Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Phage Capsid in the Resistance to UV-C Radiations

The conformational variation of the viral capsid structure plays an essential role both for the environmental resistance and acid nuclear release during cellular infection. The aim of this study was to evaluate how capsid rearrangement in engineered phages of M13 protects viral DNA and peptide bonds from damage induced by UV-C radiation. From in silico 3D modelling analysis, two M13 engineered phage clones, namely P9b and 12III1, were chosen for (i) chemical features of amino acids sequences, (ii) rearrangements in the secondary structure of their pVIII proteins and (iii) in turn the interactions involved in phage capsid. Then, their resistance to UV-C radiation and hydrogen peroxide (H2O2) was compared to M13 wild-type vector (pC89) without peptide insert. Results showed that both the phage clones acquired an advantage against direct radiation damage, due to a reorganization of interactions in the capsid for an increase of H-bond and steric interactions. However, only P9b had an increase in resistance against H2O2. These results could help to understand the molecular mechanisms involved in the stability of new virus variants, also providing quick and necessary information to develop effective protocols in the virus inactivation for human activities, such as safety foods and animal-derived materials.
Redox Homeostasis in Pancreatic β-Cells: From Development to Failure

Redox status is a key determinant in the fate of β-cell. These cells are not primarily detoxifying and thus do not possess extensive antioxidant defense machinery. However, they show a wide range of redox regulating proteins, such as peroxiredoxins, thioredoxins or thioredoxin reductases, etc., being functionally compartmentalized within the cells. They keep fragile redox homeostasis and serve as messengers and amplifiers of redox signaling. β-cells require proper redox signaling already in cell ontogenesis during the development of mature β-cells from their progenitors. We bring details about redox-regulated signaling pathways and transcription factors being essential for proper differentiation and maturation of functional β-cells and their proliferation and insulin expression/maturation. We briefly highlight the targets of redox signaling in the insulin secretory pathway and focus more on possible targets of extracellular redox signaling through secreted thioredoxin1 and thioredoxin reductase1. Tuned redox homeostasis can switch upon chronic pathological insults towards the dysfunction of β-cells and to glucose intolerance. These are characteristics of type 2 diabetes, which is often linked to chronic nutritional overload being nowadays a pandemic feature of lifestyle. Overcharged β-cell metabolism causes pressure on proteostasis in the endoplasmic reticulum, mainly due to increased demand on insulin synthesis, which establishes unfolded protein response and insulin misfolding along with excessive hydrogen peroxide production. This together with redox dysbalance in cytoplasm and mitochondria due to enhanced nutritional pressure impact β-cell redox homeostasis and establish prooxidative metabolism. This can further affect β-cell communication in pancreatic islets through gap junctions. In parallel, peripheral tissues losing insulin sensitivity and overall impairment of glucose tolerance and gut microbiota establish local proinflammatory signaling and later systemic metainflammation, i.e., low chronic inflammation prooxidative properties, which target β-cells leading to their dedifferentiation, dysfunction and eventually cell death.
Annexin Animal Models—From Fundamental Principles to Translational Research

Routine manipulation of the mouse genome has become a landmark in biomedical research. Traits that are only associated with advanced developmental stages can now be investigated within a living organism, and the in vivo analysis of corresponding phenotypes and functions advances the translation into the clinical setting. The annexins, a family of closely related calcium (Ca2+)- and lipid-binding proteins, are found at various intra- and extracellular locations, and interact with a broad range of membrane lipids and proteins. Their impacts on cellular functions has been extensively assessed in vitro, yet annexin-deficient mouse models generally develop normally and do not display obvious phenotypes. Only in recent years, studies examining genetically modified annexin mouse models which were exposed to stress conditions mimicking human disease often revealed striking phenotypes. This review is the first comprehensive overview of annexin-related research using animal models and their exciting future use for relevant issues in biology and experimental medicine.
Getting Ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer

Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat’s ability to counter the black hat’s incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal’s tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE’s variants.
Two Be or Not Two Be: The Nuclear Autoantigen La/SS-B Is Able to Form Dimers and Oligomers in a Redox Dependent Manner

According to the literature, the autoantigen La is involved in Cap-independent translation. It was proposed that one prerequisite for this function is the formation of a protein dimer. However, structural analyses argue against La protein dimers. Noteworthy to mention, these structural analyses were performed under reducing conditions. Here we describe that La protein can undergo redox-dependent structural changes. The oxidized form of La protein can form dimers, oligomers and even polymers stabilized by disulfide bridges. The primary sequence of La protein contains three cysteine residues. Only after mutation of all three cysteine residues to alanine La protein becomes insensitive to oxidation, indicating that all three cysteines are involved in redox-dependent structural changes. Biophysical analyses of the secondary structure of La protein support the redox-dependent conformational changes. Moreover, we identified monoclonal anti-La antibodies (anti-La mAbs) that react with either the reduced or oxidized form of La protein. Differential reactivities to the reduced and oxidized form of La protein were also found in anti-La sera of autoimmune patients.
Applications of Big Data Analytics to Control COVID-19 Pandemic

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.
Honokiol Prevents Non-Alcoholic Steatohepatitis-Induced Liver Cancer via EGFR Degradation through the Glucocorticoid Receptor—MIG6 Axis

Simple Summary Non-alcoholic fatty liver disease (NAFLD) is a major health problem globally linked with the growing prevalence of metabolic syndrome. A subset of patients with NAFLD progress to non-alcoholic steatohepatitis (NASH), which increases the risk of hepatocellular carcinoma (HCC). However, the mechanisms responsible for the progression to HCC are unclear, and no preventative modalities have been developed. To address this issue, the present study used the natural compound honokiol to clarify the mechanism of this process. The results illustrated that epidermal growth factor receptor (EGFR) was upregulated in mice with NASH, and treatment with honokiol inhibited EGFR and the progression to HCC. Further analysis illustrated that honokiol increased glucocorticoid receptor (GR) nuclear translocation and mitogen-inducible gene 6 (MIG6)/ERBB receptor feedback inhibitor 1 (ERRFI1) expression, thereby promoting EGFR degradation. These findings were confirmed in tissues from patients with NASH and HCC. Abstract Non-alcoholic steatohepatitis (NASH) has become a serious public health problem associated with metabolic syndrome. The mechanisms by which NASH induces hepatocellular carcinoma (HCC) remain unknown. There are no approved drugs for treating NASH or preventing NASH-induced HCC. We used a genetic mouse model in which HCC was induced via high-fat diet feeding. This mouse model strongly resembles human NASH-induced HCC. The natural product honokiol (HNK) was tested for its preventative effects against NASH progression to HCC. Then, to clarify the mechanisms underlying HCC development, human HCC cells were treated with HNK. Human clinical specimens were also analyzed to explore this study’s clinical relevance. We found that epidermal growth factor receptor (EGFR) signaling was hyperactivated in the livers of mice with NASH and human HCC specimens. Inhibition of EGFR signaling by HNK drastically attenuated HCC development in the mouse model. Mechanistically, HNK accelerated the nuclear translocation of glucocorticoid receptor (GR) and promoted mitogen-inducible gene 6 (MIG6)/ERBB receptor feedback inhibitor 1 (ERRFI1) expression, leading to EGFR degradation and thereby resulting in robust tumor suppression. In human samples, EGFR-positive HCC tissues and their corresponding non-tumor tissues exhibited decreased ERRFI1 mRNA expression. Additionally, GR-positive non-tumor liver tissues displayed lower EGFR expression. Livers from patients with advanced NASH exhibited decreased ERRFI1 expression. EGFR degradation or inactivation represents a novel approach for NASH–HCC treatment and prevention, and the GR–MIG6 axis is a newly defined target that can be activated by HNK and related compounds.
Insights into muscle stem cell dynamics during postnatal development

During development, resident stem cell populations contribute to the growth and maturation of tissue and organs. In skeletal muscle, muscle stem cells, or satellite cells (SCs), are responsible for the maturation of postnatal myofibers. However, the role SCs play in later stages of postnatal growth, and thus, when they enter a mature quiescent state is controversial. Here, we discuss the current literature regarding the role SCs play in all stages of postnatal growth, from birth to puberty onset to young adulthood. We additionally highlight the implications of SC loss or dysfunction during developmental stages, both in the context of experimental paradigms and disease settings.
Bacteriophages of Shiga Toxin-Producing Escherichia coli and Their Contribution to Pathogenicity

Shiga toxins (Stx) of Shiga toxin-producing Escherichia coli (STEC) are generally encoded in the genome of lambdoid bacteriophages, which spend the most time of their life cycle integrated as prophages in specific sites of the bacterial chromosome. Upon spontaneous induction or induction by chemical or physical stimuli, the stx genes are co-transcribed together with the late phase genes of the prophages. After being assembled in the cytoplasm, and after host cell lysis, mature bacteriophage particles are released into the environment, together with Stx. As members of the group of lambdoid phages, Stx phages share many genetic features with the archetypical temperate phage Lambda, but are heterogeneous in their DNA sequences due to frequent recombination events. In addition to Stx phages, the genome of pathogenic STEC bacteria may contain numerous prophages, which are either cryptic or functional. These prophages may carry foreign genes, some of them related to virulence, besides those necessary for the phage life cycle. Since the production of one or more Stx is considered the major pathogenicity factor of STEC, we aim to highlight the new insights on the contribution of Stx phages and other STEC phages to pathogenicity.
Genome-Wide Association Studies and Prediction of Tan Spot (Pyrenophora tritici-repentis) Infection in European Winter Wheat via Different Marker Platforms

Tan spot, caused by the fungus Pyrenophora tritici-repentis (Ptr), is a severe foliar disease of wheat (Triticum aestivum L.). Improving genetic resistance is a durable strategy to reduce Ptr-related losses. Here, we dissected Ptr-infection’s genetic basis in 372 European wheat varieties via simple sequence repeats (SSRs) plus 35k and 90k single nucleotide polymorphism (SNP) marker platforms. In our phenotypic data analyses, Ptr infection showed a significant genotypic variance and a significant negative correlation with plant height. Genome-wide association studies revealed a highly quantitative nature of Ptr infection and identified two quantitative trait loci (QTL), viz., QTs.ipk-7A and QTs.ipk-7B, which imparted 21.23 and 5.84% of the genotypic variance, respectively. Besides, the Rht-D1 gene showed a strong allelic influence on the infection scores. Due to the complex genetic nature of the Ptr infection, the potential of genome-wide prediction (GP) was assessed via three different genetic models on individual and combined marker platforms. The GP results indicated that the marker density and marker platforms do not considerably impact prediction accuracy (~40–42%) and that higher-order epistatic interactions may not be highly pervasive. Our results provide a further understanding of Ptr-infection’s genetic nature, serve as a resource for marker-assisted breeding, and highlight the potential of genome-wide selection for improved Ptr resistance.
Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.
Understanding Plant Social Networking System: Avoiding Deleterious Microbiota but Calling Beneficials

Plant association with microorganisms elicits dramatic effects on the local phytobiome and often causes systemic and transgenerational modulation on plant immunity against insect pests and microbial pathogens. Previously, we introduced the concept of the plant social networking system (pSNS) to highlight the active involvement of plants in the recruitment of potentially beneficial microbiota upon exposure to insects and pathogens. Microbial association stimulates the physiological responses of plants and induces the development of their immune mechanisms while interacting with multiple enemies. Thus, beneficial microbes serve as important mediators of interactions among multiple members of the multitrophic, microscopic and macroscopic communities. In this review, we classify the steps of pSNS such as elicitation, signaling, secreting root exudates, and plant protection; summarize, with evidence, how plants and beneficial microbes communicate with each other; and also discuss how the molecular mechanisms underlying this communication are induced in plants exposed to natural enemies. Collectively, the pSNS modulates robustness of plant physiology and immunity and promotes survival potential by helping plants to overcome the environmental and biological challenges.
Review of Recent Microwave Planar Resonator-Based Sensors: Techniques of Complex Permittivity Extraction, Applications, Open Challenges and Future Research Directions

Recent developments in the field of microwave planar sensors have led to a renewed interest in industrial, chemical, biological and medical applications that are capable of performing real-time and non-invasive measurement of material properties. Among the plausible advantages of microwave planar sensors is that they have a compact size, a low cost and the ease of fabrication and integration compared to prevailing sensors. However, some of their main drawbacks can be considered that restrict their usage and limit the range of applications such as their sensitivity and selectivity. The development of high-sensitivity microwave planar sensors is required for highly accurate complex permittivity measurements to monitor the small variations among different material samples. Therefore, the purpose of this paper is to review recent research on the development of microwave planar sensors and further challenges of their sensitivity and selectivity. Furthermore, the techniques of the complex permittivity extraction (real and imaginary parts) are discussed based on the different approaches of mathematical models. The outcomes of this review may facilitate improvements of and an alternative solution for the enhancement of microwave planar sensors’ normalized sensitivity for material characterization, especially in biochemical and beverage industry applications.
Latest Comprehensive Medical Resource Consumption in Robot-Assisted versus Laparoscopic and Traditional Open Radical Prostatectomy: A Nationwide Population-Based Cohort Study

Simple Summary Few studies have evaluated long-term medical monetary cost in patients with prostate cancer (PC) receiving open radical prostatectomy (ORP), laparoscopic radical prostatectomy (LRP), or robot-assisted radical prostatectomy (RARP). To the best of our knowledge, this is the largest and longest follow-up study to examine medical monetary cost in patients with PC undergoing ORP, LRP, or RARP. After adjustment for confounders, the medical monetary cost in the RARP group was the least compared with that in the ORP and LRP groups. Abstract Background: Few studies have evaluated long-term medical monetary cost in patients with prostate cancer (PC) receiving open radical prostatectomy (ORP), laparoscopic radical prostatectomy (LRP), or robot-assisted radical prostatectomy (RARP). To the best of our knowledge, this is the largest and longest follow-up study to examine medical monetary cost in patients with PC undergoing ORP, LRP, or RARP. After adjustment for confounders, the medical monetary cost in the RARP group was the least compared with that in the ORP and LRP groups. Purpose: To estimate long-term medical resource consumption among patients with prostate cancer (PC) receiving open radical prostatectomy (ORP), laparoscopic radical prostatectomy (LRP), or robot-assisted radical prostatectomy (RARP). Patients and Methods: Participants were men enrolled in the Taiwan Cancer Registry with localized PC diagnosis who received radical prostatectomy. After adjustment for confounders, a generalized linear mixed model was used to determine significant differences in the number of urology outpatient clinic visits required, proportion of patients being hospitalized for urinary diseases or surgical complications, and medical reimbursement for urinary diseases or surgical complications following ORP, LRP, or RARP in the first, second, and third years. Results: No differences were observed in the median number of urology outpatient clinic visits between the three types of surgical modalities up to the second year after ORP, LRP, and RARP (median: 15, 10, and seven visits, respectively; p < 0.001), but significant differences were observed in the third year. Similarly, with RARP (10.9% versus 18.7% in ORP and 9.8% in LRP; p = 0.0014), the rate of hospitalization for urinary diseases or surgical complications decreased in the third year. Medical reimbursement for urinary diseases or surgical complications reduced after RARP compared with that for ORP and LRP, with approximately 22% reduction in the first year (p = 0.0052) and 20–40% reduction in the third year (p value = 0.0024). Conclusions: Medical resource consumption in the RARP group was less compared with those in the ORP and LRP groups.