Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Differences in Coronary Artery Disease and Diabetes Revealed by scRNA-Seq and CITE-Seq of Human CD4+ T Cells

Background Despite the decades-old knowledge that diabetes mellitus (DM) is a major risk factor for cardiovascular disease (CVD), the reasons for this association are only partially understood. Among the immune cells involved in CVD development, accumulating evidence supports the critical role of T cells as drivers and modifiers of this condition. CD4+ T cells are commonly found in atherosclerotic plaques. The activity and distribution of CD4+ T cell subsets differs between the sexes. Methods Peripheral blood mononuclear cells (PBMCs) of 61 men and women who underwent cardiac catheterization were interrogated by single cell RNA sequencing (scRNA-Seq, ∼200,000 cells) combined with 49 protein markers (CITE-Seq). Coronary artery disease (CAD) was quantified using Gensini scores, with scores above 30 considered CAD+ and below 6 considered CAD-. Four pairs of groups were matched for clinical and demographic parameters. To test how DM changed cell proportions and gene expression, we compared matched groups of diabetic and non-diabetic subjects. We analyzed 41,782 single CD4+ T cell transcriptomes for sex differences in 61 mostly statin-treated coronary artery disease patients with and without DM. Results We identified 16 clusters in CD4 T cells. The proportion of cells in CD4 cluster 8 (CD4T8, CCR2+ Em) was significantly decreased in CAD+, especially among DM+ participants. The proportions of cells in CD4T2, CD4T11, CD4T16 were increased and CD4T13 was decreased in CAD+ among DM+Statin+ participants. CD4T12 was increased in DM+ participants. In female participants, CD4T8, 12, and 13 were decreased compared to in male participants. In CD4 T cells, 31 genes showed significant and coordinated upregulation in both CAD and DM. The DM gene signature was partially additive to the CAD gene signature. Conclusions We conclude that CAD and DM are clearly reflected in PBMC transcriptomes and that significant differences exist between women and men and between subjects treated with statins or not.
FastTrackNoC: A NoC with FastTrack Router Datapaths

This paper introduces FastTrackNoC, a Network-on-Chip (NoC) router architecture that reduces packet latency by bypassing its switch traversal (ST) stage. It is based on the observation that there is a bias in the direction a flit takes through a router, e.g., in a 2D mesh network, non-turning hops are preferred, especially when dimension order routing is used. FastTrackNoC capitalizes on this observation and adds to a 2D mesh router a fast-track path between the head of a single input virtual channel (VC) buffer and its most popular, opposite output. This allows non-turning flits to bypass ST logic, i.e., buffer-, input-and output multiplexing, when the required router resources are available. FastTrackNoC combines ST bypassing with existing techniques for reducing latency, namely, allocation bypassing, precomputed routing, and lookahead control signaling to allow at best incoming flits to proceed directly to link traversal (LT). Moreover, it is applied to a Dual Data Rate (DDR) router in order to maximize network throughput. Post place and route results in 28nm show the following: compared to previous DDR NoCs, FastTrackNoC offers 13-32% lower average packet latency; compared to previous multi-VC Single Data Rate (SDR) NoCs, FastTrackNoC reduces latency by 10-40% and achieves 18-21% higher throughput, and compared to single-channel SDR NoC offers up to 50% higher throughput and similar latency.
Upward Packet Popup for Deadlock Freedom in Modular Chiplet-Based Systems

Monolithic SoCs can be decomposed into disparate chiplets that support integration with advanced pack-aging technologies. This concept is promising in reducing the manufacturing cost of large scale SoCs due to the higher yield rate and reusability of chiplets. The chiplets should be designed in a modular manner without holistic system knowledge so that they can be reused in different SoCs. However, the design modularity is a major challenge to the networks-on-chip (NoCs) of chiplets.New deadlocks may occur across both the chiplets and the interposer due to the integration, even if the NoC of each individually designed chiplet is deadlock free. However, conventional deadlock freedom approaches are unsuitable to handle such deadlocks because they require holistic knowledge and violate the modularity. Although there are several modular approaches that specifically target at integration-induced deadlocks, their routing is overly restricted and the injection control incurs additional latency. They also lack flexibility in dynamically changing topologies due to their complex software algorithm and the hard-wired components.In this paper, a key insight on the chiplet integration-induced deadlocks is gained, inspired by which a deadlock recovery framework (named UPP) is proposed. Specifically, it is verified that an integration-induced deadlock always involves a stalled upward packet moving from the interposer to the connected chiplet via the vertical link. Thus, UPP detects a deadlock by discovering the upward packet and recovers the system from deadlock by transmitting the upward packet to its destination. Hybrid flow control mechanisms are proposed to enable the upward packet to bypass the buffers and be transmitted via the normal router datapath. To guarantee the ejection of the upward packet after transmission, a lightweight protocol is proposed to reserve ejection queue entries of the network interface. Experimental results show that while adhering to design modularity, UPP provides an average runtime speedup of 3.1%∼10.3% with an area overhead of less than 4%.
Relighting Backlight and Spotlight Images using the von Kries Model

: Improving the quality of backlight and spotlight images is a challenging task. Indeed, these pictures include both very bright and very dark regions with unreadable content and details. Restoring the visibility in these regions has to be performed without over-enhancing the bright regions, thus without generating unpleasant artifacts. To this end, some algorithms segment the image in bright and dark regions, re-work them separately by different enhancing functions. Other algorithms process the input image at multiple scales or with different enhancement techniques. All these methods merge the results together paying attention to the edge areas. The present work proposes a novel approach, called REK and implementing a relighting technique based on the von Kries model. REK linearly increases the channel intensities of the input image, obtaining a new brighter image, which is then summed up to the input one with weights computed from the input image and taking high values on the dark regions while low values on the bright ones. In this way, REK improves the quality of backlight and spotlight pictures without needing for segmentation and multiple analysis, while granting satisfactory performance at a computational complexity proportional to the number of image pixels.
Real-time Arabic Sign Language Recognition based on YOLOv5

: Sign language is the most common communication mode of deaf and mute community. However, hearing people do not generally know this language. So, an automatic sign langage recognition is required to facilitate and better understand interactions with such people. However, one of the main challlenges in this ﬁeld is the real-time sign recognition. That is why, deep learning-based object detection models can be used to improve the recognition performance (in terms of time and accuracy). In this paper, we present a real-time system that allows the detection and recognition of hand postures intended for the Arabic sign language alphabet. To do so, we constructed a dataset of 28 Arabic signs containing around 15,000 images acquired with different sizes of hands, lighting conditions, backgrounds and with/without accessories. We then trained and tested different variants of YOLOv5 on the constructed dataset. The conducted experiments on our ArSL real-time recognition system show that the adapted YOLOv5 is more effective than Faster R-CNN detector.
Traffic Patterns of the Migrating Endothelium: How Force Transmission Regulates Vascular Malformation and Functional Shunting During Angiogenic Remodelling

Angiogenesis occurs in distinct phases: initial spouting is followed by remodelling in which endothelial cells (ECs) composing blood vessels rearrange by migrating against the direction of flow. Abnormal remodelling can result in vascular malformation. Such is the case in mutation of the Alk1 receptor within the mouse retina which disrupts flow-migration coupling, creating mixed populations of ECs polarised with/against flow which aggregate into arteriovenous malformations (AVMs). The lack of live imaging options in vivo means that the collective EC dynamics that drive AVM and the consequences of mixed populations of polarity remain a mystery. Therefore, our goal is to present a novel agent-based model to provide theoretical insight into EC force transmission and collective dynamics during angiogenic remodelling. Force transmission between neighbouring agents consists of extrusive forces which maintain spacing and cohesive forces which maintain the collective. We performed migration simulations within uniformly polarised populations (against flow) and mixed polarity (with/against flow). Within uniformly polarised populations, extrusive forces stabilised the plexus by facilitating EC intercalation which ensures that cells remained evenly distributed. Excess cohesion disrupts intercalation, resulting in aggregations of cells and functional shunting. Excess cohesion between ECs prevents them from resolving diameter balances within the plexus, leading to prolonged flow reversals which exert a critical behaviour change within the system as they switch the direction of cell migration and traffic patterns at bifurcations. Introducing mixtures of cell polarity dramatically changed the role of extrusive forces within the system. At low extrusion, opposing ECs were able to move past each other; however, at high extrusion the pushing between cells resulted in migration speeds close to zero, forming traffic jams and disrupting migration. In our study, we produced vascular malformations and functional shunting with either excess cohesion between ECs or mixtures of cell polarity. At the centre of both these mechanisms are cell-cell adherens junctions, which are involved in flow sensing/polarity and must remodelling dynamically to allow rearrangements of cells during vascular patterning. Thus, our findings implicate junctional dysfunction as a new target in the treatment and prevention of vascular disease and AVMs.
Machine Learning-Assisted Screening of Herbal Medicine Extracts as Vaccine Adjuvants

Adjuvants are important vaccine components, composed of a variety of chemical and biological materials that enhance the vaccine antigen-specific immune responses by stimulating the innate immune cells in both direct and indirect manners to produce a variety cytokines, chemokines, and growth factors. It has been developed by empirical methods for decades and considered difficult to choose a single screening method for an ideal vaccine adjuvant, due to their diverse biochemical characteristics, complex mechanisms of, and species specificity for their adjuvanticity. We therefore established a robust adjuvant screening strategy by combining multiparametric analysis of adjuvanticity in vivo and immunological profiles in vitro (such as cytokines, chemokines, and growth factor secretion) of various library compounds derived from hot-water extracts of herbal medicines, together with their diverse distribution of nano-sized physical particle properties with a machine learning algorithm. By combining multiparametric analysis with a machine learning algorithm such as rCCA, sparse-PLS, and DIABLO, we identified that human G-CSF and mouse RANTES, produced upon adjuvant stimulation in vitro, are the most robust biological parameters that can predict the adjuvanticity of various library compounds. Notably, we revealed a certain nano-sized particle population that functioned as an independent negative parameter to adjuvanticity. Finally, we proved that the two-step strategy pairing the negative and positive parameters significantly improved the efficacy of screening and a screening strategy applying principal component analysis using the identified parameters. These novel parameters we identified for adjuvant screening by machine learning with multiple biological and physical parameters may provide new insights into the future development of effective and safe adjuvants for human use.
Editorial: Unraveling Sleep and Its Disorders Using Novel Analytical Approaches

Despite significant and meritorious research efforts over the last decades, the functions and evolutionary determinants of sleep remain one of the mysterious and relatively unexplored dimensions of physiology (Krueger et al., 2016). The recipe for deciphering such an attractive challenge in biology and medicine most probably includes ingredients such as unraveling the complexity of brain functioning during sleep (Olbrich et al., 2011), the specific roles of sleep in coordination of other body systems (Penzel et al., 2016), or defining the specific cellular and system-related pathways that regulate or are regulated by sleep. In addition, the already established and novel techniques and technological tools aiming to explore sleep-related systems, e.g., electroencephalogram (EEG), magnetoencephalogram, positron emission tomography, functional magnetic resonance image (fMRI) and several others in the case of the brain, have their own limitations and advantages (Huster et al., 2012). This Research Topic aimed to be an overarching framework and instigator for those sleep-related studies presenting novel conceptual approaches and disruptive ideas in the field. Manuscripts submitted for this special issue represent high scientific contributions to this topic. The only drawback in the Editors’ opinion is that most of the studies focused on sleep disorders rather than on deep and sophisticated analyses of physiological sleep. This Research Topic might however stimulate further research in this direction. Among the studies finally incorporated to the Research Topic, four of them focused on sleep apnea and cardio-pulmonary coupling, four additional articles focused on insomnia, sleep deprivation, and micro-sleep episodes, one focused on sleep disruption and, finally, another one focused on depression. Consistent with these topics, we have organized the rest of the document in four sections.
Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection

The transfer of power stemming from the 2020 presidential election occurred during an unprecedented period in United States history. Uncertainty from the COVID-19 pandemic, ongoing societal tensions, and a fragile economy increased societal polarization, exacerbated by the outgoing president's offline rhetoric. As a result, online groups such as QAnon engaged in extra political participation beyond the traditional platforms. This research explores the link between offline political speech and online extra-representational participation by examining Twitter within the context of the January 6 insurrection. Using a mixed-methods approach of quantitative and qualitative thematic analyses, the study combines offline speech information with Twitter data during key speech addresses leading up to the date of the insurrection; exploring the link between Trump's offline speeches and QAnon's hashtags across a 3-day timeframe. We find that links between online extra-representational participation and offline political speech exist. This research illuminates this phenomenon and offers policy implications for the role of online messaging as a tool of political mobilization.
Case Report: Adrenocortical Oncocytoma in a Patient with a Previous Contralateral Adrenalectomy for a Cortisol-Secreting Adenoma

Background Oncocytomas are uncommon benign tumors that arise in various organs and are predominantly composed of oncocytes. Adrenocortical oncocytomas are extremely rare and are generally non-functioning. Methods We report the case of a 40-year-old patient with a progressively enlarging left adrenal mass. At the age of 19 he had undergone right adrenalectomy for a cortisol-secreting adenoma. Radiologic features were not typical of an adenoma and positive uptake was detected at 18F-FDG-PET. Because of the uncertain nature of the growing lesion, it was decided to proceed to surgical resection. Results The surgeon managed to remove the left adrenal mass, sparing the normal adrenal gland, and histology was consistent with adrenocortical oncocytoma. Corticosteroid supplementation was prescribed, but at reassessment, adrenal function was found to be preserved and treatment withdrawn. Conclusions Adrenal oncocytoma is a rare diagnosis, but should be considered in the presence of a growing mass with non-specific radiologic appearance.
Translational Learnings in the Development of Chemo-Immunotherapy Combination to Bypass the Cold Tumor Microenvironment in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers, with a 5-year relative survival rate of 5%. The desmoplastic stroma found in the tumor microenvironment of PDAC is suggested to be partly responsible for the resistance to most therapeutic strategies. This review outlines the clinical results obtained with an immune checkpoint inhibitor in PDAC and discusses the rationale to use a combination of chemotherapy and immune checkpoint therapy. Moreover, essential parameters to take into account in designing an efficient combination have been highlighted.
Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
Exploring the Potential of Lactic Acid Fermentation for the Recovery of Exhausted Vanilla Beans

The market value of vanilla is constantly growing, as it is the aroma most appreciated by consumers worldwide. The key component of the aroma of vanilla beans is vanillin, which can be directly extracted from the plant, produced by chemical synthesis, or by bioconversion of natural precursors. Due to the increasing consumers' demand for products labeled as “natural,” extraction from vanilla pods results in a more valuable aroma source. Once the extraction is completed, what remains are the exhausted beans that still contain small seeds and other compounds, including varying amounts of vanillin trapped in the cellular structures of the plant. The application of fermentation of exhausted vanilla beans is proposed here as a strategy to recover “natural” vanillin and other valuable aroma compounds as a result of the metabolic conversion by lactic acid bacteria (LAB). The aim of this study was to verify the fermentability of exhausted vanilla beans by-products for their valorization, allowing the recovery of high-value molecules or new applications in food products. Design of Experiment (DoE) was used to screen a library of LAB strains to identify the best condition of fermentation in response to varying cultivation conditions. A comparison between mono and co-culture of LAB was assessed. Moreover, sensory panel tests and the evaluation of the aromatic components by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry analysis were carried out to better understand the modification of the aroma profile after fermentation. Fermentation with LAB changed the volatile profile and sensory characteristics of the exhausted vanilla beans and represents a promising method for the valorization of these by-products.
Preprocessing of Configuration Space for Improved Sampling Based Path Planning

Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ine ffective when the configuration space has a narrow passage. This paper presents two new techniques of preprocessing the configuration space. The first technique called a Random Walk to Surface (RWS), uses a random walk strategy to generate samples in narrow regions quickly, thus improving e fficiency of Probabilistic Roadmap (PRM) based planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known a priori, thus giving significant improvement over other known methods. The second method, by preprocessing the configuration space, improves the e fficiency of Rapidly Exploring Random Tree (RRT) like planners by identifying key regions of the configuration space to search for a solution path. The Experiments show a significant improvement in e fficiency for both PRM and RRT like planners.
Inhibitory Spike-Timing-Dependent Plasticity Can Account for Pathological Strengthening of Pallido-Subthalamic Synapses in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative brain disorder associated with dysfunction of the basal ganglia (BG) circuitry. Dopamine (DA) depletion in experimental PD models leads to the pathological strengthening of pallido-subthalamic synaptic connections, contributing to the emergence of abnormally synchronized neuronal activity in the external segment of the globus pallidus (GPe) and subthalamic nucleus (STN). Augmented GPe-STN transmission following loss of DA was attributed to heterosynaptic plasticity mechanisms induced by cortico-subthalamic inputs. However, synaptic plasticity may play a role in this process. Here, by employing computational modeling we show that assuming inhibitory spike-timing-dependent plasticity (iSTDP) at pallido-subthalamic synapses can account for pathological strengthening of pallido-subthalamic synapses in PD by further promoting correlated neuronal activity in the GPe-STN network. In addition, we show that GPe-STN transmission delays can shape bistable activity-connectivity states due to iSTDP, characterized by strong connectivity and strong synchronized activity (pathological states) as opposed to weak connectivity and desynchronized activity (physiological states). Our results may shed light on how abnormal reshaping of GPe-STN connectivity by synaptic plasticity during parkinsonism is related to the PD pathophysiology and contribute to the development of therapeutic brain stimulation techniques targeting plasticity-induced rewiring of network connectivity.