in place. When paramedics screened patients and they did have an advance care plan in place, 36.8% had only a Do Not Attempt Cardiopulmonary Resuscitation. Paramedics found using the Gold Standards Framework Proactive Identification Guidance to screen patients for end-of-life status useful and straightforward and considered themselves well-placed to complete this task. Future research is required to address the practicalities of implementing a paramedic screening and referral tool for end-of-life care that results in the intended outcome of supporting effective advance care planning. [ABSTRACT FROM AUTHOR]

The word "talent" is used across many sport disciplines – to describe an athlete's prowess (i.e. "he is talented"), as a term for what is sought after during assessment and selection (i.e. talent selection camps) or in reference to players to be developed (i.e. "a group of talents"). While the term has received research attention regarding its definition and criteria, its utility in practical settings is often debated. In this paper, we review several areas of concern researchers have raised for using the term "talent" and why this matters in the context of athlete development. While the notion of talent continues to resonate with coaches, scientists and practitioners, we suggest several areas for future research and recommendations for the use of this controversial term. [ABSTRACT FROM AUTHOR]

Ascidians are marine filter-feeder chordates. Botrylloides ascidians possess diverse biological properties such as whole-body regeneration (WBR), hibernation/aestivation, blastogenesis, metamorphosis, and natural chimerism. However, the absence of distinctive morphological features often makes identification difficult. Botrylloides diegensis is an ascidian that has been misidentified in previous studies and is recorded in GenBank as Botrylloides leachii owing to the high morphological similarity between the sister species. More available sequences and strategies for identification would help resolve some of the confusion surrounding its ambiguous nature. We collected several Botrylloides samples from seven locations in Aotearoa New Zealand (Dunedin, Christchurch, Picton, Nelson, Whangateau, New Plymouth, and Invercargill) and barcoded the species based on Cytochrome Oxidase I, Histone 3, 18S, and 28S ribosomal RNA markers. Network and Bayesian trees confirmed the presence of three Botrylloides species: B. diegensis, Botrylloides jacksonianum, and Botrylloides affinis anceps. [ABSTRACT FROM AUTHOR]

Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator's ability to accurately measure covariates that capture all potential sources of confounding. In practice, the most one can hope for is that covariate measurements are at best proxies of the true underlying confounding mechanism operating in a given observational study. In this article, we consider the framework of proximal causal inference introduced by Miao, Geng, and Tchetgen Tchetgen and Tchetgen Tchetgen et al. which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. We make a number of contributions to proximal inference including (i) an alternative set of conditions for nonparametric proximal identification of the average treatment effect; (ii) general semiparametric theory for proximal estimation of the average treatment effect including efficiency bounds for key semiparametric models of interest; (iii) a characterization of proximal doubly robust and locally efficient estimators of the average treatment effect. Moreover, we provide analogous identification and efficiency results for the average treatment effect on the treated. Our approach is illustrated via simulation studies and a data application on evaluating the effectiveness of right heart catheterization in the intensive care unit of critically ill patients. for this article are available online. [ABSTRACT FROM AUTHOR]

Wildfires are among the most serious disturbances that affect forest biodiversity, damaging both flora and fauna. In some burned areas, vegetation recovery is a long and complex process that requires the support of restorative actions. This study evaluated changes in forest structural features after a fire that affect habitat suitability for Rhinocryptids, an endemic group of bird species that are indicators of biodiversity in the temperate forests of South America. The study area was the Malleco National Reserve, Chile, where the main ecosystems are temperate forests. Four species from the Rhinocryptidae family of birds were used as bioindicator species. This group of species is endemic to temperate forests in South America. After fires occurred in 2002 and 2015, affecting temperate forests dominated by several Nothofagus species, two field inventories were carried out in 2016 and 2018. These inventories recorded the presence of Rhinocryptids and some forests structural features. Differences between the two stages of forest regeneration were assessed using statistical tests (T-test and Wilcoxon–Mann–Whitney test) and a classification model was proposed to determine the presence of Rhinocryptidae species. The statistical analysis of forest structural features showed that the variables more relevant in the habitat recuperation are vertical stratification (p-value < 0.001) and understory cover (p-value = 0.053). The trained decision tree to predict the presence of Rhinocryptidae species had an accuracy of 65.22%. This model showed that the coexistence of more than one Rhinocryptidae species occurs when vertical stratification is not mono-stratified and the density is lower than 335 trees ha−1. Our results can help forest managers to consider forest structure as a suitable habitat for certain major wildlife species when implementing post-fire recovery plans. [ABSTRACT FROM AUTHOR]

N,N-dimethyltryptamine (DMT) is a psychedelic substance produced within various Acacia species; however, the range of species that contain it and the quantity of the compound found within each species is relatively unknown. This work focused on the identification of DMT in 104 plant samples, representing 79 Acacia species, utilizing a reverse phase liquid chromatographic separation prior to mass spectral characterization with a robust Q-TOF-MS method. The findings of this work determined 51 DMT-containing species, 41 of which were previously unknown to contain the psychedelic. [ABSTRACT FROM AUTHOR]

Among the theoretical contributions made by Hans Loewald, some of the most meaningful address the developmental process of differentiation from a primary or original unity into "id-ego and objects." This differentiation requires "separation, loss, and restitution," and results in a shift from oneness/unity to at-onement, a connection that bears within it the trace of the separation it emends. A parallel dynamic plays out in the shift from identification to internalization in Loewald's work. The author identifies this dynamic as cohering difference, a structural interaction in both intra- and inter-psychic processes graphically illustrated by the hyphen in Loewald's concept of "at-onement." The author examines these processes and the significance of cohering difference, illustrated by Loewald's idea of "linking," and presents a clinical example in which loss, grief, identification, and internalization play key roles. [ABSTRACT FROM AUTHOR]

This work focuses on developing a forecasting model for the nonlinear system with herding. Firstly, for clear presentation of the mechanism of herding behavior, a concept called intermittent positive feedback is defined. The mathematical expression of intermittent positive feedback is formulated in terms of dead-zone functions with the beginning and ending thresholds in the positive and negative directions. Then a dynamic surrogate based on the intermittent positive feedback multi-dimensional Taylor network is established, and a method for identifying its parameters is proposed. Multi-dimensional Taylor network module simulates the long-term trends of time series. Subsequently, an optimization model based on the alternating iteration of the parameters is developed to enhance the forecast accuracy of the intermittent positive feedback part. Simulation results demonstrate that the forecasting effect of the proposed modeling method is superior to that of conventional non-mechanism data-based surrogates for herding behavior. [ABSTRACT FROM AUTHOR]

Aleurites moluccanus (candlenut) and Bertholletia excelsa (Brazil nut) are marketed as dietary supplements for weight loss. These dietary supplements have been found to sometimes be adulterated with toxic nuts/seeds from Cascabela thevetia, commonly known as yellow oleander or lucky nut. This study emphasizes the key identification parameters to differentiate the genuine and adulterated nuts. Samples were obtained from authenticated sources of the nuts and from commercial sources of dietary supplements. This study examined 38 samples, including voucher and commercial samples. All eight commercial candlenut dietary supplement samples were adulterated. Additionally, two samples sold as Brazil nuts were also found to be adulterated. Other nuts were screened for the presence of Cardiac Glycosides, but none were found to be positive. The presence of yellow oleander was confirmed in all commercial dietary supplement samples marketed as candlenut as well as in commercial samples of Brazil nut. This study provides simple key identification characters using micro-morphology and histochemical localization of cardiac glycosides in the commercial nuts, HPTLC fingerprints, and LC-DAD-Q-ToF analytical parameters to detect and identify adulteration in commercial products. [ABSTRACT FROM AUTHOR]

In the pandemic of COVID-19, identifying a person from their face became difficult due to wearing of mask. In regard to the given circumstances, the authors have remarkably put effort on identifying a person using 2D ear images based on deep convolutional neural network (CNNs). They investigated the challenges of limited data and varying environmental conditions in this regards. To deal with such challenges, the authors developed an augmentation-based light-weight CNN model using CPU enabled machine so that it can be ported into embedded devices. While applying data augmentation technique to enhance the quality and size of training dataset, the authors analysed and discussed the different augmentation parameters (rotation, flipping, zooming, and fill mode) that are effective for generating the large number of sample images of different variability. The model works well on constrained and unconstrained ear datasets and achieves good recognition accuracy. It also reduces the problem of overfitting. [ABSTRACT FROM AUTHOR]

Privacy impact assessment (PIA) has attracted the attention of privacy watchdogs and researchers for decades. This study focuses on a risk model and risk identification method, which are two crucial elements of the risk identification step in the PIA process. As a preparatory work, this article reviews national and international organizations’ current templates and guidelines and finds that PIA guidance includes multiple domains but rarely provide a risk model or a systematic risk identification method. Based on the analysis, our study offers a risk model that can capture various privacy risk realization processes. It further proposes a combination of risk identification methods that correspond to the main target domains in the PIA and the proposed risk model. This combination consists of privacy principles of a given personal information or privacy rule to check compliance with the rule, and our suggested list of risk factors is useful in inductively finding potential risk scenarios that violate social expectations of privacy. [ABSTRACT FROM AUTHOR]

Well test model identification is a challenging task due to the numerous types of well test interpretation models and the non-uniqueness of pressure responses generated by different reservoir models. An automated framework is crucial to aid in the identification of well test interpretation models. Since the identification of well test interpretation relies primarily on the various flow regimes appeared on different diagnostic plots. A novel approach is proposed for the well test model identification from the pressure transient test data using the syntactic pattern recognition in this study. In this study, the identification process of well test interpretation model is divided into six steps: preprocessing, feature primitive extraction, curve shape tracking, flow regime division, model preliminary inference, and model final validation incorporating TDS technology. The automatic identification framework developed with this method has been able to identify a variety of complex well test interpretation models correctly, and the non-uniqueness of model results can be well resolved by syntactic pattern recognition combined with TDS technology. In general, the findings of this study can help for better understanding of the process by which well test expert completes the task of model identification. [ABSTRACT FROM AUTHOR]

To what extent are Hispanic and Latinx students with gifts and talents proportionally identified? To what extent are they missing from identification (ID) due to lack of access or underidentification? This study used the Office of Civil Rights data for the years 2000, 2011–2012, 2013–2014, and 2015–2016 to investigate national and state underrepresentation of Latinx youth with gifts and talents. Schools were examined separately by Title I and non-Title I status and by locale (city, suburb, town, rural) to determine whether poverty concentration and/or school locale matter in the ID of Latinx youth. Lack of access to ID was not a major contributing factor to the underrepresentation of these students. Rather, it was the chronic underidentification of Latinx students, regardless of school Title I status or school locale. In 2015–2016 there were 588,891 Latinx students identified with gifts and talents, with between 658,544 (52.79%) and 1,164,363 (66.41%) gifted Latinx youth missing from ID. Across the United States, students with gifts and talents who are Latinx are experiencing discriminatory gifted education policies and practices. [ABSTRACT FROM AUTHOR]

Aspergillus subgenus Nidulantes with nine section forms the second largest subgenus of the fungi that comes under the genus Aspergillus. Species in this group of fungi are important as they are reported to play several important roles in the environment including influencing air quality in confined spaces, food spoilage, production of mycotoxins as well as in human pathogenicity. In the present study, 53 strains of Aspergillus subgenus Nidulantes (section: Nidulantes & Usti) isolated from Korea and preserved at the Korean Agricultural Culture Collection (KACC) were subjected to re-identification by using a combined dataset of partial β-tubulin (BenA), Calmodulin (CaM) gene sequences as well as their morphological data. We confirmed 14 species from 53 isolates in Korea. Of them, eleven species were reported in Korea previously (A. amoenus, A. baeticus, A. calidoustus, A. creber, A. insuetus, A. jensenii, A. nidulans, A. protuberus, A. sydowii, A. tabacinus and A. unguis), and three species (A. griseoaurantiacus, A. puulaauensis and A. sublatus) were previously unreported from Korea. We detailed the characteristic features of these three species, that remain unexplored in Korea. [ABSTRACT FROM AUTHOR]