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Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligence (AI) to identify SCST and describe the elements of their healthcare presentation. An AI-supported keyword search was conducted to identify SCST within the electronic medical records (EMR) of ∼1.5 million patients at a large midwestern pediatric hospital. Descriptive analyses were used to evaluate associated diagnoses and clinical presentation. A sex trafficking-related keyword was identified in.18% of patient charts. Among this cohort, the most common associated diagnostic codes were for Confirmed Sexual/Physical Assault; Trauma and Stress-Related Disorders; Depressive Disorders; Anxiety Disorders; and Suicidal Ideation. Our findings are consistent with the myriad of known adverse physical and psychological outcomes among SCST and illuminate the future potential of AI technology to improve screening and research efforts surrounding all aspects of this vulnerable population. [ABSTRACT FROM AUTHOR]

Computer simulation of energy resources has led to significant achievements in the interdisciplinary fields of energy and environment. Apart from renewable resources, fossil-fuel power generation plants can be made cleaner to satisfy the future climate targets while keeping secure and stable grid. Clean coal power plants are still among the dominant options for power generation, which are committed through energy-efficient operation, carbon capture and storage, or combination of both strategies. On the other hand, machine learning and deep learning techniques have a leading integrity in the field of simulation. This paper presents accurate models of a cleaner coal-fired supercritical (SC) unit using two types of artificial neural network, which are Elman neural network (ENN) and generalized regression neural network (GRNN). The models newly embed higher coverage range and more accurate results than previously published models. Each subsystem of the models has been structured as a multi-input single-output (MISO) component to predict the behavior of significant variables in the plant, mainly the supercritical pressure in MPa, the steam temperature in °C and the production in MW. Those variables have been intentionally selected as they are clear indicators for the energy-efficient and cleaner production. Simulation results of four sets of data have indicated satisfactory performance of both models with a bit higher superiority of the GRNN that has given negligible or zero Mean Squared Error (MSE) for all outputs, whereas the minimum MSE of the deep ENN is 3.131 × 10−3. [ABSTRACT FROM AUTHOR]

The best empirical research in political science clearly defines substantive parameters of interest, presents a set of assumptions that guarantees their identification, and uses an appropriate estimator. We argue for the importance of explicitly integrating rigorous theory into this process and focus on the advantages of doing so. By integrating a theoretical structure into one's empirical strategy, researchers can quantify the effects of competing mechanisms, consider the ex-ante effects of new policies, extrapolate findings to new environments, estimate model-specific theoretical parameters, evaluate the fit of a theoretical model, and test competing models that aim to explain the same phenomena. As a guide to such a methodology, we provide an overview of structural estimation, including formal definitions, implementation suggestions, examples, and comparisons to other methods. [ABSTRACT FROM AUTHOR]

There is heightened awareness that a whole-of-systems approach to perpetrator responses is key to addressing domestic and family violence (DFV). This paper reports on the findings from a scoping review which mapped the international literature on how health professionals identify and respond to perpetrators of DFV within a hospital setting. A comprehensive scoping review methodology was used. The search, spanning January 2010 to January 2022, yielded 12,380 publications from four databases. Eligibility for inclusion included peer-reviewed literature with any reference to inpatient hospital health professionals identifying or responding to perpetrators of DFV. Fourteen articles were included in the final review. The review presents the literature categorized by levels of prevention, from primary, secondary, through to tertiary preventive interventions. An additional category "other practices" is added to capture practices which did not fit into existing levels. Despite glimpses into how health professionals can identify, and respond to perpetrators of DFV, the current knowledge base is sparse. The review did not identify any mandated or formal procedures for identifying and/screening or responding to perpetration of abuse in hospitals. Rather, responses to perpetrators are inconsistent and rely on the motivation, skill, and self-efficacy of health professionals rather than an embedded practice that is driven and informed by hospital policy or procedures. The literature paints a picture of missed opportunities for meaningful work with perpetrators of DFV in a hospital setting and highlights a disjuncture between policy and practice. [ABSTRACT FROM AUTHOR]

The ability to effectively identify students for advanced learning opportunities has been an ongoing issue within the field of gifted education. Common criteria to guide the design and evaluation of identification systems has been essentially non-existent. In this article we provide a practical guide for evaluating and reflecting on the effectiveness of identification criteria, namely: CASA (Cost, Alignment, Sensitivity, and Access). To demonstrate their use, the CASA criteria are described and the criteria are applied to a hypothetical district with discussion about decisions and changes to support more equitable identification and services alignment. A checklist is also provided to guide district leaders with applying the CASA criteria to their own identification systems. This article builds upon previous descriptions of CASA that have been published. [ABSTRACT FROM AUTHOR]

Context: Aging and increasing comorbidities in the population are leading to more complex care for patients and primary healthcare providers. Community organizations (COs) may play a role in the services offered to support patients with chronic diseases (PCDs) but there are currently no clear guidelines to support primary healthcare providers in linking patients to COs. Objectives: The aim of this study was to describe the role of primary healthcare providers regarding linking PCDs to COs by: (1) describing linking activities; and (2) identifying the main facilitators and barriers associated with these activities. Methods: This scoping review was based on the Arksey and O'Malley method, completed by Levac, Colquhoun, and O'Brien. Related keywords were used in 7 databases to search relevant studies. After the initial screening, 135 full texts were assessed for eligibility by 2 reviewers using inclusion/exclusion criteria. Empirical studies describing activities performed by primary healthcare providers in linking PCDs to COs or describing facilitators or barriers to linking activities were included. Studies describing activities linking to other services than COs or located in emergency departments were excluded. Results: In total, 28 studies were included. Information reported in the studies was classified into 8 main linking activities: capacity development, patient identification, assessment, information, planning, referral, follow-up, and collaboration. Facilitators and barriers to these activities were related to intrapersonal characteristics of providers and patients, professional practice, work environment, relationships, and external influences. Healthcare providers' involvement was often adapted according to their field of practice. Conclusion: This scoping review details the role of primary healthcare providers when linking PCDs to COs in a collaborative and interdisciplinary context, which can be adapted to clinical practice by providers, experts, or stakeholders to support improvement in chronic care management. [ABSTRACT FROM AUTHOR]

The theoretical framework for understanding the sexual relationships developed between members of a group-analytic group is investigated and supported by clinical cases. The circumvention of the members' identification with the conductor as a leader/ego ideal/Name-of-the-Father as favoured by multi-transference and the members' and/or therapist's sliding from desire/symbolic to enjoyment/imaginary, entailing the incursion of the real, play a structural role in the patients' sexual enactment. Romantic/sexual relations constitute projective identifications in action against the group experienced as a bad object and plead unconsciously for a premature separation from it as a sufficiently good object. This study has clinical implications concerning the monitoring of transference/countertransference, exercising leadership and coping with forced termination in group analysis. [ABSTRACT FROM AUTHOR]

Executive Summary: This article highlights the importance of accounting for the dead in migration contexts, from international humanitarian law (IHL), international human rights law (IHRL), and forensic perspectives. Starting by reviewing obligations under IHL and IHRL for the processes of accounting, the article discusses forensic action and the role that accounting can play to protect the dead and clarify the fate and whereabouts of the missing for their families. Considering the complexity of missing and deceased migrant cases, this problem must be approached from several complementary angles, including States codifying international legal obligations related to accounting for the dead in domestic legal or policy frameworks; developing national mechanisms to collect, centralize, and report disaggregated information on migrant deaths; addressing migrant deaths from a public health perspective; pursuing identification efforts including participating in transnational coordination mechanisms; developing strong partnerships with civil society actors; and coupling accounting initiatives with policies that promote the search for missing migrants. [ABSTRACT FROM AUTHOR]

This phenomenological study explored how five participants used their Texas state-approved definition of dyslexia to identify children with dyslexia as different from those with other global reading difficulties. Drawing on theories from disability studies in education and dis/ability critical race theory, I interviewed participants who identified dyslexia in terms of sociocultural differences, beliefs about intelligence, and reading ability. They frequently left unexamined concerns of race and how explanations of dyslexia privileged white, middle-class children over children of color and children in low-income schools. This study contributes to research that critically examines how these definitions of dyslexia segregate students with reading difficulties based on beliefs about intelligence, sociocultural factors, and the lack of conversations about race. Implications suggest that a growing focus on dyslexia may exacerbate inequalities already present in school systems. Stakeholders should critically examine their identification practices of reading difficulties to ensure that all children receive reading support. [ABSTRACT FROM AUTHOR]

Integrating a social identity approach with Cortina's (2008) theorizing about selective incivility as modern discrimination, we examine how identification—with an organization, with one's gender, and as a feminist—shapes bystanders' interpretations and responses to witnessed incivility (i.e., interpersonal acts of disrespect) and selective incivility (i.e., incivility motivated by targets' social group membership) toward women at work. We propose that bystanders with stronger organizational identification are less likely to perceive incivility toward female colleagues as discrimination and intervene, but female bystanders with stronger gender identification are more likely to do so. Results from two-wave field data in a cross-lagged panel design (Study 1, N = 336) showed that organizational identification negatively predicted observed selective incivility 1 year later but revealed no evidence of an effect of female bystanders' gender identification. We replicated and extended these results with a vignette experiment (Study 2, N = 410) and an experimental recall study (Study 3, N = 504). Findings revealed a "dark side" of organizational identification: strongly identified bystanders were less likely to perceive incivility as discrimination, but there were again no effects of women's gender identification. Study 3 also showed that bystander feminist identification increased intervention via perceived discrimination. These results raise doubts that female bystanders are more sensitive to recognizing other women's mistreatment as discrimination, but more strongly identified feminists (male or female) were more likely to intervene. Although strongly organizationally identified bystanders were more likely to overlook women's mistreatment, they were also more likely to intervene once discrimination was apparent. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of distance-framed narratives in deterring e-cigarette use among young adults. Through an online experiment (N = 916), this study investigates how narratives with different distance cues influence participants' narrative engagement, perceived psychological distance, and compliance with the message. The findings suggest that distance-framed narratives heighten identification. Increased identification reduces perceived psychological distance, leading to more negative attitudes, decreased e-cigarette use intention, and stronger policy support. E-cigarette use status also impacts persuasive outcomes. Non-users tend to be more receptive to narratives featuring congruent distance cues in character and scenario depictions. Users are motivated by narratives that present an uncertain scenario, regardless of whether the character is portrayed as socially close or distant. [ABSTRACT FROM AUTHOR]

Objective: Basil has various biological activities, and sufficient research on its extracts can help to clarify the main components that exert biological activity. Methods: Solvents with different polarities were used to extract the basil crude extract (CE), and the total polyphenol (TP) and total flavonoid (TF) contents of extracts were measured. The compounds of extracts were identified using high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Their antioxidant and immune-enhanced activities were evaluated. Results: The CE and its four solvent fractions were obtained including petroleum ether extract (PE), ethyl acetate extract (EE), n-butanol extract (BE), and water extract (WE). Among them, BE had the highest TP and TF contents, followed by EE, and all five extracts contained maltol and arbutin. The BE and EE have better 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activities, while CE with less TP and TF contents showed better 2, 2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity than that of BE and EE. Besides, BE and EE also exert a better effect on relevant indicators of immunosuppressive mice than other extracts. Conclusion: The basil extracts (BE and EE), which have higher TP and TF contents, demonstrate better in vitro antioxidant activities and immune-enhancing effects on immunosuppressed mice. This provides a theoretical basis for the application of basil. [ABSTRACT FROM AUTHOR]

Teacher rating scales (TRS) often play a part in service eligibility decisions for gifted services. Although schools regularly use TRS to identify gifted students either as part of an informal nomination process or through behavioral rating scales, there is little research documenting the between-teacher variance in teacher ratings and the consequences of such rater dependence. To evaluate the possible benefits or disadvantages of using TRS as part of a gifted identification process, we examined the student-, teacher-, and school-level variance in TRS, controlling for student ability and achievement to determine the unique information, consistency, and potential bias in TRS. Between 10% and 25% of a students' TRS score can be attributed to the teacher doing the rating, and between-teacher standard deviations represent an effect size of one-third to one-half standard deviation. Our results suggest that TRS are not easily comparable across teachers, making it impossible to set a cut score for admission into a program (or for further screening) that functions equitably across teachers. [ABSTRACT FROM AUTHOR]

The regular monitoring of cable forces is essential for ensuring the safety of cable structures both during construction and throughout their lifetime. This paper aims at developing a vibration-based identification procedure of the axial forces, bending stiffness, and, secondarily, the crossing point position of cable networks. A model constituted by two crossing stays having small bending stiffness and negligible sag effects is considered. The in-plane direct dynamic problem is solved both numerically and through a perturbation approach. The obtained results are compared to the outcomes of a finite element model for verification purposes. The theoretical studies are also supported by experimental tests performed on a real cable-stayed bridge (Haccourt bridge), which provide insights into the dynamics of the system showing that models of cables with small bending stiffness are more appropriate than taut string models. The inverse analysis based on non-linear Bayesian regression is developed and the closed-form asymptotic formulations are used to prove that the bending stiffness, the cable forces, and the crossing point position can be separately identified from a set of observed frequencies. The implemented procedure is then applied to the tested bridge as a proof of concept, showing that the proposed in-plane identification strategy provides satisfactory results. [ABSTRACT FROM AUTHOR]

Students' talent and potential cannot be served until they are recognized by schools or caregivers. While the field of gifted education has had success in identifying many students with talents in reading and mathematics, those with spatial talents are often overlooked. This article reviews how we might identify spatial talent using traditional tests as well as observations during planned activities. Identifying spatial talent is an area in need of further research and assessment development. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer. [ABSTRACT FROM AUTHOR]

Instrumental-variables estimation is an approach commonly used to evaluate the effect of a program in case of noncompliance. However, when the binary treatment status is misreported, standard techniques are not sufficient to point identify and consistently estimate the effect of interest. We present a new command, ivbounds, that implements three partial identification strategies developed by Tommasi and Zhang (2024, Journal of Econometrics 238: 105556) to bound the heterogeneous treatment effect when both noncompliance and misreporting of treatment status are present. We illustrate the use of the command by reassessing the benefits of participating in the 401(k) pension plan on savings in the United States. [ABSTRACT FROM AUTHOR]

Given the frequent presence of slipping and guessing in item responses, models for the inclusion of their effects are highly important. Unfortunately, the most common model for their inclusion, the four-parameter item response theory model, potentially has severe deficiencies related to its possible unidentifiability. With this issue in mind, the dyad four-parameter normal ogive (Dyad-4PNO) model was developed. This model allows for slipping and guessing effects by including binary augmented variables—each indicated by two items whose probabilities are determined by slipping and guessing parameters—which are subsequently related to a continuous latent trait through a two-parameter model. Furthermore, the Dyad-4PNO assumes uncertainty as to which items are paired on each augmented variable. In this way, the model is inherently exploratory. In the current article, the new model, called the Set-4PNO model, is an extension of the Dyad-4PNO in two ways. First, the new model allows for more than two items per augmented variable. Second, these item sets are assumed to be fixed, that is, the model is confirmatory. This article discusses this extension and introduces a Gibbs sampling algorithm to estimate the model. A Monte Carlo simulation study shows the efficacy of the algorithm at estimating the model parameters. A real data example shows that this extension may be viable in practice, with the data fitting a more general Set-4PNO model (i.e., more than two items per augmented variable) better than the Dyad-4PNO, 2PNO, 3PNO, and 4PNO models. [ABSTRACT FROM AUTHOR]

Background: Ameloblastoma (AM) is a benign tumor locally originated from odontogenic epithelium that is commonly found in the jaw. This tumor makes aggressive invasions and has a high recurrence rate. This study aimed to investigate the differentially expressed genes (DEGs), biological function alterations, disease targets, and existing drugs for AM using bioinformatics analysis. Methods: The data set of AM was retrieved from the GEO database (GSE132474) and identified the DEGs using bioinformatics analysis. The biological alteration analysis was applied to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein-protein interaction (PPI) network analysis and hub gene identification were screened through NetworkAnalyst. The transcription factor-protein network was constructed via OmicsNet. We also identified candidate compounds from L1000CDS2 database. The target of AM and candidate compounds were verified using docking simulation. Results: Totally, 611 DEGs were identified. The biological function enrichment analysis revealed glycosaminoglycan and GABA (γ-aminobutyric acid) signaling were most significantly up-regulated and down-regulated in AM, respectively. Subsequently, hub genes and transcription factors were screened via the network and showed FOS protein was found in both networks. Furthermore, we evaluated FOS protein to be a therapeutic target in AMs. Candidate compounds were screened and verified using docking simulation. Tanespimycin showed the greatest affinity binding value to bind FOS protein. Conclusions: This study presented the underlying molecular mechanisms of disease pathogenesis, biological alteration, and important pathways of AMs and provided a candidate compound, Tanespimycin, targeting FOS protein for the treatment of AMs. [ABSTRACT FROM AUTHOR]

Background & Objective: Assessment of muscularis propria invasion is a crucial step in the management of urothelial carcinoma since it necessitates aggressive treatment. The diagnosis of muscle invasion is a challenging process for pathologists. Artificial intelligence is developing rapidly and being implemented in various fields of pathology. The purpose of this study was to develop an algorithm for the detection of muscularis propria invasion in urothelial carcinoma. Methods: The Training cohort consisted of 925 images from 50 specimens of urothelial carcinoma. Ninety-seven images from 10 new specimens were used as a validation cohort. Clinical validation used 127 whole specimens with a total of 617 slides. The algorithm determined areas where tumor and muscularis propria events were in nearest proximity, and presented these areas to the pathologist. Results: Analytical evaluation showed a sensitivity of 72% for muscularis propria and 65% for tumor, and a specificity of 46% and 77% for muscularis propria and tumor detection, respectively. The incorporation of the spatial proximity factor between muscularis propria and tumor in the clinical validation significantly improved the detection of muscularis propria invasion, as the algorithm managed to identify all except for one case with muscle invasive bladder cancer in the clinical validation cohort. The case missed by the algorithm was nested urothelial carcinoma, a rare subtype with unusual morphologic features. The pathologist managed to identify muscle invasion based on the images provided by the algorithm in a short time, with an average of approximately 5 s. Conclusion: The algorithm we developed may greatly aid in accurate identification of muscularis propria invasion by imitating the thought process of the pathologist. [ABSTRACT FROM AUTHOR]

Background: Historically, national programs for collecting stroke data in Australia required the use of multiple online tools. Clinicians were required to enter overlapping variables for the same patient in the different databases. From 2013 to 2016, the Australian Stroke Data Tool (AuSDaT) was built as an integrated data management solution. Objective: In this article, we have described the development, implementation, and evaluation phases of establishing the AuSDaT. Method: In the development phase, a governance structure with representatives from different data collection programs was established. Harmonisation of data variables, drawn from six programs used in hospitals for monitoring stroke care, was facilitated through creating a National Stroke Data Dictionary. The implementation phase involved a staged deployment for two national programs over 12 months. The evaluation included an online survey of people who had used the AuSDaT between March 2018 and May 2018. Results: By July 2016, data entered for an individual patient was, for the first time, shared between national programs. Overall, 119/422 users (90% female, 61% aged 30–49 years, 57% nurses) completed the online evaluation survey. The two most positive features reported about the AuSDaT were (i) accessibility of the system (including simultaneous user access), and (ii) the ability to download reports to benchmark local data against peer hospitals or national performance. More than three quarters of respondents (n = 92, 77%) reported overall satisfaction with the data collection tool. Conclusion: The AuSDaT reduces duplication and enables users from different national programs for stroke to enter standardised data into a single system. Implications: This example may assist others who seek to establish a harmonised data management solution for different disease areas where multiple programs of data collection exist. The importance of undertaking continuous evaluation of end-users to identify preferences and aspects of the tool that are not meeting current requirements were illustrated. We also highlighted the opportunities to increase interoperability, utility, and facilitate the exchange of accurate and meaningful data. [ABSTRACT FROM AUTHOR]

Viewing multiple images of a newly encountered face improves recognition of that identity in new instances. Studies examining face learning have presented high-variability (HV) images that incorporate changes that occur from moment-to-moment (e.g., head orientation and expression) and over time (e.g., lighting, hairstyle, and health). We examined whether low-variability (LV) images (i.e., images that incorporate only moment-to-moment changes) also promote generalisation of learning such that novel instances are recognised. Participants viewed a single image, six LV images, or six HV images of a target identity before being asked to recognise novel images of that identity in a face matching task (training stimuli remained visible) or a memory task (training stimuli were removed). In Experiment 1 (n = 71), participants indicated which image(s) in 8-image arrays belonged to the target identity. In Experiment 2 (n = 73), participants indicated whether sequentially presented images belonged to the target identity. Relative to the single-image condition, sensitivity to identity improved and response biases were less conservative in the HV condition; we found no evidence of generalisation of learning in the LV condition regardless of testing protocol. Our findings suggest that day-to-day variability in appearance plays an essential role in acquiring expertise with a novel face. [ABSTRACT FROM AUTHOR]

The identification of influence surfaces (ISs) for bridge structures offers an efficient tool for understanding traffic loads and assessing structural conditions. In general, ISs of a real bridge can be identified through calibration tests using calibration vehicles with known weights moving across the bridge. However, the existing methods face difficulties in considering comprehensive factors, such as the lateral movement, speed variation, and track width of the calibration vehicle, as well as bridge dynamic effects. These factors inevitably introduce inaccuracies into the task of identification. To comprehensively consider these factors, this study proposes a deep learning-based method that combines deep multilayer perceptrons (MLPs) and computer vision (CV), with deep MLP adopted to identify bridge ISs and CV employed to obtain the position coordinates of the calibration vehicle's wheels. A series of numerical simulations and field experiments on an in-service bridge were carried out to validate the proposed framework and compare it against a broadly established method to such an end—Quilligan's method. The results show the accuracy, robustness, and practicability of the proposed framework. [ABSTRACT FROM AUTHOR]

This article examines how passion affects an entrepreneur's business failure experiences. Our study explores the link between the type of passion an entrepreneur exhibits and the effect this has on the entrepreneurs' attitudes and reactions to business failure. We analyse the way in which passion type informs entrepreneurs identification with their business, and the entrepreneurial process. Entrepreneurs who experienced harmonious passion maintained an emotional distance from their business failure. Harmoniously passionate entrepreneurs had a rational perspective and were reflective, self-aware, adaptive and future oriented. Entrepreneurs who experienced obsessive passion, were defensive and reactionary about their business failure. Obsessively passionate entrepreneurs attached contingencies and experienced increased stress and conflict. Our findings suggest promising opportunities for future research on the interplay between heterogeneous passions, adaptive/maladaptive entrepreneurial action and regulated goal pursuit. [ABSTRACT FROM AUTHOR]

The rapid rise in accessibility and portability of cameras has resulted in widespread reliance on the interpretation of images by analysts and investigators in criminal proceedings. Codes of practice, guidance and jurisprudence have evolved to facilitate the admission of opinions as to the identity of offenders (or persons of interest) at trial. In this article, we explain why allowing investigators to give opinions as to identity on the basis of familiarity with images or suspects acquired during the course of an investigation is incompatible with mainstream scientific research and advice, and conducive to error. It rests on the flawed assumption that investigators can reliably identify or recognise persons in images, articulate and document the basis of these 'identifications', and avoid the risk of contamination (really cognitive bias) from their knowledge of, or exposure to, domain-irrelevant information. Jurors, who may be invited to conduct their own comparison between an image and the defendant in the dock, are similarly vulnerable to assuming the task is straightforward, as well as many of the contextual and cognitive biases confronting investigators. Using the facts and evidence in R v Yaryare [2020] EWCA Crim 1314 as a case study, we show how case information available to investigators and imaging analysts both inform their interpretations of images and is (re-)presented at trial and on appeal as independent support for their opinions. We identify substantial threats to fairness, proof and rationality and propose that only witnesses with demonstrable expertise should be permitted to testify as to the identity of persons of interest in images. [ABSTRACT FROM AUTHOR]

The author provides a checklist for educators who are selecting technically adequate tests for identifying and referring students for gifted education services and programs. The checklist includes questions related to how the test was normed, reliability and validity studies as well as questions related to types of scores, administration, and format. The author concludes that selecting technically adequate tests is a critical step in developing an identification procedure that will be inclusive, reliable, and valid for finding students who will benefit from the school's gifted education services and programs. [ABSTRACT FROM AUTHOR]

Finding all the "gifted" students who would benefit from a gifted and talented service is a perpetual concern. In this article, we focus on how to effectively implement multiple criteria in identification. First, we provide some broad background before introducing three different ways to combine multiple data points (AND, OR, and MEAN) when identifying students for gifted services. Next, we discuss how effective use of combining multiple criteria—including using two-phase identification systems—contributes to schools saving time and money while also better identifying students. To do this, we use newly introduced criteria for evaluating gifted and talented identification systems. Finally, we provide several keys for success that can help schools accomplish their identification goals effectively. [ABSTRACT FROM AUTHOR]

Scholars from very different interpretive traditions agree that Rousseau's conception of human nature and the self constitutes a pivotal point in the history of philosophy. I focus on one important aspect of his investigation into human nature and the self: the development of identity. I reconstruct his understanding of the development of identity as articulated in the Discourse on Inequality and Emile, focusing on the psychological interplay of identity and identification involved in the formation of the self. Finally, I turn to a discussion of how his theory of the development of identity informs his specifically political theory, and especially the extralegal institutions and practices he suggests for forming a strong political identity. [ABSTRACT FROM AUTHOR]

An increase in the number of individuals trying to lose weight has enabled the development of online weight-loss support communities (Weight Watchers, Dukan, etc.). This research aims to study whether and how social support in these communities affects dietary self-efficacy to improve individuals' food well-being. After an initial qualitative study with 25 community users, we formulated hypotheses and tested them in a quantitative study involving 335 users. The results show that social support contributes to dietary self-efficacy through two sequential mediators: identification with community members and motivation to comply with group norms. Our analyses also show that nutritional knowledge moderates the relationship between motivation to comply and dietary self-efficacy. The results of these two studies enrich the literature on online communities and allow us to suggest managerial recommendations for administrators of these communities and health professionals. [ABSTRACT FROM AUTHOR]

This meta-analysis synthesized 19 empirical articles reporting 123 effect sizes of character-recipient similarity on narrative processing and persuasion outcomes across different contexts, including health, environmental, and social issues. We also aimed to investigate whether the effect magnitude varies depending on how the similarity is operationalized, which perspective is adopted, and what context the narrative persuasion is placed in. The results indicated that, compared to a dissimilar counterpart, a similar character leads to stronger identification (k = 34, d = 0.14, p <.01) and self-referencing (k = 12, d = 0.16, p <.01). The effects on transportation (k = 22, d = 0.13, p =.05) and resistance (k = 12, d = −0.16, p =.05) were marginally significant. It was also found that the similarity manipulated on chosen demographic and biographic variables like occupation and living place yields the strongest impact among other variables (i.e., innate demographic and biographic variables like age and sex, psychological and behavioral variables like beliefs and behaviors). Furthermore, the similarity effect in narrative persuasion becomes intensified when combined with a first-person perspective and placed in a social issue context. By presenting a synthesis of the existing research, this meta-analytical study sought to identify areas in need of further refinement and outline future investigation directions for narrative persuasion. [ABSTRACT FROM AUTHOR]

Policies about changing gender/sex on identity documents provide insights into definitions of gender/sex, and impact especially transgender and/or nonbinary lives. We investigated these on U.S. driver's licenses and birth certificates to understand variability in these policies, including in comparison to an earlier report in 2014, and to explore what kinds of "bio/logics" (decision rules rooted in biological or biologistic thinking) might be at play. Results show that the most common requirements in 2020 included proof of gender affirming surgery, a letter from a medical doctor, and hormone therapy. Compared to 2014, results showed an increase in requirements for hormone therapy and letters from therapists or medical doctors, and a decrease in requirements for gender affirming surgery. We highlight how this suggests a shift to "pubertal bio/logics": rooting gender/sex definitions in secondary sex characteristics. This contrasts with previous requirements that pointed to "newborn bio/logics": rooted in genital definitions of gender/sex affirmed by a surgico-medical authority. Both support policy framings of gender/sex as a biophenomenon, though with different impacts for trans and/or nonbinary livability. Our study provides insights into U.S. state definitions of gender/sex, and their multiple and contradictory biological views on gender/sex, with implications especially for transgender and/or nonbinary individuals' lives. [ABSTRACT FROM AUTHOR]

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions. [ABSTRACT FROM AUTHOR]

Background: Odour discrimination and identification (DI) are markers associated with disability worsening and neuroaxonal damage in multiple sclerosis (MS). Objective: The main objective of this research is to investigate whether longitudinal change of DI predicts long-term MS disease course. Methods: This is a 6-year prospective longitudinal study on MS patients at the MS Clinic Innsbruck. Clinical, bi-annual visits assessed patients' history and Expanded Disability Status Scale (EDSS) score. DI and cognitive function were assessed at baseline (BL), Year 1 (Y1), Year 2 (Y2) and Year 6 (Y6) by the 'Sniffin' Sticks'/Symbol Digit Modalities Test. Results: Around 92 of 139 patients were available for Y6 follow-up. Mean DI scores significantly decreased over time (BL = 27.8, Y1 = 27.5, Y2 = 26.3 and Y6 = 26.3; p < 0.001) and negatively correlated with patients' age (rs = −0.120, p = 0.032) and disease duration (rs = −0.103, p = 0.041). Multivariable regression analyses revealed that lower absolute DI scores and larger DI score loss over time were associated with higher probability of EDSS worsening (per −1 point: hazard ratio (HR) = 1.40 (1.16–1.68) and 2.34 (1.27–4.21)), progression independent of relapse activity (PIRA) (HR = 1.49 (1.20–1.85) and 2.22 (1.33–3.31)) and cognitive deterioration (HR = 1.75 (1.35–2.27) and 4.29 (1.26–2.84)) at Y6, but not with time to first relapse. Conclusion: Odour DI is an irreversible marker of neuroaxonal damage, associated with PIRA, cognitive deterioration and EDSS worsening. [ABSTRACT FROM AUTHOR]

The paper presents a novel view of social norms that reflects the importance of different agent types, their specific motivations and roles. How one identifies with a role and behavioral options available to the agent is crucial for the sustainability of the social norms. The analysis of a simple case of social norm is suggested as a default model for analysis, and then the classification of subjects, enforcers, and audience is introduced. This triangular typology of agents is extended by introducing a network of interlocking patterns underlying social behavior. Lastly, the paper describes two categories of transgressions based on misidentification. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to establish the effects of simultaneous and asynchronous masking on the detection and identification of visual and auditory alarms in close temporal proximity. Background: In complex and highly coupled systems, malfunctions can trigger numerous alarms within a short period of time. During such alarm floods, operators may fail to detect and identify alarms due to asynchronous and simultaneous masking. To date, the effects of masking on detection and identification have been studied almost exclusively for two alarms during single-task performance. This research examines 1) how masking affects alarm detection and identification in multitask environments and 2) whether those effects increase as a function of the number of alarms. Method: Two experiments were conducted using a simulation of a drone-based package delivery service. Participants were required to ensure package delivery and respond to visual and auditory alarms associated with eight drones. The alarms were presented at various stimulus onset asynchronies (SOAs). The dependent measures included alarm detection rate, identification accuracy, and response time. Results: Masking was observed intramodally and cross-modally for visual and auditory alarms. The SOAs at which asynchronous masking occurred were longer than reported in basic research on masking. The effects of asynchronous and, even more so, simultaneous masking became stronger as the number of alarms increased. Conclusion: Masking can lead to breakdowns in the detection and identification of alarms in close temporal proximity in complex data-rich domains. Application: The findings from this research provide guidance for the design of alarm systems. [ABSTRACT FROM AUTHOR]

Impact force identification is of great importance for composite structural health monitoring due to the poor impact resistance of composite materials. Convex sparse regularization method based on L 1-norm tends to underestimate the amplitude of the impact force. This paper proposes a novel method using fully overlapping group sparsity based on Lp -norm regularization (FOGS L p) for impact force identification, which can localize the impact force and reconstruct its time history simultaneously with limited measurements in under-determined cases. The FOGS L p method takes more sparse prior information into account by combining the non-convex Lp -norm (0 < p < 1) and fully overlapping group sparsity to promote sparse solutions, thus improving the accuracy of the reconstructed impact force. An accelerated grouped shrinkage-thresholding algorithm is employed to solve the non-convex optimization problem under the majorization-minimization framework. The Nesterov's acceleration strategy is modified to accommodate the requirements of non-convex optimization. Simulations and experiments are conducted on composite panels to validate the effectiveness of the FOGS L p method. Results demonstrate the efficiency and robustness of the FOGS L p method to localize the impact force and reconstruct its time history simultaneously while 25 potential impact points are monitored using two sensors. Compared with L 1-norm, Lp -norm, and L 2,1-norm regularization methods, the proposed method performs best in both simulations and experiments. [ABSTRACT FROM AUTHOR]

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This article is the English translation of the 'inaugural lesson' that began Professor Mieke Bal's year as professor of the 'European Chair' at the Collège de France. How, asks Bal, to overcome Europe as Babel (where no one understands the other), Europe as nostalgia (regretting lost time and idealizing the past), the pessimism close to despair of Europe as apocalypse? While she refuses to offer a positive, practical answer, Bal does propose an approach to some of the major issues at stake in the ongoing invention of Europe. Here she dreams of 'Europe in the plural', the ongoing invention of Europe as a semiosphere that, by way of its multiple differences, encourages mutual curiosity and debate, interaction, interculturality, and sharing between cultures and languages. This is the ground, proposes Bal, for that cultural ideal of Europe in the plural. It is also the ground from which spring Bal's lectures at the Collège de France, which are to be presented in a series of articles forthcoming in Journal of Visual Culture. [ABSTRACT FROM AUTHOR]

Background and Aim: In neuroblastoma, anaplastic lymphoma kinase mutations have recently received attention as molecular targets for the treatment of neuroblastoma, as 6% to 10% of patients with neuroblastoma have anaplastic lymphoma kinase mutations. There are little data from the cases in Turkey. We aimed to detect anaplastic lymphoma kinase mutations and molecular heterogeneity in neuroblastoma using next-generation sequencing. This study is the first one with this many cases in Turkey. Methods: Next-generation sequencing analysis was performed using an Illumina MiniSeq custom gene panel. Clinically important mutations were selected for the analysis. We also gathered clinical data of the patients from Turkish Pediatric Oncology Group cohorts to associate them with anaplastic lymphoma kinase mutations. This study is a retrospective cross-sectional study. We followed STROBE guideline (https://www.equator-network.org/reporting-guidelines/strobe/) on this study. Results: We analyzed anaplastic lymphoma kinase in 108 patients with neuroblastoma, with a mean age of 43.76 months. Pathogenic anaplastic lymphoma kinase mutations were detected in 13 patients (12.04%). We noted that anaplastic lymphoma kinase mutations were primarily observed in intermediate- and high-risk patients (P =.028). R1275Q and F1174-related mutations were predominant; I1171T, L1226F, S1189F, V1135A, and G1125S mutations were rare. Duplicate samples did not exhibit any heterogeneity. Conclusions: We found that F1174 and R1275Q-related anaplastic lymphoma kinase mutations are the most common pathogenic mutations in neuroblastoma. Anaplastic lymphoma kinase mutation status did not show any heterogeneity, and the mutations were correlated with intermediate- or high-risk groups. [ABSTRACT FROM AUTHOR]

Considerable attention has recently been focused on classification and regression-based convolutional neural network (CNN) and long short-term memory (LSTM) due to their excellent performance in capturing complex spatial and temporal information characteristics for structural damage identification. However, few studies have considered structural damage identification as a classification and regression problem. In addition, bridges in practical engineering are vulnerable to various environmental and vehicle loading conditions. Hence, this study proposed a new two-stage CNN–LSTM configuration for bridge damage identification using vibration data considering the influence of temperatures. First, a classification-based CNN–LSTM is designed to perform multiclass damage detection tasks, and then a regression-based CNN–LSTM is developed for damage localization and severity prediction tasks. The performance of the proposed damage identification method was evaluated through a simulation dataset of a concrete highway bridge model and a field experiment dataset of Z24-bridge (Switzerland). In addition, a set of statistical evaluation metrics such as sparse categorical cross-entropy loss, accuracy, confusion matrix, mean squared loss, mean absolute error, mean absolute percentage error, and coefficient of determination were used to compare the damage identification performance of the proposed CNN–LSTM configuration with a regular CNN model and conventional machine learning (ML) algorithms. Prediction results indicate that the proposed CNN–LSTM model outperforms the regular CNN model and conventional ML algorithms for bridge damage identification. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been implemented recently for processing and analyzing monitored data for damage detection and identification in the field of structural health monitoring (SHM). Existing machine learning methods such as convolutional neural networks (CNNs) usually rely on inputs from a single domain (time or frequency), which may only provide partial information for damage identification. To address this issue, this work proposes a parallel convolutional neural network (P-CNN) that extracts multidimensional features assisted by a computer vision technique. The proposed network comprises a one-dimensional (1D) CNN branch, a two-dimensional (2D) CNN branch, and several fully connected layers. The efficiency and robustness of the proposed network were validated by a public experimental dataset. Our results show that (1) the features extracted by the P-CNN were separated more easily compared with those by 1D-CNN or 2D-CNN; (2) when detecting structural damages, the accuracy of the P-CNN is above 99.4%; (3) the P-CNN exhibits a robust performance when subjected to a high (5 dB) signal-to-noise ratio of the original data; and (4) when compared with traditionally used methods such as GoogLenet and Resnet, the P-CNN outperforms on many aspects of damage identification. We envision that the proposed P-CNN can be integrated into advanced SHM systems with high fidelity and intelligence. [ABSTRACT FROM AUTHOR]

Structural modal parameters are essential to provide dynamic information for data analysis of structural health monitoring (SHM) system. Because of the variation of the physical properties or the influence of the environment, the structural modal parameters may change with time during the operation, showing time-varying characteristics. Therefore, accurate identification of time-varying modal parameters shows to be an important issue for SHM. In this paper, a time-varying modal identification method is proposed by improving the multivariate variational mode decomposition (MVMD) method with autoregressive power spectrum and windowed principal component analysis (PCA). Firstly, the method for determination of initial center frequency is proposed by autoregressive power spectrum to improve the efficiency of MVMD. Secondly, intrinsic mode function for each mode is extracted using multi-channel responses by MVMD. Subsequently, instantaneous frequencies are identified through detecting the ridgeline of the synchro-squeezed short-time Fourier transform (SSTFT). Moreover, identification method for time-varying mode shapes is proposed by using the windowed PCA of the multi-channel intrinsic modes. Finally, the proposed method is verified by the numerical and practical studies. The results of the numerical study show that the method is effective for continuously varying modal parameters under impulse and random excitations. Through the data analysis of practical bridge, the capability for practical application is demonstrated. [ABSTRACT FROM AUTHOR]

Background: Several studies have demonstrated the analytical sensitivity of MALDI-TOF mass spectrometry (MALDI-TOF MS) by immunoenrichment for M-protein analysis. We report the results of a novel, low-cost, reagent-based extraction process using acetonitrile (ACN) precipitation to enrich for κ and λ light chains which can be analysed by MALDI-TOF MS. Methods: Institutional Ethics committee approval was obtained. Serum samples from patients with monoclonal gammopathy of undetermined significance (MGUS), multiple myeloma (MM), plasmacytoma, AL amyloidosis and Waldenström macroglobulinemia (WM) underwent ACN precipitation. The images obtained were overlaid on apparently healthy donor serum samples to confirm the presence of M-protein. A sample was considered positive for M-protein if there was a sharp or broad peak within the κ or λ mass/charge (m/z) range: m/z- [M + 2H]2+: 11,550–12,300 Da and λ m/z - [M + 2H]2+: 11,100–11,500 Da. Images were acquired at a m/z range of 10,000–29,000 Da. Corresponding serum protein electrophoresis (SPEP), serum immunofixation electrophoresis (IFE) and serum free light chain (sFLC) assay by nephelometry were performed for all the samples. Results: Two-hundred-and-two serum samples were included in the study: MM- 184 (91%); AL amyloidosis- 2 (1%); plasmacytoma- 8 (4%); MGUS- 6 (3%) and WM- 2 (1%). All the SPEP positive samples were identified by MALDI-TOF MS. Out of 179 samples positive for M-protein by IFE, MALDI-TOF MS was positive in 176 samples (98%). Compared to IFE, the sensitivity and specificity of M-protein identification by MALDI-TOF MS were 98.3% and 52.2%, respectively. Conclusions: This study demonstrates the feasibility of qualitatively identifying M-protein without the need for antibody-based immunoenrichment, making the technique cost-effective. [ABSTRACT FROM AUTHOR]

Two experimental studies, performed in Italy and Poland, showed that exposure to homophobic hate speech reduces well-being among LGBT+ people, particularly among those who highly identify with the LGBT+ community. Study 1 showed that exposure to homophobic words increases negative mood among highly identifying LGBT+ people, but it does not influence the negative affect of loosely identifying participants. Study 2 showed that exposure to homophobic statements reduces general life satisfaction among highly identifying LGBT+ people, as opposed to low-identifiers. [ABSTRACT FROM AUTHOR]

Background: Accurate identification of Chinese herbal medicines is the basis for their research and utilization. Molecular identification can effectively differentiate original plants from counterfeit plants. The quality of genomic DNA is an important factor affecting molecular identification. However, the processing can lead to DNA degradation of the herbal medicines, which can make it difficult for their molecular identification. Objectives: To establish a genomic DNA degradation model of Gentiana crassicaulis Radix to evaluate the effects of processing methods and storage times on genomic DNA integrity. Materials and Methods: A genomic DNA degradation model of G. crassicaulis —the original plant source of the Chinese herbal medicine G. crassicaulis Radix—was established using a steam heating method. Genomic DNA integrity of G. crassicaulis Radix was evaluated using capillary electrochromatography (CEC) fingerprinting and polymerase chain reaction (PCR) of DNA barcoding markers following different processing and drying methods, including slicing (sliced roots), no slicing (whole roots), stoving, air drying, and sweating. Results: CEC fingerprinting and DNA barcoding PCR effectively evaluated genomic DNA integrity. Compared to whole roots, sliced roots better helped maintain genomic DNA integrity. As the storage time increased, the integrity of the genomic DNA reduced; the integrity of the genomic DNA of sliced roots was greater than that of whole roots. Furthermore, the interactions between slicing and drying methods possibly reduced the genomic DNA integrity. Conclusion: A genomic DNA degradation model and an evaluation system for herbal medicines were established. Our findings can help optimize the method for processing G. crassicaulis Radix and establish the traceability of genuine herbal medicines. Key Message: The quality of genomic DNA is an important factor affecting molecular identification, which is essential to determine original plants. The results of our study can help develop a method for processing G. crassicaulis Radix and enable the traceability of genuine herbal medicines. [ABSTRACT FROM AUTHOR]

Background: Ziziphi spinosae semen (ZSS), dry mature seeds of Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H.F. Chow., are widely used in the treatment of insomnia, fright palpitations, and profuse dreaming in medical treatments. The counterfeit species of ZSS, Ziziphi mauritianae semen (ZMS), and Hovenia dulcis Thunb (HDT) are seeds from Ziziphus incurva Roxb. and Hovenia acerba Lindl., respectively. However, the pharmacopeia is not recorded for ZMS and HDT. In this article, we reviewed the identification methods of ZSS, ZMS, and HDT. Materials and Methods: The random amplified polymorphic DNA (RAPD) technique was used to identify the ZSS and its copies. Plant genome DNA was extracted from Z. jujuba Mill., Z. incurva Roxb., and H. acerba Lindl. Random primers were designed to amplify the genome. The amplified products were detected by agarose gel electrophoresis. Results: By analyzing the polymorphism of the amplified product DNA fragment, the DNA fingerprint maps of Z. jujuba Mill., Z. incurva Roxb., and H. acerba Lindl. were successfully constructed. [ABSTRACT FROM AUTHOR]

The current study conducted an online survey to understand the challenges and needs teachers face for identifying and nurturing students with twice exception. Among 896 respondent schools, 179 schools were reported to have 277 identified 2E students. The results indicated that schools with both gifted and disability classes/programs or services identified more 2E students than did schools without a special education program. Although increasing the awareness and knowledge of general education teachers is beneficial for the identification of 2E students, most of the respondents reported that they need gifted education teachers to get involved in developing students' talents. Obvious opportunity gaps existed between schools with only disability education and with gifted and disability education. An effective support system with manpower and financial assistance from governments is also essential for closing opportunity gaps and meeting the needs of both learners and teachers. [ABSTRACT FROM AUTHOR]

This article uses ethnographic and interview data about four cases in two work settings in India to examine identification as a factor in workers' reactions to workplace change. Novel technology and management practices are frequently introduced into work settings as the world of work changes. Workers tend to cooperate more with some workplace changes than with others. The previous employment relations literature has invoked interests, cultural values, and worker power to explain workers' responses to change. This article introduces an additional factor: whether a change fosters or impairs workers' identification with their work. The author examines identification at three levels—occupational, organizational, and that of the work itself—and finds that workers are more likely to cooperate with workplace change that protects and fortifies their pre-existing sources of identification. [ABSTRACT FROM AUTHOR]

The author provides information about normative scores and areas that educators should examine in determining if the test is appropriate for the group that will be administered the test. These areas include demographic characteristics, numbers tested at each grade and age level, and recency of standardization. [ABSTRACT FROM AUTHOR]

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the molecular pathogenesis, but the molecular mechanisms of PDAC are still not well understood. The purpose of this study is to further explore the molecular mechanism of PDAC through integrated bioinformatics analysis. Methods: To identify the candidate genes in the carcinogenesis and progression of PDAC, next-generation sequencing (NGS) data set GSE133684 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and Gene Ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using Integrated Interactions Database (IID) interactome database and Cytoscape. Subsequently, miRNA-DEG regulatory network and TF-DEG regulatory network were constructed using miRNet database, NetworkAnalyst database, and Cytoscape software. The expression levels of hub genes were validated based on Kaplan-Meier analysis, expression analysis, stage analysis, mutation analysis, protein expression analysis, immune infiltration analysis, and receiver operating characteristic (ROC) curve analysis. Results: A total of 463 DEGs were identified, consisting of 232 upregulated genes and 233 downregulated genes. The enriched GO terms and pathways of the DEGs include vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis, and adaptive immune system. Four hub genes (namely, cathepsin B [CCNB1], four-and-a-half LIM domains 2 (FHL2), major histocompatibility complex, class II, DP alpha 1 (HLA-DPA1) and tubulin beta 1 class VI (TUBB1)) were obtained via taking interaction of different analysis results. Conclusions: On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of PDAC and provide potential targets for further investigation. [ABSTRACT FROM AUTHOR]