Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated View on Neuronal Subsets in the Peripheral Nervous System and Their Role in Immunoregulation

The peripheral nervous system consists of sensory circuits that respond to external and internal stimuli and effector circuits that adapt physiologic functions to environmental challenges. Identifying neurotransmitters and neuropeptides and the corresponding receptors on immune cells implies an essential role for the nervous system in regulating immune reactions. Vice versa, neurons express functional cytokine receptors to respond to inflammatory signals directly. Recent advances in single-cell and single-nuclei sequencing have provided an unprecedented depth in neuronal analysis and allowed to refine the classification of distinct neuronal subsets of the peripheral nervous system. Delineating the sensory and immunoregulatory capacity of different neuronal subsets could inform a better understanding of the response happening in tissues that coordinate physiologic functions, tissue homeostasis and immunity. Here, we summarize current subsets of peripheral neurons and discuss neuronal regulation of immune responses, focusing on neuro-immune interactions in the gastrointestinal tract. The nervous system as a central coordinator of immune reactions and tissue homeostasis may predispose for novel promising therapeutic approaches for a large variety of diseases including but not limited to chronic inflammation.
A Large-Scale and Serverless Computational Approach for Improving Quality of NGS Data Supporting Big Multi-Omics Data Analyses

Various types of analyses performed over multi-omics data are driven today by next-generation sequencing (NGS) techniques that produce large volumes of DNA/RNA sequences. Although many tools allow for parallel processing of NGS data in a Big Data distributed environment, they do not facilitate the improvement of the quality of NGS data for a large scale in a simple declarative manner. Meanwhile, large sequencing projects and routine DNA/RNA sequencing associated with molecular profiling of diseases for personalized treatment require both good quality data and appropriate infrastructure for efficient storing and processing of the data. To solve the problems, we adapt the concept of Data Lake for storing and processing big NGS data. We also propose a dedicated library that allows cleaning the DNA/RNA sequences obtained with single-read and paired-end sequencing techniques. To accommodate the growth of NGS data, our solution is largely scalable on the Cloud and may rapidly and flexibly adjust to the amount of data that should be processed. Moreover, to simplify the utilization of the data cleaning methods and implementation of other phases of data analysis workflows, our library extends the declarative U-SQL query language providing a set of capabilities for data extraction, processing, and storing. The results of our experiments prove that the whole solution supports requirements for ample storage and highly parallel, scalable processing that accompanies NGS-based multi-omics data analyses.
Human Breast Milk Enhances Intestinal Mucosal Barrier Function and Innate Immunity in a Healthy Pediatric Human Enteroid Model

Breastfeeding has been associated with long lasting health benefits. Nutrients and bioactive components of human breast milk promote cell growth, immune development, and shield the infant gut from insults and microbial threats. The molecular and cellular events involved in these processes are ill defined. We have established human pediatric enteroids and interrogated maternal milk’s impact on epithelial cell maturation and function in comparison with commercial infant formula. Colostrum applied apically to pediatric enteroid monolayers reduced ion permeability, stimulated epithelial cell differentiation, and enhanced tight junction function by upregulating occludin. Breast milk heightened the production of antimicrobial peptide α-defensin 5 by goblet and Paneth cells, and modulated cytokine production, which abolished apical release of pro-inflammatory GM-CSF. These attributes were not found in commercial infant formula. Epithelial cells exposed to breast milk elevated apical and intracellular pIgR and enabled maternal IgA translocation. Proteomic data revealed a breast milk-induced molecular pattern associated with tissue remodeling and homeostasis. Using a novel ex vivo pediatric enteroid model, we have identified distinct cellular and molecular events involved in human milk-mediated improvement of human intestinal physiology and immunity.
Accuracy of Interpolated Versus In-Vineyard Sensor Climate Data for Heat Accumulation Modelling of Phenology

Background and Aims In response to global heating, accurate climate data are required to calculate climatic indices for long-term decisions about vineyard management, vineyard site selection, varieties planted and to predict phenological development. The availability of spatially interpolated climate data has the potential to make viticultural climate analyses possible at specific sites without the expense and uncertainty of collecting climate data within vineyards. The aim of this study was to compare the accuracy and precision of climatic indices calculated using an on-site climate sensor and an interpolated climate dataset to assess whether the effect of spatial variability in climate at this fine spatial scale significantly affects phonological modelling outcomes. Methods and Results Four sites comprising two topographically homogenous vineyards and two topographically diverse vineyards in three wine regions in Victoria (Australia) were studied across four growing seasons. A freely available database of interpolated Australian climate data based on government climate station records (Scientific Information for Land Owners, SILO) provided temperature data for grid cells containing the sites (resolution 0.05° latitude by 0.05° longitude, approximately 5 km × 5 km). In-vineyard data loggers collected temperature data for the same time period. The results indicated that the only significant difference between the two climate data sources was the minimum temperatures in the topographically varied vineyards where night-time thermal layering is likely to occur. Conclusion The interpolated climate data closely matched the in-vineyard recorded maximum temperatures in all cases and minimum temperatures for the topographically homogeneous vineyards. However, minimum temperatures were not as accurately predicted by the interpolated data for the topographically complex sites. Therefore, this specific interpolated dataset was a reasonable substitute for in-vineyard collected data only for vineyard sites that are unlikely to experience night-time thermal layering. Significance of the Study Access to accurate climate data from a free interpolation service, such as SILO provides a valuable tool tomanage blocks or sections within vineyards more precisely for vineyards that do not have a weather station on site. Care, nevertheless, is required to account for minimum temperature discrepancies in topographically varied vineyards, due to the potential for cool air pooling at night, that may not be reflected in interpolated climate data.
Uncovering Transcriptional Regulators and Targets of sRNAs Using an Integrative Data-Mining Approach: H-NS-Regulated RseX as a Case Study

Bacterial small RNAs (sRNAs) play a vital role in pathogenesis by enabling rapid, efficient networks of gene attenuation during infection. In recent decades, there has been a surge in the number of proposed and biochemically-confirmed sRNAs in both Gram-positive and Gram-negative pathogens. However, limited homology, network complexity, and condition specificity of sRNA has stunted complete characterization of the activity and regulation of these RNA regulators. To streamline the discovery of the expression of sRNAs, and their post-transcriptional activities, we propose an integrative in vivo data-mining approach that couples DNA protein occupancy, RNA-seq, and RNA accessibility data with motif identification and target prediction algorithms. We benchmark the approach against a subset of well-characterized E. coli sRNAs for which a degree of in vivo transcriptional regulation and post-transcriptional activity has been previously reported, finding support for known regulation in a large proportion of this sRNA set. We showcase the abilities of our method to expand understanding of sRNA RseX, a known envelope stress-linked sRNA for which a cellular role has been elusive due to a lack of native expression detection. Using the presented approach, we identify a small set of putative RseX regulators and targets for experimental investigation. These findings have allowed us to confirm native RseX expression under conditions that eliminate H-NS repression as well as uncover a post-transcriptional role of RseX in fimbrial regulation. Beyond RseX, we uncover 163 putative regulatory DNA-binding protein sites, corresponding to regulation of 62 sRNAs, that could lead to new understanding of sRNA transcription regulation. For 32 sRNAs, we also propose a subset of top targets filtered by engagement of regions that exhibit binding site accessibility behavior in vivo. We broadly anticipate that the proposed approach will be useful for sRNA-reliant network characterization in bacteria. Such investigations under pathogenesis-relevant environmental conditions will enable us to deduce complex rapid-regulation schemes that support infection.
Fluorescence-Based Real-Time Analysis of Osteoclast Development

Osteoclasts are multinucleated cells of hematopoietic origin which are critically involved in physiological and pathological bone resorption. They develop from myeloid progenitors through characteristic gene expression changes and intercellular fusion. This process is directed by M-CSF and RANKL which are also able to trigger osteoclast development from bone marrow cells in vitro. Osteoclasts are conventionally visualized by histochemical staining followed by manual counting, which hinders kinetic studies and automated quantification. Here we describe two fluorescence-based assays for the real-time analysis of myeloid cell to osteoclast development (FRAMCO) in primary mouse bone marrow cell cultures. Both assays rely on red-to-green fluorescence conversion of the membrane-targeted tdTomato/membrane-targeted eGFP (mTmG) transgene by Cre recombinase driven by the osteoclast-specific cathepsin K promoter (Ctsk-Cre). In the first assay (FRAMCO1.1), osteoclast-specific gene expression triggers red-to-green color conversion of cells carrying both the Ctsk-Cre and mTmG transgenes. In the second assay (FRAMCO1.2), red-to-green fluorescence conversion is triggered by fusion of neighboring co-cultured bone marrow cells separately carrying either the Ctsk-Cre or the mTmG transgenes. The two assays were tested using a high-content confocal fluorescence imaging system, followed by automated quantification. The FRAMCO1.1 assay showed robust red-to-green fluorescence conversion of more than 50% of the culture (including mononuclear cells) within 3 days under osteoclastogenic conditions. The FRAMCO1.2 assay showed a less robust but still readily measurable red-to-green color conversion in multinuclear cells within 5 days of differentiation. The assays required both the Ctsk-Cre and the mTmG transgenes and gave no signals in parallel macrophage cultures. The proper functioning of the two assays was also confirmed at the DNA, mRNA and bulk protein level. The assay systems were validated using lisophosphatidylcholine, a previously reported inhibitor of preosteoclast fusion. Taken together, our assays allow high-throughput automated real-time analysis of two critical aspects of osteoclast development, facilitating the screening for novel drug candidates for the pharmacological control of osteoclast-mediated bone resorption.
SDF4 Is a Prognostic Factor for 28-Days Mortality in Patients With Sepsis via Negatively Regulating ER Stress

Sepsis is a heterogeneous syndrome induced by infection and results in high mortality. Even though more than 100 biomarkers for sepsis prognosis were evaluated, prediction of patient outcomes in sepsis continues to be driven by clinical signs because of unsatisfactory specificity and sensitivity of these biomarkers. This study aimed to elucidate the key candidate genes involved in sepsis response and explore their downstream effects based on weighted gene co-expression network analysis (WGCNA). The dataset GSE63042 with sepsis outcome information was obtained from the Gene Expression Omnibus (GEO) database and then consensus WGCNA was conducted. We identified the hub gene SDF4 (stromal cell derived factor 4) from the M6 module, which was significantly associated with mortality. Subsequently, two datasets (GSE54514 and E-MTAB-4421) and cohort validation (n=89) were performed. Logistic regression analysis was used to build a prediction model and the combined score resulting in a satisfactory prognosis value (area under the ROC curve=0.908). The model was subsequently tested by another sepsis cohort (n=70, ROC= 0.925). We next demonstrated that endoplasmic reticulum (ER) stress tended to be more severe in patients PBMCs with negative outcomes compared to those with positive outcomes and SDF4 was related to this phenomenon. In addition, our results indicated that adenovirus-mediated Sdf4 overexpression attenuated ER stress in cecal ligation and puncture (CLP) mice lung. In summary, our study indicates that incorporation of SDF4 can improve clinical parameters predictive value for the prognosis of sepsis, and decreased expression levels of SDF4 contributes to excessive ER stress, which is associated with worsened outcomes, whereas overexpression of SDF4 attenuated such activation.
Optimizing Hydrogen Sensing Behavior by Controlling the Coverage in Pd Nanoparticle Films

The response of quantum-conductance-based hydrogen sensors fabricated by controllable deposition of closely spaced Pd nanoparticle films between interdigital electrodes was investigated. Three typical response regions with different conductance–hydrogen pressure correlations were observed. The response characteristics of the devices were found to depend strongly on the nanoparticle coverage. In the low H2 pressure region, higher coverage gives higher sensitivity. In the high H2 pressure region, quantitative sensing can only be realized with low nanoparticle coverage. Optimizing the coverage allows the attainment of highly sensitive hydrogen sensors with a very wide quantitative working range, extending far beyond the hydrogen pressure region associated with the α-to-β phase transition of Pd.
The rmal Transport in Semiconductors and Metals from First-Principles

Thermal Transport in Semiconductors and Metals from First-Principles by Ankit Jain Chair: Alan McGaughey The objective of this work is to study thermal transport in bulk, nano-structured, and two-dimensional semiconductors, and metals. While phonons (i.e., atomic vibrations) dominate the thermal transport in crystalline semiconductors, electrons are the major thermal and charge carriers in metals. We predict the phonon and electron transport properties from first-principles calculations by considering phonon-phonon, electron-phonon, phonon-boundary, and phononisotopes scattering and using the Boltzmann transport equation. The phonon-phonon scattering rates are obtained from harmonic and anharmonic lattice dynamics calculations. Harmonic and cubic force constants, which are required as an input for harmonic and anharmonic lattice dynamics calculations, are obtained from density functional theory and density functional perturbation theory calculations. For electron-phonon scattering, we used density functional perturbation theory to calculate mode-dependent electron-phonon coupling coefficients on coarse grids and Wannier functions to interpolate them to finer grids. At a temperature of 300 K, for porous silicon thin films with minimum feature sizes greater than 100 nm, we find that our particle-based phonon-boundary scattering model can capture the experimental trend and magnitude (within 6%) for the in-plane thermal conductivity. These results suggest the absence of coherent (i.e., wave) phonon transport phenomenon. In compound semiconductors (i.e., semiconductors with more than one atomic species), we find that thermal conductivity depends on the frequency-gap between acoustic and optical phonons and the maximum acoustic phonon frequency. For model compound semicon-
NVM Storage in IoT Devices: Opportunities and Challenges

Edge storage stores the data directly at the data collection point, and does not need to transmit the collected data to the storage central server through the network. It is a critical technology that supports applications such as edge computing and 5G network applications, with lower network communication overhead, lower interaction delay and lower bandwidth cost. However, with the explosion of data and higher real-time requirements, the traditional Internet of Things (IoT) storage architecture cannot meet the requirements of low latency and large capacity. Non-volatile memory (NVM) presents new possibilities regarding this aspect. This paper classifies the different storage architectures based on NVM and compares the system goals, architectures, features, and limitations to explore new research opportunities. Moreover, the existing solutions to reduce the write latency and energy consumption and increase the lifetime of NVM IoT storage devices are analyzed. Furthermore, we discuss the security and privacy issues of IoT devices and compare the mainstream solutions. Finally, we present the opportunities and challenges of building IoT storage systems based on NVM.
Hydroxyl groups in β-Mg2SiO4

Beta-Mg2SiO4 is likely to be an important phase within the transition zone of the Earth's mantle at a depth of 400-500 km. Recent calculations have suggested that β-Mg2SiO4 might accept OH groups into its crystal structure and that the phase could be a source for H2O in the mantle. We have obtained micro-Raman and micro-infrared spectra for well-characterized samples of synthetic β-Mg2SiO4 that show a band at 3322 ± 3 cm-1 (and perhaps a weaker feature near 3580 cm-1, which suggests that OH groups are present in the nominally anhydrous sample. From the measured absorbance of the 3322 cm-1 feature, we estimate that the OH content of the β-Mg2SiO4 sample is approximately 0.06% by weight. Also as part of this study, Raman spectroscopy has been used to study the metastable reversion of β-Mg2SiO4 to structurally disordered forsterite at 580°C and a Raman spectrum for the hydrous high pressure phase B has been obtained. -Authors
Cyclic Autoencoder for Multimodal Data Alignment Using Custom Datasets

The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned under the premise of stabilizing the compressed image features, thus the high-dimensional features of different modal data are fused at the shallow level of the model. (ii) A residual attention mechanism that helps us improve the performance of the recognition. Regions of interest in the image are enhanced and regions of disinterest are weakened, thus we can extract the features of the text regions without further increasing the depth of the model (iii) a fully convolutional network for video subtitle recognition. We choose DenseNet-121 as the backbone network for feature extraction, which effectively enabling the recognition of video subtitles in complex backgrounds. The experiments are performed on our custom datasets, and the automatic and manual evaluation results show that our method reaches the state-of-the-art.
Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.
Pseudonym Mutable Based Privacy for 5G User Identity

Privacy, identity preserving and integrity have become key problems for telecommunication standards. Significant privacy threats are expected in 5G networks considering the large number of devices that will be deployed. As Internet of Things (IoT) and long-term evolution for machine type (LTE-m) are growing very fast with massive data traffic the risk of privacy attacks will be greatly increase. For all the above issues standards’ bodies should ensure users’ identity and privacy in order to gain the trust of service providers and industries. Against such threats, 5G specifications require a rigid and robust privacy procedure. Many research studies have addressed user privacy in 5G networks. This paper proposes a method to enhance user identity privacy in 5G systems through a scheme to protect the international mobile subscriber identity (IMSI) using a mutable mobile subscriber identity (MMSI) that changes randomly and avoids the exchange of IMSIs. It maintains authentication and key agreement (AKA) structure compatibility with previous mobile generations and improves user equipment (UE) synchronization with home networks. The proposed algorithm adds no computation overhead to UE or the network except a small amount in the home subscriber server (HSS). The proposed pseudonym mutable uses the XOR function to send the MMSI from the HSS to the UE which is reducing the encryption overhead significantly. The proposed solution was verified by ProVerif.