Lifestyle Intervention’s Effect and Predictive Value on Weight Loss for University Employees

Obesity is a costly and pervasive risk factor that requires attention to reduce chronic disease rates. This study evaluated the effect of a lifestyle medicine intervention, Complete Health Improvement Program (CHIP), on reducing weight, blood pressure, lipid levels, and hemoglobin A1c. A secondary aim was to build a preliminary predictive model for computing new participants’ potential weight change from CHIP. We evaluated pre- and post-intervention biometric data of 68 individuals who completed a 10-week CHIP intervention at a Midwestern university clinic. Significant reductions (p \u3c 0.05) were observed in weight, diastolic blood pressure, total cholesterol, low-density lipoprotein, and A1c. Regression analyses indicated that the best linear model for predicting change in weight was a one-predictor model with systolic blood pressure. The CHIP intervention effectively promoted weight loss and meaningful reductions in chronic disease risk factors. Larger samples are needed for future regression analyses to create a more robust linear model

When Shall Coronavirus Disease-19 Stop? Review of Literature

In December 2019, a new coronavirus, now labeled as severe acute respiratory syndrome coronavirus 2, induced an episode of acute atypical respiratory illness started in Wuhan, Province of Hubei, China. The illness triggered by this virus was called coronavirus disease-19 (COVID-19). The infection is spread within humans and has triggered a global pandemic. The amount of death tolls continues to increase and a growing number of countries have been driven to create social barriers and lock-ups. The shortage of tailored counseling remains an issue. Epidemiological researches have shown that elderly patients are more vulnerable to serious diseases, while children tend to have milder symptoms. Here, we checked the latest understanding of this disease and found a possible explanation of the potential sequel and the expectations for the future

Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

A more accurate determination for the Probability of Failure on Demand (PFD) of the Safety Instrumented System (SIS) contributes to more SIS realiability, thereby ensuring more safety and lower cost. IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas. However, these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources, which, including high redundant systems architectures, cannot be assessed, have perfect proof test assumption, and are neglegted in partial stroke testing (PST) of impact on the system PFD. On the other hand, determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time. This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem. A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor (DC) and common cause failures (CCF). In order to simulate the proof test effectiveness, the Proof Test Coverage (PTC) factor has been incorporated into the formula. Additionally, the system PFD value has been improved by incorporating PST for the final control element into the formula. The new developed formula is modelled using the Genetic Algorithm (GA) artificial technique. The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables. The proposed model has been applicated on SIS design for crude oil test separator using MATLAB. The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality. Furthermore, the cost and associated implementation testing activities are reduced

A reinforcement learning hyper-heuristic for water distribution network optimisation

The Water Distribution Networks (WDNs) optimisation problem focuses on finding the combination of pipes from a collection of discrete sizes available to construct a network of pipes with minimum monetary cost. It is one of the most significant problems faced by WDN engineers. This problem belongs to the class of difficult combinatorial optimisation problems, whose optimal solution is hard to find, due to its large search space. Hyper-heuristics are high-level search algorithms that explore the space of heuristics rather than the space of solutions in a given optimisation problem. In this work, different selection hyper-heuristics were proposed and empirically analysed in the WDN optimisation problem, with the goal of minimising the network’s cost. New York Tunnels network benchmark was used to test the performance of these hyper-heuristics including the Reinforcement Learning (RL) hyper-heuristic method, that succeeded in achieving improved results

Bottleneck Identification in Cloudified Mobile Networks Based on Distributed Telemetry

Cloudified mobile networks are expected to deliver a multitude of services with reduced capital and operating expenses. A characteristic example is 5G networks serving several slices in parallel. Such mobile networks, therefore, need to ensure that the SLAs of customised end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualised network core, as well as tracking the performance of the radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a 2-stage distributed telemetry framework in which UEs act as early warning sensors. After UEs flag an anomaly, a ML model is activated, at network controller, to attribute the cause of the anomaly. The framework achieves 85% F1-score in detecting anomalies caused by different bottlenecks, and an overall 89% F1-score in attributing these bottlenecks. This accuracy of our distributed framework is similar to that of a centralised monitoring system, but with no overhead of transmitting UE-based telemetry data to the centralised controller. The study also finds that passive in-band network telemetry has the potential to replace active monitoring and can further reduce the overhead of a network monitoring system

Detection of equid herpesviruses among different Arabian horse populations in Egypt

Equid herpesviruses (EHVs) threaten equine health and can cause significant economic losses to the equine industry worldwide. Different equid herpesviruses, EHV‐1, EHV‐2, EHV‐4 and EHV5 are regularly detected among horse populations. In Egypt, monitoring is sporadic but EHV‐1 or EHV‐4 have been reported to circulate in the horse population. However, there is a lack of reports related to infection and health status of horses, likely due to the absence of regular diagnostic procedures. In the current study, the circulation of four infectious equid herpesviruses (EHV‐1, EHV‐2, EHV‐4 and EHV‐5) among different Arabian horse populations and donkeys residing the same farm was monitored. Different samples were collected and DNA was extracted and subjected to quantitative (q)‐PCR to detect the four equid herpesviruses using specific primers and probes. Antibody titres against EHV‐1 and EHV‐4 were tested using virus neutralization test and type‐specific ELISA. The results showed that EHV‐1, EHV‐2, EHV‐4 and EHV‐5 are endemic and can be a continuous threat for horses in the absence of vaccination programs and frequent virus reactivation. There is an urgent need for introduction of active regular surveillance measures to investigate the presence of different equid herpesviruses, and other equine viral pathogens, in various horse populations around Egypt and to establish a standardized cataloguing of equine health status

Unveiling the therapeutic potential of exogenous β-hydroxybutyrate for chronic colitis in rats: novel insights on autophagy, apoptosis, and pyroptosis

Ulcerative colitis (UC) is a chronic relapsing inflammatory disease of the colorectal area that demonstrates a dramatically increasing incidence worldwide. This study provides novel insights into the capacity of the exogenous β-hydroxybutyrate and ketogenic diet (KD) consumption to alleviate dextran sodium sulfate (DSS)-induced UC in rats. Remarkably, both interventions attenuated disease activity and colon weight-to-length ratio, and improved macro and microstructures of the damaged colon. Importantly, both β-hydroxybutyrate and KD curbed the DSS-induced aberrant NLRP3 inflammasome activation as observed in mRNA and protein expression analysis. Additionally, inhibition of the NLRP3/NGSDMD-mediated pyroptosis was detected in response to both regimens. In parallel, these modalities attenuated caspase-1 and its associated consequences of IL-1β and IL-18 overproduction. They also mitigated apoptosis as indicated by the inactivation of caspase-3. The anti-inflammatory effects of BHB and KD were confirmed by the reported decline in the levels of inflammatory markers including MPO, NFκB, IL-6, and TNF-α. Moreover, these interventions exhibited antioxidative properties by reducing ROS production and improving antioxidative enzymes. Their effectiveness in mitigating UC was also evident in the renovation of normal intestinal epithelial barrier function, as shown by correcting the discrepancies in the levels of tight junction proteins ZO-1, OCLN, and CLDN5. Furthermore, their effects on the intestinal microbiota homeostasis were investigated. In terms of autophagy, exogenous β-hydroxybutyrate upregulated BECN-1 and downregulated p62, which may account for its superiority over KD in attenuating colonic damage. In conclusion, this study provides experimental evidence supporting the potential therapeutic use of β-hydroxybutyrate or β-hydroxybutyrate-boosting regimens in UC