Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized 3D mannequin reconstruction based on 3D scanning

Purpose Currently, a common method of reconstructing mannequin is based on the body measurements or body features, which only preserve the body size lacking of the accurate body geometric shape information. However, the same human body measurement does not equal to the same body shape. This may result in an unfit garment for the target human body. The purpose of this paper is to propose a novel scanning-based pipeline to reconstruct the personalized mannequin, which preserves both body size and body shape information. Design/methodology/approach The authors first capture the body of a subject via 3D scanning, and a statistical body model is fit to the scanned data. This results in a skinned articulated model of the subject. The scanned body is then adjusted to be pose-symmetric via linear blending skinning. The mannequin part is then extracted. Finally, a slice-based method is proposed to generate a shape-symmetric 3D mannequin. Findings A personalized 3D mannequin can be reconstructed from the scanned body. Compared to conventional methods, the method can preserve both the size and shape of the original scanned body. The reconstructed mannequin can be imported directly into the apparel CAD software. The proposed method provides a step for digitizing the apparel manufacturing. Originality/value Compared to the conventional methods, the main advantage of the authors’ system is that the authors can preserve both size and geometry of the original scanned body. The main contributions of this paper are as follows: decompose the process of the mannequin reconstruction into pose symmetry and shape symmetry; propose a novel scanning-based pipeline to reconstruct a 3D personalized mannequin; and present a slice-based method for the symmetrization of the 3D mesh.
Multivariable MR can mitigate bias in two-sample MR using covariable-adjusted summary associations

Genome-Wide Association studies (GWAS) are hypothesis free studies that survey the whole genome for polymorphisms associated with a trait of interest. To increase power and to estimate the direct effects of these single nucleotide polymorphisms (SNPs) on a trait GWAS are often conditioned on a covariate (such as body mass index (BMI) or smoking status). This adjustment can introduce bias in the estimated effect of the SNP on the trait. Mendelian randomisation (MR) studies use summary statistics from GWAS estimate the causal effect of a risk factor (or exposure) on an outcome. Covariate adjustment in GWAS can bias the effect estimates obtained from MR studies using the GWAS data. Multivariable MR (MVMR) is an extension of MR that includes multiple traits as exposures. Using simulations we show that MVMR can recover unbiased estimates of the direct effect of the exposure of interest by including the covariate used to adjust the GWAS within the analysis. We show that this method provides consistent effect estimates when either the exposure or outcome of interest has been adjusted for a covariate. We apply this method to estimate the effect of systolic blood pressure (SBP) on type-2 diabetes (T2D) and waist circumference on systolic blood pressure both adjusted and unadjusted for BMI.
Pelvis perturbations in various directions while standing in staggered stance elicit concurrent responses in both the sagittal and frontal plane

Walking very slowly increases the time spent in the double support phase, which could be resembled by the staggered stance posture. Maintaining balance in this posture is important in order to continue walking safely. We therefore aimed to increase the understanding of balance recovery in staggered stance. We studied balance responses on joint- and muscle level to pelvis perturbations in various directions while standing in this posture. Ten healthy individuals participated in this study. We used one motor beside and one behind the participant to apply perturbations in mediolateral (ML), anteroposterior (AP) and diagonal directions, with a magnitude of 3, 6, 9 and 12% of the participants body weight. Meanwhile motion capture, ground reaction forces and moments, and electromyography of the muscles around the ankles and hips were recorded. The perturbations caused movements of the centre of mass (CoM) and centre of pressure (CoP) in the direction of the perturbation. Furthermore, these were often accompanied by motions in a direction different from the perturbation direction. After ML perturbations and diagonal perturbations transverse to the line between both feet, large and significant CoM and CoP deviations were present in the sagittal plane. Also, stronger responses on joint and muscle level were present after these perturbations, compared to AP and diagonal perturbations collinear with the line between both feet. The hip, knee and ankle joints significantly responded to all perturbation directions, but in different manners and modes of cooperation. To conclude, standing in a staggered stance posture makes individuals more vulnerable to perturbations in ML direction and transverse to the line between both feet, requiring larger responses on joint level as well as contributions in the sagittal plane.
Brain Tumor Diagnosis and Classification via Pre-Trained Convolutional Neural Networks

The brain tumor is the most aggressive kind of tumor and can cause low life expectancy if diagnosed at the later stages. Manual identification of brain tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment and thus reduce the chances of survival for the patient. Medical resonance imaging (MRI) is the conventional method used to diagnose brain tumors and their types. This paper attempts to eliminate the manual process from the diagnosis process and use machine learning instead. We proposed the use of pretrained convolutional neural networks (CNN) for the diagnosis and classification of brain tumors. Three types of tumors were classified with one class of non-tumor MRI images. Networks that has been used are ResNet50, EfficientNetB1, EfficientNetB7, EfficientNetV2B1. EfficientNet has shown promising results due to its scalable nature. EfficientNetB1 showed the best results with training and validation accuracy of 87.67% and 89.55%, respectively.
The ABCD study: brain heterogeneity in intelligence during a neurodevelopmental transition stage

Abstract A complex curvilinear relationship exists between intelligence and age during the neurodevelopment of cortical thickness. To parse out a more fine-grained relationship between intelligence and cortical thickness and surface area, we used a large-scale data set focusing on a critical transition juncture in neurodevelopment in preadolescence. Cortical thickness was derived from T1-weighted structural magnetic resonance images of a large sample of 9- and 11-year-old children from the Adolescent Brain Cognitive Development study. The NIH Toolbox Cognition Battery composite scores, which included fluid, crystallized, and total scores, were used to assess intelligence. Using a double generalized linear model, we assessed the independent association between the mean and dispersion of cortical thickness/surface area and intelligence. Higher intelligence in preadolescents was associated with higher mean cortical thickness in orbitofrontal and primary sensory cortices but with lower thickness in the dorsolateral and medial prefrontal cortex and particularly in the rostral anterior cingulate. The rostral anterior cingulate findings were particularly evident across all subscales of intelligence. Higher intelligence was also associated with greater interindividual similarity in the rostral cingulate. Intelligence during this key transition juncture in preadolescence appears to reflect a dissociation between the cortical development of basic cognitive processes and higher-order executive and motivational processes.
Activated or Impaired: An Overview of DNA Repair in Neurodegenerative Diseases

As the population ages, age-related neurodegenerative diseases have become a major challenge in health science. Currently, the pathology of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease, is still not fully understood. Remarkably, emerging evidence indicates a role of genomic DNA damage and repair in various neurodegenerative disorders. Here, we summarized the current understanding of the function of DNA damage repair, especially base excision repair and double strand break repair pathways, in a variety of neurodegenerative diseases. We concluded that exacerbation of DNA lesions is found in almost all types of neurodegenerative diseases, whereas the activities of different DNA repair pathways demonstrate distinct trends, depending on disease type and even brain region. Specifically, key enzymes involved in base excision repair are likely impaired in Alzheimer's disease and amyotrophic lateral sclerosis but activated in Parkinson's disease, while nonhomologous end joining is likely downregulated in most types of neurodegenerative diseases. Hence, impairment of nonhomologous end joining is likely a common etiology for most neurodegenerative diseases, while defects in base excision repair are likely involved in the pathology of Alzheimer's disease and amyotrophic lateral sclerosis but are Parkinson's disease, based on current findings. Although there are still discrepancies and further studies are required to completely elucidate the exact roles of DNA repair in neurodegeneration, the current studies summarized here provide crucial insights into the pathology of neurodegenerative diseases and may reveal novel drug targets for corresponding neurodegenerative diseases.
Human-in-the-loop energy flexibility integration on a neighbourhood level: Small and Big Data management

Modern buildings provide an enormous amount of data available from various sources ranging from modular wireless sensors to smart meters. As well as enhancing energy management and building performance, the analysis of these datasets can enhance the management of decentralized energy systems (electrical storage, PV generation, heat storage, etc.). To optimize the interaction between the building and the grid, it is essential to determine the total energy flexibility of the user and the building. A building has different possibilities for demand side management, energy storage and energy exchange for which a functional-layered approach is proposed from the user up to building and its interaction with the energy infrastructure. Central is the principle of the human-in-the-loop, where a bottom-up approach places the human needs as a central starting point for the energy interaction optimisation. The combination of Big Data with deep learning techniques offers new possibilities in the prediction of energy use and decentralized renewable energy production (e.g. from local weather data taking into account local phenomena such as urban heat islands). This combined with a more bottom-up approach of multi-agent systems with a gossip-based cooperative approach using Small Data offers decentralized control and monitoring autonomy to reduce the complexity of the energy system integration and transition. This makes it possible to relate the outcomes of the urban energy system integration on a neighbourhood level. The approach is being applied to a typically medium-sized office building. A first application of the human-in-the-loop controlling the lighting systems in the open-plan workplace of the test-bed office building showed some estimated annual energy saving of around 24%. Practical application : Analysis of a large database containing so called Big Data of clusters of buildings seems promising. Therefor there is the need to study the potential impact of utilization of big building operational data in building services industry. Besides this there is also a need for a data mining-based method for analyzing massive building operational data of a specific building, Small Data. This work sets out a general framework and method for doing both and to combine the strength of both approaches. The presented combined approach and results will be of interest to engineers and facility managers wondering what the key constraints to optimal use data to optimize low energy/carbon control strategies might have within their work.
Ginsenoside Rg1 improved diabetes through regulating the intestinal microbiota in high-fat diet and streptozotocin-induced type 2 diabetes rats.

Ginsenoside Rg1 is the active ingredient of the plant named genus Panax (ginseng), which exhibited significant bioactivities, including anti-inflammatory, antioxidation, and anti-diabetic effects. Herein, 4-week treatment of ginsenoside Rg1 remarkably decreased blood glucose levels and improved the resistance of insulin, oxidative stress, and inflammation along with lipid profile in T2D rats triggered via high-fat diet and streptozotocin (STZ). Ginsenoside Rg1 supplementation improved the intestinal microbial composition induced by type 2 diabetes (T2D), which increased the proportions of Lachnospiraceae_NK4A136_group and Lachnoclostridium and decreased the proportions of Lactobacillus. Additionally, Spearman's correlation test between the gut microflora and biomedical assays (|r| > .5) revealed that the Lachnospiraceae_NK4A136_group, Roseburia, and Romboutsia greatly affect intestinal microbiota, which may play pivotal roles in preventing T2D. Our findings illustrate the use of ginsenoside Rg1 as a dietary supplement along with prospective prebiotics due to its ability to alter the gut microflora and alleviate T2D-linked biochemical abnormalities. PRACTICAL APPLICATIONS: This was the first study to elucidate the influences of ginsenosides Rg1 in improving the intestinal microbiota of T2D in SD rats. The data illustrated that the ginsenosides Rg1 remarkably increased the proportions of SCFAs producing bacteria. Hence, ginsenoside Rg1 could be used as a potential prebiotic in the treatment of T2D through alteration of intestinal microbiota.
On the use of multi–objective evolutionary classifiers for breast cancer detection

Purpose Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. Approach Multi–Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well–known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs’ performance in terms of several well–known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. Results We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. Conclusions The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points.
Quantitative prey species detection in predator guts across multiple trophic levels by mapping unassembled shotgun reads

Quantifying species trophic interaction strengths is crucial for understanding community dynamics and has significant implications for pest management and species conservation. DNA‐based methods to identify species interactions have revolutionized these efforts, but a significant limitation is the poor ability to quantify the strength of trophic interactions, that is the biomass or number of prey consumed. We present an improved pipeline, called Lazaro, to map unassembled shotgun reads to a comprehensive arthropod mitogenome database and show that the number of prey reads detected is quantitatively predicted from the prey biomass consumed, even for indirect predation. Two feeding bioassays were performed: starved coccinellid larvae consuming different numbers of aphids (Prey Quantity bioassay), and starved coccinellid larvae consuming a chrysopid larvae that had consumed aphids (Direct and Indirect Predation bioassay). Prey taxonomic assignment against a mitochondrial genome database had high accuracy (99.8% positive predictive value) and the number of prey reads was directly related to the number of prey consumed and inversely related to the elapsed time since consumption with high significance (r2 = .932, p = 4.92E‐6). Aphids were detected up to 6 h after direct predation plus 3 h after indirect predation (9 h in total) and detection was related to the predator‐specific decay rates. Lazaro enabled quantitative predictions of prey consumption across multiple trophic levels with high taxonomic resolution while eliminating all false positives, except for a few confirmed contaminants, and may be valuable for characterizing prey consumed by field‐sampled predators. Moreover, Lazaro is readily applicable for species diversity determination from any degraded environmental DNA.
Coordinated control strategies for active steering, differential braking and active suspension for vehicle stability, handling and safety improvement

ABSTRACT In this paper, a coordinated control strategy is proposed to provide an effective improvement in handling stability of the vehicle, safety, and comfortable ride for passengers. This control strategy is based on the coordination among active steering, differential braking, and active suspension systems. Two families of controllers are used for this purpose, which are the high order sliding mode and the backstepping controllers. The control strategy was tested on a full nonlinear vehicle model in the environment of MATLAB/Simulink. Rollover avoidance and yaw stability control constraints have been considered. The control system mainly focuses on yaw stability control. When rollover risk is detected, the proposed strategy controls the roll dynamics to decrease rollover propensity. Simulation results for two different critical driving scenarios, the first one is a double lane change and the other one is a J-turn manoeuvre, show the effectiveness of the coordination strategy in stabilising the vehicle, enhancing handling and reducing rollover propensity.
Dynamic high pressure microfluidization-assisted extraction of plant active ingredients: a novel approach

Abstract The extraction method has a great influence on the yield, quality, chemical structure, and biological activities of active ingredients. Safe and efficient extraction of active ingredients is one of the important problems facing the food and pharmaceutical industry. As a pretreatment approach for the extraction of active ingredients, dynamic high pressure microfluidization (DHPM) is a promising strategy that can not only effectively increase the yield of active ingredients but also strengthen the bioactivities of active ingredients, and take the advantages of mild operating temperature and environmental friendliness. In this review, the research progress of DHPM-assisted extraction of active ingredients from plant materials in recent ten years is overviewed. The DHPM equipment, strengthening mechanism, operating procedure, critical factors and application of DHPM-assisted extraction are introduced in detail, together with the advantages and disadvantages. Furthermore, its future development trend is discussed at the end. DHPM-assisted extraction is considered as the ideal technique of better homogenization effects, less solvent consumption, more reliable operation, and so on, making it a promising method to acquire active ingredients efficiently. Therefore, this technique is worthy of further theoretical research and experimental operation.
Vancomycin‐resistant Enterococcus faecium: should we screen on admission?

Denmark has experienced an increase in the proportion of invasive vancomycin‐resistant Enterococcus faecium (VRE) since 2002 (e.g. <4% in 2015, 7.1% in 2017 and 12% in 2018). At Rigshospitalet, we employ active screening at departments with high prevalence or in case of outbreaks. This includes the collection of rectal swabs specifically for VRE screening. Our purpose was to describe the carrier prevalence of vancomycin‐resistant enterococci among acute patients admitted to the Neurointensive Care Unit, Department of Neuroanaesthesiology, Rigshospitalet, Copenhagen, Denmark (NICU). Between April 2018 and January 2019, we investigated 99 consecutive rectal swabs from patients admitted to NICU. The primary outcome was prevalence of VRE carriage. The median age was 64 years (range 23–87) and gender was equally distributed (Female = 47, Male = 46). 26 (28%) had previously been admitted within 179 days and 67 patients (72%) had no hospital admissions within 180 days prior to the admission to NICU. Of the 93 rectal swabs, 2 (2%, 95% CI 0.26–7.55%) were positive for vanA and none were positive for vanB. Routine screening of all patients at admission may be effective in hospital settings with high VRE prevalence, whereas the benefit of screening for VRE in hospitals with a low prevalence may be restricted to specific patient populations.
Sex differences in the association between sugar-sweetened beverages consumption and metabolic risks among the working-age population in Taiwan

Abstract Objective: Sugar-sweetened beverages (SSB) are linked to increased metabolic risk. However, the sex differences in the relationship between SSB intake and adverse health effects remain unclear. Therefore, the present study examined the association between SSB consumption and metabolic risks among working-age males and females from Taiwanese communities. Design: A community-based study utilised data from a comprehensive health screening project conducted by the Public Health Bureau in Changhua County, Taiwan. Metabolic risks included waist circumference, systolic blood pressure, diastolic blood pressure, total cholesterol, TAG, LDL cholesterol and fasting glucose level using serum tests. Setting: Participants were recruited in Changhua County, Taiwan. Participants: Between 2005 and 2014, 92 724 citizens participated in the health screening; our data analysis included 75 278 respondents between 30 and 64. Results: The results showed that the frequency of SSB consumption was associated with abnormal waist circumference and elevated total cholesterol, TAG, LDL and glucose in both men and women. Increased SSB consumption frequency was associated with elevated glucose and hypertension in women. Even a slight increase in SSB intake frequency was related to raising the metabolic risks. Similar patterns were evident when models included BMI; however, the associations were attenuated. In the BMI-stratified subgroup analysis, the relationship between SSB consumption and metabolic risks was more pronounced in participants without obesity. Conclusion: The present study suggests that SSB consumption carries metabolic risk among working-age Taiwanese, particularly women and those without obesity. Health promotion programmes should raise awareness of the health hazards associated with SSB.