Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulating DSC2 Expression Affects the Proliferation and Apoptosis of Prostate Cancer Cells

Background Prostate cancer threatens the life and health of men in China. Desmocollin-2 (DSC2) is a member of DSC family, abnormal expression of which can affect the invasion and metastasis of tumor cells. The aim of this study was to investigate the role of DSC2 in prostate cancer. Materials and Methods Regulating DSC2 expression in prostate cancer cells was conducted with transfection. The expression of DSC2, apoptosis-related proteins, cell cycle-related proteins and E-cadherin (E-cad)/β-catenin pathway was detected by Western blot analysis. The proliferation, clone formation ability, migration, invasion and apoptosis of transfected cells were in turn detected by cell counting kit-8 (CCK-8) assay, clone formation assay, wound healing assay, transwell assay and flow cytometry analysis. Results DSC2 expression was increased in prostate cancer cells compared with RWPE-1 cells. Inhibition of DSC2 promoted the proliferation, clone formation ability, migration and invasion while suppressed apoptosis of LNCaP cells and PC-3 cells. Inhibition of DSC2 affected the expression of apoptosis-related proteins and cell cycle-related proteins according to the changes of apoptosis and proliferation. Furthermore, inhibition of DSC2 up-regulated the expression of p-β-catenin and EGFR while down-regulated the expression of E-cad. DSC2 overexpression exerted the opposite effect of inhibition of DSC2 on LNCaP cells and PC-3 cells. Conclusion DSC2 expression was increased in prostate cancer cells. In addition, inhibition of DSC2 promoted the proliferation, clone formation ability, migration and invasion while suppressed apoptosis of LNCaP cells and PC-3 cells, which provided the fundamental basis for treatment of prostate cancer.
Simulating the infrared sky with a SPRITZ

Aims. Current hydrodynamical and semi-empirical simulations of galaxy formation and evolution have difficulties in reproducing the number densities of infrared-detected galaxies. Therefore, a phenomenological simulation tool that is new and versatile is necessary to reproduce current and predict future observations at infrared (IR) wavelengths. Methods. In this work we generate simulated catalogues starting from the Herschel IR luminosity functions of different galaxy populations to consider different populations of galaxies and active galactic nuclei (AGN) in a consistent way. We associated a spectral energy distribution and physical properties, such as stellar mass, star formation rate, and AGN contribution, with each simulated galaxy using a broad set of empirical relations. We compared the resulting simulated galaxies, extracted up to z = 10, with a broad set of observational relations. Results. Spectro-Photometric Realisations of IR-Selected Targets at all-z (SPRITZ) simulations allow us to obtain, in a fully consistent way, simulated observations for a broad set of current and future facilities with photometric capabilities as well as low-resolution IR spectroscopy, such as the James Webb Space Telescope (JWST) or the Origin Space Telescope (OST). The derived simulated catalogue contains galaxies and AGN that by construction reproduce the observed IR galaxy number density, but this catalogue also agrees with the observed number counts from UV to far-IR wavelengths, the observed stellar mass function, the star formation rate versus stellar mass plane, and the luminosity function from the radio to X-ray wavelengths. The proposed simulation is therefore ideal to make predictions for current and future facilities, in particular, but not limited to, those operating at IR wavelengths.
Shock-fronted travelling waves in a reaction-diffusion model with nonlinear forward-backward diffusion

Reaction-diffusion equations (RDEs) are often derived as continuum limits of lattice-based discrete models. Recently, a discrete model which allows the rates of movement, proliferation and death to depend upon whether the agents are isolated has been proposed, and this approach gives various RDEs where the diffusion term is convex and can become negative (Johnston et al., Sci. Rep. 7, 2017), i.e. forward-backward diffusion. Numerical simulations suggest these RDEs support shock-fronted travelling waves when the reaction term includes an Allee effect. In this work we formalise these preliminary numerical observations by analysing the shock-fronted travelling waves through embedding the RDE into a larger class of higher order partial differential equations (PDEs). Subsequently, we use geometric singular perturbation theory to study this larger class of equations and prove the existence of these shock-fronted travelling waves. Most notable, we show that different embeddings yield shock-fronted travelling waves with different properties.
Answer Identification in Collaborative Organizational Group Chat

We present a simple unsupervised approach for answer identification in organizational group chat. In recent years, organizational group chat is on the rise enabling asynchronous text-based collaboration between co-workers in different locations and time zones. Finding answers to questions is often critical for work efficiency. However, group chat is characterized by intertwined conversations and 'always on' availability, making it hard for users to pinpoint answers to questions they care about in real-time or search for answers in retrospective. In addition, structural and lexical characteristics differ between chat groups, making it hard to find a 'one model fits all' approach. Our Kernel Density Estimation (KDE) based clustering approach termed Ans-Chat implicitly learns discussion patterns as a means for answer identification, thus eliminating the need to channel-specific tagging. Empirical evaluation shows that this solution outperforms other approached.
Zero-shot Learning for Relation Extraction

Most existing supervised and few-shot learning relation extraction methods have relied on labeled training data. However, in real-world scenarios, there exist many relations for which there is no available training data. We address this issue from the perspective of zero-shot learning (ZSL) which is similar to the way humans learn and recognize new concepts with no prior knowledge. We propose a zero-shot learning relation extraction (ZSLRE) framework, which focuses on recognizing novel relations that have no corresponding labeled data available for training. Our proposed ZSLRE model aims to recognize new relations based on prototypical networks that are modified to utilize side (auxiliary) information. The additional use of side information allows those modified prototype networks to recognize novel relations in addition to recognized previously known relations. We construct side information from labels and their synonyms, hypernyms of name entities, and keywords. We build an automatic hypernym extraction framework to help get hypernyms of various name entities directly from the web. We demonstrate using extensive experiments on two public datasets (NYT and FewRel) that our proposed model significantly outperforms state-of-the-art methods on supervised learning, few-shot learning, and zero-shot learning tasks. Our experimental results also demonstrate the effectiveness and robustness of our proposed model in a combination scenario. Once accepted for publication, we will publish ZSLRE's source code and datasets to enable reproducibility and encourage further research.
The Overexpression of Keratin 23 Promotes Migration of Ovarian Cancer via Epithelial-Mesenchymal Transition

Background Keratin 23 (KRT23) is a new member of the KRT gene family and known to be involved in the development and migration of various types of tumors. However, the role of KRT23 in ovarian cancer (OC) remains unclear. This study is aimed at investigating the function of KRT23 in OC. Methods The expression of KRT23 in normal ovarian and OC tissues was determined using the Oncomine database and immunohistochemical staining. Reverse transcription quantitative polymerase chain reaction assay was used to analyze the expression of KRT23 in normal ovarian epithelial cell lines and OC cell lines. Small interfering RNA (siRNA), wound healing assay, and transwell assay were conducted to detect the effects of KRT23 on OC cell migration and invasion. Further mechanistic studies were verified by the Gene Expression Profiling Interactive Analysis platform, Western blotting, and immunofluorescence staining. Results KRT23 was highly expressed in OC tissues and cell lines. High KRT23 expression could regulate OC cell migration and invasion, and the reduction of KRT23 by siRNA inhibited the migration and invasion of OC cells in vitro. Furthermore, KRT23 mediated epithelial-mesenchymal transition (EMT) by regulating p-Smad2/3 levels in the TGF-β/Smad signaling pathway. Conclusions These results demonstrate that KRT23 plays an important role in OC migration via EMT by regulating the TGF-β/Smad signaling pathway.
Risk Factors and Outcomes of Carbapenem-Resistant Enterobacteriaceae Infection After Liver Transplantation: A Retrospective Study in a Chinese Population

Background There is an increasing prevalence of carbapenem-resistant Enterobacteriaceae (CRE) infection after liver transplantation (LT). Improved understanding of the risk factors and outcomes of CRE infections can help us to develop effective preventive strategies and even guide early treatment of high-risk LT patients. Methods This was a retrospective study involving all Chinese adult patients who underwent LT between December 2017 and September 2019 in our center. We analyzed the possible risk factors and outcomes associated with CRE infections in the first 30 days post-LT. Results A total of 387 patients underwent LT. Among them, 26 patients (6.7%) developed CRE infections within 30 days after transplantation. Patients with CRE infections had significantly lower 30-day and 180-day survival rates (80.8% vs 96.4%, p<0.001; 51.5% vs 92.4%, p<0.001). Multivariate analysis identified that intraoperative blood loss equal to or more than 1500 mL (odds ratio [OR], 3.666; 95% confidence interval [CI], 1.407–9.550; p=0.008), CRE rectal carriage within 30 days post-LT (OR, 5.516; 95% CI, 2.113–14.399; p=0.000), biliary complications (OR, 3.779; 95% CI, 1.033–13.831; p=0.045) and renal replacement therapy for more than 3 days (OR, 3.762; 95% CI, 1.196–11.833; p=0.023) were independent risk factors for CRE infections within 30 days post-LT. Conclusion CRE infections within 30 days post-LT were associated with worse outcomes. Intraoperative blood loss equal to or more than 1500 mL, CRE rectal carriage within 30 days post-LT, biliary complications and renal replacement therapy for more than 3 days were independent risk factors of CRE infections after LT.
Pharmacology, Dosing, and Side Effects of Rifabutin as a Possible Therapy for Antibiotic-Resistant Acinetobacter Infections

Abstract Acinetobacter baumannii has among the highest rates of antibiotic resistance encountered in hospitals. New therapies are critically needed. We found that rifabutin has previously unrecognized hyperactivity against most strains of A. baumannii. Here we review the pharmacology and adverse effects of rifabutin to inform potential oral dosing strategies in patients with A. baumannii infections. Rifabutin demonstrates dose-dependent increases in blood levels up to 900 mg per day, but plateaus thereafter. Furthermore, rifabutin induces its own metabolism after prolonged dosing, lowering its blood levels. Pending future development of an intravenous formulation, a rifabutin oral dose of 900–1200 mg per day for 1 week is a rational choice for adjunctive therapy of A. baumannii infections. This dosage maximizes AUC24 to drive efficacy while simultaneously minimizing toxicity. Randomized controlled trials will be needed to definitively establish the safety and efficacy of rifabutin to treat A. baumannii infections.
Towards transformation-resilient provenance detection of digital media

Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportunities for potential abuse of these capabilities. This motivates the problem of tracking the provenance of signals, i.e., being able to determine the original source of a signal. Watermarking the signal at the time of signal creation is a potential solution, but current techniques are brittle and watermark detection mechanisms can easily be bypassed by applying post-processing transformations (cropping images, shifting pitch in the audio etc.). In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations. Our detection method can be applied to domains with continuous data representations such as images, videos or sound signals. Experiments on watermarking image and audio signals show that our method can reliably detect the provenance of a signal, even if it has been through several post-processing transformations, and improve upon related work in this setting. Furthermore, we show that for specific kinds of transformations (perturbations bounded in the L2 norm), we can even get formal guarantees on the ability of our model to detect the watermark. We provide qualitative examples of watermarked image and audio samples in this https URL.
Ferroelectric tunnel junctions

My research is dedicated to the electronic properties of functional oxides. My activity specifically focuses on ferroelectric tunnel junctions in which an ultrathin layer of ferroelectric material is intercalated between two metallic electrodes. In these devices, polarization reversal induces large modifications of the tunnel resistance, leading to a non-destructive readout of the information. After a demonstration of the concept with scanning probe microscopy techniques, I have been exploring the properties of ferroelectric tunnel junctions with various ferroelectric materials (BaTiO3, BiFeO3, and PVDF). I showed that these devices possess attractive properties for applications as non-volatile binary memories. In addition, exploring the fact that polarization usually reverses by the nucleation and propagation of domains, I demonstrated a memristive behavior in the junctions associated to non-uniform configurations of ferroelectric domains. Such ferroelectric memristors can be used as artificial synapses in neuromorphic architectures. Coupled to magnetic electrodes, the resulting multiferroic tunnel junctions enable a non-volatile control of magnetism at the ferroelectric/electrode interface and of the spin-polarized current associated. Besides this main activity on tunnel devices, I explored the influence of ferroelectricity on magnetic orders and on the properties of functional oxides.
Tweet sentiment quantification: An experimental re-evaluation

Sentiment quantification is the task of training, by means of supervised learning, estimators of the relative frequency (also called “prevalence”) of sentiment-related classes (such as Positive, Neutral, Negative) in a sample of unlabelled texts. This task is especially important when these texts are tweets, since the final goal of most sentiment classification efforts carried out on Twitter data is actually quantification (and not the classification of individual tweets). It is well-known that solving quantification by means of “classify and count” (i.e., by classifying all unlabelled items by means of a standard classifier and counting the items that have been assigned to a given class) is less than optimal in terms of accuracy, and that more accurate quantification methods exist. Gao and Sebastiani 2016 carried out a systematic comparison of quantification methods on the task of tweet sentiment quantification. In hindsight, we observe that the experimentation carried out in that work was weak, and that the reliability of the conclusions that were drawn from the results is thus questionable. We here re-evaluate those quantification methods (plus a few more modern ones) on exactly the same datasets, this time following a now consolidated and robust experimental protocol (which also involves simulating the presence, in the test data, of class prevalence values very different from those of the training set). This experimental protocol (even without counting the newly added methods) involves a number of experiments 5,775 times larger than that of the original study. Due to the above-mentioned presence, in the test data, of samples characterised by class prevalence values very different from those of the training set, the results of our experiments are dramatically different from those obtained by Gao and Sebastiani, and provide a different, much more solid understanding of the relative strengths and weaknesses of different sentiment quantification methods.
Identification of Specific Trafficking Defects of Naturally Occurring Variants of the Human ABCG2 Transporter

Proper targeting of the urate and xenobiotic transporter ATP-binding transporter subfamily G member 2 (ABCG2) to the plasma membrane (PM) is essential for its normal function. The naturally occurring Q141K and M71V polymorphisms in ABCG2, associated with gout and hyperuricemia, affect the cellular routing of the transporter, rather than its transport function. The cellular localization of ABCG2 variants was formerly studied by immunolabeling, which provides information only on the steady-state distribution of the protein, leaving the dynamics of its cellular routing unexplored. In the present study, we assessed in detail the trafficking of the wild-type, M71V-, and Q141K-ABCG2 variants from the endoplasmic reticulum (ER) to the cell surface using a dynamic approach, the so-called Retention Using Selective Hooks (RUSH) system. This method also allowed us to study the kinetics of glycosylation of these variants. We found that the fraction of Q141K- and M71V-ABCG2 that passes the ER quality control system is only partially targeted to the PM; a subfraction is immobile and retained in the ER. Surprisingly, the transit of these variants through the Golgi apparatus (either the appearance or the exit) was unaffected; however, their PM delivery beyond the Golgi was delayed. In addition to identifying the specific defects in the trafficking of these ABCG2 variants, our study provides a novel experimental tool for studying the effect of drugs that potentially promote the cell surface delivery of mutant or polymorphic ABCG2 variants with impaired trafficking.
Does Workplace Bullying Produce Employee Voice and Physical Health Issues? Testing the Mediating Role of Emotional Exhaustion

Workplace bullying is a reality in organizations. Employees’ experiences of workplace bullying can produce their voice that intends to challenge the status quo at work and can damage their physical health. This study examines the effects of workplace bullying on employee voice and physical health issues and considers individuals’ emotional reactions as a critical mechanism operating between workplace bullying and its consequences in workplace situations. Emotional exhaustion mediates the influence of workplace bullying on employee voice and damaged health. Data for 694 employees from a large Taiwanese retail organization revealed that workplace bullying relates to its outcomes at work. The findings of this study show that emotional exhaustion is a critical mechanism between workplace bullying and its consequences, i.e., employee voice and health issues. A time-lag study design is applied to reduce common method bias.
Effects of a Non-Maxwellian EEDF on the scattering spectra of SRS and SBS

In a laser-irradiated plasma, Langdon heating effect would result in a super- Gaussian EEDF. In this work, using the linear analysis, we have obtained the influence of a super-Gaussian EEDF on the best-matching wavelength of the scattered wave and linear gain of SRS and SBS for various plasma conditions. The influence of a super-Gaussian EEDF on the SRS and SBS is found to be distinct: for SRS, there is a redshift or blueshift of the scattered wave depending on the plasma condition; while for SBS, a super-Gaussian EEDF results in a redshift of the scattered wave compared to a Maxwellian EEDF. Furthermore, a super-Gaussian EEDF always enhances the SRS gain, while its effect on the SBS gain depends on the ion species, ion charge and ion ratio in a complex way.
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars

A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly. Here, we propose MNCE-RL, a graph convolutional policy network for molecular optimization with molecular neighborhood-controlled embedding grammars through reinforcement learning. We extend the original neighborhood-controlled embedding grammars to make them applicable to molecular graph generation and design an efficient algorithm to infer grammatical production rules from given molecules. The use of grammars guarantees the validity of the generated molecular structures. By transforming molecular graphs to parse trees with the inferred grammars, the molecular structure generation task is modeled as a Markov decision process where a policy gradient strategy is utilized. In a series of experiments, we demonstrate that our approach achieves state-of-the-art performance in a diverse range of molecular optimization tasks and exhibits significant superiority in optimizing molecular properties with a limited number of property evaluations.
The invasion of de-differentiating cancer cells into hierarchical tissues

Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy and differentiating into a whole range of specialized cells. There is increasing evidence that tumors are structured in a very similar way, mirroring the hierarchical structure of the host tissue. In some tissues, differentiated cells can also revert to the stem cell phenotype, which increases the risk that cells that have already acquired mutations lead to long lasting clones in the tissue. Recently, the modelling community has paid special attention to the consequences of de-differentiation on cellular hierarchies. However, the adaptive significance of de-differentiation is still poorly understood and thus it is unclear under which circumstances de-differentiating cells will invade a tissue. To address this, we developed mathematical models to investigate how de-differentiation could be selected as an adaptive mechanism in the context of cellular hierarchies. We consider the cases of stepwise and jumpwise de-differentiation in this study. Our results show that the emergence of de-differentiation is driven by the combination of the properties of the cellular hierarchy and the de-differentiation pattern and derive thresholds for which de-differentiation is expected to emerge.