Terms Of Weight Research Articles

The First Exploratory Personalized Medicine Approach to Improve Bariatric Surgery Outcomes Utilizing Psychosocial and Genetic Risk Assessments: Encouraging Clinical Research.

It is predicted that by 2030, globally, an estimated 2.16 billion adults will be overweight, and 1.12 billion will be obese. This study examined genetic data regarding Reward Deficiency Syndrome (RDS) to evaluate their usefulness in counselling patients undergoing bariatric surgery and gathered preliminary data on the potential use in predicting short term (6-month) weight loss outcomes. Methods: Patients undergoing bariatric surgery (n = 34) were examined for Genetic Addiction Risk Severity (GARS) [measures the presence of risk alleles associated with RDS]; as well as their psychosocial traits (questionnaires). BMI changes and sociodemographic data were abstracted from Electronic Health Records. Results: Subjects showed ∆BMI (M = 10.0 ± 1.05 kg/m2) and a mean % excess weight loss (56 ± 13.8%). In addition, 76% of subjects had GARS scores above seven. The homozygote risk alleles for MAO (rs768062321) and DRD1 (rs4532) showed a 38% and 47% prevalence among the subjects. Of the 11 risk alleles identified by GARS, the DRD4 risk allele (rs1800955), was significantly correlated with change in weight and BMI six months post-surgery. We identified correlations with individual risk alleles and psychosocial trait scores. The COMT risk allele (rs4680) showed a negative correlation with EEI scores (r = -0.4983, p < 0.05) and PSQI scores (r = -0.5482, p < 0.05). The GABRB3 risk allele (rs764926719) correlated positively with EEI (r = 0.6161, p < 0.01) and FCQ scores (r = 0.6373, p < 0.01). The OPRM1 risk allele showed a positive correlation with the DERS score (r = 0.5228, p < 0.05). We also identified correlations between DERS and BMI change (r = 0.61; p < 0.01). Conclusions: These data support the potential benefit of a personalized medicinal approach inclusive of genetic testing and psychosocial trait questionnaires when counselling patients with obesity considering bariatric surgery. Future research will explore epigenetic factors that contribute to outcomes of bariatric surgery.

The effect of whole egg consumption on weight and body composition in adults: a systematic review and meta-analysis of clinical trials

BackgroundA limited number of studies have directly examined the effect of whole eggs on body weight and composition in adults, and they have led to inconsistent results. This study aimed to summarize the evidence on the effect of whole egg consumption on body weight and body composition in adults from clinical trials.MethodsOnline databases were searched from inception to April 2023 for clinical trials that directly or indirectly assessed the effect of whole eggs consumption on anthropometric measures including body weight, body mass index (BMI), waist circumference (WC), and fat-free mass (FFM) in adults. A random effects model was used for meta-analysis.ResultsIn total, 32 controlled clinical trials were included in the systematic review. The analyses revealed that whole egg consumption has no significant effect on body weight (n = 22), BMI (n = 13), WC (n = 10), and FFM (n = 4, P > 0.05). The subgroup analyses showed that whole egg consumption has an increasing effect on body weight and BMI in studies that lasted more than 12 weeks and in unhealthy participants (P < 0.05). A significant increasing effect on BMI was found in studies that the control group did not receive any egg (P < 0.05). Moreover, in studies that there was no significant difference in energy intake between the intervention and control groups, weight, and WC were significantly increased (P < 0.05). Additionally, in studies that participants in the control group received another food or supplement, studies with calorie restriction, and studies on healthy subjects, whole egg intake significantly decreased BMI (P < 0.05).ConclusionsAlthough whole egg consumption had no adverse effect on body composition and body weight, in overall, it might increase body weight in long term. Egg consumption beneficially affects BMI in healthy people and during weight loss diet.Systematic review registrationThis systematic review and meta-analysis is registered in the International Prospective Register of Systematic Reviews (PROSPERO, Registration number: CRD42022308045).

Pregnancy outcome in patients with a medical history of immunoglobulin A vasculitis: a case–control study

Objective Data on obstetric outcomes in patients with a history of immunoglobulin A vasculitis (IgA-V) are lacking. The aim of this study was to assess maternal, neonatal, and vasculitis outcomes during pregnancy. Method We conducted a French retrospective case–control study. Pregnancies of patients with a history of IgA-V (cases) were retrospectively studied and compared to pregnancies in women who developed IgA-V after their pregnancies and to pregnancies in healthy women (controls). Results Twenty-six pregnancies in patients with a history of IgA-V were included and compared to 15 pregnancies in women who later developed IgA-V and 52 pregnancies in healthy women. Both gestational hypertension and pre-eclampsia were more frequent in the case group than in the other groups (23% vs 0% vs 0%, p < 0.01; 12% vs 7% vs 0%, p = 0.04). Hypertensive disorder of pregnancy occurred more frequently in patients with pre-existing kidney disease (78% vs 12%, p < 0.01). Caesarean section was more often performed in the case group than in the other groups (27% vs 0% vs 10%, p = 0.04). No foetal loss or maternal deaths occurred. There were no differences in delivery term or birth weight. No vasculitis flares were observed during pregnancy. Conclusion Women with a history of IgA-V appear to be at higher risk for gestational hypertension and pre-eclampsia, especially in cases with renal involvement; however, both mother and newborn outcomes appear to be favourable.

PheSom: a term frequency-based method for measuring human phenotype similarity on the basis of MeSH vocabulary.

Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method. Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method. Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.

Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible to robustly address multi-objective and multiscale problems. BPINNs are a popular framework for data assimilation, combining the constraints of Uncertainty Quantification (UQ) and Partial Differential Equation (PDE). The relative weights of the BPINN target distribution terms are directly related to the inherent uncertainty in the respective learning tasks. Yet, they are usually manually set a-priori, that can lead to pathological behavior, stability concerns, and to conflicts between tasks which are obstacles that have deterred the use of BPINNs for inverse problems with multiscale dynamics.The present weighting strategy automatically tunes the weights by considering the multitask nature of target posterior distribution. We show that this remedies the failure modes of BPINNs and provides efficient exploration of the optimal Pareto front. This leads to better convergence and stability of BPINN training while reducing sampling bias. The determined weights moreover carry information about task uncertainties, reflecting noise levels in the data and adequacy of the PDE model.We demonstrate this in numerical experiments in Sobolev training, and compare them to analytically ε-optimal baseline, and in a multiscale Lotka-Volterra inverse problem. We eventually apply this framework to an inpainting task and an inverse problem, involving latent field recovery for incompressible flow in complex geometries.

Occams: A Text Summarization Package

Extractive text summarization selects asmall subset of sentences from a document, which gives good “coverage” of a document. When given a set of term weights indicating the importance of the terms, the concept of coverage may be formalized into a combinatorial optimization problem known as the budgeted maximum coverage problem. Extractive methods in this class are known to beamong the best of classic extractive summarization systems. This paper gives a synopsis of thesoftware package occams, which is a multilingual extractive single and multi-document summarization package based on an algorithm giving an optimal approximation to the budgeted maximum coverage problem. The occams package is written in Python and provides an easy-to-use modular interface, allowing it to work in conjunction with popular Python NLP packages, such as nltk, stanza or spacy.

Foreseeing the spatio-temporal offshore wind energy potential of India using a differential weighted ensemble created using CMIP6 datasets

Offshore wind energy assessments help in identifying suitable locations for offshore wind farms. Its importance is further amplified in the context of climate change as wind power potential is susceptible to it. The present study aimed to assess the offshore wind potential of India and its sensitivity to climate change with the help of two different ensemble variants developed using nine CMIP6 Global Climate Models (GCMs). First ensemble is created with equal emphasis on all GCMs, while differential weights derived using Shannon entropy technique is used to develop the other ensemble. Created ensembles are further compared with ERA5 data. Comparative results suggest that differential weighted ensemble is superior to uniform weights in terms of bias. Owing to this, weighted ensemble is further used to study the impact of climate change on wind power density (WPD) for the near (2021–2045) and far-future (2075–2099) periods under two shared socioeconomic pathways (SSP) scenarios, i.e., SSP2-4.5 and SSP5-8.5, the most widely used and probable scenarios. Findings suggest that WPD variation in the study area ranges between ＋10% and −20%. These variations are examined to study further the impact of climate change on geographical variations of WPD distributions. With the regions in Arabian Sea as an exception, WPD appears to increase in future scenarios. WPD varies more in far-future scenarios compared to near-future scenarios. The future variations of the WPD across study areas are prominent in the case of SSP5 - 8.5 compared to the variations noted in the case of SSP2-4.5. Findings of this study help stakeholders to understand the impact of climate change on offshore wind potential. Moreover, plots showing the variation of WPD for near and far-future scenarios complemented with additional studies help in choosing an appropriate location to tap the offshore wind potential in India.

Neural Language Model Based Attentive Term Dependence Model for Verbose Query (Student Abstract)

The query-document term matching plays an important role in information retrieval. However, the retrieval performance degrades when the documents get matched with the extraneous terms of the query which frequently arises in verbose queries. To address this problem, we generate the dense vector of the entire query and individual query terms using the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model and subsequently analyze their relation to focus on the central terms. We then propose a context-aware attentive extension of unsupervised Markov Random Field-based sequential term dependence model that explicitly pays more attention to those contextually central terms. The proposed model utilizes the strengths of the pre-trained large language model for estimating the attention weight of terms and rank the documents in a single pass without any supervision.

O-196 Evidence-based management of mosaic embryos - a single Centre experience from prospective transfer and birth outcomes of 565 mosaic embryos

Abstract Study question What are the pregnancy and neonatal outcomes of prospective transfer of 565 mosaic embryos and what are the key determinants influencing the outcome? Summary answer Mosaic (diploid/aneuploid) embryos have a significant developmental potential with good neonatal outcomes allowing personalized management and evidence based prioritization of embryos for transfer. What is known already Since the first report of healthy babies born after mosaic transfer, the gradual accumulation of knowledge on mosaic embryo transfers in the International Registry of Mosaic Embryo Transfers provided the necessary reassurance for clinical management of mosaic embryos. Large and comprehensive data from single centres with consistent clinical genetic counselling and laboratory practices for preimplantation and prenatal testing offer valuable contribution to the current practices. The aim of this study was to evaluate the pregnancy and neonatal outcomes after mosaic embryo transfers and to evaluate for associations between embryo genetic aberrations, static morphology and specific outcomes. Study design, size, duration This is a retrospective cohort study that took place in a single academic IVF centre (The CReATe Fertility Centre, Toronto, Canada), where we analysed the results from high resolution PGT-A, embryo morphologic features and pregnancy outcomes of 3529 frozen embryo transfers from 2016-2022. Associated patient demographic data, transfer outcome and neonatal outcome of 2964 euploid and 565 mosaic embryo transfers were analyzed. Participants/materials, setting, methods All patients received genetic counselling prior to frozen embryo transfer. High resolution NGS PGT-A was performed using the Illumina platform and BluGnome and NxClinical for data analysis. Clinically relevant findings were aberrations &amp;gt;10Mb and mosaicism from 25%-75%. Comparison of clinical and morphological characteristics of euploid, low-level mosaic (25%-&amp;lt;50% aneuploid cells in trophectoderm biopsy), and high-level mosaics (50% -75%) was performed with R-statistical package, Mathlab and SSPS software. p &amp;lt; 0.05 with CI 95% was considered significant. Main results and the role of chance Of the total 565 mosaic embryos transferred 77.1% had mosaicism levels ≥25%-&amp;lt;50% (Group1-n=436): 54.8% had segmental chromosome mosaic (SCM)gain/loss, 45.2% had whole chromosome mosaicism(WCM)(trisomy/monosomy); and 22.9% had mosaic levels of ≥ 50-&amp;lt;70% (Group2-n=129)(50.4% with SCM and 49.6% with WCM). The overall ongoing pregnancy rate(OPR) for mosaic embryos was 33% and the miscarriage rate was 6%. Compared with euploid embryos, mosaic embryos have significantly lower implantation (p = 0,OD0.62-CI[0.5-0.7]) and OPR(p = 0,OD0.5-CI[0.4-0.6]). Pregnancy outcomes are determined by level of mosaicism: Group1 have better implantation(p = 0.005,OD1.8-CI[1.18-2.78]) and OPR compared to Group2. High level of mosaicism independently affected the OPR of SCM(p = 0.003) and WCM(p = 0.02) embryos. WCM had lower implantation rates compared to SCM (p = 0.005,OD1.6-CI[1.15-2.27]). Mosaic monosomies had the lowest impanation and OPR of all mosaic embryos (14% and 8%,respectively). Within Group1 and Group2 good static morphology was associated with higher OPR for SCM and WCM embryos. Birth outcomes were available for 118 babies from mosaic-FET and from 802 euploid-FET. There is no difference in duration of pregnancy and birth weight between mosaic and euploid-FET. No gross-fetal anomalies were reported in the mosaic group at birth. There was one termination&amp;lt;20GW due to fetal anomalies and one stillbirth. For euploid transfers, 11 had fetal anomalies, there were 7-terminations and 2-stillbirths. Limitations, reasons for caution Although this is the largest single centre study to date that evaluated genomic, morphology and outcome data of mosaic transfers, the numbers are still low to analyze the impact of specific chromosomal aberrations on pregnancy outcome. Wider implications of the findings Mosaic embryos should be considered for transfer and prioritized based on the level of mosaicism, type of chromosomal aberration and static morphology. The implantation potential is lower than euploid embryos however established pregnancies result in healthy babies born at term and normal birth weight. Trial registration number CReATe Fertility Centre, Toronto, Canada

1717-P: Weight-Loss Differences with SGLT2 inhibitors, GLP-1 Receptor Agonists, and Repetitive Transcranial Magnetic Stimulation in Type 2 Diabetes and Obesity

Weight loss in individuals with obesity (O) and type 2 diabetes (T2D) may improve glycemic control, prevent obesity-related comorbidities and, in some cases, induce remission of T2D. Glucose-lowering drugs such as SGLT2i and GLP-1 RAs are able to control simultaneously body weight and glucose levels; their use is recommended in individuals with O and T2D. Recently, we have demonstrated the effectiveness of repetitive Transcranial Magnetic Stimulation (rTMS) in promoting weight loss up to 1 year in individuals with O, with and without T2D. The aim of this retrospective comparative study was to compare the efficacy of SGLT2i, GLP1-RA and rTMS in reducing body weight in the long term in patients with O and T2D. Data obtained from 31 patients with O and T2D were retrospectively analyzed: 11 were treated for O with high frequency rTMS for 5 weeks, 8 subjects were treated for T2D with SGLT2i for 1 year, and 12 individuals were treated for T2D with GLP1-RA for 1 year. Changes in body weight and HbA1c in the 3 groups were analyzed with ANOVA, after 6 months (FU1) and 1 year (FU2) from the beginning of the treatment. The change in body weight between the 3 groups was significant at both FU1 [−3.2±1.8% (SGLT2i) vs −3.5%±1.5% (GLP1-RA) vs −6.3±4.0% (rTMS);p=0.026] and FU2 [−2.1±2.1% (SGLT2i) vs −2.1±4.2% (GLP1-RA) vs −6.7±4.7% (rTMS); p=0.017]. Glycated hemoglobin variation was higher in the SGLT2i and GLP-1 RA groups than in rTMS at FU1 [−11.7±25.1% (SGLT2i) vs −11.9%±13.2% (GLP1-RA) vs −8.9±10.4% (rTMS)] and at FU2 [−15.9±24.2% (SGLT2i) vs −7.8±11.9% (GLP1-RA) vs −5.6±9.0% (rTMS)], although not statistically significant. TMS was found to be more effective than SGLT2i and GLP1-RAs in promoting long-term weight loss in a population with O and T2D, likely due to a prevalent effect of rTMS on appetite control at the meso-cortico-limbic system’s level. These findings set the stage for a potential combined use of rTMS with SGLT2i or GLP1-RAs in treatment of O and T2D. Disclosure A.Ferrulli: None. S.Massarini: None. C.Macrì: None. P.Senesi: None. I.Terruzzi: None. D.Cannavaro: None. L.Luzi: Advisory Panel; Eli Lilly and Company, Medtronic, Research Support; Gelesis, Speaker's Bureau; A. Menarini Diagnostics, Amgen Inc., Boehringer Ingelheim and Eli Lilly Alliance, Eli Lilly and Company, Novo Nordisk, Novartis. Funding IRCCS MultiMedica (Ricerca Corrente)

Evaluation of the Effect of Hedonic Hunger on Nutrition Change Processes and Its Relationship with BMI: A Study on University Students

Objective: The aim of this study is to evaluate the effect of hedonic hunger on nutritional change processes and its relationship with BMI in university students.&#x0D; Methods: A questionnaire consisting of sociodemographic characteristics, questions about eating habits, Power of Food Scale (PFS) and Nutrition Change Processes Scale (NPCS) were applied to 1003 undergraduate students.&#x0D; Results: Majority of the students were female and normal weight in terms of BMI. The median PFS and score of the obese students is higher than the normal ones. The median NPCS scores of obese students are higher than other BMI classifications (p&lt; .01). The median scores of food available, food present and food taste sub-factors of PFS are statistically higher in obese students than in normal-weight students (p&lt; .01). The sub-factors of NPCS that consciousness raising, dramatic relief, self-reevaluation, social liberation, contingency management, self-liberation, stimulus control median scores are statistically higher in obese students than in normal-weight students. As hedonic hunger increases, the nutritional change process increases by 13.7%. The increase in hedonic hunger affects the nutritional change processes positively by 46.1% (p&lt; .001).&#x0D; Conclusion: Hedonic hunger and nutrition change processes of obese students are higher than those of normal weight, and as hedonic hunger increases, the process of nutritional change increases, and the increase in hedonic hunger positively affects nutritional change processes.

FRI-513 - Fat versus lean tissue loss in short term versus long term weight loss: a dynamic effect with implications for NASH trials

FRI-513 - Fat versus lean tissue loss in short term versus long term weight loss: a dynamic effect with implications for NASH trials

TwIdw—A Novel Method for Feature Extraction from Unstructured Texts

This research proposes a novel technique for fake news classification using natural language processing (NLP) methods. The proposed technique, TwIdw (Term weight–inverse document weight), is used for feature extraction and is based on TfIdf, with the term frequencies replaced by the depth of the words in documents. The effectiveness of the TwIdw technique is compared to another feature extraction method—basic TfIdf. Classification models were created using the random forest and feedforward neural networks, and within those, three different datasets were used. The feedforward neural network method with the KaiDMML dataset showed an increase in accuracy of up to 3.9%. The random forest method with TwIdw was not as successful as the neural network method and only showed an increase in accuracy with the KaiDMML dataset (1%). The feedforward neural network, on the other hand, showed an increase in accuracy with the TwIdw technique for all datasets. Precision and recall measures also confirmed good results, particularly for the neural network method. The TwIdw technique has the potential to be used in various NLP applications, including fake news classification and other NLP classification problems.

How to Measure Knowledge Economy

The paper’s primary goal is to analyse the development of Knowledge Economy (KE) measurement methods ranging from those based on national income to indices identifying and combining the relevant indicators. The paper focuses on four current global and European KE level indices: Global Innovation Index (GII), Global Knowledge Index (GKI), European Innovation Scoreboard (EIS), and Digital Economy and Society Index (DESI), highlighting persistent significant differences in the perception of the very essence of KE, as there is no clear interdisciplinary definition of the initial concept of knowledge, leading to further problems with ambiguous and insufficiently specific definitions and measurement of KE. Tacit aspects of knowledge are rarely part of KE definitions or measurements, excluding a large part of the knowledge system from KE measurements. The results of the analysis show that the set of KE indicators used by the individual KE indices is heterogeneous, with the set of intersecting indicators having different weights in terms of importance. Frequent interventions in the indices by their authors were observed, such as changes in index methodology, the indicators used, main pillars (subindices), etc. Despite the high heterogeneity in the approach to measuring KE, we identified the pillars, which can be viewed as the core pillars of KE. These include, for example the level of ICT, R&amp;D, human resources, innovation, patents, and education.

Efficient FMT reconstruction based on L1-αL2 regularization via half-quadratic splitting and a two-probe separation light source strategy.

Fluorescence molecular tomography (FMT) can achieve noninvasive, high-contrast, high-sensitivity three-dimensional imaging in vivo by relying on a variety of fluorescent molecular probes, and has excellent clinical transformation prospects in the detection of tumors in vivo. However, the limited surface fluorescence makes the FMT reconstruction have some ill-posedness, and it is difficult to obtain the ideal reconstruction effect. In this paper, two different emission fluorescent probes and L 1-L 2 regularization are combined to improve the temporal and spatial resolution of FMT visual reconstruction by introducing the weighting factor α and a half-quadratic splitting alternating optimization (HQSAO) iterative algorithm. By introducing an auxiliary variable, the HQSAO method breaks the sparse FMT reconstruction task into two subproblems that can be solved in turn: simple reconstruction and image denoising. The weight factor α (α>1) can increase the weight of nonconvex terms to further promote the sparsity of the algorithm. Importantly, this paper combines two different dominant fluorescent probes to achieve high-quality reconstruction of dual light sources. The performance of the proposed reconstruction strategy was evaluated by digital mouse and nude mouse single/dual light source models. The simulation results show that the HQSAO iterative algorithm can achieve more excellent positioning accuracy and morphology distribution in a shorter time. In vivo experiments also further prove that the HQSAO algorithm has advantages in light source information preservation and artifact suppression. In particular, the introduction of two main emission fluorescent probes makes it easy to separate and reconstruct the dual light sources. When it comes to localization and three-dimensional morphology, the results of the reconstruction are much better than those using a fluorescent probe, which further facilitates the clinical transformation of FMT.

Metabolic profiles reflect weight loss maintenance and the composition of diet after very-low-energy diet

Diet and weight loss affect circulating metabolome. However, metabolite profiles induced by different weight loss maintenance diets and underlying longer term weight loss maintenance remain unknown. Herein, we investigated after-weight-loss metabolic signatures of two isocaloric 24-wk weight maintenance diets differing in satiety value due to dietary fibre, protein and fat contents and identified metabolite features that associated with successful weight loss maintenance. Non-targeted LC-MS metabolomics approach was used to analyse plasma metabolites of 79 women and men (mean age±SD 49.7±9.0 years; BMI 34.2±2.5kg/m2) participating in a weight management study. Participants underwent a 7-week very-low-energy diet (VLED) and were thereafter randomised into two groups for a 24-week weight maintenance phase. Higher satiety food (HSF) group consumed high-fibre, high-protein, and low-fat products, while lower satiety food (LSF) group consumed isocaloric low-fibre products with average protein and fat content as a part of their weight maintenance diets. Plasma metabolites were analysed before the VLED and before and after the weight maintenance phase. Metabolite features discriminating HSF and LSF groups were annotated. We also analysed metabolite features that discriminated participants who maintained ≥10% weight loss (HWM) and participants who maintained <10% weight loss (LWM) at the end of the study, irrespective of the diet. Finally, we assessed robust linear regression between metabolite features and anthropometric and food group variables. We annotated 126 metabolites that discriminated the HSF and LSF groups and HWM and LWM groups (p<0.05). Compared to LSF, the HSF group had lower levels of several amino acids, e.g. glutamine, arginine, and glycine, short-, medium- and long-chain acylcarnitines (CARs), odd- and even-chain lysoglycerophospholipids, and higher levels of fatty amides. Compared to LWM, the HWM group in general showed higher levels of glycerophospholipids with a saturated long-chain and a C20:4 fatty acid tail, and unsaturated free fatty acids (FFAs). Changes in several saturated odd- and even-chain LPCs and LPEs and fatty amides were associated with the intake of many food groups, particularly grain and dairy products. Increase in several (lyso)glycerophospholipids was associated with decrease in body weight and adiposity. Increased short- and medium-chain CARs were related to decreased body fat-free mass. Our results show that isocaloric weight maintenance diets differing in dietary fibre, protein, and fat content affected amino acid and lipid metabolism. Increased abundances of several phospholipid species and FFAs were related with greater weight loss maintenance. Our findings indicate common and distinct metabolites for weight and dietary related variables in the context of weight reduction and weight management. The study was registered in isrctn.org with identifier 67529475.

A New Term Representation Method for Gender and Age Prediction

Author Profiling is a kind of text classification method that is used for detecting the personality profiles such as age, gender, educational background, place of origin, personality traits, native language, etc., of authors by processing their written texts. Several applications like forensic analysis, security and marking are used the techniques of author profiling for finding the basic details of authors. The main problem in the domain of author profiling is preparation of suitable dataset for predicting the characteristics of authors. PAN is one organization conducting competitions on various types of shared tasks. In 2013, PAN organizers presented the task of author profiling in their series of competitions and continued this task in further years. They arranged different kinds of datasets in different varieties of languages. From 2013 onwards several researchers proposed solutions for author profiling to predict different personality features of authors by utilizing the datasets provided in PAN competitions. Researchers used different kinds of features like character based, lexical or word based, structural features, syntactic, content based, style based features for distinguishing the author’s writing styles in their texts. Most of the researchers observed that the content based features like words or phrases those are used in the text are most useful for detecting the personality features of authors. In this work, the experiment conducted with the content based features like most important words or terms for predicting age group and gender from the PAN competition datasets. Two datasets such as PAN 2014 and 2016 author profiling datasets are used in this experiment. The documents of dataset are converted in to a vector representation which is a suitable format for giving training to machine learning algorithms. The term representation in a document vector plays a crucial role to improve the performance of gender and age group prediction.The Term Weight Measures (TWMs) are such techniques used for this purpose to represent the significance of a term value in document vector representation. In this work, we developed a new TWM for representing the term value in document vector representation. The proposed TWM’s efficiency is compared with the efficiency of other existing TWMs. Two Machine Learning (ML) algorithms like SVM (Support Vector Machine) and RF (Random Forest) are considered in this experiment for estimating the accuracy of proposed approach. We recognized that the proposed TWM accomplished best accuracies for gender and age prediction in two PAN Datasets.

Perbandingan Metode Pembobotan TF-RF Dan TF-ABS Pada Kategorisasi Berita Di BDI Denpasar

BDI Denpasar is a government agency tasked with carrying out training and education for human resources of animation, crafts and art. BDI Denpasar in managing news classes in the Kabar Insan Oke service still uses conventional methods. Therefore an automatic news classification module is needed. This study was made to compare the performance level of news classification at BDI Denpasar using K-NN classification with the TF-RF and TF-ABS term weighting methods. Methods that have a high level of performance will be implemented in the news classification module. This research was carried out by collecting news documents, text preprocessing, term weighting, classification, model validation and testing. The K-NN classification uses the n_neighbhor (k), namely k=3, k=5, k=7 and k=9 using a dataset of 324 documents containing 7 classes taken from BDI Denpasar website. Based on the results of the tests performed, TF-RF method obtained a higher performance at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%. TF-ABS method with the highest performance value is found at k=9 which obtains 70% accuracy, 63% precision and 70% recall. So the method that will be implemented in the news classification module is TF-RF at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%.

BIOINSPIRED UNDERWATER ROBOT WITH SHAPE MEMORY MET-AL-BASED ACTUATORS

A bio-inspired robot was implemented in a fish whose main objective is the analysis of the interaction of the pectoral and caudal fins during the two-dimensional displacement, the pectoral and caudal fins were implemented for rotation and displacement, its pectoral fin has a dimension of 30mm high and 33mm wide, It consists of two parts, its base with cavities to house the actuator and its cover to hold and compact the fin, the caudal fin is created from a mold with cavities to house the actuator with a rigid fin at the end to have a greater movement, its dimension is 91. 69mm long, 30mm wide at the beginning and 40mm wide at the end, the robot as a whole has a dimension of 249.37mm long and 92.5mm wide with a weight of 254.5g. The actuators based on nitinol shape memory metals were used, for which tests were conducted with 2 wire gauges with diameters of 0. 5mm and 1mm, the 0.5mm cable has an activation of 60±10°C and the 1mm of 40±10°C, for which it was first analyzed the force developed by each of the actuators at different temperatures obtaining the following results, the 0. 5mm when subjected to force tests with a configuration of 60°C activation temperature with a voltage of 1.25 V at 2 A manages to lift an intermediate weight in terms of our tests of 20g reaches its final set point but with a not favorable time and having small deformations in its initial engraving, the 1mm material was subjected to the same tests with a configuration of 40°C activation temperature with a voltage of 0. 54 V at 2.25 A having favorable results in terms of reaching its final set point in the desired time and without having any final deformation, for which already established the tests it is analyzed that the two arrive to lift corresponding but the material of 0. 5mm material consumes more energy with a longer time than the 1mm material, so for the displacement of the robot the 1mm material was chosen because it has the necessary force to fulfill its objective, besides not consuming energy in excess. Once analyzed it was possible to achieve the two-dimensional displacement with pectoral and flow fins, with an oscillatory movement of the rigid flow fin, and turns with the pectoral fins, with an activation of 40°C at 0.54V with 2.25A and with the 1mm actuators a speed of 3.33 m/s was obtained.

Enhancing the Accuracy of Water-Level Forecasting with a New Parameter-Inversion Model for Estimating Bed Roughness in Hydrodynamic Models

The accurate and efficient estimation of bed roughness using limited historical observational data is well-established. This paper presents a new parameter-inversion model for estimating bed roughness in hydrodynamic models that constrains the roughness distribution between river sections. The impact of various factors on the accuracy of inversed roughness was analyzed through a numerical experiment with the number of measurement stations, observed data amount, initial bed roughness, observational noise, and the weight of the regularization term. The results indicate that increasing the number of measurement stations and the amount of observed data significantly improves the robustness of the model, with an optimal parameter setting of 3 stations and 30 observed data. The initial roughness had little impact on the model, and the model showed good noise resistance capacity, with the error significantly reduced by controlling the smoothness level of inversed roughness using a small weight of the regularization term (i.e., 100). An experiment conducted on a real river using the calibrated model parameters shows a forecasted water level RMSE of 0.041 m, 31% less than that from the Federal Emergency Management Agency. The proposed model provides a new approach to estimating bed roughness parameters in hydrodynamic models and can help in improving the accuracy of water-level forecasting.