Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative Analysis of PAIP2B to Identify a Novel Biomarker for Pancreatic Ductal Adenocarcinoma

Abstract The aim of the study was to evaluate the potential diagnostic and prognostic value of gene, Poly A-Binding Protein Interacting Protein 2B ( PAIP2B ) in pancreatic cancer. We used the gene expression data and clinical information of pancreatic adenocarcinoma patients from The Cancer Genome Atlas database and Gene Expression Omnibus database to analyze the expression of PAIP2B in pancreatic cancer samples, and validated the expression of PAIP2B in tumor tissue, using bioinformatics technology to explore the prognostic value of PAIP2B and its possible biological function. A significantly lower level of PAIP2B was observed in pancreatic cancer patients than in controls, and validated by immunohistochemistry. PAIP2B reduced the proliferation and invasion of cancer cells and had a significantly high expression in early stage. Patients with lower levels of PAIP2B had a significantly shorter median survival time than those with higher levels. DNA demethylation played an important role in PAIP2B expression. In addition, PAIP2B expression was significantly associated with the tumor-infiltrating immune cells, especially T cells CD8, T cells CD4 memory resting, macrophages M0, and dendritic cells resting. Our study also found that PAIP2B regulated miRNA function leading to disease progression in pancreatic cancer patients. Our study explored the potential value of PAIP2B as a biological link between prognosis and pancreatic cancer, and provided reference for the follow-up study on the role of PAIP2B in pancreatic cancer.
Association between Serum Follistatin-Like Protein 1 Levels and Disease Activity in Behçet’s Disease

Abstract Introduction This study sought to determine the serum levels of follistatin-like protein 1 (FSTL-1), a glycoprotein of mesenchymal origin shown to be involved in the pathogenesis of many inflammatory diseases, for example Behçet’s disease (BD), and to investigate its association with disease activity. Material and Methods The study included 45 patients followed for newly diagnosed Behçet’s disease who met the 1990 International Study Group criteria for BD and a control group of 36 healthy volunteers who were age- and gender-matched. Peripheral blood samples were collected from the patients and control groups. Serum FSTL-1 levels were measured using an enzyme-linked immunosorbent assay. The disease activity was assessed using the Behçet’s Disease Current Activity Form (BDCAF). Results A comparison of serum FSTL-1 levels in the patient and control groups found significantly higher levels of FSTL-1 in the patient group. Serum FSTL-1 levels were significantly higher in female patients compared with male patients. Female patients exhibited significantly higher BDCAF scores compared with male patients (p=0.008). Erythrocyte sedimentation rate and serum FSTL-1 levels were positively correlated. Conclusion Serum FSTL-1 levels may play a role in the aetiopathogenesis of BD and can also be a useful biomarker for determining disease activity.
Effects of environmental translocation and host characteristics on skin microbiomes of sun-basking fish

Variation in the composition of skin-associated microbiomes has been attributed to host species, geographical location and habitat, but the role of intraspecific phenotypic variation among host individuals remains elusive. We explored if and how host environment and different phenotypic traits were associated with microbiome composition. We conducted repeated sampling of dorsal and ventral skin microbiomes of carp individuals (Cyprinus carpio) before and after translocation from laboratory conditions to a semi-natural environment. Both alpha and beta diversity of skin-associated microbiomes increased substantially within and among individuals following translocation, particularly on dorsal body sites. The variation in microbiome composition among hosts was significantly associated with body site, sun-basking, habitat switch and growth, but not temperature gain while basking, sex, personality nor colour morph. We suggest that the overall increase in the alpha and beta diversity estimates among hosts were induced by individuals expressing greater variation in behaviours and thus exposure to potential colonizers in the pond environment compared with the laboratory. Our results exemplify how biological diversity at one level of organization (phenotypic variation among and within fish host individuals) together with the external environment impacts biological diversity at a higher hierarchical level of organization (richness and composition of fish-associated microbial communities).
Multimodal analysis of disinformation and misinformation

The use of disinformation and misinformation campaigns in the media has attracted much attention from academics and policy-makers. Multimodal analysis or the analysis of two or more semiotic systems—language, gestures, images, sounds, among others—in their interrelation and interaction is essential to understanding dis-/misinformation efforts because most human communication goes beyond just words. There is a confluence of many disciplines (e.g. computer science, linguistics, political science, communication studies) that are developing methods and analytical models of multimodal communication. This literature review brings research strands from these disciplines together, providing a map of the multi- and interdisciplinary landscape for multimodal analysis of dis-/misinformation. It records the substantial growth starting from the second quarter of 2020—the start of the COVID-19 epidemic in Western Europe—in the number of studies on multimodal dis-/misinformation coming from the field of computer science. The review examines that category of studies in more detail. Finally, the review identifies gaps in multimodal research on dis-/misinformation and suggests ways to bridge these gaps including future cross-disciplinary research directions. Our review provides scholars from different disciplines working on dis-/misinformation with a much needed bird's-eye view of the rapidly emerging research of multimodal dis-/misinformation.
Genome sequence of an Acinetobacter pittii strain obtained from a red-lored parrot with pneumonia

ABSTRACT Acinetobacter pittii 978-A_19 was obtained from a parrot with pneumonia. It is resistant to ampicillin, carbenicillin, cephalosporins, clindamycin, and trimethoprim + sulfamethoxazole. The genome encodes a new blaADC allele, a blaOXA-502 gene, possesses several virulence genes related to adherence and biofilm formation, and has types I, II, and IV secretion systems.
Research and Development Status of Laser Cladding on Stainless Steel Alloys: A Review

Stainless steels as one of the most appealing structural material in many fields of industries because of its resistance to corrosion, high tensile strength, durability, temperature resistant etc. To improve its surface properties and overcome issues like large heat affected zones, poor surface quality, and limited dissolvability, researchers have explored surface modifications through laser cladding. This paper gives a detailed review about the work done in recent years, in the region of laser cladding of different grades of stainless-steel alloys with different coating materials highlighting on feeding ways of cladding material, laser cladding process parameters, types of lasers employed, types of coating & composite coating materials in enhancing the surface properties of the stainless-steel substrate and their relevant applications.
Learning to Rank for Search Results Re-ranking in Learning Experience Platforms

The ability to search and retrieve the right resources in a Learning Experience Platform (LXP) is critical in helping the workforce of an enterprise to upskill and deepen their expertise effectively. To ensure the best resources are shown as high in the result set as possible to catch learners’ attention, a supervised learning approach of training and deploying a Learning to Rank (LTR) model for re-ranking is proposed. This work specifically focuses on judgement list preparation taking advantage of the learning progress data available in LXPs, as well as on defining and measuring model performance through metrics in both test and production setups. In particular, it highlights the positive impact of the deployed LTR model in production using the defined metrics like average search result click position and percentage top N clicks.
Improving learning experience to teach FPGA design at scale

The global demand for skilled engineers to facilitate the design and manufacturing of electronic chips is ever-increasing. An understanding of FPGA design tools and techniques is crucial for prospective careers in these domains. These skills are typically not taught in undergraduate engineering studies in India. In 2020, we attempted to teach the basics of FPGA design skills to undergraduate engineering students across India. This was done in project-based learning provided through three tasks in an online learning module. The initiative witnessed 872 students participating as a team of four. This experience report presents the methods adopted to teach FPGA design skills at such a scale. The report also presents and discusses participant experience gathered via feedback through semi-structured interviews. Six categories of issues were identified. Based on participant feedback, the learning module was improved and offered as 2nd edition to a similar cohort of 249 teams with 961 participants in 2021. These modifications resulted in an approximately 8% increase in the overall completion rate from 29.03% to 37.13%. The paper presents the process and experience of redesigning the learning module and contributes to the literature of issues identified and methods adapted in an attempt to teach complex engineering tools to undergraduate students which can benefit other academics aiming to teach similar essential skills.
Task dependent coarticulation of movement sequences

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co- articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.
Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based meth-ods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Clothing Recommendation Algorithm Based on Image Segmentation and Classification Recognition

Abstract: The rapid development of computer vision and deep learning algorithms has opened up new avenues for understanding and analyzing visual data. Image analysis has evolved as a useful technique in the realm of fashion for apparel detection, segmentation, and suggestion. In this paper, we propose a novel approach that combines image segmentation and similarity-based matching to segment clothing items from images and provide personalized recommendations based on their visual attributes. We aim to accomplish accurate and efficient apparel segmentation and improve overall user experience in fashion recommendation systems by employing cutting-edge deep learning models.
Communications Theory-Inspired Algorithms for Detecting Protein-Coding Regions in Prokaryotic Genomes: A Comparative Study: Using Communications Theory for Gene Detection in Prokaryotic Genomes: A Comparative Analysis of Correlation-Based Algorithms and Bayesian Classifiers

The identification of protein-coding regions in genomic DNA sequences remains a significant challenge in computational genomics, and numerous computational algorithms have been developed to address this problem. Recent advances in this field have facilitated the creation of innovative engineering methods that enable the analysis and modeling of various molecular biology processes. In this work, we propose a novel algorithm for detecting prokaryotic genes that leverages established concepts from communications theory, including correlation, the maximal ratio combining (MRC) algorithm, and filtering techniques, to generate a signal that distinguishes coding and noncoding regions based on characteristic features. The proposed algorithm is applied to multiple prokaryotic genome sequences, and Bayesian classifiers are employed to assess its performance. To further validate the proposed method, we compare its performance to that of established gene detection methods in prokaryotes, such as GLIMMER and GeneMark. This comparison underscores the value of using communications theory concepts for genomic sequence analysis and establishes the efficacy of the proposed algorithm.
Evaluating Ad Creative and Web Context Alignment with Attention Measurement

Contextual targeting is a common strategy that places marketing messages in media locations that are aligned with a target audience. The challenge of contextual targeting is knowing the ideal schema and the set of categories that provide the right audience. Refinement of the contextual targeting process has been limited by the use of metrics that are either rapid but unreliable (click through rates), or reliable but slow, expensive and inaccessible in real-time (conversions or brand awareness). In this study we evaluate the use of attention measurement as a technique for understanding the alignment between advertising creatives and media contexts. We perform the analysis using a large scale dataset of impressions categorised using the iAB V2.0 taxonomy. We evaluate the alignment by looking for consistency across time in the attention a given creative attracts over the levels of the categorisation hierarchy. Our research is the largest known study of the relationship between advertising creative, media context and consumer attention. We provide strong empirical evidence that consumer attention is related to an alignment between the creative execution and topics of media context. Furthermore, we show that this relationship transcends any specific publication by providing reliable indications of attention on new website domains.
Thermal tolerance and survival are modulated by a natural gradient of infection in differentially acclimated hosts

Wild ectotherms are exposed to multiple stressors, including parasites, which can affect their responses to environmental change. Simultaneously, unprecedented warm temperatures are being recorded worldwide, increasing both the average and maximum temperatures experienced in nature. Understanding how ectotherms, such as fishes, will react to the combined stress of parasites and higher average temperatures can help predict the impact of extreme events such as heat waves on populations. The critical thermal method (CTM), which assesses upper (CTmax) and lower (CTmin) thermal tolerance, is often used in acclimated ectotherms to help predict their tolerance to various temperature scenarios. Yet, few studies have characterized the response of naturally infected fish to extreme temperature events or how acute thermal stress affects subsequent survival. We acclimated naturally infected pumpkinseed sunfish (Lepomis gibbosus), to four ecologically relevant temperatures (10, 15, 20 and 25°C) and one future warming scenario (30°C) for three weeks, before measuring CTmax and CTmin. We also assessed individual survival the week following CTmax. Interestingly, trematode parasites causing black spot disease were negatively related to CTmax, suggesting that heavily infected fish are less tolerant to acute warming. Moreover, fish infected with yellow grub parasites showed decreased survival in the days following CTmax implying that the infection load has negative survival consequences on sunfish during extreme warming events. Our findings indicate that parasite infection and high prolonged average temperatures can affect fish thermal tolerance and survival, emphasizing the need to better understand the concomitant effects of stressors on health outcomes in wild populations. Summary statement This study shows that parasites influence thermal tolerance and survival of fish, suggesting that such stressors are important to increase the ecological relevance of thermal tolerance studies of wild animals.
Model construction and implementation of digital twin data for body workshop

Data is very important for Digital Twin Workshop (DTW). Data in DTW is not only to store it, but also needs to be associated with different physical information. Knowledge graph (KG) can acquire, integrate and utilize the information of the whole life of cycle process. This paper proposes the construction of digital twin data (DTD) by creation process and feature extraction methods of knowledge graph. The DTD combines the actual production data and the time-varying characteristics of geometry, motion and behavior model of the body workshop. It is able to realize the integration with DTW and graph database storage.
Neuroepithelial depletion schedules cessation of neurogenesis in the Drosophila optic lobes

The brain is consisted of diverse neurons arising from a limited number of neural stem cells. Drosophila neural stem cells called neuroblasts (NBs) produces specific neural lineages of various lineage sizes depending on their location in the brain. In the Drosophila visual processing centre – the optic lobes (OLs), medulla NBs derived from the neuroepithelium (NE) give rise to neurons and glia cells of the medulla cortex. The timing and the mechanisms responsible for the cessation of medulla NBs are so far not known. In this study, we show that the termination of medulla NBs during pupal development is determined by the exhaustion of the NE stem cell pool. Altering NE-NB transition during larval neurogenesis disrupts the timely termination of medulla NBs. Medulla NBs terminate neurogenesis via a combination of cell death, terminal symmetric division, and a switch to gliogenesis. We show that temporal progression is not required for the termination of medulla NBs. The timing of NB cessation can be altered through the acquisition of a glial cell fate via Glial cells missing, or through conversion to type II NB cell fate via Tailless, or by inhibition of differentiation via Prospero knockdown. As the Drosophila OL shares a similar mode of division with mammalian neurogenesis, determining how and when these progenitors cease proliferation during development can have important implications for mammalian brain size determination and regulation of its overall function.
Claudin-4 polymerizes after the incorporation of just two extracellular claudin-3 residues

Tight junctions play a pivotal role in the functional integrity of the human body by forming barriers crucial for tissue compartmentalization and protecting the body from external threats. Essential components of tight junctions are the transmembrane claudin proteins, which can polymerize into tight junction strands and meshworks. This study delves into the structural determinants of claudin polymerization, utilizing the close homology yet strong difference in polymerization capacity between claudin-3 and claudin-4. Through a combination of sequence alignment and structural modeling, critical residues in the second extracellular segment are pinpointed. Molecular dynamics simulations provide insights into the interactions of and the conformational changes induced by the identified extracellular segment 2 residues, shedding light on the intricacies of claudin polymerization. Live-STED imaging demonstrates that introduction of these residues from claudin-3 into claudin-4 significantly enhances polymerization in non-epithelial cells. In tight junction-deficient epithelial cells, mutated claudin-4 not only influences tight junction morphology but also partially restores barrier function. Understanding the structural basis of claudin polymerization is of paramount importance, as it offers insights into the dynamic nature of tight junctions. This knowledge could be applied to targeted therapeutic interventions, offering insight to repair or prevent barrier defects associated with pathological conditions, or introduce temporary barrier openings during drug delivery.