Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Classification of Glioma Subgroup, Which Is Highly Correlated With the Clinical Characteristics and Tumor Tissue Characteristics, Based on the Expression Levels of Gβ and Gγ Genes

Purpose Glioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be a reference standard for subgroup classification. Glioma has been diagnosed based on histopathology, grade, and molecular markers including IDH mutation, chromosome 1p/19q loss, and H3K27M mutation. This subgroup classification cannot fully meet the current needs of clinicians and researchers. We, therefore, present a new subgroup classification for glioma based on the expression levels of Gβ and Gγ genes to complement studies on glioma and Gβγ subunits, and to support clinicians to assess a patient’s tumor status. Methods Glioma samples retrieved from the CGGA database and the TCGA database. We clustered the gliomas into different groups by using expression values of Gβ and Gγ genes extracted from RNA sequencing data. The Kaplan–Meier method with a two-sided log-rank test was adopted to compare the OS of the patients between GNB2 group and non-GNB2 group. Univariate Cox regression analysis was referred to in order to investigate the prognostic role of each Gβ and Gγ genes. KEGG and ssGSEA analysis were applied to identify highly activated pathways. The “estimate” package, “GSVA” package, and the online analytical tools CIBERSORTx were employed to evaluate immune cell infiltration in glioma samples. Results Three subgroups were identified. Each subgroup had its own specific pathway activation pattern and other biological characteristics. High M2 cell infiltration was observed in the GNB2 subgroup. Different subgroups displayed different sensitivities to chemotherapeutics. GNB2 subgroup predicted poor survival in patients with gliomas, especially in patients with LGG with mutation IDH and non-codeleted 1p19q. Conclusion The subgroup classification we proposed has great application value. It can be used to select chemotherapeutic drugs and the prognosis of patients with target gliomas. The unique relationships between subgroups and tumor-related pathways are worthy of further investigation to identify therapeutic Gβγ heterodimer targets.
APP, PSEN1, and PSEN2 Variants in Alzheimer’s Disease: Systematic Re-evaluation According to ACMG Guidelines

The strategies of classifying APP, PSEN1, and PSEN2 variants varied substantially in the previous studies. We aimed to re-evaluate these variants systematically according to the American college of medical genetics and genomics and the association for molecular pathology (ACMG-AMP) guidelines. In our study, APP, PSEN1, and PSEN2 variants were collected by searching Alzforum and PubMed database with keywords “PSEN1,” “PSEN2,” and “APP. ” These variants were re-evaluated based on the ACMG-AMP guidelines. We compared the number of pathogenic/likely pathogenic variants of APP, PSEN1, and PSEN2. In total, 66 APP variants, 323 PSEN1 variants, and 63 PSEN2 variants were re-evaluated in our study. 94.91% of previously reported pathogenic variants were re-classified as pathogenic/likely pathogenic variants, while 5.09% of them were variants of uncertain significance (VUS). PSEN1 carried the most prevalent pathogenic/likely pathogenic variants, followed by APP and PSEN2. Significant statistically difference was identified among these three genes when comparing the number of pathogenic/likely pathogenic variants (P < 2.2 × 10–16). Most of the previously reported pathogenic variants were re-classified as pathogenic/likely pathogenic variants while the others were re-evaluated as VUS, highlighting the importance of interpreting APP, PSEN1, and PSEN2 variants with caution according to ACMG-AMP guidelines.
Effects of exogenous oxalic acid on ripening and decay incidence in mango fruit during storage at controlled atmosphere

Experiment was carried out with the fruit of Shengxin mango cultivar (Mangifera indica L.). The fruit were dipped in 0 (as control) and 5 mmol/L oxalic acid solutions for 10 min, respectively, and then stored at controlled atmosphere(6% CO2+ 2% O2, 14±1℃). Changes in flesh firmness, soluble solid content (SSC), pH value, disease index and fruit decay, production of active oxygen species (AOS), such as superoxide ( ) and hydrogen peroxide (H2O2), content of reduced forms of ascorbate (ASA) and glutathione (GSH), and superoxide dismutase (SOD) activity in fruit were investigated during storage. The results showed that, as compared with control, in treated fruit, flesh firmness presented high, increases of SSC content were alleviated, and disease index and fruit decay decreased as well. Moreover, AOS production was inhibited, and ASA loss was reduced and increasing SOD activity was induced by oxalic acid treatment. It was suggested that the effects of exogenous oxalic acid on enhancement of antioxidant capacity and decrease of AOS production were associated with retardation of the ripening process and decrease of decay incidence in mango fruit during storage, and oxalic acid treatment was an alternative method for prolonging the storage life of mango fruit.
The Paraventricular Thalamus as a Critical Node of Motivated Behavior via the Hypothalamic-Thalamic-Striatal Circuit

In this review, we highlight evidence that supports a role for the paraventricular nucleus of the thalamus (PVT) in motivated behavior. We include a neuroanatomical and neurochemical overview, outlining what is known of the cellular makeup of the region and its most prominent afferent and efferent connections. We discuss how these connections and distinctions across the anterior-posterior axis correspond to the perceived function of the PVT. We then focus on the hypothalamic-thalamic-striatal circuit and the neuroanatomical and functional placement of the PVT within this circuit. In this regard, the PVT is ideally positioned to integrate information regarding internal states and the external environment and translate it into motivated actions. Based on data that has emerged in recent years, including that from our laboratory, we posit that orexinergic (OX) innervation from the lateral hypothalamus (LH) to the PVT encodes the incentive motivational value of reward cues and thereby alters the signaling of the glutamatergic neurons projecting from the PVT to the shell of the nucleus accumbens (NAcSh). The PVT-NAcSh pathway then modulates dopamine activity and resultant cue-motivated behaviors. As we and others apply novel tools and approaches to studying the PVT we will continue to refine the anatomical, cellular, and functional definitions currently ascribed to this nucleus and further elucidate its role in motivated behaviors.
Bandgap Engineering and Near-Infrared-II Optical Properties of Monolayer MoS2: A First-Principle Study

The fluorescence-based optical imaging in the second near-infrared region (NIR-II, 1,000–1,700 nm) has broad applications in the biomedical field, but it is still difficult to find new NIR-II fluorescence materials in the two dimension. As a crucial characteristic of the electronic structure, the band structure determines the fundamental properties of two-dimensional materials, such as their optical excitations and electronic transportation. Therefore, we calculated the electronic structures and optical properties of different crystalline phases (1T phase and 2H phase) of pure monolayer MoS2 films and found that the 1T phase has better absorption and thus better fluorescence in the NIR-II window. However, its poor stability makes the 1T-phase MoS2 less useful in vivo bioimaging. By introducing vacancy defects and doping with foreign atoms, we successfully tuned the bandgap of the monolayer 2H-MoS2 and activated it in the NIR-II. Our results show that by engineering the vacancy defects, the bandgap of the 2H phase can be tailored to around 1 eV, and there are three candidates of vacancy structures that exhibit strong absorption in the NIR-II.
Single-Target Regulators Constitute the Minority Group of Transcription Factors in Escherichia coli K-12

The identification of regulatory targets of all transcription factors (TFs) is critical for understanding the entire network of genome regulation. A total of approximately 300 TFs exist in the model prokaryote Escherichia coli K-12, but the identification of whole sets of their direct targets is impossible with use of in vivo approaches. For this end, the most direct and quick approach is to identify the TF-binding sites in vitro on the genome. We then developed and utilized the gSELEX screening system in vitro for identification of more than 150 E. coli TF-binding sites along the E. coli genome. Based on the number of predicted regulatory targets, we classified E. coli K-12 TFs into four groups, altogether forming a hierarchy ranging from a single-target TF (ST-TF) to local TFs, global TFs, and nucleoid-associated TFs controlling as many as 1,000 targets. Using the collection of purified TFs and a library of genome DNA segments from a single and the same E. coli K-12, we identified here a total of 11 novel ST-TFs, CsqR, CusR, HprR, NorR, PepA, PutA, QseA, RspR, UvrY, ZraR, and YqhC. The regulation of single-target promoters was analyzed in details for the hitherto uncharacterized QseA and RspR. In most cases, the ST-TF gene and its regulatory target genes are adjacently located on the E. coli K-12 genome, implying their simultaneous transfer in the course of genome evolution. The newly identified 11 ST-TFs and the total of 13 hitherto identified altogether constitute the minority group of TFs in E. coli K-12.
The Enterovirus Theory of Disease Etiology in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Critical Review

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system disease whose etiological basis has not been established. Enteroviruses (EVs) as a cause of ME/CFS have sometimes been proposed, as they are known agents of acute respiratory and gastrointestinal infections that may persist in secondary infection sites, including the central nervous system, muscle, and heart. To date, the body of research that has investigated enterovirus infections in relation to ME/CFS supports an increased prevalence of chronic or persistent enteroviral infections in ME/CFS patient cohorts than in healthy individuals. Nevertheless, inconsistent results have fueled a decline in related studies over the past two decades. This review covers the aspects of ME/CFS pathophysiology that are consistent with a chronic enterovirus infection and critically reviews methodologies and approaches used in past EV-related ME/CFS studies. We describe the prior sample types that were interrogated, the methods used and the limitations to the approaches that were chosen. We conclude that there is considerable evidence that prior outbreaks of ME/CFS were caused by one or more enterovirus groups. Furthermore, we find that the methods used in prior studies were inadequate to rule out the presence of chronic enteroviral infections in individuals with ME/CFS. Given the possibility that such infections could be contributing to morbidity and preventing recovery, further studies of appropriate biological samples with the latest molecular methods are urgently needed.
Identification of Early Diagnostic and Prognostic Biomarkers via WGCNA in Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, and the outcome of the patients remains dismal for the lack of effective biomarkers of early detection. Recent studies have elucidated the landscape of genomic alterations of gastric cancer and reveal some biomarkers of advanced-stage gastric cancer, however, information about early-stage biomarkers is limited. Here, we adopt Weighted Gene Co-expression Network Analysis (WGCNA) to screen potential biomarkers for early-stage STAD using RNA-Seq and clinical data from TCGA database. We find six gene clusters (or modules) are significantly correlated with the stage-I STADs. Among these, five hub genes, i.e., MS4A1, THBS2, VCAN, PDGFRB, and KCNA3 are identified and significantly de-regulated in the stage-I STADs compared with the normal stomach gland tissues, which suggests they can serve as potential early diagnostic biomarkers. Moreover, we show that high expression of VCAN and PDGFRB is associated with poor prognosis of STAD. VCAN encodes a large chondroitin sulfate proteoglycan that is the main component of the extracellular matrix, and PDGFRB encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor (PDGF) family. Consistently, Gene Ontology (GO) analysis of differentially expressed genes in the STADs indicates terms associated with extracellular matrix and receptor ligand activity are significantly enriched. Protein-protein network interaction analysis (PPI) and Gene Set Enrichment Analysis (GSEA) further support the core role of VCAN and PDGFRB in the tumorigenesis. Collectively, our study identifies the potential biomarkers for early detection and prognosis of STAD.
The Effects of Calcium, Phosphate, and Vitamin D

Secondary hyperparathyroidism is characterized by an increase in parathyroid (PT) cell number, and parathyroid hormone (PTH) synthesis and secretion. It is still unknown as to what stimuli regulate PT cell proliferation and how they do this. We have studied rats with dietary-induced secondary hyper- and hypoparathyroidism, rats given 1,25dihydroxyvitamin D3 (1,25(OH)2D3) and rats after 5/6 nephrectomy for the presence of PT cell proliferation and apoptosis. PT cell proliferation has been measured by staining for proliferating cell nuclear antigen (PCNA) and apoptosis by in situ detection ofnuclearDNA fragmentation and correlated with serum biochemistry and PTH mRNA levels. A low calcium diet led to increased levels of PTH mRNA and a 10-fold increase in PT cell proliferation. A low phosphate diet led to decreased levels of PTH mRNA and the complete absence of PT cell proliferation.
(ROPO) BEHAVIOUR OF CONSUMERS – SOUTH INDIA PERSPECTIVE

P. BALATHANDAYUTHAM Assistant professor Department of Business Administration Annamalai University DR. R. SRITHARAN Assistant Professor Department of Business Administration Annamalai University Abstract In the past, individual had to rely on traditional media that were non-interactive to learn about products and services. Companies could communicate and market their goods and services to consumer through media vehicles such as television, newspapers, and magazines. Moreover consumers could not communicate with the vendors through the same means nor did they have control over what they heard or read. Now a new and interactive medium has been developed with eliminated some of the disadvantages of traditional forms of media. This revolutionary non-traditional form of media is known as World Wide Web. ROPO behavior is one of the recent topic in consumer behaviour, that is to be studied to develop a strong strategies in buyer behaviour field of study. This study made an attempt to identify the important strategies to measure the ROPO behaviour.
Monogenic Autoinflammatory Diseases: State of the Art and Future Perspectives

Systemic autoinflammatory diseases are a heterogeneous family of disorders characterized by a dysregulation of the innate immune system, in which sterile inflammation primarily develops through antigen-independent hyperactivation of immune pathways. In most cases, they have a strong genetic background, with mutations in single genes involved in inflammation. Therefore, they can derive from different pathogenic mechanisms at any level, such as dysregulated inflammasome-mediated production of cytokines, intracellular stress, defective regulatory pathways, altered protein folding, enhanced NF-kappaB signalling, ubiquitination disorders, interferon pathway upregulation and complement activation. Since the discover of pathogenic mutations of the pyrin-encoding gene MEFV in Familial Mediterranean Fever, more than 50 monogenic autoinflammatory diseases have been discovered thanks to the advances in genetic sequencing: the advent of new genetic analysis techniques and the discovery of genes involved in autoinflammatory diseases have allowed a better understanding of the underlying innate immunologic pathways and pathogenetic mechanisms, thus opening new perspectives in targeted therapies. Moreover, this field of research has become of great interest, since more than a hundred clinical trials for autoinflammatory diseases are currently active or recently concluded, allowing us to hope for considerable acquisitions for the next few years. General paediatricians need to be aware of the importance of this group of diseases and they should consider autoinflammatory diseases in patients with clinical hallmarks, in order to guide further examinations and refer the patient to a specialist rheumatologist. Here we resume the pathogenesis, clinical aspects and diagnosis of the most important autoinflammatory diseases in children.
AUGMENTED REALITY AND VISUAL ARTS

This research explores the use of augmented reality technologies in the field of visual arts, focusing on the way that visitors and artwork interact. The bibliographic research focuses on exploring the definitions of augmented reality and interaction. The historical overview defines the two above-mentioned fields and looks into their connection with art, via exploration of their visual characteristics. We present the implementation methodology for such enhanced art-work and the process of interaction with the observer. Our research extends beyond typical presentation and citation of augmented and interactive visual works as it sets two main objectives a) discuss the way the artistic work which use augmented reality technologies interacts with the visitor, as well as b) pres-ent how human senses are stimulated through visual art work when new interactive augmented reality technologies are employed.
PASAN: Detecting Peripheral Access Concurrency Bugs within Bare-Metal Embedded Applications

Concurrency bugs might be one of the most challenging software defects to detect and debug due to their non-deterministic triggers caused by task scheduling and interrupt handling. While different tools have been proposed to address concurrency issues, protecting peripherals in embedded systems from concurrent accesses impose unique challenges. A naïve lock protection on a certain memory-mapped I/O (MMIO) address still allows concurrent accesses to other MMIO addresses of a peripheral. Meanwhile, embedded peripherals such as sensors often employ some internal state machines to achieve certain functionalities. As a result, improper locking can lead to the corruption of peripherals’ on-going jobs (we call transaction corruption ) thus corrupted sensor values or failed jobs. In this paper, we propose a static analysis tool namely PAS AN to detect peripheral access concurrency issues for embedded systems. PAS AN automatically ﬁnds the MMIO address range of each peripheral device using the parser-ready memory layout documents, extracts the peripheral’s internal state machines using the corresponding device drivers, and detects concurrency bugs of peripheral accesses automatically. We evaluate PAS AN on seven different embedded platforms, including multiple real time operating systems (RTOSes) and robotic aerial vehicles (RAVs). PAS AN found 17 true positive concurrency bugs in total from three different platforms with the bug detection rates ranging from 40% to 100%. We have reported all our ﬁndings to the corresponding parties. To the best of our knowledge, PAS AN is the ﬁrst static analysis tool detecting the intrinsic problems in concurrent peripheral accesses for embedded systems.
Detection of DoS/DDoS attacks: the UBM and GMM approach

There are many different kinds of traditional techniques used for DoS/DDos attack detection, some of them include Artificial Intelligence, IDS, DPI, and most of them are well known and have remained unchanged during the last few years. In this work we implement two novel techniques called GMM and UBM, which are normally used in other scientific or engineering areas, to detect DoS/DDos cyberattacks using a real traffic dataset (CICIDS2017). Three experimental scenarios were implemented, including UBM, GMM, and a Random Forest alternative. The obtained results show new opportunities to use these novel methods in other approaches and explore new solutions to important problems like DoS/DDoS cyber-attack detection, which are closely related to a lot of services on the current Internet. The main goal of this work is to show the potential of UBM and GMM techniques in this simple problem and explore new applications in more complex scenarios.
REALIZATION OF SENSING APPLICATIONS IN NANOPHOTONIC WAVEGUIDE USING CyGEL SOLUTION

The vast increase in the amount of data transfer around the world each day, there has been a necessity for rapid development in optical communication technologies. Using an all optical scheme to replace the current electronic format shows promise in the next generation for optical communication. On viewing this, people from different areas are dong many works for the development for photonic circuits. Specific to the realm of photonics, there has been considerable effort to create wave guiding structures capable of sustaining high-density photonic integrated circuits. With demand for increased speed and bandwidth being the main impetus, innovative propagation mechanisms are necessary to supplant conventional techniques limited by the diffraction limit. In addition, there is propagation loss to contend with during guiding through passive components as well as wave dispersion at transmission bit rates approaching 40 Gbps and higher.
Meta-Active Learning for Node Response Prediction in Graphs

Meta-learning is an important approach to improve machine learning performance with a limited number of observations for target tasks. However, when observations are unbalancedly obtained, it is difficult to improve the performance even with meta-learning methods. In this paper, we propose an active learning method for meta-learning on node response prediction tasks in attributed graphs, where nodes to observe are selected to improve performance with as few observed nodes as possible. With the proposed method, we use models based on graph convolutional neural networks for both predicting node responses and selecting nodes, by which we can predict responses and select nodes even for graphs with unseen response variables. The response prediction model is trained by minimizing the expected test error. The node selection model is trained by maximizing the expected error reduction with reinforcement learning. We demonstrate the effectiveness of the proposed method with 11 types of road congestion prediction tasks.