Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept, Content, and Context Perspectives of Quality of Agrofood Products: Reflections on Some Consumer Decision-Making-Purchase Scenarios

Quality attributes in agrofood products can be somewhat difficult to identify and observe. The quality of the same agrofood product in two different market shelves would, most likely not be exactly the same when compared to each other, even if both belong to the same batch. There are quality attributes peculiar/specific to one product, which stands it unique from the other. The basics/fundamentals underlying such peculiarities/specificities can be found either in concept, content, and context perspectives of quality. It appears however that no publication has deliberated on these three aspects together, that is, concept, content, and context perspectives of quality of agrofood products, particularly on how it contributes to the decision-making to purchase an agrofood product. We, therefore, in this current work, looked at concept, content, and context perspectives of quality of agrofood products, specifically discussing some reflections on some consumer decision-making purchase scenarios. Each of these, “concept,” “content,” and “context” perspectives independently project very important meanings to the quality of agrofood products. There appears a thin line that would separate concept, content, and context perspectives of quality in the choice/decision-making of purchase of agrofood products. To solely depend on either concept, content, or context perspective of quality will likely provide the consumer with insufficient information about the (given/specific) agrofood product. Interaction between any two will most likely improve the information. Obviously, the interaction between the three, would most likely provide sufficient information about the quality and help consumers make a more informed decision of purchase.
Characterization and elimination of stochastically generated persister subpopulation in mycobacteria

Mycobacterium tuberculosis (MTB) is able to persist in the host for long periods of time, even during antibiotic treatment. Eliminating persister cells, which are implicated as the primary reason for treatment failure, is essential for shortening TB treatment regimen. Here, we report a novel methodology, Per-Sort, to identify and sort miniscule numbers of translationally dormant mycobacterial cells within an isogenic mycobacterium population. Using Per-Sort we have discovered that translationally dormant cells pre-exist (under optimal growth conditions) as a fraction of a percent of isogenic mycobacterial cultures, suggesting they are generated stochastically as a bet hedging strategy. We show that this pre-existing translationally dormant subpopulation of cells are tolerant to antibiotics, small in size, low in oxidative metabolism, and expand in number upon nutrient starvation. Finally, through transcriptional profiling at single cell resolution, we’ve determined that the pre-existing persisters are a heterogeneous mix of vapC30, mazF, and relA/spoT overexpressing cells that are eliminated and sensitized to antibiotic killing through induction of respiration. Highlights We have developed a novel method, Per-Sort, to isolate a small proportion of translationally dormant cells that pre-exist in Mycobacterium spp. cultures growing under optimal conditions but dramatically increase in proportion under stressful conditions. The pre-existing translationally dormant cells have lower oxygen consumption, significantly longer lag phase to initiate growth in nutrient rich medium, and high tolerance to >10x MIC concentrations of isoniazid (INH) and rifampicin (RIF), indicating they are a subpopulation of persister cells. Single-cell expression profiling demonstrated that the persisters are a heterogenous mixture of toxin (VapC30 and MazF) and alarmone response (RelA/SpoT) expressing cells. A shared outcome of high toxin and alarmone response is reduced cellular oxidative metabolism, which we demonstrate is reversed upon addition of L-cysteine to reduce the proportion of pre-existing persister cells and increase killing by INH and RIF.
MorphoBERT: a Persian NER System with BERT and Morphological Analysis

Named Entity refers to person, organization and location names, and sometimes date, time, money and percent expressions as well. Named entity Recognition (NER) systems are developed to extract these essential information units from a text. Persian is a less-developed language in many natural language processing tasks such as NER. In this paper we present our system, MorphoBERT, submitted to the First Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019)(Taghizadeh et al., 2019). We train the BERT model (Devlin et al., 2019a) on a large volume of Persian texts to get a highly accurate representation of tokens and then we apply a BiLSTM (bidirectional LSTM) on vector representations to label tokens. Persian is a rich language in terms of morphology and word parts may convey grammatical and semantic information. To inform the model of this information we analyze texts morphologically to split the lemma and afﬁx(es) of each word and then we train the model on the analyzed texts. The test data, provided by the organizers, contains in-domain and out-of-domain texts. Our system achieves the ﬁrst rank among all participated systems with a total high precision, recall and F1 of 87.0, 83.8, 85.4, respectively.
AtyNegar at NSURL-2019 Task 8: Semantic Question Similarity in Arabic

Measurement of semantic similarity plays an important role in many areas of natural language processing. Several approaches have been proposed to determine the similarity of sentences in different languages but many of them are not extendable in all languages. According to the complicated Arabic language structure and lack of necessary resources and tools, the Arabic semantic similarity measurement is challenging. In this paper, we proposed a supervised method for Arabic semantic question similarity measurement. Forty-one features (lexical, syntactic and semantic) are extracted from two question phrases, then the best distinctive features are selected by using SelectKBest algorithm. Finally, for sentences classification and determining the similarity score, SVM used. The system participated in task8 of NSURL 2019 . The results of using this method on the data set of NSURL 2019 have a F-measure of 82.58 percent, which have improved the basic method.
Arabic Dialogue Act Recognition for Textual Chatbot Systems

Automatic Dialogue Acts Recognition is considered a crucial step for semantic extraction in Natural Language Understanding and Di-alogue Systems. In this paper, we introduce our work aiming to recognize the dialogue acts of the users in a Textual Dialogue sys-tem using Levantine Arabic dialect. Our Di-alogue acts have 8 types: Greeting, Good-bye, Thanks, Conﬁrm, Negate, Ask_repeat, Ask_for_alt, and Apology. Various Machine Learning algorithms -with different features have been used to detect the correct speech act categories: Logistic Regression, SVM, Multi-nomial NB, Extra Trees Classiﬁer, Random Forest Classiﬁer. We also used the Voting En-semble method to make the best prediction from each classiﬁer. We compared the results of the proposed models on a hand-crafted corpus in the restaurants orders and airline ticket-ing domain. The SVM algorithm with 2-gram has given the best results.
Hidden Markov-based Part-of-Speech Tagger for Igbo Resource-Scarce African Language

Igbo is a resource-scarce Nigerian African language of Bantu language phylum, lacking elctronic linguistic resources in sufficient quantity and quality for the development of human language technologies. Developing Natural Language Processing (NLP) pipeline tools for such a language could be challenging, due to the need to balance the linguistics semantics robustness of the tool with computational parsimony. A Part-of-Speech (POS) tagger is a challenging NLP tool to develop for the language because of its morphological richness poses computational linguistics challenge that could affect the effectiveness of the entire NLP system. In this paper, the experience in developing a POS tagger for the language using the Hidden Markov Model (HMM is presented. It is an on-going project, developed using a small corpus. The results give an approximate accuracy score of 73%, which needs to be improved upon .
NSURL-2019 Task 8: Semantic Question Similarity in Arabic

Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared task (Semantic Question Similarity in Arabic). The task was organized as part of the first workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) The goal of the task is to predict whether two questions are semantically similar or not, even if they are phrased differently. A total of 9 teams participated in the task. The datasets created for this task are made publicly available to support further research on Arabic Q2Q.
Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

Background There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa. Methods We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases. Results Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8+ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases. Conclusion PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.
Acid- and Volume-Sensitive Chloride Currents in Human Chondrocytes

Chondrocytes face extreme alterations of extracellular osmolarity and pH, which force them to appropriately regulate their cell volume (CV) and cellular pH. Perturbations of these mechanisms lead to chondrocyte death and ultimately to osteoarthritis (OA), the most common chronic joint diseases worldwide. OA hallmarks are altered cartilage hydration and severe fluid acidification. Impaired CV regulation and acidotoxicity contribute to disease progression and volume-sensitive anion channels are upregulated in OA. This study assessed the effect of hypotonicity and extracellular acidification on chondrocyte Cl– conductances and CV regulation. Cl– currents and membrane potentials were measured in human C28/I2 cells and primary human chondrocytes using the patch clamp technique. Intracellular pH was assessed by BCECF fluorescence, CV measurements were performed using the Coulter method, and cell viability/cell death by a resazurin assay. Hypotonic cell swelling caused activation of a volume-sensitive outwardly rectifying (VSOR) Cl– current followed by a regulatory volume decrease (RVD), which was attenuated by the Cl– channel blocker DCPIB. Extracellular, but not intracellular acidification to pH ≤ 5.0 elicited an acid-sensitive outwardly rectifying (ASOR) Cl– conductance. Activation of either current depolarized the cell membrane potential. Under simultaneous hypotonic and acidic stimulation, VSOR and ASOR currents transiently coactivated, giving rise to a mixed current phenotype. Over time the VSOR current gradually vanished and the residual conductance showed a pure ASOR current phenotype. Extracellular acidification caused an isotonic CV gain and a complete suppression of RVD under hypotonic conditions. The results suggest that deactivation of the VSOR current under acidic conditions impairs CV regulation in chondrocytes, which is likely to compromise chondrocyte viability.
Seismogenic Faulting of the 2016 Mw 6.0 Hutubi Earthquake in the Northern Tien Shan Region: Constraints From Near-Field Borehole Strain Step Observations and Numerical Simulations

Constraining the seismotectonic faulting that occurred as a result of the 2016 Mw 6.0 Hutubi earthquake provides valuable information about the deformation in the northern Tien Shan region. However, due to the lack of surface rupturing and high quality near-field teleseismic data, the exact nature of the faulting remains controversial. In our study, we analyze the coseismic strain time series of the Mw 6.0 Hutubi earthquake using strain data collected from nearby borehole stations. The tensile and compressive coseismic strain steps account for most of the recorded borehole data in this region. Employing a numerical model that is based on elastic dislocation theory, we reproduce the observed tensile and compressive coseismic strain steps using source parameters that were generated through seismic wave inversion, seismic reflection data, and aftershock relocation. By conducting a comparative analysis between the predicted and observed coseismic strain steps, we study the seismogenic faulting of the Mw 6.0 Hutubi earthquake. The results indicate that when the source parameters are 292°/62°/80° (strike/dip/rake), the predicted tensile and compressive characteristics for 13/16 channels are consistent with the observational data. Based on these results, we infer that the seismogenic faulting, which is located near the Horgos-Manas-Tugulu fault, can be characterized as a high-angle blind back-thrust fault with a north-dipping fault plane. Providing constraints on the seismogenic faulting associated with the 2016 Mw 6.0 Hutubi earthquake also yields to understand the mechanism of the overall deformation pattern in the northern Tien Shan region.
Dosimetry for New Radiation Therapy Approaches Using High Energy Electron Accelerators

We have performed dosimetry studies using electron beams with energies up to 50 MeV, which exceed current clinical energy ranges and approaches the bottom end of the very high energy electron range. 50 MeV electron beams can reach deep-seated tumors. In contrast to photon beams, electron beams can be generated with ultra-high dose rates by linear accelerators, which could enable FLASH radiotherapy of deep-seated tumors. The response of radiochromic film and alanine is compared with dose measurements using an ionisation chamber. Energy dependence is not observed within the measurement uncertainty in the investigated energy range from 15 to 50 MeV.
Gnu-RL: A Practical and Scalable Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy

We propose Gnu-RL: a novel approach that enables real-world deployment of reinforcement learning (RL) for building control and requires no prior information other than historical data from existing Heating Ventilation and Air Conditioning (HVAC) controllers. In contrast with existing RL agents, which are opaque to expert interrogation and need ample training to achieve reasonable control performance, Gnu-RL is much more attractive for real-world applications. Furthermore, Gnu-RL avoids the need to develop and calibrate simulation environments for each building or system under control, thus making it highly scalable. To achieve this, we bootstrap the Gnu-RL agent with domain knowledge and expert demonstration. Specifically, Gnu-RL adopts a recently-developed Differentiable Model Predictive Control (MPC) policy, which encodes domain knowledge on planning and system dynamics, making it both sample-efficient and interpretable. Prior to any interaction with the environment, a Gnu-RL agent is pre-trained on historical data using imitation learning, enabling it to match the behavior of the existing controller. Once it is put in charge of controlling the environment, the agent continues to improve its policy end-to-end, using a policy gradient algorithm. We evaluate Gnu-RL in both simulation studies and a real-world testbed. Firstly, we validated the Gnu-RL agent is indeed practical and scalable by applying it to three commercial reference buildings in simulation, and demonstrated the superiority of the Differentiable MPC policy compared to a generic neural network. In another simulation experiment, our approach saved 6.6% of the total energy compared to the best published RL result for the same environment, while maintaining a higher level of occupant comfort. Finally, Gnu-RL was deployed to control the airflow to a real-world conference room with a Variable-Air-Volume (VAV) system during a 3-weeks period. Our results show that Gnu-RL reduced cooling demand by 16.7% compared to the existing controller and tracked the temperature set-point better.
Altered Functional Connectivity Differences in Salience Network as a Neuromarker of Suicide Risk in Euthymic Bipolar Disorder Patients

Objective The occurrence of death by suicide in patients diagnosed with bipolar disorder is as much as 60 times greater than in the general population. Even during the state of euthymia patients are characterized by suicide risk. The aim of the study is to investigate the baseline brain activity in euthymic bipolar disorder patients in regard to suicide risk. We hypothesized that patients compared to healthy control group will demonstrate altered functional connectivity among resting state networks which will be directly related to current suicide risk. Method 41 subjects were enrolled in the study consisting control group (n = 21) and euthymic bipolar disorder patients group (n = 20). Functional magnetic resonance imaging was used to evaluate resting state brain activity and ROI–ROI functional connectivity analysis was performed. Suicidal risk was estimated using The Suicide Behaviors Questionnaire-Revised. Results A two sample t-test revealed decreased functional connectivity between regions involved in the salience network in patients compared to the control group. This decrease was negatively correlated with current suicide risk. Conclusion Obtained results suggest the association between risk of suicide and activity of regions responsible for functions such as learning from mistakes, prospective thinking, and sensory integration.
Contact Force Measurements in Cam and Follower Lubricated Contacts

Cam-follower contacts are characterized by continuous changes of operating parameters such as speed, load and geometry, so that both numerical simulations and experimental measurements are very complex. An experimental apparatus has been specifically designed and realized to perform tests with cam–follower pairs. The apparatus allows measurements of contact forces with a custom-made dynamometer and of lubricant film thickness and shape with optical interferometry. After an introductory review of experimental works on cam-follower pairs mainly related to force measurements, the test rig used for the experimental work reported in the paper is briefly described. Some trends of all components of the contact forces measured during the rotation of a circular eccentric cam and an engine spline cam are presented. Tests have been performed at different rotational speeds and preloads. The friction trends can be associated to mixed and boundary lubrication regimes. Some unattended friction peaks are found in tests with the spline cam and possible explanations are given.
Ring trial of 2nd generation RT‐QuIC diagnostic tests for sporadic CJD

Real‐time quaking‐induced conversion (RT‐QuIC) assays detect prion‐seeding activity in a variety of human biospecimens, including cerebrospinal fluid and olfactory mucosa swabs. The assay has shown high diagnostic accuracy in patients with prion disorders. Recently, advances in these tests have led to markedly improved diagnostic sensitivity and reduced assay times. Accordingly, an algorithm has been proposed that entails the use of RT‐QuIC analysis of both sample types to diagnose sporadic Creutzfeldt‐Jakob disease with nearly 100% accuracy. Here we present a multi‐center evaluation (ring trial) of the reproducibility of these improved “second generation” RT‐QuIC assays as applied to these diagnostic specimens.
In vitro Targetability Validation of Peptide-Functionalized Mesoporous Silica Nanoparticles in the Presence of Serum Proteins

Demonstration of receptor-mediated targeting of nanoparticles to specific organs and/or cell types is an integral aim in many bionanomedicine development projects. However, engagement of targeted receptors with ligands on nanocarriers, which is the cornerstone of the active targeting concept, is challenging to study under biologically relevant conditions and thus often stays overlooked. In this work, we utilize an in-house established bioassay for in vitro targetability validation of mesoporous silica nanoparticles (MSNs), functionalized with high-affinity peptide ligands to somatostatin receptors via protective group chemistry, ensuring the correct orientation of the peptide's pharmacophore. We demonstrate that targeted nanoparticles, but not scrambled peptide-decorated counterparts, specifically engage the targeted receptors in living cells in culture media containing serum protein. The importance of being able to exclude false positives originating from the premature detachment of targeting peptides from the MSNs is highlighted.