Neuronal SAM68 differentially regulates alternative last exon splicing and ensures proper synapse development and function.

Alternative splicing in the 3'UTR of mammalian genes plays a crucial role in diverse biological processes, including cell differentiation and development. SAM68 is a key splicing regulator that controls the diversity of 3'UTR isoforms through alternative last exon (ALE) selection. However, the tissue/cell type-specific mechanisms underlying the splicing control at the 3' end and its functional significance remain unclear. Here, we show that SAM68 regulates ALE splicing in a dose-dependent manner and the neuronal splicing is differentially regulated depending on the characteristics of the target transcript. Specifically, we found that SAM68 regulates interleukin-1 receptor-associated protein splicing through the interaction with U1 small nuclear ribonucleoprotein. In contrast, the ALE splicing of protocadherin-15 (Pcdh15), a gene implicated in several neuropsychiatric disorders, is independent of U1 small nuclear ribonucleoprotein but modulated by the calcium/calmodulin-dependent protein kinase signaling pathway. We found that the aberrant ALE selection of Pcdh15 led to a conversion from a membrane-bound to a soluble isoform and consequently disrupted its localization into excitatory and inhibitory synapses. Notably, the neuronal expression of the soluble form of PCDH15 preferentially affected the number of inhibitory synapses. Moreover, the soluble form of PCDH15 interacted physically with α-neurexins and further disrupted neuroligin-2-induced inhibitory synapses in artificial synapse formation assays. Our findings provide novel insights into the role of neuron-specific alternative 3'UTR isoform selections in synapse development.

Efficacy and Clinical Outcomes of mRNA COVID-19 Vaccine in Pregnancy: A Systematic Review and Meta-Analysis.

BACKGROUND: The world has witnessed one of the largest pandemics, dubbed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of December 2020, the USA alone reported 98,948 cases of coronavirus disease 2019 (COVID-19) infection during pregnancy, with 109 related maternal deaths. Current evidence suggests that unvaccinated pregnant women infected with SARS-CoV-2 are at a higher risk of experiencing complications related to COVID-19 compared to nonpregnant women. This review aimed to provide healthcare workers and non-healthcare workers with a comprehensive overview of the available information regarding the efficacy of vaccines in pregnant women. SUMMARY: We performed a systematic review and meta-analysis following PRISMA guidelines. The search through the database for articles published between December 2019 and October 2021 was performed. A comprehensive search was performed in PubMed, Scopus, and EMBASE databases for research publications published between December 2019 and October 2021. We focused on original research, case reports, case series, and vaccination side effect by authoritative health institutions. Phrases used for the Medical Subject Heading [MeSH] search included ("COVID-19" [MeSH]) or ("Vaccine" [MeSH]) and ("mRNA" [MeSH]) and ("Pregnant" [MeSH]). Eleven studies were selected and included, with a total of 46,264 pregnancies that were vaccinated with mRNA-containing lipid nanoparticle vaccine from Pfizer/BioNTech and Moderna during pregnancy. There were no randomized trials, and all studies were observational (prospective, retrospective, and cross-sectional). The mean maternal age was 32.2 years, and 98.7% of pregnant women received the Pfizer COVID-19 vaccination. The local and systemic adverse effects of the vaccination in pregnant women were analyzed and reported. The local adverse effects of the vaccination (at least 1 dose) such as local pain, swelling, and redness were reported in 32%, 5%, and 1%, respectively. The systemic adverse effects such as fatigue, headaches, new onset or worsening of muscle pain, chills, fever, and joint pains were also reported in 25%, 19%, 18%, 12%, 11%, and 8%, respectively. The average birthweight was 3,452 g. Among these pregnancies, 0.03% were stillbirth and 3.68% preterm (<37 weeks) births. KEY MESSAGES: The systemic side effect profile after administering the COVID-19 mRNA vaccine to pregnant women was similar to that in nonpregnant women. Maternal and fetal morbidity and mortality were lowered with the administration of either one or both the doses of the mRNA COVID-19 vaccination.

IKZF1 rs4132601 and rs11978267 gene polymorphisms and paediatric systemic lupus erythematosus; relation to lupus nephritis.

The demographic factors, the socioeconomic status and the ethnicity of populations are important players that determine the incidence, the prevalence and the spectrum of systemic lupus erythematosus (SLE) clinical presentations in different populations. Therefore, the purpose of the present research was to investigate the possible association between the Ikaros family zinc finger 1 gene (IKZF1) rs4132601 and rs11978267 single nucleotide polymorphisms (SNPs) and SLE susceptibility and clinical presentations including lupus nephritis (LN) among Egyptian paediatric patients. After DNA extraction from Ethylenediaminetetraacetic acid (EDTA) blood samples for 104 paediatric SLE (pSLE) patients and 286 healthy controls, the investigated SNPs (IKZF1 rs4132601 and rs11978267) were genotyped using TaqMan-Real-time Polymerase chain reaction (PCR). The G allele, GG and GT genotypes of IKZF1 rs4132601 were associated with pSLE (pc<.001, OR 2.97, 3.2 and 2.25, respectively). The GG and GA haplotype were more frequent in pSLE patients than other haplotypes (pc<.001, OR 3.47 and pc = .004, OR = 2.8, respectively). The studied SNPs have no impact on the distinctive features of pSLE. The rs4132601 TG genotype was significantly associated with proliferative LN (pc = .03) The IKZF1 rs4132601 can be considered a risk factor for SLE in the cohort of Egyptian children. The TG genotype of the IKZF1 rs4132601 may predispose to proliferative LN.

Correlates of childhood caries: A study in Qatar.

BACKGROUND: The prevalance of dental caries in children in Qatar is high, which necessitates preventive efforts. AIM: To identify the sociodemographic and behavioural correlates of dental caries in the primary dentition of children 4- to 8-year-olds in Qatar. DESIGN: Weighted data from the Qatar Child Oral Health Survey 2017 were analysed for caries prevalence (dmft>0) and experience (dmft). Sociodemographic and behavioural variables were also drawn from the survey. RESULTS AND CONCLUSION: Among the 1154 children, caries prevalence was 69.3% (95%CI [63.4, 74.5]) and experience at 3.8 dmft (95%CI [3.3, 4.2]). The prevalence ratio (PR) 0.82 (0.72, 0.94) was lower among younger than in older children; those for non-Qatari nationality Arabic PR 0.91 (0.82, 1.00) and Other PR 0.75 (0.57, 0.99) than for Qatari nationality; those attending international kindergartens/schools PR 0.89 (0.80, 0.99) than independent schools; and whose parents had university-level education PR 0.85 (0.75,0.95) than did not. Caries prevalence was lower among those toothbrushing by age 3 years PR 0.88 (0.80,0.99) than later; children with low/intermediate sugar exposures PR 0.85 (0.74,0.97) and 0.89 (0.79,1.00) than those with high exposures; children with a dental check-up PR 0.68 (0.53,0.87) than those without; and children who drank bottled water with some fluoride PR 0.89 (0.80,0.99) than those who did not. Findings were similar for dmft. In conclusion caries prevalence varied but was high across sociodemographic correlates indicating vulnerablity. Interventions focusing on behaviours - such as toothbrushing, reducing sugar intake, check-up and encouraging intake of water with fluoride - are needed.

Condition Assessment of Natural Ester-Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties.

Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil.

Neuron-derived neurotrophic factor protects against dexamethasone-induced skeletal muscle atrophy.

Skeletal muscle atrophy caused by various conditions including aging, nerve damage, and steroid administration, is a serious health problem worldwide. We recently reported that neuron-derived neurotrophic factor (NDNF) functions as a muscle-derived secreted factor, also known as myokine, which exerts protective actions on endothelial cell and cardiomyocyte function. Here, we investigated whether NDNF regulates skeletal muscle atrophy induced by steroid administration and sciatic denervation. NDNF-knockout (KO) mice and age-matched wild-type (WT) mice were subjected to continuous dexamethasone (DEX) treatment or sciatic denervation. NDNF-KO mice exhibited decreased gastrocnemius muscle weight and reduced cross sectional area of myocyte fiber after DEX treatment or sciatic denervation compared with WT mice. Administration of an adenoviral vector expressing NDNF (Ad-NDNF) or recombinant NDNF protein to gastrocnemius muscle of WT mice increased gastrocnemius muscle weight after DEX treatment. NDNF-KO mice showed increased expression of ubiquitin E3-ligases, including atrogin-1 and MuRF-1, in gastrocnemius muscle after DEX treatment, whereas Ad-NDNF reduced expression of atrogin-1 and MuRF-1 in gastrocnemius muscle of WT mice after DEX treatment. Pretreatment of cultured C2C12 myocytes with NDNF protein reversed reduced myotube diameter and increased expression of atrogin-1 and MuRF-1 after DEX stimulation. Treatment of C2C12 myocytes increased Akt phosphorylation. Pretreatment of C2C12 myotubes with the PI3-kinase/Akt inhibitor reversed NDNF-induced increase in myotube fiber diameter after DEX treatment. In conclusion, our findings indicated that NDNF prevents skeletal muscle atrophy in vivo and in vitro through reduction of ubiquitin E3-ligases expression, suggesting that NDNF could be a novel therapeutic target of muscle atrophy.

Distinct Expression of SLM2 Underlies Splicing-Dependent Trans-Synaptic Signaling of Neurexin Across GABAergic Neuron Subtypes.

The mammalian brain contains multiple types of neuronal cells with complex assemblies and distinct structural and functional properties encoded by divergent gene programs. There is increasing evidence that alternative splicing (AS) plays fundamental roles in transcriptomic diversity and specifying synaptic properties of each neuronal cell type. However, the mechanisms underlying AS regulation and whether it controls synapse formation across GABAergic interneurons have not been fully elucidated. Here we show the differential expression levels of Sam68-like molecule 2 (SLM2), a major splicing regulator of neurexin (NRX), in GABAergic neuronal subtypes and its contribution to GABAergic synapse specification. Cortical SLM2 is strongly expressed not only in excitatory neurons but also in a subpopulation of GABAergic interneurons, especially in VIP-positive neurons that are originated from late-born caudal ganglionic eminence (GE)- derived cells. Using artificial synapse formation assay, we found that GE containing cortices form a strong synapse with LRRTM2, a trans-synaptic receptor of the alternatively spliced segment 4 (AS4)(-) of NRX. SLM2 knock-down reduced the NRX AS4(-) isoform expression and hence weaken LRRTM2-induced synapse formation. The addition of NRX AS4(-) was sufficient to rescue the synaptic formation by LRRTM2 in SLM2 knock-down neurons. Thus, our findings suggest a novel function of SLM2 in modifying network formation of a specific population of GABAergic interneurons and contribute to a better understanding of the roles AS plays in regulating synapse specificity and neuronal molecular diversity.

Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission.

Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC-side three-phase current, and the upper and lower bridges' currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extraction. Then, two classification algorithms-multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)-are used for classification. The effectiveness of the proposed framework is validated by a two-terminal simulation model of the MMC-high-voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published results.

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map.

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian-Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

Polymeric Nanocomposites for Environmental and Industrial Applications.

Polymeric nanocomposites (PNC) have an outstanding potential for various applications as the integrated structure of the PNCs exhibits properties that none of its component materials individually possess. Moreover, it is possible to fabricate PNCs into desired shapes and sizes, which would enable controlling their properties, such as their surface area, magnetic behavior, optical properties, and catalytic activity. The low cost and light weight of PNCs have further contributed to their potential in various environmental and industrial applications. Stimuli-responsive nanocomposites are a subgroup of PNCs having a minimum of one promising chemical and physical property that may be controlled by or follow a stimulus response. Such outstanding properties and behaviors have extended the scope of application of these nanocomposites. The present review discusses the various methods of preparation available for PNCs, including in situ synthesis, solution mixing, melt blending, and electrospinning. In addition, various environmental and industrial applications of PNCs, including those in the fields of water treatment, electromagnetic shielding in aerospace applications, sensor devices, and food packaging, are outlined.

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters.

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method.

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time.

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System.

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors.

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV-40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

Glycine receptor α4 subunit facilitates the early embryonic development in mice.

Oviduct fluid is essential for the fertilization and subsequent preimplantation development. Glycine is abundant in oviduct fluid and is reported to be critical for preimplantation development of fertilized eggs in mammals. However, the mechanism by which glycine exerts its action on fertilized eggs is yet to be understood. Here we show that glycine regulates the preimplantation development of mouse fertilized eggs via glycine receptors. Among them, the alpha-4 subunit (Glra4) and the ß subunit are expressed in mouse fertilized eggs, and lacking Glra4 inhibits embryonic development to the blastocyst stage, decreases the number of cells in the blastocysts and the litter size. Thus, we identify a novel function of the glycine receptor, which is considered to act mainly as a neurotransmitter receptor, as a regulator of embryonic development and our data provide new insights into the interactions between oviduct milieu and mammalian fertilized egg.

The Effect of Antenatal Vaginal Progesterone Administration on Uterine, Umbilical, and Fetal Middle Cerebral Artery Doppler Flow: A Cohort Study.

OBJECTIVE: This study aimed to evaluate the effect of vaginal progesterone (P) administration during the second and third trimesters of pregnancy on Doppler velocimetry of uterine, umbilical, and middle cerebral vessels. STUDY DESIGN: A prospective cohort study conducted on 80 women at risk for preterm labor. Uterine artery, umbilical artery, and middle cerebral artery (MCA) Doppler indices were measured before and after 1 week of administration of 200 mg twice daily vaginal P. The primary outcome parameter was the change of MCA pulsatility index (PI) after P administration. Secondary outcomes included changes in uterine artery and umbilical artery Doppler measurement. RESULTS: There was no significant changes of umbilical artery resistance index (RI) (0.69 ± 0.049 vs. 0.68 ± 0.041), umbilical artery PI (1.14 ± 0.118 vs. 1.11 ± 0.116), uterine artery RI (0.66 ± 0.12 vs. 0.66 ± 0.107), uterine artery PI (1.00 ± 0.26 vs. 1.016 ± 0.24), and MCA PI (1.27 ± 0.18 vs. 1.26 ± 0.23) measurements before and after 1 week of P administration, respectively. CONCLUSION: Administration of vaginal P has no significant effects on uterine artery, umbilical artery, and MCA Doppler indices.

Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods.

In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges' currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection. The two-terminal MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN, the SoftMax classifier performed better in detection and classification accuracy as well as testing speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based DNN and SoftMax classifier.

ARID5B rs10821936 and rs10994982 gene polymorphisms and acute lymphoblastic leukemia: relation to disease susceptibility and outcome.

ARID5B rs10821936 and rs10994982 single nucleotide polymorphism (SNP) have been associated with the risk of acute lymphoblastic leukemia (ALL) in different ethnic populations. We investigated the association between the ARID5B rs10821936 C > T, rs10994982 A > G, and susceptibility to ALL in a cohort of Egyptian individuals and investigated their role in relation to disease outcome. Real-time PCR typing was done for ARID5B rs10821936 and rs10994982 SNPs for 128 pediatric ALL (pALL), 45 adult ALL (aALL), and 436 healthy controls. Significant risk associations were found between the C allele (p < 0.001, OR = 2.02), CC genotype (p < 0.001, OR = 2.72), CT genotype (p = 0.011, OR = 1.45) of ARID5B rs10821936 and pediatric ALL especially T-ALL and adult ALL (p < 0.05). The CA haplotype (C allele of rs10821936 + A allele of rs10994982) was associated with the risk of ALL either pediatric ALL or adult ALL (p < 0.001). In the studied Egyptian population, it can be concluded that the C allele, CC, and CT genotypes of ARID5B rs10821936 and the CA haplotype may be a susceptibility risk factor for pediatric and adult ALL. However, the SNPs of ARID5B rs10821936 and rs10994982 were not found to be strongly associated with ALL outcomes.