UniVar: A variant interpretation platform enhancing rare disease diagnosis through robust filtering and unified analysis of SNV, INDEL, CNV and SV.

Background: Interpreting the pathogenicity of genetic variants associated with rare diseases is a laborious and time-consuming endeavour. To streamline the diagnostic process and lighten the burden of variant interpretation, it is crucial to automate variant annotation and prioritization. Unfortunately, currently available variant interpretation tools lack a unified and comprehensive workflow that can collectively assess the clinical significance of these types of variants together: small nucleotide variants (SNVs), small insertions/deletions (INDELs), copy number variants (CNVs) and structural variants (SVs).

Facial Anti-Spoofing Using "Clue Maps".

Spoofing attacks (or Presentation Attacks) are easily accessible to facial recognition systems, making the online financial system vulnerable. Thus, it is urgent to develop an anti-spoofing solution with superior generalization ability due to the high demand for spoofing attack detection. Although multi-modality methods such as combining depth images with RGB images and feature fusion methods could currently perform well with certain datasets, the cost of obtaining the depth information and physiological signals, especially that of the biological signal is relatively high.

Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem.

Effect of differing flap reconstruction strategies in perineal closure following advanced pelvic oncological resection: a retrospective cohort study.

Introduction: Advancing approaches to locally invasive pelvic malignancy creates a large tissue defect resulting in perineal wound complications, dehiscence, and perineal hernia. Use of reconstructive flaps such as vertical rectus abdominus myocutaneous (VRAM) flap, gracilis, anterolateral thigh and gluteal flaps have been utilised in our institution to address perineal closure. The authors compared outcomes using different flap techniques along with primary perineal closure in advanced pelvic oncological resection.

Assessment of inter-centre agreement across multidisciplinary team meetings for patients with retroperitoneal sarcoma.

Background: Decision-making in the management of patients with retroperitoneal sarcoma is complex and requires input from a number of different specialists. The aim of this study was to evaluate the levels of agreement in terms of resectability, treatment allocation, and organs proposed to be resected across different retroperitoneal sarcoma multidisciplinary team meetings.

Methods: The CT scans and clinical information of 21 anonymized retroperitoneal sarcoma patients were sent to all of the retroperitoneal sarcoma multidisciplinary team meetings in Great Britain, which were asked to give an opinion about resectability, treatment allocation, and organs proposed to be resected.

An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons.

Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area.

Encouraging Patients to Ask Questions: Development and Pilot Testing of a Question Prompt List for Patients Undergoing a Biopsy for Suspected Prostate Cancer.

This study assessed the acceptability and feasibility of a question prompt list (QPL) to facilitate informed treatment decision-making in men with suspected localised prostate cancer, which involves values-based choices between options with similar efficacy but different side effects. The QPL was developed through iterative consultation with consumers, clinicians and researchers. Acceptability was assessed using study-specific questions regarding QPL satisfaction and usefulness and qualitative interviews.

An Optimization Framework for Data Collection in Software Defined Vehicular Networks.

A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data.

Multi-modal characterization and simulation of human epileptic circuitry.

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear.

Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies.

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient.

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G.

Prevention and management of complications in pelvic exenteration.

Pelvic exenteration is widely recognised as the gold standard of care for locally advanced tumours of the pelvis. Surgery in pursuit of curative resection comes at the cost of significant morbidity. Perioperative complications are commonplace with the majority managed without further surgical intervention.

Human neocortical expansion involves glutamatergic neuron diversification.

The neocortex is disproportionately expanded in human compared with mouse, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues.

What Matters to Us: Impact of Telemedicine During the Pandemic in the Care of Patients With Sarcoma Across Scotland.

Purpose: In Scotland, approximately 350 sarcoma cases are diagnosed per year and treated in one of the five specialist centers. Many patients are required to travel long distances to access specialist care. The COVID-19 pandemic brought a number of rapid changes into the care for patients with cancer, with increasing utilization of telemedicine.

A Comprehensive Open-Source Simulation Framework for LiFi Communication.

Light Fidelity (LiFi) is a new candidate for wireless networking that utilizes the visible light spectrum and exploits the existing lighting infrastructure in the form of light-emitting diodes (LEDs). It provides point-to-point and point-to-multipoint communication on a bidirectional channel at very high data rates. However, the LiFi has small coverage, and its optical gain is closely related to the receiver's directionality vis-à-vis the transmitter, therefore it can experience frequent service outages.

Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex.

Viral genetic tools that target specific brain cell types could transform basic neuroscience and targeted gene therapy. Here, we use comparative open chromatin analysis to identify thousands of human-neocortical-subclass-specific putative enhancers from across the genome to control gene expression in adeno-associated virus (AAV) vectors. The cellular specificity of reporter expression from enhancer-AAVs is established by molecular profiling after systemic AAV delivery in mouse.

Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among researchers.

Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach.

The recent outbreak of COVID-19 has brought the entire world to a standstill. The rapid pace at which the virus has spread across the world is unprecedented. The sheer number of infected cases and fatalities in such a short period of time has overwhelmed medical facilities across the globe.

Modelling of Chest Wall Motion for Cardiorespiratory Activity for Radar-Based NCVS Systems.

Chest wall motion can provide information on critical vital signs, including respiration and heartbeat. Mathematical modelling of chest wall motion can reduce an extensive requirement of human testing in the development of many biomedical applications. In this paper, we propose a mathematical model that simulates a chest wall motion due to cardiorespiratory activity.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics.

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges.

Design and Implementation of a Self-Powered Smart Water Meter.

Smart cities require interactive management of water supply networks and water meters play an important role in such a task. As compared to fully mechanical water meters, electromechanical water meters or fully electronic water meters can collect real-time information through automatic meter reading (AMR), which makes them more suitable for smart cities applications. In this paper, we first study the design principles of existing water meters, and then present our design and implementation of a self-powered smart water meter.