Number of Recent Stressful Life Events and Incident Cardiovascular Disease: Moderation by Lifetime Depressive Disorder

Objective We investigated whether number of recent stressful life events is associated with incident cardiovascular disease (CVD) and whether this relationship is stronger in adults with a history of clinical depression. Methods Prospective data from 28,583 U.S. adults (mean age = 45 years) initially free of CVD who participated in Waves 1 (2001–2002) and 2 (2004–2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were examined. Number of past-year stressful life events (Wave 1), lifetime depressive disorder (Wave 1), and incident CVD (Wave 2) were determined by structured interviews. Results There were 1069 cases of incident CVD. Each additional stressful life event was associated with a 15% increased odds of incident CVD [Odds Ratio (OR) = 1.15, 95% Confidence Interval (CI): 1.11, 1.19]. As hypothesized, a stressful life events by lifetime depressive disorder interaction was detected (P = 0.003). Stratified analyses indicated that stressful life events had a stronger association with incident CVD among adults with (OR = 1.18, 95% CI: 1.10, 1.27, n = 4908) versus without (OR = 1.10, 95% CI: 1.07, 1.14, n = 23,675) a lifetime depressive disorder. Conclusion Our findings suggest that a greater number of recent stressful life events elevate the risk of new-onset CVD and that this risk is potentiated in adults with a history of clinical depression

Hybrid-aligned nematic liquid-crystal modulators fabricated on VLSI circuits

A new method for fabricating analog light modulators on VLSI devices is described. The process is fully compatible with devices fabricated by commercial VLSI foundries, and the assembly of the modulator structures requires a small number of simple processing steps. The modulators are capable of analog amplitude or phase modulation and can operate at video rates and at low voltages (2.2 V). The modulation mechanism and the process yielding the modulator structures are described. Experimental data are presented

Analysis and Simulations of Abnormalities for Induction Motor Using PSCAD

Induction motors are most important part of any network or industry because most of the industries rely on this to drive their equipment. Due to various faults that take place in the motor it interrupts the normal functioning of the motor and leads to system failure or loss. So, it is necessary to reduce this stress factors to a safer level for normal functioning of the motor and this can be done by using various types of protective devices. In this paper we have mentioned different types of abnormalities and how to analyse these abnormalities using PSCAD. By simulating we can know proper working of any system and how that system would behave if same configuration existing at that time during fault. DOI: 10.17762/ijritcc2321-8169.150513

Cardiovascular Risk Factors as Differential Predictors of Incident Atypical and Typical Major Depressive Disorder in U.S. Adults

Objectives While the association between major depressive disorder (MDD) and future cardiovascular disease (CVD) is established, less is known about the relationship between CVD risk factors and future depression, and no studies have examined MDD subtypes. Our objective was to determine whether hypertension, tobacco use, and body mass index (BMI) differentially predict atypical and typical MDD in a national sample of U.S. adults. Methods We examined prospective data from 22,915 adults with no depressive disorder history at baseline who participated in Wave 1 (2001-2002) and Wave 2 (2004-2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). CVD risk factors (Wave 1) and incident MDD subtypes (Wave 2) were determined by structured interviews. Results There were 252 atypical and 991 typical MDD cases. In fully-adjusted models, baseline hypertension (OR=0.58, 95% CI: 0.43-0.76), former tobacco use (OR=1.46, 95% CI: 1.20-1.78), and BMI (OR=1.32, 95% CI: 1.25-1.40; all p’s<0.001) predicted incident atypical MDD versus no MDD, whereas no CVD risk factor predicted incident typical MDD. Baseline hypertension (OR=0.52, 95% CI: 0.39-0.70), former tobacco use (OR=1.53, 95% CI: 1.22-1.93), and BMI (OR=1.26, 95% CI: 1.18-1.36; all p’s<0.001) also predicted incident atypical MDD versus typical MDD. Conclusions Our study is the first to report that CVD risk factors differentially predict MDD subtypes, with hypertension (protective factor), former tobacco use (risk factor), and BMI (risk factor) being stronger predictors of incident atypical versus typical MDD. Such evidence could provide insights into the etiologies of MDD subtypes and inform interventions tailored to MDD subtype

Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos

Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a particular dataset, however, perform poorly when tested on another dataset. This occurs due to a significant domain shift between the datasets resulting from the use of different tools, sensors, data resolution etc. In this paper, we highlight this domain shift in the commonly performed cataract surgery and propose a novel end-to-end Unsupervised Domain Adaptation (UDA) method called the Barlow Adaptor that addresses the problem of distribution shift without requiring any labels from another domain. In addition, we introduce a novel loss called the Barlow Feature Alignment Loss (BFAL) which aligns features across different domains while reducing redundancy and the need for higher batch sizes, thus improving cross-dataset performance. The use of BFAL is a novel approach to address the challenge of domain shift in cataract surgery data. Extensive experiments are conducted on two cataract surgery datasets and it is shown that the proposed method outperforms the state-of-the-art UDA methods by 6%. The code can be found at https://github.com/JayParanjape/Barlow-AdaptorComment: MICCAI 202

From Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics

In the past decade, there has been significant advancement in designing wearable neural interfaces for controlling neurorobotic systems, particularly bionic limbs. These interfaces function by decoding signals captured non-invasively from the skin's surface. Portable high-density surface electromyography (HD-sEMG) modules combined with deep learning decoding have attracted interest by achieving excellent gesture prediction and myoelectric control of prosthetic systems and neurorobots. However, factors like pixel-shape electrode size and unstable skin contact make HD-sEMG susceptible to pixel electrode drops. The sparse electrode-skin disconnections rooted in issues such as low adhesion, sweating, hair blockage, and skin stretch challenge the reliability and scalability of these modules as the perception unit for neurorobotic systems. This paper proposes a novel deep-learning model providing resiliency for HD-sEMG modules, which can be used in the wearable interfaces of neurorobots. The proposed 3D Dilated Efficient CapsNet model trains on an augmented input space to computationally `force' the network to learn channel dropout variations and thus learn robustness to channel dropout. The proposed framework maintained high performance under a sensor dropout reliability study conducted. Results show conventional models' performance significantly degrades with dropout and is recovered using the proposed architecture and the training paradigm

Associations between immigrant status and pharmacological treatments for diabetes in U.S. adults

Objectives: Although treatment disparities in diabetes have been documented along racial/ethnic lines, it is unclear if immigrant groups in the United States experience similar treatment disparities. Our objective was to determine whether immigrant status is associated with differences in pharmacological treatment of diabetes in a nationally representative sample of adults with diabetes. We were specifically interested in differences in treatment with oral hypoglycemic agents (OHA) and insulin. Method: Respondents were 2,260 adults from National Health and Nutritional Examination Survey (NHANES) 2003–2012 with a self-reported diabetes diagnosis. Immigrant status was indicated by birth within (U.S.-born) or outside (foreign-born) the 50 U.S. States or Washington, DC. Multinomial logistic regression analyses examined associations between immigrant status and (a) treatment with OHAs only and (b) treatment with insulin only or insulin and OHA combination therapy, using no treatment as the reference group. Results: Adjusting for demographics, diabetes severity and duration, cardiovascular disease (CVD), and CVD risk factors, being foreign-born versus U.S.-born was not associated with treatment with OHAs only (odds ratio [OR] = 1.59; 95% confidence interval [CI] [0.97, 2.60]). However, being foreign-born was associated with decreased odds (OR = 0.53; 95% CI [0.28, 0.99]) of treatment with insulin. Conclusions: Pharmacological treatment of diabetes differs along immigrant status lines. To understand these findings, studies capturing the processes underlying treatment differences in diabetes among immigrants are needed. Findings raise the possibility that integrating information about a patient’s immigrant status, in addition to racial/ethnic identity, may be an important component of culturally sensitive diabetes care

Multi-Response Optimization of WEDM Process Parameters for Machining of Superelastic Nitinol Shape-Memory Alloy Using a Heat-Transfer Search Algorithm

Nitinol, a shape-memory alloy (SMA), is gaining popularity for use in various applications. Machining of these SMAs poses a challenge during conventional machining. Henceforth, in the current study, the wire-electric discharge process has been attempted to machine nickel-titanium (Ni55.8Ti) super-elastic SMA. Furthermore, to render the process viable for industry, a systematic approach comprising response surface methodology (RSM) and a heat-transfer search (HTS) algorithm has been strategized for optimization of process parameters. Pulse-on time, pulse-off time and current were considered as input process parameters, whereas material removal rate (MRR), surface roughness, and micro-hardness were considered as output responses. Residual plots were generated to check the robustness of analysis of variance (ANOVA) results and generated mathematical models. A multi-objective HTS algorithm was executed for generating 2-D and 3-D Pareto optimal points indicating the non-dominant feasible solutions. The proposed combined approach proved to be highly effective in predicting and optimizing the wire electrical discharge machining (WEDM) process parameters. Validation trials were carried out and the error between measured and predicted values was negligible. To ensure the existence of a shape-memory effect even after machining, a differential scanning calorimetry (DSC) test was carried out. The optimized parameters were found to machine the alloy appropriately with the intact shape memory effect

Correlation of Neck Circumference with BMI and Waist Circumference in a High-Risk Urban Cohort

Background Problem Statement: Obesity is a global epidemic requiring accurate ongoing clinical assessment, as it increases risk of multiple health conditions, such as CVD. Body mass index (BMI) is the most widely used anthropometric measure. Waist circumference (WC) is an accurate measure of visceral fat. Neck circumference (NC) is a newer, simple assessment tool positively correlated with percent body fat, BMI and WC. NC is easier to measure than WC, and unaffected by external factors like lean muscle mass for BMI, and abdominal distension, respiration, and patient discomfort for WC. Hypothesis: NC, less studied in black patients, who are at higher risk of CVD, is hypothesized to correlate with BMI and WC in a predominantly black, urban patient cohort. Project Objectives: Investigate the feasibility of integrating NC measurements into routine clinical assessments, emphasizing its simplicity and potential advantages Explore the correlation of NC with BMI and WC in a predominantly black, urban patient cohort with a higher risk of obesity-related conditions

Performance of automated and manual coding systems for occupational data: A case study of historical records

Occupational data are a common source of workplace exposure and socioeconomic information in epidemiologic research. We compared the performance of two occupation coding methods, an automated software and a manual coder, using occupation and industry titles from U.S. historical records