Miracle-dz/UniFAR-FTUs huggingface dataset:
Adipocyte “Fatty Acid Binding Protein” Gene Polymorphisms (rs1054135, rs16909196 and rs16909187) in Jordanians with Obesity and Type 2 Diabetes Mellitus

ABSTRACT Background Obesity, type 2 diabetes mellitus (T2DM), and dyslipidemia may result from the interactions of genetic and environmental factors. There are controversial reports concerning the association of polymorphisms (rs1054135, rs16909196 and rs16909187) in the gene of adipocyte fatty acid binding protein (FABP4) with obesity and T2DM. Therefore, we designed this study to determine the association of these polymorphisms with obesity, T2DM, and dyslipidemia among Jordanian subjects. Methods The study was approved by the National Center for Diabetes, Endocrinology, and Genetics (NCDEG) Institutional Review Board (IRB). A total of 397 subjects were enrolled in the study and divided into four groups as described in materials and methods section. The fatty acid binding protein 4 (FABP4) gene containing (rs1054135, rs16909196 and rs16909187) single nucleotide polymorphisms (SNP) was amplified by polymerase chain reaction (PCR) followed by Sanger DNA sequencing of the PCR product. Results None of the three SNPs were associated with T2DM (p > 0.05). The rs16909187 and rs16909196 were significantly associated with obesity. The wild type (CC) of rs16909187 was significantly higher among the overweight and obese group compared with normal weight controls (OD = 2.17, 95% CI = 1.18 - 3.96, p =0.01). The wild type of rs16909196 (AA) was significantly higher among the overweight and obese group compared to controls, (OD = 2.26, 95% CI = 1.24 - 4.14, p = 0.01). These results may indicate that the wild-type may be a risk factor for obesity. Only the rs1054135 SNP was significantly associated with increased low density lipoprotein (LDL) levels in the overweight and obese group compared with the controls (p = 0.03). Conclusions The wild-type genotypes of rs16909196 and rs16909187 may be risk factors for obesity but not T2DM. None of the three SNPs was associated with T2DM.
Complexes à automorphismes et homéomorphie différentiable

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Relationship Between Implicit Intelligence Beliefs and Maladaptive Self-Regulation of Learning

Objectives. Although prior research has uncovered shifts in computer science (CS) students’ implicit beliefs about the nature of their intelligence across time, little research has investigated the factors contributing to these changes. To address this gap, two studies were conducted in which the relationship between ineffective self-regulation of learning experiences and CS students’ implicit intelligence beliefs at different times during the semester was assessed. Participants. Participants for Studies 1 (n = 536) and 2 (n = 222) were undergraduate students enrolled in introductory- and upper-level CS courses at a large, public, Midwestern university. Race-ethnicity information was not collected due to IRB concerns about possible secondary identification of participants from underrepresented groups. Study Method. Participants completed a condensed version of the Implicit Theories of Intelligence Scale [16, 54] and the Lack of Regulation Scale from the Student Perceptions of Classroom Knowledge Building scale [51, 53] at the beginning (Studies 1 and 2), middle (Study 2), and end (Studies 1 and 2) of semester-long undergraduate CS courses. Survey responses were analyzed using path analyses to investigate how students’ lack of regulation experiences throughout the semester predicted their implicit intelligence beliefs at the beginning (Study 2) and end (Studies 1 and 2) of the semester. Findings. Results from Study 1 indicate that undergraduate CS students come to more strongly believe that their intelligence is a fixed, unchanging entity from the beginning until the end of the semester. Moreover, participants’ responses to the lack of regulation scale were predictive of their implicit intelligence beliefs at the end of the semester. Results from Study 2 indicate that ineffective self-regulation experiences early in the semester enhance CS students’ belief in the unchanging nature of intelligence (i.e., during the first half of the semester). Taken altogether, these findings provide evidence that self-regulation experiences influence students’ beliefs about the malleability of intelligence. Conclusions. Findings align with Bandura's [4] contention that students’ behaviors and experiences influence their values and beliefs. Students who experienced poor self-regulated learning came to view intelligence as more of a fixed, unalterable entity than their more successfully self-regulated peers. Findings suggest that CS instructors can positively affect student motivation and engagement by embedding self-regulated learning strategy instruction into their courses and helping CS students adopt an incremental-oriented (e.g., growth-oriented) belief system about their intellectual abilities.
Disentangling Affordances of Online Collaboration Tools for Mutual Aid in Emergencies: Insights from the COVID-19 Lockdown

With the uncertain trajectory of COVID-19 conditions worldwide, there lies the potential for emergencies to arise, abruptly yielding mass social and economic disruption. Gaining insight into how digital technologies may be leveraged for effective emergency response is therefore pertinent. Emergency-induced needs may prompt citizens to organize mutual aid initiatives where people give what they can and get what they need in response. An increasingly prominent technology used for emergency response, online collaboration tools (OCTs), enables the appropriate match between the supply of aid and its relevant demand in mutual aid initiatives by mediating information and interactions between participants. Through analysis of mutual aid cases during the 2022 Shanghai COVID-19 lockdown, this study elucidates the benefits OCTs provide through the lens of affordance theory and identifies five key affordances of OCTs for emergency mutual aid: persistent accessibility, iterative modifiability, structured consolidation and retrieval, multisynchronous participation, and multichannel broadcasting. We illustrate how such affordances are actualized and how their enabling features work across information flow processes, specifically highlighting benefits of software minimalism with implications for practitioners and future software design in emergency situations. This study contributes to the body of knowledge on OCTs and affordances by disentangling its role in emergency responses.
Urdu Speech Emotion Recognition: A Systematic Literature Review

Research on Speech Emotion Recognition is becoming more mature day by day, and a lot of research is being carried out on Speech Emotion Recognition in resource-rich languages like English, German, French, and Chinese. Urdu is among the top 10 languages spoken worldwide. Despite its importance, few studies have worked on Urdu Speech emotion as Urdu is recognized as a resource-poor language. The Urdu language lacks publicly available datasets, and for this reason, few researchers have worked on Urdu Speech Emotion Recognition. To the best of our knowledge, no review has been found on Urdu Speech Emotion recognition. This study is the first systematic literature review on Urdu Speech Emotion Recognition, and the primary goal of this study is to provide a detailed analysis of the literature on Urdu Speech Emotion Recognition which includes the datasets, features, pre-processing, approaches, performance metrics, and validation methods used for Urdu Speech Emotion Recognition. This study also highlights the challenges and future directions for Urdu Speech Emotion Recognition.
Memristive-based Mixed-signal CGRA for Accelerating Deep Neural Network Inference

In this paper, a mixed-signal coarse-grained reconfigurable architecture (CGRA) for accelerating inference in deep neural networks (DNNs) is presented. It is based on performing dot-product computations using analog computing to achieve a considerable speed improvement. Other computations are performed digitally. In the proposed structure (called MX-CGRA), analog tiles consisting of memristor crossbars are employed. To reduce the overhead of converting the data between analog and digital domains, we utilize a proper interface between the analog and digital tiles. In addition, the structure benefits from an efficient memory hierarchy where the data is moved as close as possible to the computing fabric. Moreover, to fully utilize the tiles, we define a set of micro instructions to configure the analog and digital domains. Corresponding context words used in the CGRA are determined by these instructions (generated by a companion compiler tool). The efficacy of the MX-CGRA is assessed by modeling the execution of state-of-the-art DNN architectures on this structure. The architectures are used to classify images of the ImageNet dataset. Simulation results show that, compared to the previous mixed-signal DNN accelerators, on average, a higher throughput of 2.35 × is achieved.
Robust Adaptive Soft Landing Control of an Electromagnetic Valve Actuator for Camless Engines

This paper deals with a signal‐based robust adaptive approximation technique for a proportional derivative (PD) regulator which is applied to an electromagnetic valve actuator control for camless internal combustion engines. PD regulators generate very high spikes in the presence of unavoidable noise. These spikes cause high power dissipation and poor dynamic performance with a lack of precision. The presented method allows the reduction of the noise and not robust nonlinear uncertainties effects by using minimum variance analysis. The technique with which the PD regulator is approximated does not depend on the model of the controlled system. Hence, the technique is quite general and can be applied to any type of system. In addition, this paper describes a feasible real‐time self‐tuning of an approximated discrete PD regulator using a backward Euler technique. The main contribution of the paper is the presentation of an approximated PD controller using a minimum variance control strategy together with a weighted least squares method to adapt the parameters of this approximated controller. This control law realises a robust control technique with respect to the noise and nonlinear uncertainties. Moreover, a comparison with the approximate PD controller proposed in MATLAB by Mathworks is provided. The presented technique ensures a good dynamic performance, including low dissipation as well as accurate positioning and soft landing control.
AMC: Towards Trustworthy and Explorable CRDT Applications with the Automerge Model Checker

Conflict-free Replicated Data Types (CRDTs) enable local-first operations and asynchronous collaboration without the need for always-on centralised services. CRDTs can have a high overhead, so implementations need to be optimised, but this optimisation can lead to bugs despite the use of test suites and fuzzing. Furthermore, using CRDTs in applications is complex, observing unexpected conflict resolution, issues synchronising documents and difficulties implementing appropriate data models. Automerge is a library, exposing a JSON CRDT, that sees users having difficulties in modelling their problems, understanding their edge cases and implementing applications correctly. We introduce the Automerge Model Checker (AMC), empowering application developers to check properties about their implementations and explore them dynamically. AMC can check a range of applications as well as being able to check properties about the core of Automerge itself, helping to make more trustworthy Automerge applications. AMC is available open-source at github.com/jeffa5/automerge-model-checker.
The Postphenomenological Impact of Conversational Artificial Intelligence on Autonomy and Psychological Integrity

potentially deception in letting people think that CAI is demonstrating actual empathy or is an agent). Sedlakova and Trachsel show that CAI lacks duties because it is not a moral subject, and that this lack of duties could cause harm to patients. Somewhat similarly, CAI has a power over patients when it is in a therapeutic alliance based on care. In human-tohuman relationships, that imbalance can be rectified through “reciprocity. ” This reciprocity cannot exist within a relationship involving CAI as one party. In humans, that asymmetry can avoid leading to a power imbalance through reciprocity, which requires the “compassionate other” to have an ability to selfinterpret (to have “self-esteem” which can be affected by the person suffering). The patient cannot give to the provider if there is nothing within the provider to receive such a gift. But this is the case with CAI; there is no ‘myself’ for CAI to interpret. Indeed, there is no value for ‘selves’ in general for CAI. If there is no ‘self’ to receive, then there is no reciprocity in the therapeutic relationship. Being a therapeutic relationship, suffering is naturally involved, making it an asymmetric relationship. And as argued earlier, an asymmetric relationship without reciprocity is one prone to power imbalance and harm. One particularly interesting potential harm is the way by which patient could be denied an aspect of what Ricoeur views as a “good life,” which is aiming at the good, with and for others. In a relationship, by engaging in care for another person, you view them as having a “self. ” So, either the patient falsely values the CAI’s non-existent “self” (in the same way they might value a therapist’s “self”), or the patient recognizes that lack of self-hood and is denied the ability to value the CAI in a way that might otherwise benefit the patient to do. By this benefit, I mean that acts of caring could potentially help restore a patient from the “passivity” that Ricoeur notes as being inherent to suffering. Either way, a harm could come about through the denial of genuine reciprocity in a therapeutic alliance.
Effect of blending ratio on morphological, chemical, and thermal characteristics of PLA/PCL and PLLA/PCL electrospun fibrous webs

Abstract Scaffolding structures made of biocompatible and biodegradable polymers are employed in tissue engineering applications to allow cells to perform the activities required to generate new tissue. However, the morphological, chemical, and thermal compatibility of these surfaces with the target tissue should be investigated before cell transplantation. Polymers like polylactic acid (PLA), poly (L-lactide) (PLLA), and polycaprolactone (PCL), which are synthetic biomaterials with high biocompatibility and adjustable biodegradability, have piqued the interest of tissue engineering researchers for years. In the study, electrospun fibrous surfaces with various ratios of PLA/PCL and PLLA/PCL blends (100/0, 10/90, 20/80, 30/70, 40/60, 50/50, and 0/100) are produced. The morphological, chemical, and thermal properties of the suggested surfaces are examined. Although smooth fiber formation is not detected on some surfaces, fibrous surfaces with fiber diameters of 0.962–1.733 µm and porosities of 19.83–29.54% have been observed. Chemical analyses, on the other hand, reveal no solvent residue on surfaces produced with organic solvent systems, indicating that the harmful effect of solvent systems has been eradicated from the fiber surface. Thermal analyses also provide information regarding the crystallinity of surfaces, which will assist future biodegradability research. According to the thermal analysis, the PLA/PCL and PLLA/PCL blend ratios have a significant impact on the surface crystallinity. Graphical Abstract
Ceramic-based electromagnetic wave absorbing materials and concepts towards lightweight, flexibility and thermal resistance

Electromagnetic wave (EMW) absorbing materials have attracted much attention in recent years due to the dramatical increase of high-frequency electronic components and devices, which generate electromagnetic (EM) pollution and cause serious electromagnetic interference (EMI). Ceramics and associated (nano)composites are widely investigated as EMW absorbing materials because of their excellent mechanical properties, chemical/thermal stabilities, and oxidation/corrosion resistance. In addition to outstanding EMW absorbing performance, lightweight, flexibility and thermal resistance at high temperatures strongly affect their practical applications. Therefore, this review highlights the recent progress of advanced ceramic-based EMW absorbing materials by evaluating their vital EMW absorption parameters. First, the fundamentals of EMW absorption are briefly summarized, followed by the effects of phase/chemical composition, micro/nano structure, and morphology on the EMW absorbing performance and associated mechanisms. Furthermore, modern strategies for the preparation of lightweight, flexible and thermal resistant EMW absorbing materials are comprehensively reviewed. Finally, the perspectives of advanced-ceramics as EMW absorbing materials are discussed as well.
Music Therapy with Autistic Children: Creating an Intersubjective Field of Play through Improvisation

ABSTRACT This article considers the value of improvisational music therapy in cultivating intersubjective states between autistic clients and therapists. The authors first describe the potential therapeutic resources in live and individually-tailored musical experiences for autistic people, followed by a theoretical explanation of music and intersubjectivity. Finally, four music therapy cases from The Nordoff-Robbins Center for Music Therapy at NYU highlight various perspectives about music therapy and intersubjectivity with autistic children. Video excerpts of sessions are included.
Rapid appearance of negative emotion during oral fentanyl self-administration in male and female rats

Opioid use disorder has become an epidemic in the United States, fueled by the widespread availability of fentanyl, which produces rapid and intense euphoria followed by severe withdrawal and emotional distress. We developed a new preclinical model of fentanyl seeking in outbred male and female rats using volitional oral self-administration that can be readily applied in labs without intravascular access. Using a traditional two lever operant procedure, rats learned to take oral fentanyl vigorously, escalated intake across sessions, and readily reinstated responding to conditioned cues after extinction. Oral self-administration also revealed individual and sex differences that are essential to studying substance use risk propensity. During a behavioral economics task, rats displayed inelastic demand curves and maintained stable intake across a wide range of fentanyl concentrations. Oral SA was also neatly patterned, with distinct “ loading” and “ maintenance” phases of responding within each session. Using our software DeepSqueak, we analyzed thousands of ultrasonic vocalizations (USVs), which are innate expressions of current emotional state in rats. Rats produced 50 kHz USVs during loading then shifted quickly to 22 kHz calls despite ongoing maintenance oral fentanyl taking, reflecting a transition to negative reinforcement. Using fiber photometry, we found that the lateral habenula differentially processed drug-cues and drug-consumption depending on affective state, with potentiated modulation by drug cues and consumption during the negative affective maintenance phase. Together, these results indicate a rapid progression from positive to negative reinforcement occurs even within an active drug taking session, revealing a within-session opponent process. Significance Statement The United States opioid epidemic is defined by rampant and treatment resistant fentanyl use. Better understanding of neural substrates underlying this phenomenon is essential to slowing the opioid crisis. Intravenous and vapor self-administration (SA) are the standard models for studying fentanyl use in rodents, however they many carry pragmatic downsides. Here, we used a novel oral fentanyl self-administration model that provides key translational and technical benefits and can be readily applied in other labs to study the neurobiology of fentanyl SA. This method captured individual and sex differences necessary for studying substance use risk propensity and uncovered a rapid shift in affective state in rats, suggesting and shift from positive to negative reinforcement within each fentanyl taking session.
xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs

Genome-wide association studies (GWAS) have identified thousands of disease-associated non-coding variants, posing urgent needs for functional interpretation. Molecular quantitative trait loci (xQTLs) such as eQTLs serve as an essential intermediate link between these non-coding variants and disease phenotypes and have been widely used to discover disease-risk genes from many population-scale studies. However, mining and analyzing the xQTLs data presents several significant bioinformatics challenges, particularly when it comes to integration with GWAS data. Here, we developed xQTLbiolinks as the first comprehensive and scalable tool for xQTLs data retrieval, quality controls, and pre-processing from 75 human tissues and cell types. In addition, xQTLbiolinks provided a robust colocalization module through integration with GWAS data. The result generated by xQTLbiolinks can be flexibly visualized or stored in standard R objects that can easily be integrated with other R packages and custom pipelines. We applied xQTLbiolinks to cancer GWAS summary statistics as case studies and demonstrated its robust utility and reproducibility. xQTLbiolinks will profoundly accelerate the interpretation of disease-associated variants, thus promoting a better understanding of disease etiologies. xQTLbiolinks is available at https://github.com/lilab-bioinfo/xQTLbiolinks.
Uncovering Co-regulatory Modules and Gene Regulatory Networks in the Heart through Machine Learning-based Analysis of Large-scale Epigenomic Data

The availability of large scale epigenomic data from different cell types and conditions has provided valuable information to evaluate and learn features that predict co-binding of transcription factors (TF). However, previous attempts to develop models for predicting motif cooccurrence were not scalable for global analysis of any combination of motifs or cross-species predictions. Further, mapping co-regulatory modules (CRM) to their gene regulatory networks (GRN) is crucial in understanding the underlying function. Currently, there is no comprehensive pipeline to locate CRM and GRN on a large scale with speed and accuracy. In this study, we analyzed and evaluated different TF binding characteristics that would facilitate co-binding with biological significance to identify all possible clusters of co-binding TFs. We curated the UniBind database, which contains ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and potential regulatory networks they operate on. We narrowed our focus to study heart related regulatory motifs. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.