Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiation Induction as a Response to Irradiation in Neural Stem Cells In Vitro

Radiotherapy plays a significant role in brain cancer treatment; however, the use of this therapy is often accompanied by neurocognitive decline that is, at least partially, a consequence of radiation-induced damage to neural stem cell populations. Our findings describe features that define the response of neural stem cells (NSCs) to ionizing radiation. We investigated the effects of irradiation on neural stem cells isolated from the ventricular-subventricular zone of mouse brain and cultivated in vitro. Our findings describe the increased transcriptional activity of p53 targets and proliferative arrest after irradiation. Moreover, we show that most cells do not undergo apoptosis after irradiation but rather cease proliferation and start a differentiation program. Induction of differentiation and the demonstrated potential of irradiated cells to differentiate into neurons may represent a mechanism whereby damaged NSCs eliminate potentially hazardous cells and circumvent the debilitating consequences of cumulative DNA damage.
Mental Health of Refugees and Torture Survivors: A Critical Review of Prevalence, Predictors, and Integrated Care

Civilian war trauma and torture rank among the most traumatic life experiences; exposure to such experiences is pervasive in nations experiencing both internal and external conflict. This has led to a high volume of refugees resettling throughout the world with mental health needs that primary care physicians may not be screening for and prepared to effectively address. In this article, we review the literature on demographics, predictors, mental health outcomes of torture, and integrated care for the mental health needs of refugees. We searched PubMed and PSYCINFO databases for original research articles on refugees and mental health published in the English language between 2010 and present. Nine percent of 720 adults in conflict areas in Nepal, with predominance of literate married males, met the threshold for Post-Traumatic Stress Disorder (PTSD), 27.5% for depression, and 22.9% for anxiety. While, PTSD rate has been documented as high as 88.3% among torture survivors from Middle East (ME), Central Africa (CA), South Asia (SA), Southeast Europe (SE). Depression was recorded as high as 94.7% among 131 African torture survivors and anxiety as high as 91% among 55 South African torture survivors. Torture severity, post-migration difficulties, and wait time to receive clinical services were significantly associated with higher rate of mental health symptoms. Mental health screening is not a standard component of initial physical exams for refugees, yet these individuals have had high trauma exposure that should inform clinical care. Integrated care models are lacking but would greatly benefit this community to prevent progression to greater severity of mental health symptoms.
3D Tdoa Problem Solution with Four Receiving Nodes

Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two different solutions that cannot be discarded according to mathematical standards. In this paper, a new methodology to solve the 3D TDOA problems in a sensor network with four beacons is proposed. A confidence interval, which is defined in this paper as a sphere, is defined to use positioning algorithms with four different nodes. It is proven that the separation between solutions in the four-beacon TDOA problem allows the transformation of the problem into an analogous one in which more receivers are implied due to the geometric properties of the intersection of hyperboloids. The achievement of the distance between solutions needs the application of genetic algorithms in order to find an optimized sensor distribution. Results show that positioning algorithms can be used 96.7% of the time with total security in cases where vehicles travel at less than 25 m/s.
Targeting Bacterial Biofilms by the Green Tea Polyphenol EGCG

Bacterial biofilms are multicellular aggregates in which cells are embedded in an extracellular matrix of self-produced biopolymers. Being refractory to antibiotic treatment and host immune systems, biofilms are involved in most chronic infections, and anti-biofilm agents are being searched for urgently. Epigallocatechin-3-gallate (EGCG) was recently shown to act against biofilms by strongly interfering with the assembly of amyloid fibres and the production of phosphoethanolamin-modified cellulose fibrils. Mechanistically, this includes a direct inhibition of the fibre assembly, but also triggers a cell envelope stress response that down-regulates the synthesis of these widely occurring biofilm matrix polymers. Based on its anti-amyloidogenic properties, EGCG seems useful against biofilms involved in cariogenesis or chronic wound infection. However, EGCG seems inefficient against or may even sometimes promote biofilms which rely on other types of matrix polymers, suggesting that searching for ‘magic bullet’ anti-biofilm agents is an unrealistic goal. Combining molecular and ecophysiological aspects in this review also illustrates why plants control the formation of biofilms on their surfaces by producing anti-amyloidogenic compounds such as EGCG. These agents are not only helpful in combating certain biofilms in chronic infections but even seem effective against the toxic amyloids associated with neuropathological diseases.
The changing ecology of primate parasites: Insights from wild‐captive comparisons

Host movements, including migrations or range expansions, are known to influence parasite communities. Transitions to captivity—a rarely studied yet widespread human‐driven host movement—can also change parasite communities, in some cases leading to pathogen spillover among wildlife species, or between wildlife and human hosts. We compared parasite species richness between wild and captive populations of 22 primate species, including macro‐ (helminths and arthropods) and micro‐parasites (viruses, protozoa, bacteria, and fungi). We predicted that captive primates would have only a subset of their native parasite community, and would possess fewer parasites with complex life cycles requiring intermediate hosts or vectors. We further predicted that captive primates would have parasites transmitted by close contact and environmentally—including those shared with humans and other animals, such as commensals and pests. We found that the composition of primate parasite communities shifted in captive populations, especially because of turnover (parasites detected in captivity but not reported in the wild), but with some evidence of nestedness (holdovers from the wild). Because of the high degree of turnover, we found no significant difference in overall parasite richness between captive and wild primates. Vector‐borne parasites were less likely to be found in captivity, whereas parasites transmitted through either close or non‐close contact, including through fecal‐oral transmission, were more likely to be newly detected in captivity. These findings identify parasites that require monitoring in captivity and raise concerns about the introduction of novel parasites to potentially susceptible wildlife populations during reintroduction programs.
Assessing treatment integrity in personalized CBT: the inventory of therapeutic interventions and skills

ABSTRACT The third wave of cognitive behavioral therapy (CBT) has increased the heterogeneity of today’s CBT practice, while developments in patient-focused research are paving the road to the empirical personalization of CBT. This paper presents the development and psychometric properties of a therapy video rating instrument, which was designed to adequately assess the treatment integrity (adherence and competence) of modern, personalized CBT. The Inventory of Therapeutic Interventions and Skills (ITIS) was developed based on two existing CBT adherence and competence scales and augmented with third wave content and overarching therapeutic strategies. The instrument was then applied by graduate students and post-graduate clinicians to rate N = 185 therapy videos from N = 70 patients treated at a university outpatient clinic. Descriptive results, inter-rater reliability, item structure, and associations with session outcome and alliance were examined. Average inter-rater reliability was excellent for Interventions items and good for Skills items. Intercorrelations were low between Interventions items, but higher and significant between Skills items, which loaded on a single factor. Several ITIS items were shown to be predictive of session outcome and alliance, even after controlling for the nested data structure. Implications of these results for future research and clinical training are discussed.
Striatal functional connectivity in chronic ketamine users: a pilot study

ABSTRACT Background: The striatum supports motivated behavior and impulse control. Altered striatal activation and connectivity has been observed in link with impulse control dysfunction in individuals with drug addiction. Objectives: We examined how resting state functional connectivity (rsFC) of the striatum is altered as a result of chronic ketamine misuse. Methods: Thirty-six ketamine users (10 women) and 20 healthy controls (9 women) completed an assessment with the Barratt Impulsiveness Scale (BIS-11) and magnetic resonance imaging. In SPM we examined voxel-wise connectivities of the caudate, pallidum, putamen, and ventral striatum in ketamine users (versus healthy controls) and in association with BIS-11 score and duration of use, all at a corrected threshold. Results: Compared to controls, ketamine users showed higher connectivity between caudate and dorsal anterior cingulate cortex and between pallidum and bilateral cerebellum. In ketamine users, putamen showed higher connectivity with the left orbitofrontal cortex (OFC) in association with both BIS-11 score and months of ketamine use. Mediation analyses suggest that the connectivity z score mediated the relationship between impulsivity and duration of use. Conclusions: These preliminary findings highlighted altered striatal connectivity in chronic ketamine users, and the potential role of putamen OFC connectivity in supporting the correlation between impulsivity and duration of ketamine use. If replicated in a larger sample, these findings may represent neural markers of ketamine misuse.
Human glycan expression patterns influence Group A streptococcal colonization of epithelial cells

Colonization of the oropharynx is the initial step in Group A Streptococcus (GAS) pharyngeal infection. We have previously reported that the highly virulent M1T1 GAS clone attaches to oral epithelial cells via M1 protein interaction with blood group antigen carbohydrate structures. Here, we have identified that colonization of human oral epithelial cells by GAS serotypes M3 and M12 is mediated by human blood group antigens [ABO(H)] and Lewis (Le) antigen expression. Removal of linkage‐specific fucose, galactose, N‐acetylgalactosamine, and sialic acid modulated GAS colonization, dependent on host ABO(H) blood group and Le expression profile. Furthermore, N‐linked glycans from human salivary glycoproteins, when released and purified, were potent inhibitors of M1, M3, and M12 GAS colonization ex vivo. These data highlight the important role played by human protein glycosylation patterns in GAS attachment to oral epithelial cell surfaces. —De Oliveira, D. M. P., Everest‐Dass, A. , Hartley‐Tassell, L. , Day, C. J., Indraratna, A. , Brouwer, S. , Cleary, A. , Kautto, L. , Gorman, J., Packer, N. H. , Jennings, M. P., Walker, M. J., Sanderson‐Smith, M. L. Human glycan expression patterns influence Group A streptococcal colonization of epithelial cells. FASEB J. 33, 10808–10818 (2019). www.fasebj. org
Model‐based analysis on systemic availability of co‐administered cannabinoids after controlled vaporised administration

The most important two medicinal cannabinoids are Δ9‐tetrahydrocannabinol (THC) and cannabidiol (CBD). Vaporised administration is superior due to its higher systemic availability, lower individual variability and faster drug delivery. Although it is common THC is co‐administered with CBD, the influence of CBD on the pharmacokinetics, especially the systemic availability of THC after vaporised administration, is unknown.
Contractile acto-myosin network on nuclear envelope remnants positions human chromosomes for mitosis

To ensure proper segregation during mitosis, chromosomes must be efficiently captured by spindle microtubules and subsequently aligned on the mitotic spindle. The efficacy of chromosome interaction with the spindle can be influenced by how widely chromosomes are scattered in space. Here, we quantify chromosome-scattering volume (CSV) and find that it is reduced soon after nuclear envelope breakdown (NEBD) in human cells. The CSV reduction occurs primarily independently of microtubules and is therefore not an outcome of interactions between chromosomes and the spindle. We find that, prior to NEBD, an acto-myosin network is assembled in a LINC complex-dependent manner on the cytoplasmic surface of the nuclear envelope. This acto-myosin network remains on nuclear envelope remnants soon after NEBD, and its myosin-II-mediated contraction reduces CSV and facilitates timely chromosome congression and correct segregation. Thus, we find a novel mechanism that positions chromosomes in early mitosis to ensure efficient and correct chromosome–spindle interactions.
Negative symptoms in the clinical high‐risk state for psychosis: Connection with cognition and primacy in impacting functioning

In the clinical high‐risk (CHR) state for psychosis, both negative symptoms and lower cognitive function have been associated with poorer daily functioning. Recent evidence suggests that negative symptoms share overlapping variability with cognition and may partially mediate the relationship between cognition and functioning. However, the nature of this overlap is unknown, and the reverse mediation model remains untested leaving the precise nature of these associations unclear.
TeO 2 bolometers with Cherenkov signal tagging: towards next-generation neutrinoless double beta decay experiments

CUORE, an array of 988 TeO 2 bolometers, is about to be one of the most sensitive experiments searching for neutrinoless double-beta decay. Its sensitivity could be fur-ther improved by removing the background from α radioactivity. A few years ago it was pointed out that the signal from β s can be tagged by detecting the emitted Cherenkov light, which is not produced by α s. In this paper we conﬁrm this possibility. For the ﬁrst time we measured the Cherenkov light emitted by a CUORE crystal, and found it to be 100 eV at the Q -value of the decay. To completely reject the α background, we compute that one needs light detectors with baseline noise below 20 eV RMS, a value which is 3-4 times smaller than the average noise of the bolometric light detectors we are using. We point out that an improved light detector technology must be developed to obtain TeO 2 bolometric experiments able to probe the inverted hierarchy of neutrino masses.
A Specification-based Intrusion Detection Mechanism for LEACH Protocol

With the improvement of wireless communication and embedded technology, WSN is used at various fields. Meanwhile, because WSN is resource constrained, it is more vulnerable than other networks. To solve the security problem of WSN, we can use the traditional secure mechanism like as cryptography and authentication. But the traditional secure mechanism is not enough for all security issues that may be happened in WSN, especially attacks caused by the compromised node. So, we need the IDS as the second secure mechanism for WSN. In this paper, we propose the Specification-based Intrusion Detection Mechanism that makes LEACH, which is one of the clustering routing protocol for WSN, more reliable and safety.
Viral Genome Deep Classifier

The task of virus classification into subtypes is an important concern in many categorization studies, e.g., in virology or epidemiology. Therefore, the problem of virus subtyping has been a subject of considerable interest in the last decade. Although there exist several virus subtyping tools, they are often dedicated to a specific family of viruses. Even specialized methods, however, often fail to correctly subtype viruses, such as HIV or influenza. To address these shortcomings, we present a viral genome deep classifier (VGDC)—a tool for an automatic virus subtyping, which employs a deep convolutional neural network (CNN). The method is universal and can be applied for subtyping any virus, as confirmed by experiments on dengue, hepatitis B and C, HIV-1, and influenza A datasets. For all considered virus types, the obtained classification rates are very high with the corresponding values of the F1-score ranging from about 0.85 to 1.00 depending on the virus type and the considered number of subtypes. For HIV-1 and influenza A, the VGDC significantly outperforms the leading competitors, including CASTOR and COMET. The VGDC source code is freely available to facilitate easy usage and comparison with future approaches.
Using Constrained NMPC to Control a Cable-Suspended Payload With Two Quadrotors

A nonlinear MPC controller is developed in this work to control the suspended payload position carried with two quadrotors by cables. The dynamic model is derived using the Euler-Lagrange equation. Given high nonlinear complex dynamics and constrained environments, a constrained NMPC controller is applied to handle the control tasks. These tasks are presented by the payload tracking a reference trajectory while the constraints are employed. The constraints to be considered include the limits on the quadrotors control inputs, the payload position, the swing angles and the orientation of both quadrotors. The MATLAB simulation results are obtained against a constrained linear MPC controller. The system is also tested under disturbance to show the nonlinear control performance against the linear counterpart.
UMLBP-A Novel Approach for Face Recognition System using OPENCV

Several face recognition algorithms have been proposedin the last decade which provide a biometric authentication process for various utilities. In the present work, a novel algorithm for face verification considering both lightening and shape information for representing the face images, has been presented. In this approach, the region of interest in face is first divided into 8x8 regionfrom which Uniform Mean Local Binary Pattern (UMLBP) histograms are extracted and concatenated into asingle histogram with enhanced feature efficiently representing the face. The matching is done using a K nearest neighbor classifier with minimum error based similarity measure. A number of performed trials reveal that the given approach is better overall considered methods (basic LBP, MLBP and ULBP methods). All experiments have been performed on ORL database which include evaluating the efficacy of the approach over different face angles, illumination and rotation of the query image. Testing was performed using 10 cross validation scheme and average results have been considered. Recognition rate has been recorded as 90 percent on 3 face training of single person and 99 percent on 9 face training of single person. The proposed method also allows for the fast feature extraction and testing for a given platform using OPENCV.
Different Controlling Method of Closed Loop DC-DC Buck Converter: A review

Over the last few years, DC/DC buck converters have been the subject of great interest due to its higher utility for different applications. In this review paper, we explain about various types of controlling method of dc-dc buck converter for closed loop feedback system. The study is focused on various specified application such as wireless network, computer server, computer CPU, telephonic system etc. Here we consider about linear, nonlinear, voltage control, current control, and switch mode controller for design the closed loop system. In linear controlling method, we consider about PID controller, its tuning process, and finding other system parameter. But linear systems fails to work properly in real-time applications, hence we have to think about nonlinear controller and due to this reason, we consider switching mode controller, Hysteretic current and voltage mode controller etc.
Get-a-Doc: A Doctor Recommender System

A number of web and mobile based applications exist for finding and booking a doctor’s appointment through the application. However, these applications list multiple specialists instead of providing the perfect match of doctor according to user preferences. Though the present apps provide filters to shortlist the selection, yet a user needs to browse through the entire list of doctors. Moreover, only a single filter can be applied at once, in general. The current work presents a web-based application, Get-a-doc, which provides a user with the best match out of available doctors according to his preferences. A distributed approach to information retrieval has been employed, whereby the database of doctors’ data has been stored on a NoSQL based data store, MongoDb. This database has been sharded, into 3 divisions for scalability of the application. Results demonstrate the efficiency of the proposed approach. Our main motivation for building the project was to reduce the effort and hassle that the user needs to go through when he has to go through a list of doctors, to find a suitable match accoding to his/her needs.
Brain Tumor Classification Using Hybrid Model Of PSO And SVM Classifier

Medical image processing is the very significant process for any disease diagnosis now a days. MRI is usually used to detect the presence of tumor and its type . The process which is followed in classification of brain tumor is very complicated. There are various steps for the medical image processing like image segmentation, image extraction and image classification. Different types of features are extracted from the segmented MRI images like intensity, shapes and texture based features. The feature selection approach is used to select the small subset of features from MRI image which minimize redundancy and maximize relevance to the target. In this paper, online database of MRI images containing brain tumor is taken then a machine learning model is developed by using the Particle Swarm Optimization(PSO) algorithm for feature selection and then Support Vector Machine(SVM) classifier is used to classify the type of tumor in present brain MRI images.