Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Simulation of Associative Reasoning

Modeling human behavior is a popular area of research. Special attention is then focused on activities related to knowledge processing. It is the knowledge that has a fundamental influence on an individual's decisionmaking and its dynamics. The subject of research is both the representation of knowledge and the procedures of their processing. The processing also comprises associative reasoning. Associations significantly influence the knowledge base used in processing stimuli and thus participate in creating a knowledge context that is further used for knowledge derivation and decision making. This paper focuses on the area of associative knowledge processing. There are already classical approaches associated with developing probabilistic neural networks, which can also be used with modifications at a higher abstraction level. This paper aims to show that associative processing of knowledge can be described with these approaches and simulated. The article will present a possible implementation of the model of knowledge storage and associative processing on the individual's knowledge base. The behavior of this model will be demonstrated in experiments.
Parallel Privacy-preserving Computation of Minimum Spanning Trees

In this paper, we propose a secret sharing based secure multiparty computation (SMC) protocol for computing the minimum spanning trees in dense graphs. The challenges in the design of the protocol arise from the necessity to access memory according to private addresses, as well as from the need to reduce the round complexity. In our implementation, we use the single-instruction-multiple-data (SIMD) operations to reduce the round complexity of the SMC protocol; the SIMD instructions reduce the latency of the network among the three servers of the SMC platform. We present a state-of-the-art parallel privacy-preserving minimum spanning tree algorithm which is based on Prim’s algorithm for finding a minimum spanning tree (MST) in dense graphs. Performing permutation of the graph with sharemind to be able to perform the calculation of the MST on the shuffled graph outside the environment. We compare our protocol to the state of the art and find that its performance exceeds the existing protocols when being applied to dense graphs.
Bio-Inspired Protocols for Embodied Multi-Agent Systems

Bio-Inspired approaches and techniques are being used in different domains and applications in artificial intelligence, including the agent domain. Some agents are able to move from one system to another to establish new relationships. In biology, ecological relations are concepts responsible for classifying the relationships between living beings in an ecosystem, depending on the behavior and function that each one can assume. The objective of this work is to propose bio-inspired protocols based on ecological relations: Predation, Inquilinism, and Mutualism. These protocols aims to preserve agents’ knowledge as they can live as a tenant in another physical body waiting for a similar hardware to predate, or acquire and transmit knowledge by interacting with other agents while sharing the same physical body. To validate these protocols, a study case and a scenario are implemented, tested, and evaluated in a real environment.
Electrophysiological Evidence of Dissociation Between Explicit Encoding and Fast Mapping of Novel Spoken Words

Existing behavioral, neuropsychological and functional neuroimaging data suggest that at least two major cognitive strategies are used for new word learning: fast mapping (FM) via context-dependent inference and explicit encoding (EE) via direct instruction. However, these distinctions remain debated at both behavioral and neurophysiological levels, not least due to confounds related to diverging experimental settings. Furthermore, the neural dynamics underpinning these two putative processes remain poorly understood. To tackle this, we designed a paradigm presenting 20 new spoken words in association with pictures in either FM or EE settings, closely matched for auditory and visual features and overall task demands. We tested word acquisition using a range of behavioral measures as well as passive event-related potential (ERP) responses, an established measure of word memory trace activation, and compared brain activity elicited by novel FM and EE words before and after the learning session. Behavioral data obtained in free recall, recognition and semantic word-picture matching tasks indicated successful acquisition of new words after just 10 exposures. Crucially, we found no behavioral evidence of different acquisition outcomes between FM and EE learning. ERP data, which exhibited the main response peaks at ~170, 250, and 520 ms, also indicated successful learning, with statistically different responses between novel and familiar words present only before, but not after the training, suggesting rapid formation of new neural memory circuits matching in activation those for previously known words. Furthermore, already at the earliest peak, we found different topographic distributions for the two learning types, with left-lateralized FM dynamics, suggestive of core language system involvement, and more diffuse activity for EE items, possibly suggesting the role of attention/executive control network. A similar effect also manifested later, at ~520 ms. Our data suggest that while both EE and FM learning can be successful for rapid word acquisition at the behavioral level, the diverging electrophysiological patterns suggest a dissociation between the neural systems underpinning these learning strategies.
Angiotensin-Converting Enzyme (ACE) Inhibitors and Angiotensin Receptor Blockers (ARB) Are Protective Against ICU Admission and Mortality for Patients With COVID-19 Disease

Introduction: Corona Virus disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic. The aim of this study was to investigate the impact of being on an Angiotensin-Converting Enzyme Inhibitors (ACEI) and/or Angiotensin Receptor Blockers (ARB) on hospital admission, on the following COVID-19 outcomes: disease severity, ICU admission, and mortality. Methods: The charts of all patients consecutively diagnosed with COVID-19 from the 24th of February to the 16th of June of the year 2020 in Jaber Al-Ahmed Al-Sabah hospital in Kuwait were checked. All related patient information and clinical data was retrieved from the hospitals electronic medical record system. The primary outcome was COVID-19 disease severity defined as the need for Intensive Care Unit (ICU) admission. Secondary outcome was mortality. Results: A total of 4,019 COVID-19 patients were included, of which 325 patients (8.1%) used ACEI/ARB, users of ACEI/ARB were found to be significantly older (54.4 vs. 40.5 years). ACEI/ARB users were found to have more co-morbidities; diabetes (45.8 vs. 14.8%) and hypertension (92.9 vs. 13.0%). ACEI/ARB use was found to be significantly associated with greater risk of ICU admission in the unadjusted analysis [OR, 1.51 (95% CI: 1.04–2.19), p = 0.028]. After adjustment for age, gender, nationality, coronary artery disease, diabetes and hypertension, ICU admission was found to be inversely associated with ACEI use [OR, 0.57 (95% CI: 0.34–0.88), p = 0.01] and inversely associated with mortality [OR, 0.56 (95% CI: 0.33–0.95), p = 0.032]. Conclusion: The current evidence in the literature supports continuation of ACEI/ARB medications for patients with co-morbidities that acquire COVID-19 infection. Although, the protective effects of such medications on COVID-19 disease severity and mortality remain unclear, the findings of the present study support the use of ACEI/ARB medication.
Spillover Effects When Taking Turns in Dyadic Coping: How Lingering Negative Affect and Perceived Partner Responsiveness Shape Subsequent Support Provision

When experiencing personal distress, people usually expect their romantic partner to be supportive. However, when put in a situation to provide support, people may at times (still) be struggling with issues of their own. This interdependent nature of dyadic coping interactions as well as potential spillover effects is mirrored in the state-of-the-art research method to behaviorally assess couple’s dyadic coping processes. This paradigm typically includes two videotaped 8-min dyadic coping conversations in which partners swap roles as sharer and support provider. Little is known about how such dyadic coping interactions may feed back into one another, impacting the motivation and ability to be a responsive support provider. In three behavioral studies, we examined how sharers’ experiences may spill over to affect their own support provision in a subsequent dyadic coping interaction. We hypothesized that the extent to which sharers perceive their partner as responsive to their self-disclosure increases the quality of their own subsequent support provision (Hypothesis 1), whereas sharers’ lingering negative affect reduces the quality of their own subsequent support provision (Hypothesis 2). In line with our first hypothesis, perceived partner responsiveness predicted the provision of higher-quality support, though primarily as perceived by the partner. Sharers who perceived their partner to have been more responsive were somewhat more likely to subsequently engage in positive dyadic coping and were rated as more responsive by their partners. Negative dyadic coping behavior was unaffected. Evidence for our second hypothesis was mixed. While lingering negative affect did not affect positive dyadic coping behavior or perceived support, it did increase the chances of negative dyadic coping behavior. However, given the very low occurrences of negative affect and negative dyadic coping, these findings should be interpreted with caution. Taken together, these findings suggest that support interactions may feed back into one another, highlighting the complex and interdependent nature of dyadic coping. The strongest and most consistent findings concerned the spillover effect of perceived partner responsiveness on subsequent perceived support quality, speaking to the key role of believing that one’s partner is responsive to one’s needs in promoting healthy relationship functioning.
Targeted RNA-Based Oxford Nanopore Sequencing for Typing 12 Classical HLA Genes

Identification of human leukocyte antigen (HLA) alleles from next-generation sequencing (NGS) data is challenging because of the high polymorphism and mosaic nature of HLA genes. Owing to the complex nature of HLA genes and consequent challenges in allele assignment, Oxford Nanopore Technologies’ (ONT) single-molecule sequencing technology has been of great interest due to its fitness for sequencing long reads. In addition to the read length, ONT’s advantages are its portability and possibility for a rapid real-time sequencing, which enables a simultaneous data analysis. Here, we describe a targeted RNA-based method for HLA typing using ONT sequencing and SeqNext-HLA SeqPilot software (JSI Medical Systems GmbH). Twelve classical HLA genes were enriched from cDNA of 50 individuals, barcoded, pooled, and sequenced in 10 MinION R9.4 SpotON flow cell runs producing over 30,000 reads per sample. Using barcoded 2D reads, SeqPilot assigned HLA alleles to two-field typing resolution or higher with the average read depth of 1750x. Sequence analysis resulted in 99–100% accuracy at low-resolution level (one-field) and in 74–100% accuracy at high-resolution level (two-field) with the expected alleles. There are still some limitations with ONT RNA sequencing, such as noisy reads, homopolymer errors, and the lack of robust algorithms, which interfere with confident allele assignment. These issues need to be inspected carefully in the future to improve the allele call rates. Nevertheless, here we show that sequencing of multiplexed cDNA amplicon libraries on ONT MinION can produce accurate high-resolution typing results of 12 classical HLA loci. For HLA research, ONT RNA sequencing is a promising method due to its capability to sequence full-length HLA transcripts. In addition to HLA genotyping, the technique could also be applied for simultaneous expression analysis.
Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks

Background and Purpose It is extremely important to predict the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, which is a key predictor of recurrence and helps determine the treatment strategy before liver resection or liver transplantation. In this study, we demonstrate that a deep learning approach based on contrast-enhanced MR and 3D convolutional neural networks (CNN) can be applied to better predict MVI in HCC patients. Materials and Methods This retrospective study included 114 consecutive patients who were surgically resected from October 2012 to October 2018 with 117 histologically confirmed HCC. MR sequences including 3.0T/LAVA (liver acquisition with volume acceleration) and 3.0T/e-THRIVE (enhanced T1 high resolution isotropic volume excitation) were used in image acquisition of each patient. First, numerous 3D patches were separately extracted from the region of each lesion for data augmentation. Then, 3D CNN was utilized to extract the discriminant deep features of HCC from contrast-enhanced MR separately. Furthermore, loss function for deep supervision was designed to integrate deep features from multiple phases of contrast-enhanced MR. The dataset was divided into two parts, in which 77 HCCs were used as the training set, while the remaining 40 HCCs were used for independent testing. Receiver operating characteristic curve (ROC) analysis was adopted to assess the performance of MVI prediction. The output probability of the model was assessed by the independent student’s t-test or Mann-Whitney U test. Results The mean AUC values of MVI prediction of HCC were 0.793 (p=0.001) in the pre-contrast phase, 0.855 (p=0.000) in arterial phase, and 0.817 (p=0.000) in the portal vein phase. Simple concatenation of deep features using 3D CNN derived from all the three phases improved the performance with the AUC value of 0.906 (p=0.000). By comparison, the proposed deep learning model with deep supervision loss function produced the best results with the AUC value of 0.926 (p=0.000). Conclusion A deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction.
Ion Channels Orchestrate Pancreatic Ductal Adenocarcinoma Progression and Therapy

Pancreatic ductal adenocarcinoma is a devastating disease with a dismal prognosis. Therapeutic interventions are largely ineffective. A better understanding of the pathophysiology is required. Ion channels contribute substantially to the “hallmarks of cancer. ” Their expression is dysregulated in cancer, and they are “misused” to drive cancer progression, but the underlying mechanisms are unclear. Ion channels are located in the cell membrane at the interface between the intracellular and extracellular space. They sense and modify the tumor microenvironment which in itself is a driver of PDAC aggressiveness. Ion channels detect, for example, locally altered proton and electrolyte concentrations or mechanical stimuli and transduce signals triggered by these microenvironmental cues through association with intracellular signaling cascades. While these concepts have been firmly established for other cancers, evidence has emerged only recently that ion channels are drivers of PDAC aggressiveness. Particularly, they appear to contribute to two of the characteristic PDAC features: the massive fibrosis of the tumor stroma (desmoplasia) and the efficient immune evasion. Our critical review of the literature clearly shows that there is still a remarkable lack of knowledge with respect to the contribution of ion channels to these two typical PDAC properties. Yet, we can draw parallels from ion channel research in other fibrotic and inflammatory diseases. Evidence is accumulating that pancreatic stellate cells express the same “profibrotic” ion channels. Similarly, it is at least in part known which major ion channels are expressed in those innate and adaptive immune cells that populate the PDAC microenvironment. We explore potential therapeutic avenues derived thereof. Since drugs targeting PDAC-relevant ion channels are already in clinical use, we propose to repurpose those in PDAC. The quest for ion channel targets is both motivated and complicated by the fact that some of the relevant channels, for example, KCa3.1, are functionally expressed in the cancer, stroma, and immune cells. Only in vivo studies will reveal which arm of the balance we should put our weights on when developing channel-targeting PDAC therapies. The time is up to explore the efficacy of ion channel targeting in (transgenic) murine PDAC models before launching clinical trials with repurposed drugs.
Liraglutide Alleviates Hepatic Steatosis and Liver Injury in T2MD Rats via a GLP-1R Dependent AMPK Pathway

Non-alcoholic fatty liver disease (NAFLD), ranging from non-alcoholic fatty liver to non-alcoholic steatohepatitis, can be prevalent in patients with type 2 diabetes mellitus (T2DM). However, no antidiabetic drug has been approved for the treatment of NAFLD in T2DM patients. Multiple daily injections of basal-bolus insulin are often the final therapeutic option for T2DM. We found that insulin treatment aggravated hepatic steatosis and oxidative stress in Zucker diabetic fatty (ZDF) rats. In addition to glycaemic control, we demonstrated the stimulatory role of liraglutide in relieving hepatic steatosis and liver injury in ZDF rats. Interestingly, liraglutide could also alleviate insulin-aggravated hepatic fatty accumulation. The glucagon-like peptide-1 (GLP-1) agonists liraglutide and Ex-4 activated the expression of peroxisome proliferator-activated receptor alpha (PPARα) via a GLP-1 receptor-dependent 5′ AMP-activated protein kinase pathway. As a nuclear transcription factor, PPARα could mediate the effect of GLP-1 in alleviating hepatic steatosis by differentially regulating the expression of its target genes, including acetyl CoA carboxylase and carnitine palmitoyl transferase la both in vitro and in vivo. Moreover, GLP-1 could relieve liver injury by decreasing oxidative stress stimulated by hepatic steatosis. Insulin might aggravate hepatic steatosis and liver injury by inhibiting GLP-1R expression. The findings indicate the feasibility of liraglutide treatment combined with basal insulin in attenuating hepatic steatosis and liver injury in ZDF rats. This knowledge, and the evidence for the underlying mechanism, provide a theoretical basis for the combination treatment recommended by the latest clinical practice guidelines for T2DM.
Immunological Considerations for Schistosoma Vaccine Development: Transitioning to Endemic Settings

Despite mass drug administration programmes with praziquantel, the prevalence of schistosomiasis remains high. A vaccine is urgently needed to control transmission of this debilitating disease. As some promising schistosomiasis vaccine candidates are moving through pre-clinical and clinical testing, we review the immunological challenges that these vaccine candidates may encounter in transitioning through the clinical trial phases in endemic settings. Prior exposure of the target population to schistosomes and other infections may impact vaccine response and efficacy and therefore requires considerable attention. Schistosomes are known for their potential to induce T-reg/IL-10 mediated immune suppression in populations which are chronically infected. Moreover, endemicity of schistosomiasis is focal whereby target and trial populations may exhibit several degrees of prior exposure as well as in utero exposure which may increase heterogeneity of vaccine responses. The age dependent distribution of exposure and development of acquired immunity, and general differences in the baseline immunological profile, adds to the complexity of selecting suitable trial populations. Similarly, prior or concurrent infections with other parasitic helminths, viral and bacterial infections, may alter immunological responses. Consequently, treatment of co-infections may benefit the immunogenicity of vaccines and may be considered despite logistical challenges. On the other hand, viral infections leave a life-long immunological imprint on the human host. Screening for serostatus may be needed to facilitate interpretation of vaccine responses. Co-delivery of schistosome vaccines with PZQ is attractive from a perspective of implementation but may complicate the immunogenicity of schistosomiasis vaccines. Several studies have reported PZQ treatment to induce both transient and long-term immuno-modulatory effects as a result of tegument destruction, worm killing and subsequent exposure of worm antigens to the host immune system. These in turn may augment or antagonize vaccine immunogenicity. Understanding the complex immunological interactions between vaccine, co-infections or prior exposure is essential in early stages of clinical development to facilitate phase 3 clinical trial design and implementation policies. Besides well-designed studies in different target populations using schistosome candidate vaccines or other vaccines as models, controlled human infections could also help identify markers of immune protection in populations with different disease and immunological backgrounds.
Selecting Classification Methods for Small Samples of Next-Generation Sequencing Data

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.