Miracle-dz/UniFAR-FTUs huggingface dataset:
Very late reactions in the patch test with fragrance mix I and oak moss absolute (Evernia prunastri, INCI): Data of the Information Network of Departments of Dermatology (IVDK)

Information Network of Departments of Dermatology (IVDK), Institute at the University Medical Center Göttingen, Göttingen, Germany Clinic and Policlinic for Dermatology, Medical Faculty Carl Gustav Carus, TU Dresden, Dresden, Germany Department of Dermatology, Erlangen University Hospital, Erlangen, Germany Dermatologic Center, BG Trauma Hospital Hamburg, Hamburg, Germany Department of Dermatology, Venereology and Allergology, St. Josef Hospital, Ruhr University Bochum, Bochum, Germany Department of Dermatology, Occupational Dermatology, University Medical Center, Heidelberg, Germany Department of Dermatology, Bielefeld Medical Center, Bielefeld, Germany Department of Dermatology, University of Mainz, Mainz, Germany Department of Dermatology, Venereology and Allergology, University Medical Center Göttingen, Göttingen, Germany Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland Department of Dermatology and Allergology, Academic Teaching Hospital Dresden-Friedrichstadt, Dresden, Germany Department of Dermatology and Venereology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany Department of Allergology, Photodermatology and Occupational Dermatology, Dermatologikum Hamburg, Hamburg, Germany Department of Dermatology, SLK Hospital Heilbronn, Heilbronn, Germany
Betulinic acid induces autophagy-dependent apoptosis via Bmi-1/ROS/AMPK-mTOR-ULK1 axis in human bladder cancer cells

Betulinic acid (BA), a pentacyclic triterpenoid isolated from tree bark, exhibits antitumor effects against solid malignancies and triggers autophagy and/or apoptosis in human cancer cells. Nonetheless, the relationship between autophagy and apoptosis and the potential modulatory actions of BA on autophagy-dependent bladder cancer cell death remain unclear. The present study showed that BA exposure significantly suppressed viability, proliferation, and migration of EJ and T24 human bladder cancer cells. These effects reflected caspase 3-mediated apoptosis and could be attenuated or abolished by inhibiting ROS production with N-acetyl-L-cysteine, inhibiting autophagy with chloroquine, or silencing ATG7 with targeted siRNA. BA-induced autophagy was evidenced by epifluorescence imaging of lentivirus-induced expression of mCherry-GFP-LC3B and increased expression of two autophagy-related proteins, LC3B-II and TEM. Moreover, enhanced AMPK phosphorylation and decreased mTOR and ULK-1 phosphorylation suggested BA activates autophagy via the AMPK/mTOR/ULK1 pathway. Accordingly, exposure to dorsomorphin (Compound C), an AMPK inhibitor, and AICAR, an AMPK activator, respectively inhibited and stimulated BA-induced autophagy in EJ and T24 cells. The effects of Bmi-1 overexpression in vitro and decreased Bmi-1 expression in BA-treated T24 cell xenografts in nude mice suggested that downregulation of Bmi-1 is the underlying mechanism in BA-mediated, autophagy-dependent apoptosis.
Skeletal Muscle Deconditioning in Breast Cancer Patients Undergoing Chemotherapy: Current Knowledge and Insights From Other Cancers

Breast cancer represents the most commonly diagnosed cancer while neoadjuvant and adjuvant chemotherapies are extensively used in order to reduce tumor development and improve disease-free survival. However, chemotherapy also leads to severe off-target side-effects resulting, together with the tumor itself, in major skeletal muscle deconditioning. This review first focuses on recent advances in both macroscopic changes and cellular mechanisms implicated in skeletal muscle deconditioning of breast cancer patients, particularly as a consequence of the chemotherapy treatment. To date, only six clinical studies used muscle biopsies in breast cancer patients and highlighted several important aspects of muscle deconditioning such as a decrease in muscle fibers cross-sectional area, a dysregulation of protein turnover balance and mitochondrial alterations. However, in comparison with the knowledge accumulated through decades of intensive research with many different animal and human models of muscle atrophy, more studies are necessary to obtain a comprehensive understanding of the cellular processes implicated in breast cancer-mediated muscle deconditioning. This understanding is indeed essential to ultimately lead to the implementation of efficient preventive strategies such as exercise, nutrition or pharmacological treatments. We therefore also discuss potential mechanisms implicated in muscle deconditioning by drawing a parallel with other cancer cachexia models of muscle wasting, both at the pre-clinical and clinical levels.
NMDARs Drive the Expression of Neuropsychiatric Disorder Risk Genes Within GABAergic Interneuron Subtypes in the Juvenile Brain

Medial ganglionic eminence (MGE)-derived parvalbumin (PV)+, somatostatin (SST)+and Neurogliaform (NGFC)-type cortical and hippocampal interneurons, have distinct molecular, anatomical, and physiological properties. However, the molecular mechanisms regulating their maturation remain poorly understood. Here, via single-cell transcriptomics, we show that the obligate NMDA-type glutamate receptor (NMDAR) subunit gene Grin1 mediates transcriptional regulation of gene expression in specific subtypes of MGE-derived interneurons, leading to altered subtype abundances. Notably, MGE-specific early developmental Grin1 loss results in a broad downregulation of diverse transcriptional, synaptogenic and membrane excitability regulatory programs in the juvenile brain. These widespread gene expression abnormalities mirror aberrations that are typically associated with neurodevelopmental disorders. Our study hence provides a road map for the systematic examination of NMDAR signaling in interneuron subtypes, revealing potential MGE-specific genetic targets that could instruct future therapies of psychiatric disorders.
Characterization of the Primary Human Trophoblast Cell Secretome Using Stable Isotope Labeling With Amino Acids in Cell Culture

The placental villus syncytiotrophoblast, the nutrient-transporting and hormone-producing epithelium of the human placenta, is a critical regulator of fetal development and maternal physiology. However, the identities of the proteins synthesized and secreted by primary human trophoblast (PHT) cells remain unknown. Stable Isotope Labeling with Amino Acids in Cell Culture followed by mass spectrometry analysis of the conditioned media was used to identify secreted proteins and obtain information about their relative rates of synthesis in syncytialized multinucleated PHT cells isolated from normal term placental villus tissue (n = 4/independent placenta). A total of 1,344 proteins were identified, most of which have not previously been reported to be secreted by the human placenta or trophoblast. The majority of secreted proteins are involved in energy and carbon metabolism, glycolysis, biosynthesis of amino acids, purine metabolism, and fatty acid degradation. Histone family proteins and mitochondrial proteins were among proteins with the slowest synthesis rate whereas proteins associated with signaling and the plasma membrane were synthesized rapidly. There was a significant overlap between the PHT secretome and proteins known be secreted to the fetal circulation by the human placenta in vivo. The generated data will guide future experiments to determine the function of individual secreted proteins and will help us better understand how the placenta controls maternal and fetal physiology.
Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan

Purpose The clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans. Methods We retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: “negative”, “equivocal”, and “suspicious” for the presence of PCa. Results Univariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case. Conclusion Our multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.
Defensive Behavior and Morphometric Variation in Apis mellifera Colonies From Two Different Agro-Ecological Zones of North-Western Argentina

European lineages of Apis mellifera were first introduced into America for beekeeping purposes. A subsequent introduction and accidental release of A. m. scutellata resulted in hybridization events that gave rise to Africanized populations that rapidly spread throughout the continent. In Argentina, Africanized honey bees (AHBs) have been mostly detected in northern regions of the territory, and represent a valuable genetic resource for the selection of stocks with advantageous characteristics for beekeeping. The objective of the present study was to profile honey bee colonies of wild origin with potential beneficial traits for apiculture using morphological, molecular and behavioral traits. Honey bee colonies chosen for evaluation were located in two different agro-ecological regions in north-western Argentina (Tucumán province): The Chaco Depressed Plain (Leales apiary) and the Piedmont (Famaillá apiary). Each apiary was surveyed three times during the 2017–2018 season (mid-season, wintertime, and early spring) for: brood population, phoretic Varroa level and defensive behavior (run, fly, sting, and hang). At the midpoint of the beekeeping season colonies were also characterized by morphometry (45 variables) and mitochondrial haplotypes (COI–COII intergenic region). Apiaries studied showed similar patterns throughout the beekeeping season, for most of the characteristics monitored. However, significant variation in defensive behavior parameters was found between apiaries at the different times of evaluation. Twelve of 45 morphometric variables also showed significant differences between apiaries. The mitochondrial haplotype analysis revealed a high representation of African A4 and A1 haplotypes (91%) in both apiaries. Haplotype variation was associated with morphometric and behavioral traits. Multivariate analyses [principal component analysis (PCA) and principal coordinate analysis (PCoA)] including morphometric and behavior variables explained 65.3% (PCA) and 48.1% (PCoA) of the variability observed between colonies in the first two components. Several morphometric parameters and “fly” behavior were mainly associated with the separation of the colonies. The results from this study point to a possible association between morphometric and behavioral variation and the adaptation of honey bee colonies to differential agro-ecological conditions. We discuss how the detected variation between apiaries can be used for the selection and preservation of honey bee ecotypes in regional breeding programs.
Relationship Between Chinese Adolescents’ Sleep Status and Problem Behaviors: The Mediating Role of Mental Health

Adolescents’ problem behaviors constitute a critical indicator of crime, and they play an important role in the growth and development of adolescents and social stability. Using the 2014–2015 school year follow-up data from the China Education Panel Survey, this study investigated the relationship between sleep time, sleep disorders, and adolescents’ problem behaviors. Further, we analyzed the mediating effect of mental health status, a key factor influencing adolescent growth and development. The results showed that the frequency of problem behaviors among adolescents was significantly and positively associated with sleep disorders and short sleep time. In addition, mental health status is a channel through which sleep time and sleep disorders affect adolescents’ problem behaviors.
Should I Start My Own Venture? Moderating Effect of Pull Factors on the Relationship of Push Factors with Women Entrepreneurial Intentions

Abstract Extant literature of women entrepreneurial intention (EI) has not studied the interplay of push and pull factors well, creating a lacuna in literature. In this paper, we explored how barriers within their professional careers, i.e. push factors led to women EI, and how interpersonal motivators and contextual pull factors moderated that relationship. Adopting the push-pull framework and analyzing survey-based responses from 302 working women using structural-equation-modeling, we find that the need for recognition (NFR) and the glass ceiling (GC) effect have positive relationships, whereas family support (FS) has a negative relationship with EIs. Moreover, the NFR and FS moderate GC-EI relationship. Our study contributes towards the women entrepreneurship literature by focusing on the interplay of push and pull factors. The paper also provides important managerial and policy implications for organizations and policy makers.
Ensuring a Robust Multimodal Conversational User Interface During Maintenance Work

It has been shown that the provision of a conversational user interface proves beneficial in many domains. But, there are still many challenges when applied in production areas, e.g. as part of a virtual assistant to support workers in knowledge-intensive maintenance work. Regarding input modalities, touchscreens are failure-prone in wet environments and the quality of voice recognition is negatively affected by ambient noise. Augmenting a symmetric text- and voice-based user interface with gestural input poses a good solution to provide both efficiency and a robust communication. This paper contributes to this research area by providing results on the application of appropriate head and one-hand gestures during maintenance work. We conducted an elicitation study with 20 participants and present a gesture set as its outcome. To facilitate the gesture development and integration for application designers, a classification model for head gestures and one for one-hand gestures were developed. Additionally, a proof-of-concept for operators’ acceptance regarding a multimodal conversational user interface with support of gestural input during maintenance work was demonstrated. It encompasses two usability testings with 18 participants in different realistic, but controlled settings: notebook repair (SUS: 82.1) and cutter head maintenance (SUS: 82.7).
Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation

Much of the recent progress in sequential and session-based recommendation has been driven by improvements in model architecture and pretraining techniques originating in the field of Natural Language Processing. Transformer architectures in particular have facilitated building higher-capacity models and provided data augmentation and training techniques which demonstrably improve the effectiveness of sequential recommendation. But with a thousandfold more research going on in NLP, the application of transformers for recommendation understandably lags behind. To remedy this we introduce Transformers4Rec, an open-source library built upon HuggingFace’s Transformers library with a similar goal of opening up the advances of NLP based Transformers to the recommender system community and making these advancements immediately accessible for the tasks of sequential and session-based recommendation. Like its core dependency, Transformers4Rec is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. In order to demonstrate the usefulness of the library and the applicability of Transformer architectures in next-click prediction for user sessions, where sequence lengths are much shorter than those commonly found in NLP, we have leveraged Transformers4Rec to win two recent session-based recommendation competitions. In addition, we present in this paper the first comprehensive empirical analysis comparing many Transformer architectures and training approaches for the task of session-based recommendation. We demonstrate that the best Transformer architectures have superior performance across two e-commerce datasets while performing similarly to the baselines on two news datasets. We further evaluate in isolation the effectiveness of the different training techniques used in causal language modeling, masked language modeling, permutation language modeling and replacement token detection for a single Transformer architecture, XLNet. We establish that training XLNet with replacement token detection performs well across all datasets. Finally, we explore techniques to include side information such as item and user context features in order to establish best practices and show that the inclusion of side information uniformly improves recommendation performance. Transformers4Rec library is available at https://github.com/NVIDIA-Merlin/Transformers4Rec/
On the Choice of Fairness: Finding Representative Fairness Metrics for a Given Context

It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Various notions of fairness have been de-ﬁned, though choosing an appropriate metric is cumbersome. Trade-offs and impossibility theorems make such selection even more complicated and controversial. In practice, users (perhaps regular data scientists) should understand each of the measures and (if possible) manually explore the combinatorial space of different measures before they can decide which combination is preferred based on the context, the use case, and regulations. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that automatically discovers the correlations and trade-offs between different pairs of measures for a given context. Our framework dramatically reduces the exploration space by ﬁnding a small subset of measures that represent others and highlighting the trade-offs between them. This allows users to view unfairness from various perspectives that might otherwise be ignored due to the sheer size of the exploration space. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark data sets.
Hypertriglyceridemic waist phenotype and associated factors in individuals with arterial hypertension and/or diabetes mellitus

Abstract Cardiovascular diseases are among the main causes of death in Brazil and worldwide. The literature indicates the hypertriglyceridemic waist phenotype (HTWP) as an accessible alternative for the identification of cardiovascular and metabolic risk. The present study aimed to identify the prevalence and factors associated with HTWP in individuals diagnosed with arterial hypertension (AH) and/or diabetes mellitus type 2 (DM2). A cross-sectional study was conducted with individuals diagnosed with AH and/or DM2. The study data were collected through semi-structured interviews containing socio-demographic information, lifestyle, health care, in addition to anthropometric assessment, blood pressure measurement and biochemical blood tests. The prevalence of HTWP was estimated and bivariate and multivariate logistic regression was used to assess the factors associated with HTWP. Of the 788 individuals analysed, 21⋅5 % had the HTWP. In the adjusted model, the following variables remained associated with a greater chance of presenting HTWP: sex, age, body mass index (BMI) and very-low-density lipoprotein (VLDL). Being female increased the chance of HTWP by 7⋅7 times (OR 7⋅7; 95 % CI 3⋅9, 15⋅2). The one-year increase in age increased the chance of HTWP by 4 % (OR 1⋅04; 95 % CI 1⋅02, 1⋅06). The addition of 1 mg/dl of VLDL-c increased the chance of HTWP by 15 % (odds ratio (OR) 1⋅15; 95 % confidence interval (CI) 1⋅12, 1⋅18), as well as the increase of 1 kg/m2 in the BMI increased the chance of this condition by 20 % (OR 1⋅20; 95 % CI 1⋅15, 1⋅27). The prevalence of HTWP was associated with females, older age, higher BMI, higher VLDL-c and risk waist/height ratio.
Stylistic Retrieval-based Dialogue System with Unparallel Training Data

The ability of a dialog system to express consistent language style during conversations has a direct, positive impact on its usability and on user satisfaction. Although previous studies have demonstrated that style transfer is feasible with a large amount of parallel data, it is often impossible to collect such data for different styles. In this paper, instead of manually constructing conversation data with a certain style, we propose a flexible framework that adapts a generic retrieval-based dialogue system to mimic the language style of a specified persona without any parallel data. Our approach is based on automatic generation of stylized data by learning the usage of jargon, and then rewriting the generic conversations to a stylized one by incorporating the jargon. In experiments we implemented dialogue systems with five distinct language styles, and the result shows our framework significantly outperforms baselines in terms of the average score of responses' relevance and style degree, and content diversity. A/B testing on a commercial chatbot shows that users are more satisfied with our system. This study demonstrates the feasibility of building stylistic dialogue systems by simple data augmentation.
The neglected unity-in-variety principle: A holistic rather than a single-factor approach in conceptualising a visual merchandise display

ABSTRACT While retailers cannot stress enough the fact that, in reality, a variety of store design cues combine to make a merchandise’s visual display (VMD), the product presentation research focuses mostly on investigating the effect of a single retail display cue, at a time, on consumer product evaluations. But, is “unity-in-variety” a neglected principle in the product presentation research? Scholars are increasingly suggesting more systematic study to identify combinations of key store cues that shape a product’s display for more pertinent for the retailing practice exploratory research. In response, the present article reviews the relevant literature and organises the findings to propose a qualitative typology of VMD cues that can capture, holistically, the VMD construct. The proposed typology is then confirmed in two studies. The identified VMD cues are classified into five key VMD element categories (factors) comprising display fixtures, materials, organisation, staging technique and lighting, and all factors load on one higher-order VMD construct.