Miracle-dz/UniFAR-FTUs huggingface dataset:
A Zn2+-triggered two-step mechanism of CLIC1 membrane insertion and activation into chloride channels

ABSTRACT The chloride intracellular channel (CLIC) protein family displays the unique feature of altering its structure from a soluble form to a membrane-bound chloride channel. CLIC1, a member of this family, is found in the cytoplasm or in internal and plasma membranes, with membrane relocalisation linked to endothelial disfunction, tumour proliferation and metastasis. The molecular switch promoting CLIC1 activation remains under investigation. Here, cellular Cl− efflux assays and immunofluorescence microscopy studies have identified intracellular Zn2+ release as the trigger for CLIC1 activation and membrane insertion. Biophysical assays confirmed specific binding to Zn2+, inducing membrane association and enhancing Cl− efflux in a pH-dependent manner. Together, our results identify a two-step mechanism with Zn2+ binding as the molecular switch promoting CLIC1 membrane insertion, followed by pH-mediated activation of Cl− efflux.
JAGUAR AND PUMA IN BRAZILIAN SEMI-ARID REGION – SCAPEGOATS FOR WEAK GOVERNANCE?

Exclusively Brazilian, the Caatinga is a seasonally dry tropical forest where the endangered jaguar (Panthera onca) and puma (Puma concolor) co-occur with the lowest regional Human Development Indexes. New land uses challenge traditional livelihoods and add threats to species historically poached in retaliation for livestock depredation. Chronicle biodiversity conflicts became acute after a reported increase in depredation allegedly because of those changes and conflicts among stakeholders. Using the framework of human dimensions of wildlife management, pioneer research on the vulnerabilities of rural communities to jaguar-and-puma conflicts was led in ‘Boqueirão da Onça’, within and surrounding that polygon of protected areas. The aim was to identify, describe, understand, and predict human behaviour, and link the outcomes with the IUCN natural resources governance concept. Negative attitudes arose from 72% and 35.2% participants towards the proximity of jaguar and proximity of puma, respectively. When asked about institutions working for wildlife protection, and institutions working for people facing problems caused by carnivores, 64.9% and 88.8%, respectively, were unaware of them. Regarding beliefs about co-occurrence with jaguars or pumas, 80.9% and 82.9%, respectively, mentioned more problems than benefits, with 74.5% believing in the increase of both species’ populations due to the creation of protected areas. This scenario may ease jaguar and puma to become scapegoats for human-wildlife and broader social conflicts, unless values such as justice and transparency are pursued.
Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.
An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong–weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was >80%, but CAS speech was higher for SW (>80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children’s speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy.
CRNDE/ETS1/GPR17 Facilitates the Proliferation, Migration, and Invasion of Glioma

Background Numerous lncRNAs were found as regulatory factors for occurrence and progression of various tumors, but there is still less research on the role of lncRNAs in malignant progression of glioma. Methods Bioinformatics analysis analyzed differential genes (DEGs) in the TCGA database. MTT, flow cytometry, and Transwell assays were performed to test the proliferation, apoptosis, migration, and invasion of cells. qRT-PCR and western blot were conducted to detect RNA and protein expressions of each gene, respectively. CHIP assay verified the binding relationship between genes. FISH assayed subcellar location of CRNDE, and xenograft in nude mice was performed for in vivo verification. Results CRNDE was upregulated in glioma cells, and overexpression of CRNDE facilitated malignant progression of glioma cells. CRNDE regulated occurrence and development of glioma through the CRNDE-ETS1-GPR17 axis. ETS1 was proved to target promoter region of GPR17. Overexpression of CRNDE promoted the binding between ETS1 and the promoter region of GPR17, thus, promoting the transcription of GPR17, while silencing of GPR17 inhibited promotion of CRNDE on proliferation, migration, and invasion of glioma cells. Conclusions These results demonstrated that CRNDE regulated GPR17 expression by binding ETS1, a transcription factor, thereby affecting glioma development. The results also indicated that CRNDE could serve as a possible therapeutic target and prognostic biomarker for glioma.
An Infinite System of Fractional Order with p-Laplacian Operator in a Tempered Sequence Space via Measure of Noncompactness Technique

In the current study, a new class of an infinite system of two distinct fractional orders with p-Laplacian operator is presented. Our mathematical model is introduced with the Caputo–Katugampola fractional derivative which is considered a generalization to the Caputo and Hadamard fractional derivatives. In a new sequence space associated with a tempered sequence and the sequence space c0 (the space of convergent sequences to zero), a suitable new Hausdorff measure of noncompactness form is provided. This formula is applied to discuss the existence of a solution to our infinite system through applying Darbo’s theorem which extends both the classical Banach contraction principle and the Schauder fixed point theorem.
Two-Stage Stochastic Program for Dynamic Coordinated Traffic Control Under Demand Uncertainty

This study develops a cell-based two-stage stochastic program to address the dynamic, spatial and stochastic characteristics of traffic flow for arterial adaptive signal control. To capture demand uncertainty, we formulate the adaptive coordinated traffic signal control as a two-stage stochastic program. To capture dynamic and spatial features of traffic flow, Cell Transmission Model (CTM) is embedded in the two-stage formulation. We incorporate the concept of Phase Clearance Reliability (PCR) to decompose the original two-stage stochastic formulation into separable sub-problems, which greatly enhances solution efficiency. A gradient-based solution algorithm is developed to solve the problem. Numerical examples are constructed to investigate the importance of capturing (or ignoring) each of the dynamic, spatial and stochastic features for traffic control. The results show that failure to account for any of these three traffic flow features will incur a certain extent of delay performance degradation, especially for heavy traffic. Finally, this study validates the findings through VISSIM, with promising results for the newly developed stochastic formulation.
Homosalate boosts the release of tumor-derived Extracellular Vesicles with anti-anoikis properties

Eukaryotic cells, including cancer cells, secrete highly heterogeneous populations of extracellular vesicles (EVs). EVs could have different subcellular origin, composition and functional properties, but tools to distinguish between EV subtypes are scarce. Here, we tagged CD63- or CD9-positive EVs secreted by triple negative breast cancer cells with Nanoluciferase enzyme, to set-up a miniaturized method to quantify secretion of these two EV subtypes directly in the supernatant of cells. We performed a cell-based high-content screening to identify clinically-approved drugs able to affect EV secretion. One of the identified hits is Homosalate, an anti-inflammatory drug found in sunscreens which robustly increased EVs’ release. Comparing EVs induced by Homosalate with those induced by Bafilomycin A1, we discovered that: 1) the two drugs act on EVs generated in distinct subcellular compartments and 2) EVs released upon treatment with Homosalate, but not with Bafilomycin A1, conferred anti-anoikis properties to another recipient tumor cell line. In conclusion, we identified a new drug modifying EV release and demonstrated that under influence of different drugs, triple negative breast cancer cells release EV subpopulations from different subcellular origins harboring distinct functional properties.
Real-time Text Classiﬁcation of User-Generated Content on Social Media: Systematic Review

—The aim of this systematic review is to determine the current state of the art in the real-time classiﬁcation of user-generated content from social media. Focus is on the identiﬁcation of the main characteristics of data used for training and testing; the types of text processing and normalisation that is required; the machine learning methods used most commonly; and how these methods compare to one another in terms of classiﬁcation performance. Relevantstudies were selected from subscription-based digital libraries, free-to-access bibliographies, and self-curated repositories, then screened for relevance with key information extracted and structured against the following facets: natural language processing methods, data characteristics, classiﬁcation methods and evaluation results. A total of 25 studies published between 2014 and 2018 covering 15 types of classiﬁcation algorithms were included in this review.
Discopersicus hexagrammatus n. sp. (Rhabditida: Tylenchidae), the second species of the genus

Abstract Discopersicus hexagrammatus n. sp. , is described and illustrated from a population associated with the rhizosphere of carrot (Daucus carota L.) in Dezful, Khuzestan province, south-western Iran. Based on morphological and morphometric data, the new species is characterized by a 601-734 µm long body, a prominent perioral labial disc and oblique amphidial slits, 10-11 µm long stylet, vulva at 65-67% of body length, 15.5-17 µm long spicules, and an elongate conoid tail with a pointed terminus. It is the second species of the genus Discopersicus and differs from its counterpart D. iranicus, by its anterior position of vulva, shorter stylet, lower M.B. ratio, different shape of tail tip, and shorter spicules in male specimens. A molecular phylogenetic analysis using the D2-D3 expansion segments of 28S rDNA sequences, placed the new species in close relationship with five sequences of the genus Boleodorus.
Dual Output and High Voltage Gain DC-DC Converter for PV and Fuel Cell Generators Connected to DC Bipolar Microgrids

This paper introduces a new topology for a DC-DC converter with bipolar output and high voltage gain. The topology was designed with the aim to use only one active power switch. Besides the bipolar multiport output and high voltage gain this converter has another important feature, namely, it has a continuous input current. Due to the self-balancing bipolar outputs, the proposed topology is suitable for bipolar DC microgrids. Indeed, the topology balancing capability can achieve the two symmetrical voltage poles of bipolar DC microgrids. Furthermore, it is possible to create a midpoint in the output of the converter that can be directly connected to the ground of the DC power supply, avoiding common-mode leakage currents in critical applications such as transformerless grid-connect PV systems. The operating principle of the proposed topology will be supported by mathematical analysis. To validate and verify the characteristics of the presented topology, several experimental results are shown.
Automatic Methods and Neural Networks in Arabic Texts Diacritization: A Comprehensive Survey

Arabic diacritics are signs used in Arabic orthography to represent essential morphophonological and syntactic information. It is a common practice to leave out those diacritics in written Arabic. Most Arabic electronic texts lack such diacritics. The processing of those texts for various purposes of Natural Language Processing is a complicated task. Diacritized words are necessary for applications such as machine translation, sentiment analysis, and speech synthesis. To address this problem, several studies proposed automatic systems to restore diacritics in Arabic texts. The present paper presents an in-depth survey of 56 most recent Arabic diacritization studies. Based on the diacritization approach, the studies are grouped into four sections in terms of method; rule-based, simple statistical, hybrid, and Neural Networks. While rule-based methods such as morphological analyzers and lexicon retrievals were the earliest approaches, results indicated that they are still valuable tools that can aid in the process of diacritization. Effective statistical methods that produced diacritics with acceptable accuracy include Hidden Markov Model, n-grams, and Support Vector Machines. They are often accompanied by either rule-based or neural networks in hybrid systems. Neural networks, specifically Bidirectional Long Short Term Memory, reached very high diacritization accuracy levels. Studies employing neural networks focused on evaluating and comparing the efficacy of several types of neural networks or a hybrid of them, testing alternatives of input units or suggested schemes for partial daicritization. The study synthesizes the results of the studies, identifies research gaps, and offers recommendations for future research.
Effect of Poly(acrylamide-acrylic acid) on the Fire Resistance and Anti-Aging Properties of Transparent Flame-Retardant Hydrogel Applied in Fireproof Glass

Poly(acrylamide-acrylic acid) (P(AM-co-AA)) was synthesized via the copolymerization of acrylamide and acrylic acid and well characterized by Fourier transform infrared spectroscopy. Afterward, the obtained P(AM-co-AA) was blended with flame retardants to prepare transparent flame-retardant hydrogel applied in the fireproof glass. The influence of poly(acrylamide-acrylic acid) on fire resistance and anti-aging properties of the transparent flame-retardant hydrogels were studied by assorted analysis methods. The optical transparency analysis shows that the light transmittance of the transparent flame-retardant hydrogel gradually decreases with the decreasing mass ratio of acrylamide to acrylic acid in P(AM-co-AA). Heat insulation testing shows that the heat insulation performance of fireproof glass applying the transparent flame-retardant hydrogel firstly decreases and then increases with decreasing mass ratio of acrylamide to acrylic acid in P(AM-co-AA). When the mass ratio of acrylamide to acrylic acid is 1:2, the obtained P(AM-co-AA) endows the resulting flame-retardant hydrogel applied in fireproof glass with the lowest light transmittance of 81.3% and lowest backside temperature of 131.4 °C at 60 min among the samples, which is attributed to the formation of a more dense and expanded char to prevent the heat transfer during combustion, as supported by the digital photos of char residues. The results of TG analysis indicate that P(AM-co-AA) imparts high thermal stability to the resulting hydrogels due to the hydrogen bonds between carboxyl and amide groups. The accelerated aging test shows that the transparent flame-retardant hydrogel containing P(AM-co-AA) is less affected by aging conditions. Especially, when the mass ratio of acrylamide to acrylic acid in P(AM-co-AA) is 4:1, the resulting transparent flame-retardant hydrogel shows a light transmittance of 82.9% and backside temperature of 173.1 °C at 60 min after 7 aging cycles, exhibiting the best comprehensive properties among the samples.
Novel Markov Chain Based URLLC Link Adaptation Method for 5G Vehicular Networking

Advanced driver-assistance system features in current vehicles (as precursor to fully autonomous, connected vehicular systems) will rely on 5G network links. Specifically, enabling various real-time operational aspects (e.g. emergency messaging), performance of ultra-reliable low-latency communications (URLLC) specified by 3GPP Release 16 is of direct relevance. A key challenge in achieving URLLC goals is the natural tension between simultaneously achieving high reliability (low block error rate or BLER) and low latency. When operating in a dynamic (i.e. significant time-varying channels due to vehicular mobility) environment, link adaptation is fundamental to URLLC implementation. In this paper, we introduce a new Markov Chain model for link adaptation that predicts the three key performance indicators (KPIs): end-to-end link latency, throughput and BLER as a function of the modulation and coding scheme used. The predictions from the Markov Chain model are supported by Monte Carlo simulations for various mobility scenarios, to explore how 5G network parameters may be adapted to achieve desired benchmarks, e.g. maximizing link throughput while providing strict latency and BLER bounds.
Geraniol Averts Methotrexate-Induced Acute Kidney Injury via Keap1/Nrf2/HO-1 and MAPK/NF-κB Pathways

Objectives: Geraniol, a natural monoterpene, is an essential oil component of many plants. Methotrexate is an anti-metabolite drug, used for cancer and autoimmune conditions; however, clinical uses of methotrexate are limited by its concomitant renal injury. This study investigated the efficacy of geraniol to prevent methotrexate-induced acute kidney injury and via scrutinizing the Keap1/Nrf2/HO-1, P38MAPK/NF-κB and Bax/Bcl2/caspase-3 and -9 pathways. Methods: Male Wister rats were allocated into five groups: control, geraniol (orally), methotrexate (IP), methotrexate and geraniol (100 and 200 mg/kg). Results: Geraniol effectively reduced the serum levels of creatinine, urea and Kim-1 with an increase in the serum level of albumin when compared to the methotrexate-treated group. Geraniol reduced Keap1, escalated Nrf2 and HO-1, enhanced the antioxidant parameters GSH, SOD, CAT and GSHPx and reduced MDA and NO. Geraniol decreased renal P38 MAPK and NF-κB and ameliorated the inflammatory mediators TNF-α, IL-1β, IL-6 and IL-10. Geraniol negatively regulated the apoptotic mediators Bax and caspase-3 and -9 and increased Bcl2. All the biochemical findings were supported by the alleviation of histopathological changes in kidney tissues. Conclusion: The current findings support that co-administration of geraniol with methotrexate may attenuate methotrexate-induced acute kidney injury.
Multimodal Imaging-Based Potential Visualization of the Tumor Microenvironment in Bone Metastasis

Bone metastasis (BM) is the most common malignant bone tumor and a significant cause of morbidity and mortality for patients with cancer. Compared to other metastatic organs, bone has unique characteristics in terms of the tumor microenvironment (TME). Precise assessments of the TME in BM could be an important step for developing an optimized management plan for patient care. Imaging approaches for BM have several advantages, such as biopsy not being required, multiple site evaluation, and serial assessment in the same sites. Owing to the developments of new imaging tracers or imaging modalities, bone TME could be visualized using multimodal imaging techniques. In this review, we describe the BM pathophysiology, diagnostic principles of major imaging modalities, and clinically available imaging modalities to visualize the TME in BM. We also discuss how the interactions between various factors affecting the TME could be visualized using multimodal imaging techniques.
Comment on Hinds et al , page 1121 JAK 2 V 617 F clonal disorders : fate or chance ?

In this issue of Blood, Hinds et al identify several novel polymorphic genomic loci that are associated with an increased risk of developing JAK2 V617F–driven clonal hematopoiesis and myeloproliferative neoplasms (MPNs). Their findings affirm the notion that, although the acquisition of somatic mutations in hematopoietic stem cell (HSC) genomes is an infrequent and apparently stochastic process, the fate of mutant cells and their clonal progeny is profoundly influenced by heritable genetic polymorphisms present in the host’s genome.
Multi-View Representation Learning via Dual Optimal Transportation

Recently, multi-view representation learning has gained rapid growth in various fields. Most of previous multi-view learning methods rely on strong notions of distances that often provide no useful gradients in deep network training, which greatly degrades the performance in merging complementary information of views. To address this challenge, a multi-view representation learning network with dual optimal transportation (MDOT-Net) is proposed to capture fusion representations embedded in a common manifold with the weak topology. In MDOT-Net, the multi-view representation learning is modelled as an optimal transportation (OT) problem in manifold fitting, which is further decomposed into the intra-view OT and the inter-view OT. The intra-view OT is implemented by a view-specific adversarial variational network, which accurately captures local manifold structures within views by leveraging fusion knowledge. The inter-view OT is implemented by a view-fusion adversarial inference network, which models fusion representations compatible with diversities of sub-manifolds by utilizing view-specific knowledge. The two OTs boost mutually by providing prior knowledge to each other in multi-view representation learning. Finally, numerous experiments are conducted on four benchmark datasets, and the results demonstrate MDOT-Net is competitive against state-of-the-art algorithms.