NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. RESULTS: NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. AVAILABILITY AND IMPLEMENTATION: Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.

Evaluation of a multi-component early warning system for pastoralist populations in Doolo zone, Ethiopia: mixed-methods study.

BACKGROUND: This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019-2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components. METHODS: We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019-January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others. RESULTS: 1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community's response expectations not being met. CONCLUSIONS: Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.

A foundational atlas of autism protein interactions reveals molecular convergence.

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

Heart failure patients monitoring using IoT-based remote monitoring system.

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm.

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT.

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.

Millimeter-wave channel modeling in a VANETs using coding techniques.

The Vehicular ad-Hoc Network (VANET) is envisioned to ensure wireless transmission with ultra-high reliability. In the presence of fading and mobility of vehicles, error-free information between Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) requires extensive investigation. The current literature lacks in designing an ultra-reliable comprehensive tractable model for VANET using millimeter wave. Ultra-reliable communication is needed to support autonomous vehicular communication. This article aims to provide a comprehensive tractable model for VANET over millimeter waves using Space-Time-Block-Coding (STBC) concatenated with Reed Solomon (RS) coding. The designed model provides the fastest way of designing and analyzing VANET networks on 60 GHz. By using the derived BER expressions and Reed Solomon coded doppler expression ultra-reliable vehicular networks can be build meeting the demands of massive growing volume of traffic. The performance of the model is compared with previous BER computational techniques and existing VANET communication systems, i.e., IEEE 802.11bd and 3rd generation partnership project vehicle to everything (3GPP V2X). The findings show that our proposed approach outperforms IEEE 802.11bd and the results are comparable with V2X NR. Packet Error Rate (PER), Packet Reception Ratio (PRR) and throughput are used as performance metrics. We have also evaluated the model on higher velocities of vehicles. Further, the simulation and numerical findings show that the proposed system surpass the existing BER results comprising of various modulation and coding techniques. The simulation results are verified by the numerical results there-by, showing the accuracy of our derived expressions.

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review.

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT's layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.

Preparation and Photovoltaic Evaluation of CuO@Zn(Al)O-Mixed Metal Oxides for Dye Sensitized Solar Cell.

In this manuscript, a series of dye-sensitized solar cells (DSSCs) were fabricated as a function of post-processing temperature based on mesoporous CuO@Zn(Al)O-mixed metal oxides (MMO) in conjunction with dye N719 as the main light absorber; the proposed CuO@Zn(Al)O geometry was, in turn, attained using Zn/Al-layered double hydroxide (LDH) as a precursor via combination of co-precipitation and hydrothermal techniques. In particular, the dye loading amount onto the deposited mesoporous materials was anticipated via regression equation-based UV-Vis technique analysis, which evidently demonstrated a robust correlation along with the fabricated DSSCs power conversion efficiency. In detail, of the DSSCs assembled, CuO@MMO-550 exhibited short-circuit current (JSC) and open-circuit voltage (VOC) of 3.42 (mA/cm2) and 0.67 (V) which result in significant fill factor and power conversion efficiency of 0.55% and 1.24%, respectively. This could mainly be due to the relatively high surface area of 51.27 (m2/g) which in turn validates considerable dye loading amount of 0.246 (mM/cm-2).

Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock.

Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.

Characterization of the immunoglobulin lambda chain locus from diverse populations reveals extensive genetic variation.

Immunoglobulins (IGs), crucial components of the adaptive immune system, are encoded by three genomic loci. However, the complexity of the IG loci severely limits the effective use of short read sequencing, limiting our knowledge of population diversity in these loci. We leveraged existing long read whole-genome sequencing (WGS) data, fosmid technology, and IG targeted single-molecule, real-time (SMRT) long-read sequencing (IG-Cap) to create haplotype-resolved assemblies of the IG Lambda (IGL) locus from 6 ethnically diverse individuals. In addition, we generated 10 diploid assemblies of IGL from a diverse cohort of individuals utilizing IG-Cap. From these 16 individuals, we identified significant allelic diversity, including 36 novel IGLV alleles. In addition, we observed highly elevated single nucleotide variation (SNV) in IGLV genes relative to IGL intergenic and genomic background SNV density. By comparing SNV calls between our high quality assemblies and existing short read datasets from the same individuals, we show a high propensity for false-positives in the short read datasets. Finally, for the first time, we nucleotide-resolved common 5-10 Kb duplications in the IGLC region that contain functional IGLJ and IGLC genes. Together these data represent a significant advancement in our understanding of genetic variation and population diversity in the IGL locus.

Conditional Anonymous Remote Healthcare Data Sharing Over Blockchain.

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an "on-chain & off-chain" model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance.

Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach.

Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

The psychological impact of the ongoing COVID-19 pandemic on health-care workers in primary health-care centers in resource-poor settings.

Background: Health-care workers' psychological status is essential to Preventive control measures in a weak and unstable health system with poor infrastructural constraints. This study examines the psychological impact of the ongoing coronavirus disease 2019 (COVID-19) pandemic on the health-care providers working in primary health-care settings in Sudan. Materials and Methods: This is a health facility-based cross-sectional study conducted in primary health-care units in White Nile State, Sudan. The psychological impact of stress and anxiety was determined using the Depression Anxiety Stress Scale 21 (DASS-21). A self-administered questionnaire measured depression, anxiety, and stress. The population of this study included health professionals working in health centers, including physicians, nurses, technicians, pharmacists, and other support staff. Results: A total of 167 health professionals were systematically recruited. The mean anxiety score in the study population was 8.26 & 9.0 (corresponding to mild anxiety). Participants without anxiety constituted 26.35% (n = 44) of the participants. Women were significantly more likely to be affected than men (P = 0.0). Age (21-40 years), female nurses, and other health-care workers (anesthesiology, public health, health education, occupational health, psychiatry, etc.) could be strong predictors of psychological disorders (P-value of 0.0). Conclusion: This study provided evidence for primary health care at its preparatory levels, as they are the first line of protection against the COVID-19 pandemic. Addressing the high-risk population is a high priority in the preliminary phase.

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach.

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author's research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher's scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author's profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The self-citation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect self-citation must be included.

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Assessing the Psychological Impact of the Pandemic COVID -19 in Uninfected High-Risk Population.

PURPOSE: To assess the impact of the COVID-19 pandemic on the psyche of uninfected people with chronic diseases in the Elduim community, White Nile State, Sudan, during the COVID -19 pandemic. METHODS: We used a generalized anxiety disorder scale (GAD -7) and a patient health questionnaire (PHQ-9) for psychological assessment. The study included two hundred thirty-four participants; all participants with a chronic disease but not infected with COVID -19 were between 24 and 65 years of age. Residents of the study area were randomly selected. Descriptive statistics and a t-test were used for associations with a p-value of 0.05 or less. RESULTS: This study found that anxiety rated by GAD 7 was either mild (18, 7.7%), moderate (98, 41.9%), or severe (41, 17.5%) among participants. PHQ 9-rated depression showed 22 (9.4%) mild depression, most of them in participants aged 36-44 years. Participants with kidney disease showed major depression 11 (42.31%). Factors that significantly affected anxiety scores were age 24-35 years (P =0.002), university graduates (P < 0.000), married (P < 0.000), those with diabetes and hypertension (P =0.041), and urban residents (P < 0.023). Those who had secondary education were married and smoked were significantly more likely to have major depression than those with another educational status (p < 0.05). CONCLUSION: COVID 19 pandemic had a significant impact on the psyche of uninfected people with chronic diseases in Sudan, and significant associated factors were identified. Unique interventions are strongly recommended to reduce the psychological impact of the COVID 19 pandemic.

Mid- and long-term outcomes of thoracic endovascular aortic repair in acute and subacute uncomplicated type B aortic dissection.

BACKGROUND: Uncomplicated type B aortic dissection (un-TBAD) has been managed conservatively with medical therapy to control the heart rate and blood pressure to limit disease progression, in addition to radiological follow-up. However, several trials and observational studies have investigated the use of thoracic endovascular aortic repair (TEVAR) in un-TBAD and suggested that TEVAR provides a survival benefit over medical therapy. Outcomes of TEVAR have also been linked with the timing of intervention. AIMS: The scope of this review is to collate and summarize all the evidence in the literature on the mid- and long-term outcomes of TEVAR in un-TBAD, confirming its superiority. We also aimed to investigate the relationship between the timing of TEVAR intervention and results. METHODS: We carried out a comprehensive literature search on multiple electronic databases including PubMed, Scopus, and EMBASE to collate and summarize all research evidence on the mid- and long-term outcomes of TEVAR in un-TBAD, as well as its relationship with intervention timing. RESULTS: TEVAR has proven to be a safe and effective tool in un-TBAD, offering superior mid- and long-term outcomes including all-cause and aorta-related mortality, aortic-specific adverse events, aortic remodeling, and need for reintervention. Additionally, performing TEVAR during the subacute phase of dissection seems to yield optimal results. CONCLUSION: The evidence demonstrating a survival advantage in favor TEVAR over medical therapy in un-TBAD means that with further research, particular trials and observational studies, TEVAR could become the gold-standard treatment option for un-TBAD patients.