Miracle-dz/UniFAR-FTUs huggingface dataset:
6,7,4′-Trihydroxyflavanone Mitigates Methamphetamine-Induced Neurotoxicity in SH-SY5y Cells via Nrf2/heme Oxyganase-1 and PI3K/Akt/mTOR Signaling Pathways

Methamphetamine (METH) is a synthetic psychostimulant drug that has detrimental effects on the health of its users. Although it has been investigated as a cause of neurodegenerative disease due to its neurotoxicity, whether small molecules derived from natural products attenuate these side effects remains elusive. 6,7,4′-trihydroxyflavanone (THF) is a flavanone family that possesses various pharmacological activities, including anti-rheumatic, anti-ischemic, anti-inflammatory, anti-osteoclastogenic, and protective effects against METH-induced deactivation of T cells. However, little is known about whether THF protects neuronal cells from METH-induced neurotoxicity. Here, we investigated the protective effects of THF on neurotoxicity induced by METH exposure by enhancing the Nrf2/HO-1 and PI3K/Akt/mTOR signaling pathways in SH-SY5y cells. Treatment with THF did not lead to cytotoxicity, but attenuated METH-induced neurotoxicity by modulating the expression of apoptosis-related proteins, METH-induced oxidative stress, and PI3K/Akt/mTOR phosphorylation in METH-exposed SH-SY5y cells. Moreover, we found THF induced Nrf2 nuclear translocation and HO-1 expression. An inhibitor assay confirmed that the induction of HO-1 by THF attenuates METH-induced neurotoxicity. Therefore, we suggest that THF preserves neuronal cells from METH-induced neurotoxicity by upregulating HO-1 expression through the Nrf2 and PI3K/Akt/mTOR signaling pathways. Thus, THF has therapeutic potential for use in the treatment of METH-addicts suffering from neurodegenerative diseases.
Melatonin in Cancer Treatment: Current Knowledge and Future Opportunities

Melatonin is a pleotropic molecule with numerous biological activities. Epidemiological and experimental studies have documented that melatonin could inhibit different types of cancer in vitro and in vivo. Results showed the involvement of melatonin in different anticancer mechanisms including apoptosis induction, cell proliferation inhibition, reduction in tumor growth and metastases, reduction in the side effects associated with chemotherapy and radiotherapy, decreasing drug resistance in cancer therapy, and augmentation of the therapeutic effects of conventional anticancer therapies. Clinical trials revealed that melatonin is an effective adjuvant drug to all conventional therapies. This review summarized melatonin biosynthesis, availability from natural sources, metabolism, bioavailability, anticancer mechanisms of melatonin, its use in clinical trials, and pharmaceutical formulation. Studies discussed in this review will provide a solid foundation for researchers and physicians to design and develop new therapies to treat and prevent cancer using melatonin.
On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.
Antioxidant Content of Selected Medicinal Plants Used by Kaani Tribes of Kanyakumari District in Tamilnadu India

Objectives: Despite the great advances observed in modern medicine in recent decades, plants still make an important contribution to health care. The objectives of this study were to determine the moisture content and antioxidant capacity of the selected medicinal plants. Methods and Materials: The medicinal plants (20) were collected from Kanyakumari district of Tamilnadu. The moisture content of the medicinal plants was analyzed using Infra red moisture analyzer. For the analysis of antioxidants by DPPH (2,2-diphenyl-1-picryl hydrazyl assay) & FRAP (Ferric Reducing Antioxidant Power), phenol by Folin-ciocalteau method and flavonoids by aluminium chloride method the leaves was shade dried, powdered and stored in brown bottle containers. The aqueous and ethanol extracts of the medicinal plants were evaluated for their antioxidant capacity All recorded values of Moisture and Antioxidant analysis are mean of triplicates. Results and Discussion: The moisture content of the selected medicinal plants ranged from 36.00 to 89.86%. Blepharis Maderaspatens had highest DPPH activity in both aqueous (8.870) and ethanolic extracts (71.82) expressed in mg of GAE/g dry weight. The antioxidant activity AOA was 5.6 times higher in ethanolic extracts. The Mean DPPH activity of the selected medicinal plants was found to be 3.402±2.22 in aqueous extract and 19.114±17.01 in ethanolic extract. In FRAP, Blepharis Maderaspatens (633.76) had the highest AOA in ethanol extracts and Valarai (300.70) in water extracts expressed in μM of (Ascorbic Acid Equivalence Capacity) AAEC/g. The Mean FRAP value of the selected medicinal plants was found to be 145.44±129.53 in ethanolic extract and 117.96±79.17 in aqueous extracts. Blepharis Maderaspatens had the highest phenolic content in both aqueous (12.8) and ethanolic extracts (35.6). Conclusion: The study concluded that there was a wide difference in antioxidant content among the medicinal plants. The ethanolic extracts revealed the presence of high concentrations of AOA, Total Phenol Content (TPC), and Total Flavanoid Content (TFC) in the medicinal plants than water. Further investigation in quantification of individual antioxidant components in these medicinal plants possessing good free radical scavenging ability is needed.
Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning

In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions.
Extracellular Vesicle Associated Non-Coding RNAs in Lung Infections and Injury

Extracellular vesicles (EVs) refer to a heterogenous population of membrane-bound vesicles that are released by cells under physiological and pathological conditions. The detection of EVs in the majority of the bodily fluids, coupled with their diverse cargo comprising of DNA, RNA, lipids, and proteins, have led to the accumulated interests in leveraging these nanoparticles for diagnostic and therapeutic purposes. In particular, emerging studies have identified enhanced levels of a wide range of specific subclasses of non-coding RNAs (ncRNAs) in EVs, thereby suggesting the existence of highly selective and regulated molecular processes governing the sorting of these RNAs into EVs. Recent studies have also illustrated the functional relevance of these enriched ncRNAs in a variety of human diseases. This review summarizes the current state of knowledge on EV-ncRNAs, as well as their functions and significance in lung infection and injury. As a majority of the studies on EV-ncRNAs in lung diseases have focused on EV-microRNAs, we will particularly highlight the relevance of these molecules in the pathophysiology of these conditions, as well as their potential as novel biomarkers therein. We also outline the current challenges in the EV field amidst the tremendous efforts to propel the clinical utility of EVs for human diseases. The lack of published literature on the functional roles of other EV-ncRNA subtypes may in turn provide new avenues for future research to exploit their feasibility as novel diagnostic and therapeutic targets in human diseases.
Efficacy of live attenuated and inactivated influenza vaccines among children in rural India: A 2-year, randomized, triple-blind, placebo-controlled trial

Background Influenza is a cause of febrile acute respiratory infection (FARI) in India; however, few influenza vaccine trials have been conducted in India. We assessed absolute and relative efficacy of live attenuated influenza vaccine (LAIV) and inactivated influenza vaccine (IIV) among children aged 2 to 10 years in rural India through a randomized, triple-blind, placebo-controlled trial conducted over 2 years. Methods and findings In June 2015, children were randomly allocated to LAIV, IIV, intranasal placebo, or inactivated polio vaccine (IPV) in a 2:2:1:1 ratio. In June 2016, vaccination was repeated per original allocation. Overall, 3,041 children received LAIV (n = 1,015), IIV (n = 1,010), nasal placebo (n = 507), or IPV (n = 509). Mean age of children was 6.5 years with 20% aged 9 to 10 years. Through weekly home visits, nasal and throat swabs were collected from children with FARI and tested for influenza virus by polymerase chain reaction. The primary outcome was laboratory-confirmed influenza-associated FARI; vaccine efficacy (VE) was calculated using modified intention-to-treat (mITT) analysis by Cox proportional hazards model (PH) for each year. In Year 1, VE was 40.0% (95% confidence interval (CI) 25.2 to 51.9) for LAIV and 59.0% (95% CI 47.8 to 67.9) for IIV compared with controls; relative efficacy of LAIV compared with IIV was −46.2% (95% CI −88.9 to −13.1). In Year 2, VE was 51.9% (95% CI 42.0 to 60.1) for LAIV and 49.9% (95% CI 39.2 to 58.7) for IIV; relative efficacy of LAIV compared with IIV was 4.2% (95% CI −19.9 to 23.5). No serious adverse vaccine-attributable events were reported. Study limitations include differing dosage requirements for children between nasal and injectable vaccines (single dose of LAIV versus 2 doses of IIV) in Year 1 and the fact that immunogenicity studies were not conducted. Conclusions In this study, we found that LAIV and IIV vaccines were safe and moderately efficacious against influenza virus infection among Indian children. Trial registration Clinical Trials Registry of India CTRI/2015/06/005902.
Buzzing Homes: Using Citizen Science Data to Explore the Effects of Urbanization on Indoor Mosquito Communities

Simple Summary Many mosquito species can transmit pathogens and may pose a risk to human health. With increasing urbanization and alteration of natural habitats, the composition of mosquito communities is changing, with some species thriving particularly well in and adjacent to human settlements. In the present study, indoor mosquito collections submitted to the citizen science project ‘Mückenatlas’ were used to investigate the composition, abundance, and diversity of species of different urbanization levels, and to detect preferences for less or more urbanized areas. We found that species richness and diversity decreases with increasing urbanization, and some important vector species are captured most frequently in densely urbanized regions. Our results highlight the importance of long-term mosquito monitoring to learn how these vectors respond to habitat change caused by humans. Only with sufficient knowledge about the ecology of mosquitoes can we assess risks, plan counter strategies, and take action. Abstract Urbanization has been associated with a loss of overall biodiversity and a simultaneous increase in the abundance of a few species that thrive in urban habitats, such as highly adaptable mosquito vectors. To better understand how mosquito communities differ between levels of urbanization, we analyzed mosquito samples from inside private homes submitted to the citizen science project ‘Mückenatlas’. Applying two urbanization indicators based on soil sealing and human population density, we compared species composition and diversity at, and preferences towards, different urbanization levels. Species composition between groups of lowest and highest levels of urbanization differed significantly, which was presumably caused by reduced species richness and the dominance of synanthropic mosquito species in urban areas. The genus Anopheles was frequently submitted from areas with a low degree of urbanization, Aedes with a moderate degree, and Culex and Culiseta with a high degree of urbanization. Making use of citizen science data, this first study of indoor mosquito diversity in Germany demonstrated a simplification of communities with increasing urbanization. The dominance of vector-competent species in urban areas poses a potential risk of epidemics of mosquito-borne diseases that can only be contained by a permanent monitoring of mosquitoes and by acquiring a deeper knowledge about how anthropogenic activities affect vector ecology.
LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Central to any medical domain is the challenging patient to medical professional assignment task, aimed at getting the right patient to the right medical professional at the right time. This task is highly complex and involves partially conflicting objectives such as minimizing patient wait-time while providing maximal level of care. To tackle this challenge, medical institutions apply common scheduling heuristics to guide their decisions. These generic heuristics often do not align with the expectations of each specific medical institution. In this article, we propose a novel learning-based online optimization approach we term Learning-Based Assignment (LBA), which provides decision makers with a tailored, data-centered decision support algorithm that facilitates dynamic, institution-specific multi-variate decisions, without altering existing medical workflows. We adapt our generic approach to two medical settings: (1) the assignment of patients to caregivers in an emergency department; and (2) the assignment of medical scans to radiologists. In an extensive empirical evaluation, using real-world data and medical experts’ input from two distinctive medical domains, we show that our proposed approach provides a dynamic, robust and configurable data-driven solution which can significantly improve upon existing medical practices.
A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.
Zebrafish Models to Study New Pathways in Tauopathies

Tauopathies represent a vast family of neurodegenerative diseases, the most well-known of which is Alzheimer’s disease. The symptoms observed in patients include cognitive deficits and locomotor problems and can lead ultimately to dementia. The common point found in all these pathologies is the accumulation in neural and/or glial cells of abnormal forms of Tau protein, leading to its aggregation and neurofibrillary tangles. Zebrafish transgenic models have been generated with different overexpression strategies of human Tau protein. These transgenic lines have made it possible to highlight Tau interacting factors or factors which may limit the neurotoxicity induced by mutations and hyperphosphorylation of the Tau protein in neurons. Several studies have tested neuroprotective pharmacological approaches. On few-days-old larvae, modulation of various signaling or degradation pathways reversed the deleterious effects of Tau mutations, mainly hTauP301L and hTauA152T. Live imaging and live tracking techniques as well as behavioral follow-up enable the analysis of the wide range of Tau-related phenotypes from synaptic loss to cognitive functional consequences.
A New Tool to Study Parkinsonism in the Context of Aging: MPTP Intoxication in a Natural Model of Multimorbidity

The diurnal rodent Octodon degus (O. degus) is considered an attractive natural model for Alzheimer’s disease and other human age-related features. However, it has not been explored so far if the O. degus could be used as a model to study Parkinson’s disease. To test this idea, 10 adult male O. degus were divided into control group and MPTP-intoxicated animals. Motor condition and cognition were examined. Dopaminergic degeneration was studied in the ventral mesencephalon and in the striatum. Neuroinflammation was also evaluated in the ventral mesencephalon, in the striatum and in the dorsal hippocampus. MPTP animals showed significant alterations in motor activity and in visuospatial memory. Postmortem analysis revealed a significant decrease in the number of dopaminergic neurons in the ventral mesencephalon of MPTP animals, although no differences were found in their striatal terminals. We observed a significant increase in neuroinflammatory responses in the mesencephalon, in the striatum and in the hippocampus of MPTP-intoxicated animals. Additionally, changes in the subcellular expression of the calcium-binding protein S100β were found in the astrocytes in the nigrostriatal pathway. These findings prove for the first time that O. degus are sensitive to MPTP intoxication and, therefore, is a suitable model for experimental Parkinsonism in the context of aging.
Utilising Induced Pluripotent Stem Cells in Neurodegenerative Disease Research: Focus on Glia

Induced pluripotent stem cells (iPSCs) are a self-renewable pool of cells derived from an organism’s somatic cells. These can then be programmed to other cell types, including neurons. Use of iPSCs in research has been two-fold as they have been used for human disease modelling as well as for the possibility to generate new therapies. Particularly in complex human diseases, such as neurodegenerative diseases, iPSCs can give advantages over traditional animal models in that they more accurately represent the human genome. Additionally, patient-derived cells can be modified using gene editing technology and further transplanted to the brain. Glial cells have recently become important avenues of research in the field of neurodegenerative diseases, for example, in Alzheimer’s disease and Parkinson’s disease. This review focuses on using glial cells (astrocytes, microglia, and oligodendrocytes) derived from human iPSCs in order to give a better understanding of how these cells contribute to neurodegenerative disease pathology. Using glia iPSCs in in vitro cell culture, cerebral organoids, and intracranial transplantation may give us future insight into both more accurate models and disease-modifying therapies.
An Argon-Ion-Induced Pale Green Mutant of Arabidopsis Exhibiting Rapid Disassembly of Mesophyll Chloroplast Grana

Argon-ion beam is an effective mutagen capable of inducing a variety of mutation types. In this study, an argon ion-induced pale green mutant of Arabidopsis thaliana was isolated and characterized. The mutant, designated Ar50-33-pg1, exhibited moderate defects of growth and greening and exhibited rapid chlorosis in photosynthetic tissues. Fluorescence microscopy confirmed that mesophyll chloroplasts underwent substantial shrinkage during the chlorotic process. Genetic and whole-genome resequencing analyses revealed that Ar50-33-pg1 contained a large 940 kb deletion in chromosome V that encompassed more than 100 annotated genes, including 41 protein-coding genes such as TYRAAt1/TyrA1, EGY1, and MBD12. One of the deleted genes, EGY1, for a thylakoid membrane-localized metalloprotease, was the major contributory gene responsible for the pale mutant phenotype. Both an egy1 mutant and F1 progeny of an Ar50-33-pg1 × egy1 cross-exhibited chlorotic phenotypes similar to those of Ar50-33-pg1. Furthermore, ultrastructural analysis of mesophyll cells revealed that Ar50-33-pg1 and egy1 initially developed wild type-like chloroplasts, but these were rapidly disassembled, resulting in thylakoid disorganization and fragmentation, as well as plastoglobule accumulation, as terminal phenotypes. Together, these data support the utility of heavy-ion mutagenesis for plant genetic analysis and highlight the importance of EGY1 in the structural maintenance of grana in mesophyll chloroplasts.