Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter

The COVID-19 pandemic has disrupted people’s lives and caused significant economic damage around the world, but its impact on people’s mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user’s PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model’s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern

Medical diagnosis using machine learning: a statistical review

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results

Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the above challenge, this paper proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Secondly, a differential privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Thirdly, a non-cooperative game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism

Information-Centric Wireless Sensor Networking Scheme with Water-Depth-Awareness Content Caching for Underwater IoT

The existing underwater Internet of Things (UIoT) is based on IP architecture, which is not conducive to the efficient storage and distribution of huge amounts of content generated in underwater. Actively pushing all content to users causes much unnecessary resource consumption in the UIoT. The information centric networking (ICN) architecture opens new horizons up for these challenges. However, the slowness of underwater propagation speed makes traditional ICN not suitable for UIoT, especially considering about delay time. In this paper, we propose an information-centric wireless sensor networking scheme with water-depth-aware content caching (ICWSN-WDA) to solve the above challenges. First, we design a naming scheme and a hybrid communication mode suitable for ICWSN-WDA. The communication mode in underwater we design is divided into push and pull traffic, which balances energy consumption and delay time. Secondly, we define a push level to decide which water depth the content actively pushes to, finding a suitable junction point of two modes. Thirdly, as the water depth is deeper, it becomes more difficult to replace sensor battery. To save energy consumption of deep-water sensors, water-depth-aware caching mechanism is proposed based on water depth, popularity and senor energy. Our extensive evaluation confirms the effectiveness of our proposed scheme, and it balances energy consumption constraints and latency

An anonymous device to device access control based on secure certificate for internet of medical things systems: an anonymous D2D access control scheme for IoMT

The Internet of Medical Things (IoMT) is structured upon both the sensing and communication infrastructure and computation facilities. The IoMT provides the convenient and cheapest ways for healthcare by aiding the remote access to the patients’ physiological data and using machine learning techniques for help in diagnosis. The communication delays in IoMT can be very harmful to healthcare. Device to device (D2D) secure communication is a vital area that can reduce communication delays; otherwise, caused due to the mediation of a third party. To substantiate a secure D2D communication framework, some schemes were recently proposed to secure D2D based communication infrastructure suitable for IoMT-based environments. However, the insecurities of some schemes against device physical capture attack and non-provision of anonymity along with related attacks are evident from the literature. This calls for a D2D secure access control system for realizing sustainable smart healthcare. In this article, using elliptic curve cryptography, a certificate based D2D access control scheme for IoMT systems (D2DAC-IoMT) is proposed. The security of the proposed D2DAC-IoMT is substantiated through formal and informal methods. Moreover, the performance analysis affirms that the proposed scheme provides a good trade-off between security and efficiency compared with some recent schemes

Challenges in QCD matter physics --The scientific programme of the Compressed Baryonic Matter experiment at FAIR

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 (sNN= 2.7--4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials (μB> 500 MeV), effects of chiral symmetry, and the equation of state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2024, in the context of the worldwide efforts to explore high-density QCD matter

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty

Measuring the burden of arboviral diseases: the spectrum of morbidity and mortality from four prevalent infections

<p>Abstract</p> <p>Background</p> <p>Globally, arthropod-borne virus infections are increasingly common causes of severe febrile disease that can progress to long-term physical or cognitive impairment or result in early death. Because of the large populations at risk, it has been suggested that these outcomes represent a substantial health deficit not captured by current global disease burden assessments.</p> <p>Methods</p> <p>We reviewed newly available data on disease incidence and outcomes to critically evaluate the disease burden (as measured by disability-adjusted life years, or DALYs) caused by yellow fever virus (YFV), Japanese encephalitis virus (JEV), chikungunya virus (CHIKV), and Rift Valley fever virus (RVFV). We searched available literature and official reports on these viruses combined with the terms "outbreak(s)," "complication(s)," "disability," "quality of life," "DALY," and "QALY," focusing on reports since 2000. We screened 210 published studies, with 38 selected for inclusion. Data on average incidence, duration, age at onset, mortality, and severity of acute and chronic outcomes were used to create DALY estimates for 2005, using the approach of the current Global Burden of Disease framework.</p> <p>Results</p> <p>Given the limitations of available data, nondiscounted, unweighted DALYs attributable to YFV, JEV, CHIKV, and RVFV were estimated to fall between 300,000 and 5,000,000 for 2005. YFV was the most prevalent infection of the four viruses evaluated, although a higher proportion of the world's population lives in countries at risk for CHIKV and JEV. Early mortality and long-term, related chronic conditions provided the largest DALY components for each disease. The better known, short-term viral febrile syndromes caused by these viruses contributed relatively lower proportions of the overall DALY scores.</p> <p>Conclusions</p> <p>Limitations in health systems in endemic areas undoubtedly lead to underestimation of arbovirus incidence and related complications. However, improving diagnostics and better understanding of the late secondary results of infection now give a first approximation of the current disease burden from these widespread serious infections. Arbovirus control and prevention remains a high priority, both because of the current disease burden and the significant threat of the re-emergence of these viruses among much larger groups of susceptible populations.</p

Dysferlin Forms a Dimer Mediated by the C2 Domains and the Transmembrane Domain In Vitro and in Living Cells

Dysferlin was previously identified as a key player in muscle membrane repair and its deficiency leads to the development of muscular dystrophy and cardiomyopathy. However, little is known about the oligomerization of this protein in the plasma membrane. Here we report for the first time that dysferlin forms a dimer in vitro and in living adult skeletal muscle fibers isolated from mice. Endogenous dysferlin from rabbit skeletal muscle exists primarily as a ∼460 kDa species in detergent-solubilized muscle homogenate, as shown by sucrose gradient fractionation, gel filtration and cross-linking assays. Fluorescent protein (YFP) labeled human dysferlin forms a dimer in vitro, as demonstrated by fluorescence correlation spectroscopy (FCS) and photon counting histogram (PCH) analyses. Dysferlin also dimerizes in living cells, as probed by fluorescence resonance energy transfer (FRET). Domain mapping FRET experiments showed that dysferlin dimerization is mediated by its transmembrane domain and by multiple C2 domains. However, C2A did not significantly contribute to dimerization; notably, this is the only C2 domain in dysferlin known to engage in a Ca-dependent interaction with cell membranes. Taken together, the data suggest that Ca-insensitive C2 domains mediate high affinity self-association of dysferlin in a parallel homodimer, leaving the Ca-sensitive C2A domain free to interact with membranes