Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel GEMIN4 Variant in a Consanguineous Family Leads to Neurodevelopmental Impairment with Severe Microcephaly, Spastic Quadriplegia, Epilepsy, and Cataracts

Pathogenic variants in GEMIN4 contribute to a hereditary disorder characterized by neurodevelopmental features, microcephaly, cataracts, and renal abnormalities (known as NEDMCR). To date, only two homoallelic variations have been linked to the disease. Moreover, clinical features associated with the variants have not been fully elucidated yet. Here, we identified a novel variant in GEMIN4 (NM_015721:exon2:c.440A>G:p.His147Arg) in two siblings from a consanguineous Saudi family by using whole exome sequencing followed by Sanger sequence verification. We comprehensively investigated the patients’ clinical features, including brain imaging and electroencephalogram findings, and compared their phenotypic characteristics with those of previously reported cases. In silico prediction and structural modeling support that the p.His147Arg variant is pathogenic.
Machine Learning Schemes for Anomaly Detection in Solar Power Plants

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize recent advances in machine learning to accurately and timely detect different anomalies and condition monitoring. This paper addresses this issue by evaluating different machine learning techniques and schemes and showing how to apply these approaches to solve anomaly detection and detect faults on photovoltaic components. For this, we apply distinct state-of-the-art machine learning techniques (AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest) to detect faults/anomalies and evaluate their performance. These models shall identify the PV system's healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such complex solution space.
Computational Investigations on Interactions Between DNA and Flavonols

Today, the main task of researchers is to study and develop drugs that are less toxic and have lesser side effects. The principal motive of this research is to study and analyze the interaction between naturally active compounds flavonoids with biomolecule DNA. Since the interaction between DNA and ligand is essential in drug designing, this study will provide a good base for further research and development of less toxic and more efficient drugs for various diseases. The selected compounds for this study are Kaempferide, Kaempferol, Morin, and Rutin. They all fall into the category ‘flavonols’ of flavonoids. Computational methods are implemented for theoretical drug designing. These are molecular optimization, molecular docking, and molecular dynamics. Computational results are compared with experimental data from previous studies. Molecular docking gives the most preferred orientation of ligands within DNA, and Molecular Dynamics provides the details about the DNA-ligand complex with respect to time. Free energy calculations were also performed by implementing MMPBSA and MMGBSA calculations.
Intraoperative lactic acid concentration during liver transplantation and cutoff values to predict early mortality: a retrospective analysis of 3,338 cases

Background We aimed to explore the distribution of intraoperative lactic acid (LA) level during liver transplantation (LT) and determine the optimal cutoff values to predict post-LT 30-day and 90-day mortality. Methods Intraoperative LA data from 3,338 patients were collected between 2008 to 2019 and all-cause mortalities within 30 and 90 days were retrospectively reviewed. Of the three LA levels measured during preanhepatic, anhepatic, and neohepatic phase of LT, the peak LA level was selected to explore the distribution and predict early post-LT mortality. To determine the best cutoff values of LA, we used a classification and regression tree algorithm and maximally selected rank statistics with the smallest P value. Results The median intraoperative LA level was 4.4 mmol/L (range: 0.5–34.7, interquartile range: 3.0–6.2 mmol/L). Of the 3,338 patients, 1,884 (56.4%) had LA levels > 4.0 mmol/L and 188 (5.6%) had LA levels > 10 mmol/L. Patients with LA levels > 16.7 mmol/L and 13.5–16.7 mmol/L showed significantly higher 30-day mortality rates of 58.3% and 21.2%, respectively. For the prediction of the 90-day mortality, 8.4 mmol/L of intraoperative LA was the best cutoff value. Conclusions Approximately 6% of the LT recipients showed intraoperative hyperlactatemia of > 10 mmol/L during LT, and those with LA > 8.4 mmol/L were associated with significantly higher early post-LT mortality.
Clinical status, biochemical profile and management of a single cohort of patients with arginase deficiency

Arginase deficiency is a rare autosomal recessive urea cycle disorder (UCD) caused by mutations in the ARG1 gene encoding arginase that catalyses the hydrolysis of arginine to ornithine and urea. Patients have hyperargininaemia and progressive neurological impairment but generally suffer fewer metabolic decompensations compared to other UCDs. The objective is to describe the clinical features, biochemical profile, neuroradiological findings and experience of managing children with arginase deficiency. Twenty‐year retrospective review of patient medical records at a single metabolic centre was performed. Six patients from three unrelated families were identified. Mean age at first symptom was 3.3 (1.5–9.0) years, while mean age at diagnosis was 8.8 (0.16–15.92) years. Four patients developed spastic diplegia and two of six with spastic quadriplegia with classical features including hyperreflexia, clonus and toe walking. This resulted in gait abnormalities that have been monitored using the GAITRite system and required Achilles tendon release in five children. Generalised tonic‐clonic seizures and/or absences were present in three of six children and were controlled with anticonvulsants. All patients had moderate learning difficulties. Neuroimaging showed cerebral/cerebellar atrophy in four patients and basal ganglia abnormalities in two. Arginine levels were universally elevated throughout follow‐up despite protein restriction, essential amino acid supplementation and ammonia scavengers, and neurological outcome was generally poor. Two patients died following severe metabolic decompensation in adolescence. Children with arginase deficiency continue to present a management challenge of what appears to be an inexorable course of neurocognitive impairment. Further insight into disease mechanisms may provide insight into novel treatment strategies.
Studi Literatur Human Activity Recognition (HAR) Menggunakan Sensor Inersia

Human activity recognition (HAR) is one of the topics that is being widely researched because of its diverse implementation in various fields such as health, construction, and UI / UX. As MEMS (Micro Electro Mechanical Systems) evolves, HAR data acquisition can be done more easily and efficiently using inertial sensors. Inertial sensor data processing for HAR requires a series of processes and a variety of techniques. This literature study aims to summarize the various approaches that have been used in existing research in building the HAR model. Published articles are collected from ScienceDirect, IEEE Xplore, and MDPI over the past five years (2017-2021). From the 38 studies identified, information extracted are the overview of the areas of HAR implementation, data acquisition, public datasets, pre-process methods, feature extraction approaches, feature selection methods, classification models, training scenarios, model performance, and research challenges in this topic. The analysis showed that there is still room to improve the performance of the HAR model. Therefore, future research on the topic of HAR using inertial sensors can focus on extracting and selecting more optimal features, considering the robustness level of the model, increasing the complexity of classified activities, and balancing accuracy with computation time.
Circulating markers in metastatic cancer: new perspectives.

Metastasis is one of the leading causes of cancerrelated death resulting from the final outcome of multiple processes such as epithelial-mesenchymal transition (EMT) and the reverse (MET), extracellular matrix (ECM) remodeling, immune system activation and reprogramming of the tumor microenvironment. This special issue on “Noninvasive strategies in metastatic dissemination” focuses on recent advances and novel studies in exosomes as well as other cellular processes influencing metastasis in cancer. The special issue highlights the areas covering novel insights into the role of phenotypic plasticity and EMTassociated heterogeneity in circulating tumor cells (CTCs), exosomes in cancer development and ECM remodeling in tumor progression and metastasis.
Working in the Sphere of Food Trade and Service in Armenia: Patterns of Precariousness

The food trade and service sector is considered to be the most uncertain, with a strong expression of instability both worldwide and in Armenia. Currently, the term precariousness is widely used in the field to describe the uncertainty and risks in the field. Research on food trade and services in Armenia has been conducted recently, but there have been no sociological studies on patterns of precariousness in the field. The study of patterns of precariousness provides an in-depth look at sectoral uncertainty and risk in relation to broader social structures. The purpose of this article is to study the patterns of precariousness expressed in the field through secondary analysis, which will provide opportunities for deeper analysis in the future, emphasizing the expressions of social injustice and inequality.
Gate Tunnelling Current Model for Nanoscale MOSFETs with Varying Surface Potential

ABSTRACT At nanoscale, the channel of metal oxide semiconductor field effect transistors (MOSFETs) acts like a potential well within which electron energy levels are quantized and a nonzero wavefunction is obtained at the oxide–semiconductor interface. As a result the gate tunnelling current has emerged a significant constraint in respect of scaling of ultrathin gate oxides. We have developed an analytical model considering varying surface potential with applied voltage for evaluating gate tunnelling current through thin dielectrics in nanoscale MOSFETs. The electron wavefunction has been calculated by treating the band profile in the channel as a triangular potential well. The tunnelling probability through the gate oxide has been evaluated using Jeffreys–Wentzel–Kramers–Brillouin approximation method. The tunnelling current density is estimated from the evaluated interface wavefunction along with the tunnelling probability. The results from the present model compare well with the published Mondal–Dutta model and the experimental data. The novelty of the present model lies in its simplicity and its analyticity requiring much less computational efforts for its implementation.
The great dichotomy: How experiences of history and transcendence explain Ukraine’s political life

The article uses Eric Voegelin’s ontology to address domestic processes in contemporary Ukraine. It explains how interpretations of experiences of history and transcendence evoke political order and justice. It also outlines the nature of political symbols deriving from these experiences. The article argues that Ukraine’s social architecture is constructed according to a set of arrangements that are generally regarded as moral and functional under given circumstances. As a result, it provides political elites a platform from which to build a plan of action and gain legitimacy. The article not only shows how Voegelin’s ontology can be used to explain Zelensky’s 2019 presidential election victory but also highlights its interpretative advantages over competing analytical approaches from within the frameworks of institutionalism and behaviorism.
Semi-Analytical Model Using the Uniform Theory of Diffraction for Single Feed High Gain Antenna Structure

The recent advancement in commercial electromagnetic computational solvers, along with the computational processing resources gives a huge advantage for antenna designers to study complex structures and optimize their performance. In addition, the huge leap in processors speeds, and the advent of parallel processing techniques made it possible to reduce the computation time dramatically. Consequently, this made it less attractive for designers to explore fast analytical models for their structures. The main drawback of the full and blind dependence on commercial solvers in the design process is the lack of the physical insight for the operating mechanism of the structure. On the other hand, despite that numerical and analytical series solutions can give an accurate solution to the electromagnetic problem being studied, they hardly can give an insight on the internal interactions and the operating mechanism of the structure. In this work we provide a semi-analytical model along with a thorough physical insight for a single feed high gain antenna structure. The provided model not only gives a powerful insight into understanding the radiation mechanism, but also is very computationally effective, and allows the use of antenna theory intuition, where the structure can be perceived as an antenna array, and the principles of array factor, power tapering, etc. can be intuitively used to understand and manipulate the structure radiation behavior.
The emergence of a social marketing framework to increase hand washing among rural women in Telangana, India while conducting Participatory Action Research (PAR)

This project examines the feasibility of employing methods associated with the Participatory Action Research (PAR) approach to develop a Social Marketing campaign around hand washing in Southern India. Historically, media and large-scale behavior change communications campaigns have driven public awareness on a myriad of health topics. With that said, these efforts can fail to produce measurable results often due to an absence of important insights into the target audience, and therefore a lack of cultural relevance in messaging efforts. Blending a participatory approach with traditional campaign development tactics that are inherently more top-down, may create more tailored and targeted communication’s strategies. This work applied a unique approach to creating a Social Marketing campaign by engaging the target audience as co-creators in the development process. Focus groups were held with mothers in five study villages selected in Medchal Mandal, an area located in the state of Telangana. During the qualitative analysis process, there was a natural emergence of core Social Marketing campaign components. Participants organically provided suggestions for various campaign concepts within the same framework that is commonly used by Social Marketing professionals. Their suggestions for where, when and how a campaign should be carried out generally mapped on to the Marketing Mix or 4Ps. The study was successful in producing rich insights into the target audience using a feasible approach for community engagement. It also has exciting implications for future work around participatory campaign development and increased synergy between the fields of participatory research and Social Marketing. It was concluded that moving forward, this blended approach should be further investigated as a potential method for igniting social change.
Image Adjustment for Multi-Exposure Images Based on Convolutional Neural Networks

SUMMARY In this paper, we propose an image adjustment method for multi-exposure images based on convolutional neural networks (CNNs). We call image regions without information due to saturation and object moving in multi-exposure images lacking areas in this paper. Lacking areas cause the ghosting artifact in fused images from sets of multi-exposure images by conventional fusion methods, which tackle the artifact. To avoid this problem, the proposed method estimates the information of lacking areas via adaptive inpainting. The proposed CNN consists of three networks, warp and reﬁnement, detection, and inpainting networks. The second and third networks detect lacking areas and estimate their pixel values, respectively. In the experiments, it is observed that a simple fusion method with the proposed method outperforms state-of-the-art fusion methods in the peak signal-to-noise ratio. Moreover, the proposed method is applied for various fusion methods as pre-processing, and results show obviously reducing artifacts.
Orf disease in local goat; clinical and phylogenetic study in Al-Qadisiyah governorate, Iraq

Article history: Received February 20, 2021 Accepted May 20, 2021 Available online November 20, 2021 This study aimed to diagnose orf virus ORFV from infected goats in Al-Qadisiyah Governorate of Iraq, using polymerase chain reaction and construction of phylogenetic tree. Clinical examination was made on 400 goats in different herds and crusty scabs were collected from forty infected goats. All scabs were subjected to (PCR), sequencing, and phylogenetic analysis involving the presence of GIF gene GM-CSF inhibitor factor, a characteristic gene of the ORFV. The typical proliferative skin lesion with overall orf prevalence results by PCR was 10% with low mortality rates and high significance variation with age and gender. Two positive PCR amplicons were sent for partial-genebased sequencing and Phylogenetic analysis. Multiple alignments of sequences presented close identities with two Iraqi global isolates of the virus. This study didn't only report novel sequences from the local orf virus isolates, but it also provides important data about transmission of local sheep orf virus into local goat during closely rearing and grazing and orf outbreak in small ruminant in Iraq might be suspected.
Effects of an Electric Field on the Conformational Transition of the Protein: Pulsed and Oscillating Electric Fields with Different Frequencies

The effect of pulsed and oscillating electric fields with different frequencies on the conformational properties of all-α proteins was investigated by molecular dynamics simulations. The root mean square deviation, the root mean square fluctuation, the dipole moment distribution, and the secondary structure analysis were used to assess the protein samples’ structural characteristics. In the simulation, we found that the higher frequency of the electric field influences the rapid response to the secondary structural transitions. However, the conformational changes measured by RMSD are diminished by applying the electrical field with a higher frequency. During the dipole moment analysis, we found that the magnitude and frequency of the dipole moment was directly related to the strength and frequency of the external electric field. In terms of the type of electric fields, we found that the average values of RMSD and RMSF of whole molecular protein are larger when the protein is exposed in the pulsed electric field. Concerning the typical sample 1BBL, the secondary structure analysis showed that two alpha-helix segments both transit to turns or random coils almost simultaneously when it is exposed in a pulsed electric field. Meanwhile, two segments present the different characteristic times when the transition occurs in the condition of an oscillating electric field. This study also demonstrated that the protein with fewer charged residues or more residues in forming α-helical structures display the higher conformational stability. These conclusions, achieved using MD simulations, provide a theoretical understanding of the effect of the frequency and expression form of external electric fields on the conformational changes of the all-α proteins with charged residues and the guidance for anticipative applications.
Natural Hazard Mitigation: Professionalization of the Policy Making Process

Liability and increased federal involvement in issues of public safety has resulted in the development of a bewildering array of agencies and entities at all levels of government* In spite of this increased involvement, losses from catastrophic natural hazardous events are continuing to increase at an alarming rate. Although there is an increased federal involvement, primary responsibility and authority for dealing with the problems associated with natural hazard exposure rests with the states and local governments. However, the capacity of state and local governments to deal with these problems is significantly constrained by geophysical, ecological, and sociopolitical factors. The purpose of this paper is to discuss the context in which natural hazard problems are defined and policies adopted in the United States. Further we will examine the barriers to the adoptation and implementation of natural hazard mitigation policies. Professionalization of the policy adoptation and implementation process is presented as a basis for increased success in reducing societal risks to natural hazasrds.
A graph sequence neural architecture for code completion with semantic structure features

Code completion plays an important role in intelligent software development for accelerating coding efficiency. Recently, the prediction models based on deep learning have achieved good performance in code completion task. However, the existing models cannot avoid three drawbacks: (i) In the existing models, the code representation loses the information (parent–child information between nodes) and lacks many effective features (orientation between nodes). (ii) The known code structure information is not fully utilized, which will cause the model to generate completely irrelevant results. (iii) Simple sequence modeling ignores repeated patterns and structural information. Besides, previous works cannot capture the characteristics of correlation and directionality between nodes. In this paper, we propose a Code Completion approach named CC‐GGNN, which is graph model based on Gated Graph Neural Networks (GGNNs) to address the problems. We introduce a new architecture to obtain the effective code features from code representation. In order to utilize the known information, we propose Classification Mechanism, which classifies the representation of the node using the known parent node and constructs training graph in the model. The experimental results show that our model outperforms the state‐of‐the‐art methods MRR@5 at most 9.2% and ACC at most 11.4% in datasets.