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. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Tenosynovial giant cell tumor: a case report.

BACKGROUND: This case reports the synchronous diagnosis of two rare unrelated diseases; leiomyosarcoma and tenosynovial giant cell tumor of the knee. It focuses on the challenges of diagnosing tenosynovial giant cell tumor, including cognitive biases in clinical medicine that delay diagnosis. It also demonstrates the pathogenic etiology of tenosynovial giant cell tumor, evidenced by the transient deterioration of the patients' knee symptoms following the administration of prophylactic granulocyte colony-stimulating factor given as part of the chemotherapeutic regime for leiomyosarcoma. CASE PRESENTATION: A 37-year-old Caucasian man presented with a left groin lump and left knee pain with swelling and locking. Investigations including positron emission tomography-computed tomography and biopsy revealed leiomyosarcoma in a lymph node likely related to the spermatic cord, with high-grade uptake in the left knee that was presumed to be the primary site. His knee symptoms temporarily worsened each time granulocyte colony-stimulating factor was administered with each cycle of chemotherapy for leiomyosarcoma to help combat myelosuppressive toxicity. Subsequent magnetic resonance imaging and biopsy of the knee confirmed a tenosynovial giant cell tumor. His knee symptoms relating to the tenosynovial giant cell tumor improved following the completion of his leiomyosarcoma treatment. CONCLUSIONS: Tenosynovial giant cell tumor remains a diagnostic challenge. We discuss the key clinical features and investigations that aid prompt diagnosis. The National Comprehensive Cancer Network clinical practice guidelines for soft tissue sarcoma have recently been updated to include the pharmacological management of tenosynovial giant cell tumor. Our case discussion provides an up-to-date review of the evidence for optimal management of patients with tenosynovial giant cell tumor, with a particular focus on novel pharmacological options that exploit underlying pathogenesis.

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. METHODS: A prospectively maintained database of patients undergoing advanced pelvic oncological resection in a single tertiary hospital was retrospectively analysed. This study included consecutive patients between 2014 and 2021 according to the Strengthening The Reporting of Cohort Studies in Surgery (STROCSS) criteria. Primary outcome measures were the frequency of postoperative perineal complications between primary closure, VRAM, gluteal and thigh (anterolateral thigh and gracilis) reconstruction. RESULTS: One hundred twenty-two patients underwent advanced pelvic resection with perineal closure. Of these, 40 patients underwent extra-levator abdominoperineal resection, and 70 patients underwent pelvic exenteration. Sixty-four patients received reconstructive flap closure, which included VRAM (22), gluteal (21) and thigh flaps (19). Perineal infection and dehiscence rates were low. Infection rates were lower in the flap group despite a higher rate of radiotherapy ( P <0.050). Reoperation rates were infrequent (<10%) but specific for each flap, such as donor-site hernia following VRAM and flap dehiscence after thigh flap reconstruction. CONCLUSIONS: In patients who are at high risk of postoperative perineal infections, reconstructive flap closure offers acceptable outcomes. VRAM, gluteal and thigh flaps offer comparable outcomes and can be tailored to the individual patient.

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. The main outcome was inter-centre reliability, which was quantified using overall agreement, as well as the chance-corrected Krippendorff's alpha statistic. Based on the latter, the level of agreement was classified as: 'slight' (0.00-0.20), 'fair' (0.21-0.40), 'moderate' (0.41-0.60), 'substantial' (0.61-0.80), or 'near-perfect' (>0.80). RESULTS: Twenty-one patients were reviewed at 12 retroperitoneal sarcoma multidisciplinary team meetings, giving a total of 252 assessments for analysis. Consistency between centres was only 'slight' to 'fair', with rates of overall agreement and Krippendorff's alpha statistics of 85.4 per cent (211 of 247) and 0.37 (95 per cent c.i. 0.11 to 0.57) for resectability; 80.4 per cent (201 of 250) and 0.39 (95 per cent c.i. 0.33 to 0.45) for treatment allocation; and 53.0 per cent (131 of 247) and 0.20 (95 per cent c.i. 0.17 to 0.23) for the organs proposed to be resected. Depending on the centre that they had attended, 12 of 21 patients could either have been deemed resectable or unresectable, and 10 of 21 could have received either potentially curative or palliative treatment. CONCLUSIONS: Inter-centre agreement between retroperitoneal sarcoma multidisciplinary team meetings was low. Multidisciplinary team meetings may not provide the same standard of care for patients with retroperitoneal sarcoma across Great Britain.

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. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon's steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.

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. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.

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. Feasibility was determined via the proportion of men given the QPL according to medical records and the completion of standardised measures of decisional outcomes. Quantitative data were analysed using descriptive and univariate statistics. Qualitative data were thematically analysed. Fifty-two men consented; 34 provided data for analysis. The QPL recipients reported moderate-high content satisfaction (70.6%) and perceived usefulness in guiding appointments when receiving biopsy results (64.7%). Two main qualitative themes also indicated the QPL acceptability: (1) the freedom to ask-acceptable timing, flexible usage and usefulness of the QPL, and (2) satisfaction with the QPL content. However, only 18.4% of eligible men received the QPL, indicating limited feasibility. The QPL is safe and acceptable, but further research is needed regarding how to facilitate the uptake of the question prompt list in clinical practice.

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. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state.

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. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

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. Boundaries of resection are expanding, resulting in increasing incidence of excision of major vascular structures and bone. Optimisation of patients is paramount prior to such significant surgical insult. Specialist centres with designated multidisciplinary teams should be used whenever possible. Addressing anaemia and nutrition play a significant role in prehabilitation. Intra-operatively consideration should be given to prevention of empty pelvis syndrome, perineal reconstruction, safe control of vascular structures and minimising risk of fistulae. Post-operative complications are common however employment of enhanced recovery protocols, minimally invasive surgery and opiate sparing analgesia protocols may in time lead to improvements for patients. Enteric fistulae and urine leak remain the most devastating and risk reduction strategies should be employed. Early recognition and aggressive management of complications is essential.

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. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.

Human neocortical expansion involves glutamatergic neuron diversification.

The neocortex is disproportionately expanded in human compared with mouse1,2, 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 depth3. 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. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer's disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

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. We aimed to evaluate how the utilization of telemedicine affects professionals and patients across Scotland and care delivery, at the Beatson West of Scotland Cancer Centre Sarcoma Unit. METHODS: Between June 8 and August 25, 2020, we invited patients and professional sarcoma multidisciplinary team members to participate in separate online anonymous survey questionnaires, to assess their attitudes toward telemedicine. Data were extracted, and descriptive statistics were performed. RESULTS: Patient satisfaction (n = 64) with telemedicine was high (mean = 9.4/10) and comparable with traditional face-to-face appointments (mean = 9.5/10). Patients were receptive to the use of telemedicine in certain situations, with patients strongly opposed to being told bad news via telemedicine (88%). Providers recommended the use of telemedicine in certain patient populations and reported largely equivalent workloads when compared with traditional consultations. Providers reported that telemedicine should be integrated into regular practice (66%), with patients echoing this indicating a preference for a majority of telemedicine appointments (57%). CONCLUSION: Telemedicine in sarcoma care is favorable from both clinician and patient perspectives. Utilization of telemedicine for patients with rare cancers such as sarcomas is an innovative approach to the delivery of care, especially considering the time and financial pressures on patients who often live a distance away from specialist centers. Patients and providers are keen to move toward a more flexible, mixed system of care.

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. To provide reliable coverage, the LiFi is integrated with other networking technologies such as wireless fidelity (WiFi) thus forming a hybrid system. The hybrid LiFi/WiFi system faces many challenges including but not limited to seamless integration with the WiFi, support for mobility, handover management, resource sharing, and load balancing. The existing literature has addressed one or the other aspect of the issues facing LiFi systems. There are limited free source tools available to holistically address these challenges in a scalable manner. To this end, we have developed an open-source simulation framework based on the network simulator 3 (ns-3), which realizes critical aspects of the LiFi wireless network. Our developed ns-3 LiFi framework provides a fully functional AP equipped with the physical layer and medium access control (MAC), a mobility model for the user device, and integration between LiFi and WiFi with a handover facility. Simulation results are produced to demonstrate the mobility and handover capabilities, and the performance gains from the LiFi-WiFi hybrid system in terms of packet delay, throughput, packet drop ratio (PDR), and fairness between users. The source code of the framework is made available for the use of the research community.

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. Over 30% of enhancer-AAVs produce specific expression in the targeted subclass, including both excitatory and inhibitory subclasses. We present a collection of Parvalbumin (PVALB) enhancer-AAVs that show highly enriched expression not only in cortical PVALB cells but also in some subcortical PVALB populations. Five vectors maintain PVALB-enriched expression in primate neocortex. These results demonstrate how genome-wide open chromatin data mining and cross-species AAV validation can be used to create the next generation of non-species-restricted viral genetic tools.

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. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel patterns due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 50 States of the US and Washington D.C, the capital city of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of family members and high income are less interested in reopening the economy. The accuracy of the model is reasonable (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi-squared test indicates that this model has high goodness-of-fit. This study provides clear insights for public and corporate policymakers on potential areas to allocate resources, and directional guidance on potential policy options they can undertake to improve socioeconomic conditions, to mitigate the impact of pandemic in the current situation, and in the future as well.

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. The rapid pace of the spread of the novel coronavirus makes it imperative that its' spread be forecasted well in advance in order to plan for eventualities. An accurate early forecasting of the number of cases would certainly assist governments and various other organizations to strategize and prepare for the newly infected cases, well in advance. In this work, a novel method of forecasting the future cases of infection, based on the study of data mined from the internet search terms of people in the affected region, is proposed. The study utilizes relevant Google Trends of specific search terms related to COVID-19 pandemic along with European Centre for Disease prevention and Control (ECDC) data on COVID-19 spread, to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and UK. For this purpose, a hybrid GWO-LSTM model is developed, where the network parameters of Long Short Term Memory (LSTM) network are optimized using Grey Wolf Optimizer (GWO). The results of the proposed model are compared with the baseline models including Auto Regressive Integrated Moving Average (ARIMA), and it is observed that the proposed model achieves much better results in forecasting the future trends of the spread of infection. Using the proposed hybrid GWO-LSTM model incorporating online big data from Google Trends, a reduction in Mean Absolute Percentage Error (MAPE) values for forecasting results to the extent of about 98% have been observed. Further, reduction in MAPE by 74% for models incorporating Google Trends was observed, thus, confirming the efficacy of utilizing public sentiments in terms of search frequencies of relevant terms online, in forecasting pandemic numbers.

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. Chest wall motion due to respiration is simulated based on the optimal chemical-mechanical respiratory control-based mechanics. The theory of relaxation oscillation system is applied to model the motion due to cardiac activity. The proposed mathematical chest wall model can be utilized in designing and optimizing different design parameters for radar-based non-contact vital sign (NCVS) systems.