Regular Weight Research Articles

Jogging and weight training associated with increased high-density lipoprotein cholesterol levels in Taiwanese adults.

Although previous studies have shown that aerobic and resistance exercise increase high-density lipoprotein cholesterol (HDL-C) levels, the optimal type of exercise has not been determined. Therefore, the purpose of this study was to investigate the association of jogging (a type of aerobic exercise) and weight training (a type of resistance exercise) with HDL-C levels in Taiwanese adults. The data used in this cross-sectional study were obtained from the Taiwan Biobank (TWB), which is a national health resource that contains the genetic information of Taiwanese volunteers aged 30-70years. A total of 75,635 subjects (47,881 women and 27,754 men) were included in this study. The subjects were divided into four groups: jogging (n =2,278), weight training (n =522), mixed exercise (n =519), and no exercise (n =72,316). The TWB data were collected through questionnaires (e.g. basic characteristics, lifestyle factors, and disease history), biochemical tests, and anthropometric measurements. Compared with no exercise, jogging, weight training, and mixed exercise were all associated with higher HDL-C levels (β=2.5470, 2.6249, and 3.2117, respectively). As seen, the β value was highest for the mixed exercise group, followed by weight training and then jogging (p for trend <0.0001). In the current study, jogging and weight training were individually associated with higher levels of HDL-C. Engaging in both activities was associated with much higher levels of HDL-C. Our findings suggest that regular jogging and weight training might play an important role in increasing HDL-C levels.

On a criterion for the existence of unconditional bases of reproducing kernels in Fock spaces with radial regular weight

We consider the spaces Fφ of entire functions f such that fe−φ∈L2(C), where φ(z)=φ(|z|) is a radial subharmonic function with some regularity property. It was proved that Fφ has Riesz basis of normalized reproducing kernels if and only if (φ(er))″ is bounded above.

Regularizing autoencoders with wavelet transform for sequence anomaly detection

Nowadays, systems or entities are usually monitored by devices, generating large amounts of time series. Detecting anomalies in them help prevent potential losses, thus arousing much research interest. Existing studies have adopted autoencoders to detect anomalies, where reconstruction errors are used to indicate outliers. However, sometimes autoencoders may also reconstruct anomalies well due to the learned general features in latent spaces. To solve the above problem, we propose to regularize autoencoders to grasp specific features of normal sequences. Specifically, spectral unique patterns are captured by statistical analysis on discrete wavelet transform (DWT) coefficients of input sequences, restricting latent spaces to reflect unique patterns of normal sequences in both time and frequency domains. Furthermore, a Weight Controller calculating sample-adaptive regularization weights is designed to fully utilize the regularization effect. Extensive experiments on three public benchmarks demonstrate the effectiveness and superiority of the proposed model compared with state-of-the-art algorithms.

Contribution of Lipid Mediators in Divergent Outcomes following Acute Bacterial and Viral Lung Infections in the Obese Host.

Obesity is considered an important comorbidity for a range of noninfectious and infectious disease states including those that originate in the lung, yet the mechanisms that contribute to this susceptibility are not well defined. In this study, we used the diet-induced obesity (DIO) mouse model and two models of acute pulmonary infection, Francisella tularensis subspecies tularensis strain SchuS4 and SARS-CoV-2, to uncover the contribution of obesity in bacterial and viral disease. Whereas DIO mice were more resistant to infection with SchuS4, DIO animals were more susceptible to SARS-CoV-2 infection compared with regular weight mice. In both models, neither survival nor morbidity correlated with differences in pathogen load, overall cellularity, or influx of inflammatory cells in target organs of DIO and regular weight animals. Increased susceptibility was also not associated with exacerbated production of cytokines and chemokines in either model. Rather, we observed pathogen-specific dysregulation of the host lipidome that was associated with vulnerability to infection. Inhibition of specific pathways required for generation of lipid mediators reversed resistance to both bacterial and viral infection. Taken together, our data demonstrate disparity among obese individuals for control of lethal bacterial and viral infection and suggest that dysregulation of the host lipidome contributes to increased susceptibility to viral infection in the obese host.

Adaptively Regularized Bases-Expansion Subspace Optimization Methods for Electrical Impedance Tomography.

In this work, to deal with the difficulties in choosing regularization weighting parameters and low spatial resolution problems in difference electrical impedance tomography (EIT), we propose two adaptively regularized bases-expansion subspace optimization methods (AR-BE-SOMs). Firstly, an adaptive L1-norm based total variation (TV) regularization is introduced under the framework of BE-SOM. Secondly, besides the additive regularization method, an adaptive weighted L2-norm multiplicative regularization is further proposed. The regularized objective functions are optimized by conjugate gradient method, where the unknowns in both methods are updated alternatively between induced contrast current (ICC) and conductivity domain. Both numerical and experimental tests are conducted to validate the proposed methods, where AR-BE-SOMs show better edge-preserving, anti-noise performance, lower relative errors, and higher structure similarity indexes than BE-SOM. Unlike the common regularization techniques in EIT, the proposed regularization factors can be obtained adaptively during the optimization process. More importantly, AR-BE-SOMs perform well in reconstructions of some challenging geometry with sharp corners such as the "heart and lung" phantoms, deformation phantoms, triangles and even rectangles. It is expected that the proposed AR-BE-SOMs will find their applications for high-quality lung health monitoring and other clinical applications.

Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.

More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA-disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.

Homoeopathic management of polycystic ovarian syndrome – A case series

Introduction: Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder in females of the reproductive age group. It is characterised by menstrual irregularities, enlarged ovaries, infertility, high levels of male hormones, excess hair on the face and body, acne and obesity. PCOS can result from abnormal function of the hypothalamic-pituitary-ovarian axis. Women with PCOS have an increased risk of diabetes, high blood pressure, heart disease and endometrial cancer. Case Summary: A case series of seven PCOS patients treated with indicated constitutional homoeopathic medicines at Nehru Homoeopathic Medical College and Hospital, New Delhi, India, is presented here. Overall improvement was assessed by improvement in signs and symptoms like restoration of regular menstrual cycles, weight loss, and disappearance of ovarian cysts, as evident in ultrasonography reports, and improved scores of the integrative Medicine Outcome Scale (IMOS) scale and quality of life (WHOQOL-BREF) . The resolution of cysts in the ovaries, along with marked symptomatic improvement and improvement in quality of life in all seven patients, shows that homoeopathy has a potential for treating PCOS. Gradation of IMOS scale reduced to 1 in all seven patients after treatment showing complete recovery. One patient, who had presented with infertility, conceived during the treatment. Homoeopathic medicines such as Natrum muriaticum, Lycopodium clavatum, Silicea terra, Pulsatilla nigricans and Sepia were found most useful.

A general deep hybrid model for bioreactor systems: Combining first principles with deep neural networks

Numerous studies have reported the use of hybrid semiparametric systems that combine shallow neural networks with First Principles for bioprocess modeling. Here we revisit the general bioreactor hybrid model and introduce some deep learning techniques. Multi-layer networks with varying depths were combined with First Principles equations in the form of deep hybrid models. Deep learning techniques, namely the adaptive moment estimation method (ADAM), stochastic regularization and depth-dependent weights initialization were evaluated in a hybrid modeling context. Modified sensitivity equations are proposed for the computation of gradients in order to reduce CPU time for the training of deep hybrid models. The methods are illustrated with applications to a synthetic dataset and a pilot 50 L MUT+ Pichia pastoris process expressing a single chain antibody fragment. All in all, the results point to a systematic generalization improvement of deep hybrid models over its shallow counterpart. Moreover, the CPU cost to train the deep hybrid models is shown to be lower than for the shallow counterpart. In the pilot 50L MUT+ Pichia pastoris data set, the prediction accuracy was increased by 18.4% and the CPU decreased by 43.4%.

Analysis of Balanced Nutrition Consumption Behavior During the Covid-19 Pandemic At Binatama Health Vocational School, Yogyakarta

Teenagers’ food consumption patterns during the COVID-19 pandemic are very important, to keep the body healthy and avoid infectious diseases such as Covid-19. This study aims to analyze the consumption behavior of balanced nutrition that still needs attention from the students of SMK Kesehatan Binatama so that it can be used to produce health promotion programs for students. This research is a qualitative descriptive study. Secondary data from the results of previous studies. Both closed behavior (such as knowledge, attitudes) and open behavior of students in consuming balanced nutrition, where students' understanding still answered incorrectly, or their attitude negative. It is presented and concluded. The results showed that the knowledge, attitudes, and roles of teachers towards students regarding balanced nutrition during the covid 19 pandemic were mostly good, only in terms of behavior that required attention, namely about the lack of action in terms of 1) maintaining regular weight gain during the covid-19 pandemic for normal weight (54 out of 84); 2) eat fruit at least 2 times a day during the covid-19 pandemic (47 out of 84); 3). reducing consumption of sweet, salty and fatty foods during the covid-19 pandemic (52 out of 84); and 4). do physical activity/sports for at least 30 minutes a day during the covid-19 pandemic (59 out of 84). Then the role of parents is mostly good, the only thing that needs attention is regarding excessive consumption of snacks during the COVID-19 pandemic has not received the attention of parents (43 out of 84).

Carleman estimate for the Navier–Stokes equations and applications

For linearized Navier–Stokes equations, we first derive a Carleman estimate with a regular weight function. Then we apply it to establish conditional stability for the lateral Cauchy problem and finally we prove conditional stability estimates for the inverse source problem of determining a spatially varying divergence-free factor of a source term.

Increase in admission rates and symptom severity of childhood and adolescent anorexia nervosa in Europe during the COVID-19 pandemic: data from specialized eating disorder units in different European countries

BackgroundThe COVID-19 pandemic, associated with confinement and social isolation, seems to have impacted the course of many mental disorders in children and adolescents. An increase in hospital admission rates for juvenile anorexia nervosa (AN) has been documented in many regions of the world. However, data from Europe are scarce.MethodsWe asked clinicians in specialized eating disorder units in hospitals of maximum care in France, Germany, Italy, Spain, Sweden, and the Netherlands to report on (i) overall (inpatient and outpatient) and (ii) inpatient admission rates for adolescents with AN during 2019 and 2020. Additionally, a modified version of the COVID Isolation Eating Scale (CIES) was used to assess the child and adolescent psychiatrists’ estimations of a possible increase in symptom severity in children and adolescents with AN during the COVID-19 pandemic and to (iii) inquire about the contributing factors perceived by the caring professionals.ResultsFour out of six representatives of European hospitals described a higher rate of overall admissions during the pandemic. Three hospitals out of six reported an increase in inpatient admissions, and two centres had constant high numbers of admissions of both outpatients and inpatients. The clinicians perceived a higher symptom severity in 2020 than in 2019, especially involving more frequent use of social media, longer duration of exercising, and more restrictive eating. They supposed an increase in social media consumption, a perceived “loss of control”, and a lack of in-person assessments and weight controls as the main contributing factors for the deterioration in AN numbers and symptomatology.ConclusionsThe COVID-19 pandemic seems to have had a deep impact on symptom severity in AN, which is mirrored by a large increase in admission rates across Europe. An increase in exercise, social media consumption, a perceived “loss of control”, and a lack of face-to-face health care seem to have contributed to this development. Further investigation is required to identify which factors may lead to the increase in incidence and deterioration of childhood and adolescent AN. Possible preventive means for the future could include educating paediatricians and health care workers about AN, regular weight assessment, and home-based treatments.

Time-lapse adaptive inversion on resistivity monitoring data with Lp norm regularization and cross-time weight

Time-lapse electrical resistivity tomography (ERT) can describe the dynamic processes occurring underground and has been widely employed in the fields of hydrology, environmental management, and engineering. The time-lapse inversion method is based on the space- and time-dimension regularization technique as it considers complementary information about more than one time interval and reduces the potential causes of the geophysical data inversion of the artifact. However, the L2 norm inversions smear out the models and cannot distinguish between the resistivity interfaces. Moreover, the determination of the two optimal regularization parameters for time-lapse inversion remains challenging, and the fixed-time regularization parameter smooths out the low-sensitivity area and inhibits the real variation on the target body of the deep region. To overcome this problem, we propose a time-lapse ERT inversion method of adaptively imaging the time-lapse model and focusing on the resistivity variation on the time dimension. This method is based on the Lp norm regularization provided by the M-estimator and uses the adaptive regularization parameters based on the model roughness and data misfit. In addition, a cross-time matrix improving the sensitivity in the deep region of model space is added to the time regularization term. We present two synthetic examples and a field dataset to validate the method. Numerical simulation and field dataset inversion results revealed that the proposed time-lapse inversion strategy can avoid the excessive smoothness caused by the low sensitivity in the model deep region and can thus enhance the response from the true ground variation to improve the accuracy of the inversion results. Owing to the adaptive regularization parameters that can preserve the dynamic balance between the objective function components, the inversion iteration progress can remain stable and can maintain the trend toward convergence.

Mechanical and Characterization Behavior of Light Weight Aggregate Concrete Using Sintered Fly Ash Aggregates and Synthetic Fibers

This study focuses into the usage of sintered fly ash aggregate in the production of lightweight concrete. To minimize the environmental impacts would alleviate the waste disposal issues related with fly ash and contribute in the decrease of environmental pollution. Construction materials are in high need in the domestic sector, which is growing increasingly rare day by day. In India, the power sector is currently reliant on thermal power plants, which generate a massive amount of fly ash, estimated to be roughly 200 million tons each year. Fly ash is a waste product produced by thermal power stations when pulverized coal is burned. The use of industrial waste as a building material is a significant step towards long-term sustainability. The mechanical characteristics of sintered fly ash aggregates were discussed. According to the results, the specific gravity of these aggregates is 16–46% lower than that of regular weight aggregates, and they have higher water absorption. The 28-day comp strengths of sintered fly ash aggregate concretes range from 27 to 74MPa, with densities ranging from 1651 to 2017 kg/m3. According to the study, sintered fly ash aggregate concrete is also one of the materials that might be used to develop structural concrete. Sintered fly ash aggregate was used to replace natural aggregate (12%). The compressive, tensile, and flexural strengths of concrete produced with natural aggregate and sintered fly ash aggregate were compared. Also, concretes were conducted to a microstructure test and a SEM analysis.

Laparoscopic treatment of duodenal atresia associated with situs inversus abdominis in a newborn

Introduction. A combination of congenital duodenal obstruction (CDO) and situs inversus abdominis (SIA) is an extremely rare case. The present article describes a laparoscopic technique applied for the care in such patient.Case presentation. A newborn girl was admitted to the hospital on the 4th day of her life with regular bile vomiting and weight loss. Examination revealed CDO combined with SIA. After preoperative preparation and condition stabilization, the child was operated on and Kimura duodenoduodenoanastomosis was put. After the performed treatment, duodenal patency was restored. The patient was discharged home in a satisfactory state. She had complete enteral feeding with a good dynamics of weight gain.Conclusion. The described case demonstrates the effectiveness of laparoscopic approach in patients with CDO and SIA hospitalized in a regional children`s hospital.

Prognostic Observational Analysis of BMI, Leptin, and Adiponectin in Children With Acute Lymphocytic Leukemia Undergoing Remission-Induction Chemotherapy.

BackgroundThe survival rate of children and adolescents with acute lymphoblastic leukemia (ALL) has progressively improved. However, ALL survivors often have adverse effects after treatment, such as an increased risk of obesity. Obesity has been associated with reduced survival.ObjectiveWe investigated the relationship between obesity, adipocytokine levels, and ALL short-term outcomes.MethodsWeight and height were measured, and body mass index (BMI) was calculated at patient diagnosis and discharge. Leptin and Adiponectin levels and Minimal Residual Disease (MRD) were measured before therapy, at days 19 of remission-induction therapy, and at the end of remission-induction therapy (days 46). The relationship between BMI, adipocytokine levels, and MRD was then determined.ResultsCompared to the normal BMI group, children with an abnormal increase in BMI had an increase in MRD at day 19 and 46 (P = 0.04 and P = 0.008), and showed a positive correlation (P = 0.014). In addition, we found a positive correlation between weight, hip circumference at diagnosis and at day 19, and MRD at day 46. Both BMI and fat concentric distribution affected the outcome of ALL children. A higher BMI was also associated with a significant increase in Leptin levels at diagnosis. Leptin resistance should be considered in ALL children with high BMI.ConclusionBMI affects the outcome of ALL patients. Early interventions such as regular weight, height monitoring, and dietary assessments should be preferably initiated during remission-induction chemotherapy.

Monitoring Body Length of Hokkaido Native Horse’s Foals Using a Drone

Monitoring Body Length of Hokkaido Native Horse’s Foals Using a Drone

Holistic fine-grained global glomerulosclerosis characterization: from detection to unbalanced classification.

Purpose: Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-grained quantitative analysis of multiple GGS subtypes (e.g., obsolescent, solidified, and disappearing glomerulosclerosis) is typically a resource extensive manual process. Very few automatic methods, if any, have been developed to bridge this gap for such analytics. We present a holistic pipeline to quantify GGS (with both detection and classification) from a whole slide image in a fully automatic manner. In addition, we conduct the fine-grained classification for the subtypes of GGS. Our study releases the open-source quantitative analytical tool for fine-grained GGS characterization while tackling the technical challenges in unbalanced classification and integrating detection and classification. Approach: We present a deep learning-based framework to perform fine-grained detection and classification of GGS, with a hierarchical two-stage design. Moreover, we incorporate the state-of-the-art transfer learning techniques to achieve a more generalizable deep learning model for tackling the imbalanced distribution of our dataset. This way, we build a highly efficient WSI-to-results GGS characterization pipeline. Meanwhile, we investigated the largest fine-grained GGS cohort as of yet with 11,462 glomeruli and 10,619 nonglomeruli, which include 7841 globally sclerotic glomeruli of three distinct categories. With these data, we apply deep learning techniques to achieve (1) fine-grained GGS characterization, (2) GGS versus non-GGS classification, and (3) improved glomeruli detection results. Results: For fine-grained GGS characterization, when pretrained on the larger dataset, our model can achieve a 0.778-macro- score, compared to a 0.746-macro- score when using the regular ImageNet-pretrained weights. On the external dataset, our best model achieves an area under the curve (AUC) score of 0.994 when tasked with differentiating GGS from normal glomeruli. Using our dataset, we are able to build algorithms that allow for fine-grained classification of glomeruli lesions and are robust to distribution shifts. Conclusion: Our study showed that the proposed methods consistently improve the detection and fine-grained classification performance through both cross validation and external validation. Our code and pretrained models have been released for public use at https://github.com/luyuzhe111/glomeruli.

Relationship between functional capacity and socioeconomic status in a cohort of Turkish heart failure patients.

New York Heart Association (NYHA) classification predicts prognosis for heart failure (HF) patients. Socioeconomic status (SES) has the potential to affect treatment strategy and disease course. The present objective was to investigate whether SES of Turkish HF patients affected NYHA classification. Turkish research team-HF (TREAT-HF) is a questionnaire study with 52 questions, the purpose of which is to aid in the assessment of various qualities of HF patients. The 2013 TREAT-HF cohort included 503 patients from 11 centers in Turkey. Patients were divided into 2 groups according to NYHA functional classification: I-II, a better functional classification, or III-IV, a poorer functional classification. In addition, patients were analyzed according to gender. Evaluated were NYHA functional classifications of 459 (326 males and 133 females) patients with a mean age of 59.7±14.07 years and a mean ejection fraction (EF) of 31.7±9.22%. Total monthly family income level of ≥1000 TL, younger age, higher EF, and male gender were independently associated with better NYHA functional classification. In addition to EF, regular weight control was found to be independently associated with better NYHA classification for females, while younger age and income of ≥1000 TL were independently associated with better NYHA classification for males. The present results demonstrated that SES was associated with NYHA functional classification in a cohort of Turkish HF patients.

Possible etiological factors related with fatty liver diseases other than alcohol

Non-alcoholic Fatty liver disease (NAFLD) is an ailment that is associated with various causative factors other than alcohol consumption. It affects a substantial number of individuals both young and old irrespective of sex around the globe and its incidence is increasing/rises globally. The data was collected by searching various websites, consulting reports and analysed the data with respect to possible causative factors related to NAFLD in humans. The data available indicated that several factors such as drug addiction/abuse, unhealthy dietary practices, lifestyle habits, diabetic, metabolic disorders, obesity, sedentary lifestyles etc. might be related to non-alcoholic fatty liver disorders in human or exaggerate the NAFLD. Thus, there is a necessity to prevent exposure to such chemicals and/or factors to reduce or prevent this disease and there is also a necessity for an awareness program about the causative factors of NAFLD among the community which could be useful in prevention/control of the ailment. Further, some sort of regular physical exercise, activity and excess weight loss might be useful to check/ control the occurrence/ progression of NAFLD. NAFLD can be prevented/ reduced/ stopped by adaptation of healthy lifestyles in day-to-day life, regular exercise to reduce the weight, and adoption of these in life may be useful in management/ control of disease. Keywords: Fatty liver disease; Non-alcoholic liver disease; Non-alcoholic steatohepatitis; Cirrhosis; Metabolic disorders.

Optimization of seismic-guided 3-D marine magnetotelluric imaging in a complex fold-thrust belt setting in NW Borneo, Malaysia

SUMMARY The use of seismic-derived structure tensor to guide the inversion of non-seismic data has been gaining increasing attention because of its ability to constrain the inversion result to honour structural information from seismic reflectivity. While the cross-gradient method has been recently adapted to this problem, there is a need to further examine how the choice of the various regularization weighting factors and the design of the starting model impact the image-guided results. In this paper focused on marine magnetotellurics (MT), we study several practical aspects of seismic-guided electromagnetic (EM) inversion to see on what can be improved from the current practice. We then show practical examples of how the cross-gradient method can be applied to get a more geologically pleasing model that fits the data. We also show that there are subjective choices in how to effectively apply the method. First, using a field-realistic synthetic model, we established what would be the suitable regularization weights in the vertical and horizontal directions. Secondly, for the actual 3-D survey data, we compared the use of structure tensors derived from anisotropic pre-stack depth migration (APSDM) reflectivity, high-fidelity full-waveform inversion (FWI) velocity and acoustic impedance inversion volumes to guide cross-gradient anisotropic resistivity inversion with initial half-space resistivity starting models. We determined post-facto the optimum structure tensor weights using well logs as the ground truth. We found that the initial model built using horizon-based interpolated resistivity logs from multiple wells in the anisotropic inversion guided with seismic FWI velocity gave the best match to well logs as compared to the other options. However, given that resistivity logs from multiple wells may not always be available for a robust initial model construction in frontier exploration, we suggest that the structure tensor from APSDM reflectivity data is still preferred since the APSDM reflectivity data have well-defined structural information.