Miracle-dz/UniFAR-FTUs huggingface dataset:
Aberrant expression of imprinted lncRNA MEG8 causes trophoblast dysfunction and abortion

Long noncoding RNAs (lncRNAs) are a group of noncoding RNAs whose nucleotides are longer than 200 bp. Previous studies have shown that they play an important regulatory role in many developmental processes and biological pathways. However, the contributions of lncRNAs to placental development are largely unknown. Here, our study aimed to investigate the lncRNA expression signatures in placental development by performing a microarray lncRNA screen. Placental samples were obtained from pregnant C57BL/6 female mice at three key developmental time points (embryonic day E7.5, E13.5, and E19.5). Microarrays were used to analyze the differential expression of lncRNAs during placental development. In addition to the genomic imprinting region and the dynamic DNA methylation status during placental development, we screened imprinted lncRNAs whose expression was controlled by DNA methylation during placental development. We found that the imprinted lncRNA Rian may play an important role during placental development. Its homologous sequence lncRNA MEG8 (RIAN) was abnormally highly expressed in human spontaneous abortion villi. Upregulation of MEG8 expression in trophoblast cell lines decreased cell proliferation and invasion, whereas downregulation of MEG8 expression had the opposite effect. Furthermore, DNA methylation results showed that the methylation of the MEG8 promoter region was increased in spontaneous abortion villi. There was dynamic spatiotemporal expression of imprinted lncRNAs during placental development. The imprinted lncRNA MEG8 is involved in the regulation of early trophoblast cell function. Promoter methylation abnormalities can cause trophoblastic cell defects, which may be one of the factors that occurs in early unexplained spontaneous abortion.
Investigation of Leukocyte Telomere Length and Genetic Variants in Chromosome 5p15.33 as Prognostic Markers in Lung Cancer

Background: Lung cancer remains the leading cause of cancer mortality with relatively few prognostic biomarkers. We investigated associations with overall survival for telomere length (TL) and genetic variation in chromosome 5p15.33, an established telomere maintenance locus. Methods: Leukocyte TL was measured after diagnosis in 807 patients with non–small cell lung cancer (NSCLC) from the Princess Margaret Cancer Center in Toronto and assessed prospectively in 767 NSCLC cases from the Copenhagen City Heart Study and the Copenhagen General Population Study. Associations with all-cause mortality were tested for 723 variants in 5p15.33, genotyped in 4,672 NSCLC cases. Results: Short telomeres (≤10th percentile) were associated with poor prognosis for adenocarcinoma in both populations: TL measured 6 months after diagnosis [HR = 1.65; 95% confidence intervals (CI), 1.04–2.64] and for those diagnosed within 5 years after blood sampling (HR = 2.42; 95% CI, 1.37–4.28). Short TL was associated with mortality in never smokers with NSCLC (HR = 10.29; 95% CI, 1.86–56.86) and adenocarcinoma (HR = 11.31; 95% CI, 1.96–65.24). Analyses in 5p15.33 identified statistically significant prognostic associations for rs56266421-G in LPCAT1 (HR = 1.86; 95% CI, 1.38–2.52; P = 4.5 × 10−5) in stage I–IIIA NSCLC, and for the SLC6A3 gene with OS in females with NSCLC (P = 1.6 × 10−3). Conclusions: Our findings support the potential clinical utility of TL, particularly for adenocarcinoma patients, while associations in chromosome 5p15.33 warrant further exploration. Impact: This is the largest lung cancer study of leukocyte TL and OS, and the first to examine the impact of the timing of TL measurement. Our findings suggest that extremely short telomeres are indicative of poor prognosis in NSCLC.
Effect of Acid‐Suppressive Strategies on Pazopanib Efficacy in Patients With Soft‐Tissue Sarcoma

Pazopanib (PAZ), a tyrosine kinase inhibitor used in the treatment of soft tissue sarcoma (STS), should not be administered with acid‐suppressive medications (ASMs) due to decreased drug solubility. Common practice for patients requiring ASM with PAZ is to separate administration by 12 hours; however, there is little real‐world evidence describing clinical outcomes using this strategy. The aim of this study was to determine whether concomitant ASM impacted efficacy and adverse event rates in patients with STS receiving PAZ. Medical records were retrospectively reviewed for patients with STS who received PAZ from June 2011 to July 2017. Patients were stratified into two groups, PAZ with or without ASM (PAZ + ASM or PAZ only). The primary objective was to determine whether progression‐free survival (PFS) differed between groups. Secondary objectives were to determine overall survival (OS) and occurrence of grade 3/4 toxicities. Ninety‐one patients were included in the study, 42 patients in the PAZ + ASM group and 49 in the PAZ only group. Median PFS was significantly shorter in the PAZ + ASM group than the PAZ only group (5.3 vs. 6.7 months). The PAZ + ASM group also had a 74% higher relative risk of progression or death than the PAZ only group, but there was no difference in OS. Regarding adverse events, the PAZ + ASM group trended toward lower levels of grade 3/4 hypertension (19% vs. 37%). These results suggest that ASM should be avoided in patients with STS receiving PAZ. Larger studies are needed to further elucidate the impact of ASM use with PAZ in clinical practice.
Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large‐scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse‐variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin‐like growth factor receptor 1 and other membrane receptors (OR: 0.82–1.18, p values: 6.96 × 10−4–3.28 × 10−8), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.
Synthetic disaccharide standards enable quantitative analysis of stored heparan sulfate in MPS IIIA murine brain regions.

Heparan sulfate (HS) is a complex polysaccharide from the glycosaminoglycan (GAG) family that accumulates in tissues in several neurological lysosomal storage diseases known as mucopolysaccharidosis (MPS) disorders. The quantitation of HS in biological samples is important for studying MPS disorders but is very challenging due to its high molecular weight and heterogeneity. Recently, acid catalysed butanolysis followed by LC-MS/MS analysis has emerged as a promising method for the determination of HS. Butanolysis of HS produces fully desulfated disaccharide cleavage products which are detected by LC-MS/MS. Herein we describe the synthesis of butylated HS disaccharide standards and their use for determining the identity of major product peaks in LC-MS chromatograms from butanolysis of HS as well as the related GAGs heparin and heparosan. Furthermore, synthesis of a d9-labelled disaccharide internal standard enabled the development of a quantitative LC-MS/MS assay for HS. The assay was utilised for the analysis of MPS IIIA mouse brain tissues revealing significant differences in abundance and in the regional accumulation of the various HS disaccharides in affected mice.
Psychopharmacological Treatment of Schizophrenia Over Time in 30 908 Inpatients: Data From the AMSP Study

Abstract Background Psychotropic drugs are the cornerstone of schizophrenia treatment, often requiring lifelong treatment. Data on pharmacotherapy in inpatient settings are lacking. Methods Prescription data of schizophrenic inpatients within the time period 2000–2015 were obtained from the database of the Drug Safety Program in Psychiatry (AMSP). Data were collected at 2 index dates per year; the prescription patterns and changes over time were analyzed. Results Among 30 908 inpatients (mean age 41.6 years, 57.8% males), the drug classes administered most often were antipsychotics (94.8%), tranquilizers (32%), antidepressants (16.5%), antiparkinsonians (16%), anticonvulsants (14.1%), hypnotics (8.1%), and lithium (2.1%). The use of second-generation antipsychotics significantly increased from 62.8% in 2000 to 88.9% in 2015 (P < .001), whereas the prescription of first-generation antipsychotics decreased from 46.6% in 2000 to 24.7% in 2015 (P < .001). The administration of long-acting injectable antipsychotics decreased from 15.2% in 2000 to 11.7% in 2015 (P = .006). Clopazine was the most often used antipsychotic, having been used for 21.3% of all patients. Polypharmacy rates (≥5 drugs) increased from 19% in 2000 to 26.5% in 2015. Psychiatric polypharmacy (≥3 psychotropic drugs) was present in 44.7% of patients. Conclusions Combinations of antipsychotics and augmentation therapies with other drug classes are frequently prescribed for schizophrenic patients. Though treatment resistance and unsatisfactory functional outcomes reflect clinical necessity, further prospective studies are needed on real-world prescription patterns in schizophrenia to evaluate the efficacy and safety of this common practice.
Construct Validity of the Professional Quality of Life (ProQoL) Scale in a Sample of Child Protection Workers

Abstract The Professional Quality of Life (ProQOL) scale is one of the most widely used measures of compassion satisfaction and fatigue despite there being little publicly available evidence to support its validity. This study, conducted among a sample of 310 child protection workers, assessed the construct validity of this measure using confirmatory factor analysis (CFA) and bifactor modeling. The CFA failed to confirm the adequacy of the three‐factor structure proposed by Stamm (2010). In response, a bifactor model postulating a factor structure with a general factor in addition to independent factors (compassion satisfaction, job burnout, and secondary traumatic stress) was proposed, highlighting the unidimensionality of the ProQOL while allowing for each subscale to be used separately. Moreover, this bifactor model of the ProQOL was moderately correlated with the Posttraumatic Disorder Checklist, r = −. 427, p < . 001, and strongly correlated with scales of well‐being at work, r = . 694, p < . 001, and psychological distress at work, r = −. 666, p < . 001, thus supporting the ProQOL's convergent validity. No associations were found between the ProQOL and the Life Event Checklist, which supports the ProQOL's discriminant validity. Overall, the results indicated that compassion satisfaction and compassion fatigue represent higher and lower levels of the same construct rather than two different constructs. Researchers and clinicians could therefore compute a single score to rate professionals’ individual levels of professional quality of life.
Overexpression of lncRNA Gm15621 alleviates apoptosis and inflammation response resulting from sevoflurane treatment through inhibiting miR‐133a/Sox4

Sevoflurane is the most widely used anesthetic administered by inhalation. Exposure to sevoflurane can elicit learning deficits and abnormal cognitive disorder. In this study, we investigated the function of long noncoding RNA (lncRNA) Gm15621. Primary hippocampal neuron cells were used to analyze the function of lncRNA Gm15621 in vitro. The tunel, inflammation markers, and cell survival rates were detected to evaluate the function of lncRNA Gm15621. Dual‐luciferase reporter assay was used to identify the interaction between microRNA 133a and Gm15621. We found that lncRNA Gm15621 located in the cytoplasm. The expression of lncRNA Gm15621 was decreased with the development of sevoflurane exposure. Overexpression of lncRNA Gm15621 significantly reduced the apoptosis and cell survival rates. The inflammation response was also attenuated in lncRNA Gm15621 overexpressed group. The dual‐luciferase assay revealed that miR‐133a was the direct target of lncRNA Gm15621. In addition, we also found that Sox4 was a downstream target of miR‐133a and lncRNA Gm15621 exerted its biological functions by regulating the expression of Sox4. In summary, our findings revealed that lncRNA Gm15621 ameliorated the sevoflurane‐induced neurotoxicity and the important role of Gm15621/miR‐133a/Sox4 axis in cognitive disorder.
Simulink Modeling of Multiphase Induction Motors

The subject of the article is a computer model of a multi-phase squirrel cage induction motor with a stator number phases greater than 3. This model is written in the Matlab / Simulink language and is presented graphically in the form of a multi-terminal block. A system of differential equations describing a multiphase motor was introduced by the use of Simulink library blocks. This system of equations takes into account the interaction of higher spatial harmonics. The block of equations is connected to the external power circuit by means of the so-called ideal transformers. This allows for modeling not only the electric drive, but also the internal waveforms of the converter and the power supply system. Application of the model was illustrated by the operation of a nine-phase motor powered by a frequency-controlled voltage source inverter at a variable supply sequence and referred to measurements.
Defect Classification of Printed Circuit Boards based on Transfer Learning

In this paper, we propose a transfer learning based method to classify PCB images into two classes - i) True Defect and ii) Pseudo Defect A pre-trained Inception-V3 model is used for transfer learning and the mid-level representations of the PCB images are extracted from the output of an intermediate layer of the pre-trained model. These mid-level representations of the PCB images are used to train a small adaptation network, which makes the overall network suitable for classification of PCB images. In order to tackle overfitting, regularization strategies are implemented. The results of our experiment, conducted on real world PCB images, show a significant improvement in the classification accuracy as compared to previous work.
Detection of DDoS Attack on SDN Control plane using Hybrid Machine Learning Techniques

Software Defined Network (SDN) provides a promising solution over traditional networks by decoupling the control plane and network plane. With the help of this feature, controller can get global view of the entire network. Since the controller acts as a core part of the SDN environment, there is a serious threat towards the controller in terms of security. A Distributed Denial of Service (DDoS) attack is the most potential attack in SDN environment. DDoS attack prevents the authorized user to access the available resources for infinite amount of time. In this paper, we have proposed the hybrid machine learning model to protect the controller from DDoS attacks. And our experimental results clearly manifest that the hybrid machine learning model provides more accuracy, detection rate and less false alarm rate compared to simple machine learning models.
Design and Implementation of IoT Based Smart Energy Meter

Modern day smart grid technology relies heavily on communication networks for two-way communication between load, generation, transmission, and control centre. As a part of the smart grid, smart meters use advanced metering infrastructures (AMI) that are widely distributed and interconnected to the communication network. Such integrated infrastructure has made the power network easy to diagnose and control. But unfortunately, this has made power system networks vulnerable to various forms cyber-attacks that have caused major concerns to power researchers in recent times. As a first step towards such cyber-attack related researches and searching for possible mitigation techniques, it is necessary that a smart meter be developed that has the feature of network integration. The present contribution reports the design, fabrication, and operation of an Internet of Things (IoT) based smart meter using Arduino to serve as an integral part of a smart grid system
A Comparative Study of Machine Learning and Deep Learning Techniques for Sentiment Analysis

In this day and age an increasing number of people are using online social networks and services to not only connect and communicate but also to voice their opinions. Sentiment Analysis is the identifying and categorizing of these opinions to determine the public’ s opinion towards a particular topic, problem, product etc. The importance of Sentiment analysis is increasing day by day. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Deep Learning is a subfield of machine learning concerned with algorithms that are neural implementations, most commonly seen as neural networks, neural beliefs, etc. It is crucial to employ the most feasible and accurate technique while analyzing sentiments for a given data as this affects both producers as well as consumers. This paper puts forward a study that compares various Machine learning, Deep learning as well as their hybrid techniques. It compares their accuracy for Sentiment Analysis and thus it can be concluded that in most cases Deep learning techniques give better results. However, in some cases the difference in the accuracies of the two techniques is not substantial enough and thus it is better to use Machine Learning methods as they are easier to implement.
Analysis and Implementation of Message Authentication Code (MAC) Algorithms for GOOSE Message Security

There is growing awareness towards cybersecurity threats in power systems. IEC 61850 standard facilitates communication between different Intelligent Electronic devices (IEDs) and eases interoperable operation with set data and message structures. An unwanted consequence of this standardized communication over ethernet is increased viability to cyber threats. The IEC 62351-6 standard stipulates the use of digital signatures for ensuring integrity in IEC 61850 message exchanges. However, the digital signatures result in higher computational times which makes it very difficult to use for Generic Object-Oriented Substation Events (GOOSE) messages. This short communication article proposes implementation of the Message Authentication Code (MAC) algorithms, such as Hash-based Message Authentication Code (HMAC) and Advanced Encryption Standard-Galois Message Authentication Code (AES-GMAC), for GOOSE message integrity. Lab tests are run to observe their timing performances and feasibility for GOOSE.
Detection of Anomaly in Train Speed for Intelligent Railway Systems

Anomaly detection has been applied to diverse critical applications or systems since anomalous behaviors could lead to fatal situations during the operation. In intelligent transportation systems, anomaly detection also plays an important role by allowing the system administrator to assess the imminent emergence of any incidents. In this paper, we address real-time anomaly detection that has not yet been thoroughly explored in railway system. We propose an online anomaly detection scheme in train speed form railway systems using machine learning approaches. We adopt the Bayesian statistical learning model to represent normal behavior of train speed changes and detect the anomaly based on the occurrence probability of each speed change observation. While the Bayesian statistical learning model can detect sudden speed changes, it may not be able to detect malicious behavior of an intelligent attacker who gradually reduces or increases the train speed to cause the collision between two subsequent trains. We thus propose a linear regression model that takes into account time duration and travel distance from the departure station to detect anomaly. We evaluate the proposed scheme through comprehensive simulations. The results show that the proposed scheme efficiently detects anomalous speed change by accurate predictions from the learning phase and it outperforms a baseline approach with an improvement in sensitivity by up to 22%.
Clusters of Features Using Complementary Information Applied to Gender Classification From Face Images

Face recognition performance by computers has been shown to be more accurate than that of humans. However, a bias with soft-biometrics features has been detected. This bias reduces recognition performance when gender is used. Feature selection for gender classification from face images is a difficult problem since faces contain symmetrical and redundant features. We argue that traditional methods, based on mutual information using pairs of features to estimate the relevance and redundancy among features, fail to select the right set of features in cases where there are strong spatial correlations among features, which is the case with facial images. In this paper, a new method is proposed fusing a filter and a wrapper to measure the relationships among image features, and to select feature clusters based on mutual information for gender classification. We applied this method on nine face datasets using an SVM classifier. We were able to achieve 98.2% correct gender classification in the testing partition using the UND, 95.56% with the Morph II, 98.33% on the LFW, and 98.66% on celebA databases. We validated the results using a cross-test with three different datasets: COFW, Adience, and Image of Groups, that were not used to define the parameters of our method. Additionally, the method was tested with a Random Forest. All the results achieved are better than those previously published on the same databases, and with a significantly smaller number of total features.
A Novel Approach for Agile Software Development Methodology Selection Using Fuzzy Inference System

An Agile Methodology of software development has been more popular in the software industry in terms of fast software delivery, less documentation, more satisfaction and more interactions between developers and users. In this paper Fuzzy Inference System based model is proposed to select an appropriate agile method for successful software development. A number of agile methods such as Extreme Programming (XP), Scrum, Feature Driven Development (FDD) and Dynamic System Development Method (DSDM) have been proposed in the literature. Selection of an appropriate method for a given project is a pivotal and a challenging issue. As a solution to this problem, the projects are categorized into four classes namely, Small scale, Medium scale, large scale and safety critical projects, where project size is estimated based on programmer months (PM) and the proposed model is applied to select the most appropriate agile method for each of the project category. MATLAB Simulation results reveal that the proposed model is efficient in predicting the suitability of one of the agile methods.