Miracle-dz/UniFAR-FTUs huggingface dataset:
Eight-Hour Time-Restricted Feeding: A Strong Candidate Diet Protocol for First-Line Therapy in Polycystic Ovary Syndrome

We aimed to investigate the effects of a 6-week program of 8 h time-restricted feeding (TRF) diet in polycystic ovary syndrome (PCOS), as determined by anthropometric, hormonal, metabolic profiles, and fecal calprotectin level. Thirty women diagnosed with PCOS underwent a 6-week 8 h TRF diet intervention. Age, anthropometric features (body mass index (BMI), waist-to-hip ratio (WHR)) and biochemical results were recorded. Free androgen index (FAI, defining hyperandrogenism) and the homeostatic model assessment-insulin resistance (HOMA-IR) were calculated. Baseline (pre-diet) and 6-week post-diet findings were compared. Mean age was 25.57 ± 2.67 years. BMI (p < 0.001) and WHR (p = 0.001) were found to have significantly decreased after the diet, as well as the percentage of patients defined to have hyperandrogenism (p = 0.016). Reproductive hormone levels, FAI (p < 0.001) and HOMA-IR (p < 0.001) were improved significantly. Metabolic parameters associated with glucose and lipid profiles were also significantly improved after the diet. Additionally, fecal calprotectin levels demonstrated a significant decrease from pre-diet to post-diet (p < 0.001). In conclusion, a 6-week diet intervention with 8 h TRF may be a suitable and effective intermittent fasting protocol that can be used as a first-line option in PCOS.
Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.
Prescribed-Time Control for Stochastic High-Order Nonlinear Systems With Parameter Uncertainty

This brief focuses on the problem of prescribed-time mean-square control for uncertain stochastic high-order $(p$ -normal) nonlinear systems. Compared to existing results, a more general model is considered. First, a prescribed-time mean-square stable controller is constructed with the assistance of the power integrator technique, the time-varying prescribed-time function and a non-scaling framework. Then, based on the stability theorem of stochastic systems and the useful proposition of the selected Lyapunov function, the prescribed-time mean-square stability of all signals is analyzed. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed control strategy.
Dynamique quantique hors-équilibre et systèmes désordonnés pour des atomes ultrafroids bosoniques

This thesis deals with two general themes, both of which may be studied in ultracold atom systems: on the one hand, the out of equilibrium dynamics of closed quantum systems, and on the other hand the impact of disorder on a strongly correlated system at zero temperature. The first part is focused on mean field completely connected models. We show that the dynamics can be solved and mapped onto classical equations of motion for collective coordinates. We applied this technique to address the problem of the dynamical transition. This phenomenon is signalled by singular behaviour of time averages of observables as a function of the parameters of the quench. We showed that the dynamical transition occurs in the BoseHubbard model, the Ising model in a transverse field and the Jaynes-Cummings model. These results hint for the existence of a strong link between the quantum phase transition at zero temperature and the dynamical transition. In the second part, we studied the impact of fluctuations on the dynamical transition, using field theory techniques on a relativistic theory with O(N) symmetry. At leading order in N, the dynamical transition bear similarities with a critical phenomenon. At the transition, correlation functions have a two-time scaling law, and there is a diverging lengthscale as the transition is approached. Besides, the fixed point may be described in terms of a gas of massless effective particles, which is a peculiar case of the much more general light cone effect. Finally, we relate the fixed point dynamics to the quench dynamics from the symmetric to the broken symmetry phase. The third part of this thesis is about the disordered Bose-Hubbard model and the nature of its phase transitions. We used and extended the quantum cavity method coupled with the replica trick, within the mean-field cavity approximation introduced by Ioffe, Mezard. This method provides analytical estimates for the phase diagram and about the nature of the quantum phase transition. We find that the conventional transition, with power law exponents, is changed into an activation-like transition in some regions. This effect is accompanied by continuously varying critical exponents at the conventional transition. The method also give access to the large tails of superfluid susceptibility, which allows to characterize the Bose glass phase.
Coefficients of Reaction‐Diffusion Processes Derived From Patterns in Rocks

Self‐organizing diffusion‐reaction systems naturally form complex patterns under far‐from‐equilibrium conditions. A representative example is the rhythmic concentration pattern of Fe‐oxides in Zebra rocks; these patterns include reddish‐brown stripes, rounded rods, and elliptical spots. Similar patterns are observed in the banded iron formations, which are presumed to have formed in the early earth under global glaciation. We propose that such patterns can be used directly (e.g., by computer‐vision‐analysis) to infer basic quantities relevant to their formation giving information on generalized chemical gradients. Here we present a phase‐field model that quantitatively captures the distinct Zebra rock patterns based on the concept of phase separation that describes the process forming Liesegang stripes. We find that diffusion coefficients (the bulk self‐diffusivities of the native species and the mobility of the reaction product) play an essential role in controlling the appearance of regular stripe patterns as well as the transition from stripes to spots. The numerical results are carefully benchmarked with well‐established empirical spacing law, width law, timing law and the Matalon‐Packter law. Using this model, we invert for the important process parameters originating from the intrinsic material properties, the self‐diffusivity ratio and the mobility of Fe‐oxides with a series of Zebra rock samples. This study allows a quantitative prediction of the generalized chemical gradients in mineralized source rocks without intrusive measurements, providing a better intuition for the mineral exploration space.
Perceptions of Health and Wellbeing Among Employees in a Work Integration Social Enterprise in Sweden

Background: Work Integration Social Enterprises (WISEs) constitute an important vehicle for providing employment opportunities for disadvantaged groups. Objective: The goal of this qualitative case study is to explore perceptions of health and wellbeing among employees working in a WISE located in the Gävleborg region, in east central Sweden. Methods: Data were gathered using 16 in-depth, semi-structured interviews with the social enterprise employees. Results: Findings were categorized into three main categories: the importance of financial independence and societal benefits; team spirit and a sense of belonging; and improved quality of life and wellbeing. Conclusion: The participants perceived that working in the WISE gave them a feeling of freedom and increased their self-esteem because of the possibility to earn an income. Also, they were satisfied with their job (e.g., with regard to work quality and flexibility) and believed that their work contributed to society. Moreover, through working in a WISE, the participants felt a sense of belonging and togetherness through interaction with co-workers and managers, and an improved quality of life for themselves and their families.
A-B-3—Associations and dissociations of reading and arithmetic: Is domain-specific prediction outdated?

Reading and arithmetic are core domains of academic achievement with marked impact on career opportunities and socioeconomic status. While associations between reading and arithmetic are well established, evidence on underlying mechanisms is inconclusive. The main goal of this study was to reevaluate the domain-specificity of established predictors and to enhance our understanding of the (co-)development of reading and arithmetic. In a sample of 885 German-speaking children, standard domain-specific predictors of reading and arithmetic were assessed before and/or at the onset of formal schooling. Reading and arithmetic skills were measured at the beginning and end of second grade. Latent variables were extracted for all relevant constructs: Grapheme-phoneme processing (phonological awareness, letter identification), RAN (RAN-objects, RAN-digits), number system knowledge (number identification, successor knowledge), and magnitude processing (non-symbolic and symbolic magnitude comparison), as well as the criterion measures reading and arithmetic. Four structural equation models tested distinct research questions. Grapheme-phoneme processing was a specific predictor of reading, and magnitude processing explained variance specific to arithmetic. RAN explained variance in both domains, and it explained variance in reading even after controlling for arithmetic. RAN and number system knowledge further explained variance in skills shared between reading and arithmetic. Reading and arithmetic entail domain-specific cognitive components, and they both require tight networks of visual, verbal, and semantic information, as reflected by RAN. This perspective provides a useful background to explain associations and dissociations between reading and arithmetic performance.
Cerebrovascular Reactivity Is Not Associated With Therapeutic Intensity in Adult Traumatic Brain Injury: A Validation Study

Within traumatic brain injury (TBI) care, there is growing interest in pathophysiological markers as surrogates of disease severity, which may be used to improve and individualize care. Of these, assessment of cerebrovascular reactivity (CVR) has been extensively studied given that it is a consistent, independent factor associated with mortality and functional outcome. However, to date, the literature supports little-to-no impact of current guideline-supported therapeutic interventions on continuously measured CVR. Previous work in this area has suffered from a lack of validation studies, given the rarity of time-matched high-frequency cerebral physiology with serially recorded therapeutic interventions; thus, we undertook a validation study. Utilizing the Winnipeg Acute TBI database, we evaluated the association between daily treatment intensity levels, as measured through the therapeutic intensity level (TIL) scoring system, and continuous multi-modal–derived CVR measures. CVR measures included the intracranial pressure (ICP)-derived pressure reactivity index, pulse amplitude index, and RAC index (a correlation between the pulse amplitude of ICP and cerebral perfusion pressure), as well as the cerebral autoregulation measure of near-infrared spectroscopy-based cerebral oximetry index. These measures were also derived over a key threshold for each day and were compared to the daily total TIL measure. In summary, we could not observe any overall relationship between TIL and these CVR measures. This validates previous findings and represents only the second such analysis to date. This helps to confirm that CVR appears to remain independent of current therapeutic interventions and is a potential unique physiological target for critical care. Further work into the high-frequency relationship between critical care and CVR is required.
Post hoc experimental designs improve genetic trial analyses: A case study of cherrybark oak (Quercus pagoda Raf.) genetic evaluation in the western Gulf region, USA

Oaks (Quercus spp.) are widespread hardwood trees in the Northern Hemisphere and of high ecological, economic, and social values. Optimal experimental design of genetic trials is essential for accurate estimates of genetic parameters and improving the genetic merit of breeding stock. Here, we evaluate the use of post hoc row-column factors combined with spatial adjustment to improve genetic analyses of parents and individual trees in field progeny tests of plantation hardwoods, using cherrybark oak (Quercus pagoda Raf.) as an example. For tree height, post hoc incomplete blocking reduced ~14% more of the within-block environmental variance compared to the randomized complete block design (RCBD) model. Incomplete blocking also improved the heritability estimates for height by 7% to 14% compared to the original RCBD model. No clinal trend for growth breeding values was identified due to provenances. Our approach warrants the initial selection for height as early as age ~10 based on its moderate narrow-sense heritability of 0.2; however, diameter and volume need longer evaluation times. The post hoc incomplete blocking is more efficient and promising to improve the genetic analysis of Q. pagoda to minimize the environmental heterogeneity influences. Adjusting competition and spatial effects, including the distance principal components and autoregressive residual structure notably improves the model fit based on the observed reductions in AICs and BICs. Employing our approach is promising for hardwood genetic improvement in the southern USA.
Complete Response of Rare Sarcomatoid Upper Tract Urothelial Carcinoma Variant: Case Report and Literature Review

Infiltrating urothelial carcinoma sarcomatoid variant is a rare variant of urothelial carcinoma. We report a case of a 68-year-old female with a history of hematuria. CT scan with contrast showed a mass in the 1/3 distal of the right ureter. The biopsy result showed a high-grade infiltrating urothelial carcinoma. A radical nephroureterectomy was performed but at the follow-up after 3 months, there was a recurrent mass and gemcitabine-cisplatin chemotherapy was given. Since a high-grade infiltrating urothelial carcinoma sarcomatoid variant was an aggressive tumor, we need to give more attention to evaluating this tumor.
Applying a neoscore in locally advanced rectal cancer is beneficial in predicting local recurrences after surgery

Background and aim The current study was undertaken to provide more detailed prognostic models for early prediction of local recurrences and local recurrence free survival (RFS) using different radiologic and pathologic features of locally advanced rectal carcinomas treated with neoadjuvant chemoradiation (CRT). Methods One hundred patients with locally advanced rectal carcinomas decided to receive neoadjuvant CRT were retrospectively recruited, Hazard ratios (HR) were determined in the two cox regression models and only significant ratios were considered for pointing, Models were built to determine their important effects of different predictors including: pathologic T (T), pathologic N (N), grade (G), clinical stage (cTNM), site (S), perineural invasion (PNI), and response to CRT (R) on 3-year RFS, goodness of performance of each model was measured by Harrell’s C index. Results HR of 1st group of models: T+N, T+N+G, T+N+G+S, T+N+G+S+PNI, and T+N+G+S+PNI+R were summated and categorized into scores, these scores were significantly correlated with the risk of recurrence (Somer’s D = 0.5, p<0.0001) & Harrell’s C index = 0.751, (Somer’s D = 0.6, p<0.0001) & its Harrell’s C index = 0.794, (Somer’s D = 0.7, p<0.0001) & C index = 0.826, Somer’s D = 0.7, p<0.0001) & C index = 0.827, and (Somer’s D = 0.7, p<0.0001) & C index = 0.843 respectively. The 2nd group of models including: cTNM stage, cTNM+G, cTNM+G+S, cTNM+G+S+PNI, cTNM+G+S+PNI+R scores which were significantly correlated with the HR of LRR (Somer’s D = 0.2, 0.5, 0.6, 0.6, & 0.6 respectively), (p = 0.006, <0.0001, <0.0001, <0.0001, <0.0001 respectively), the corresponding Harrell’s C indices were 0.595, 0.743, 0.782, 0.795, & 0.813 respectively. Conclusion We propose that the addition of biologic factors to staging of rectal cancer provide precise stratification and association with local recurrences in patients received preoperative CRT.
Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort

This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)–based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index–related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.
Splash erosion and surface deformation following a drop impact on the soil with different hydrophobicity levels and moisture content

The splash phenomenon and the scale of the surface deformation of post-fire soils in the variants of various hydrophobicity and moisture content were studied. Splash erosion is the result of the impact of a single water drop and was analysed using high-speed cameras, while the surface deformation was parameterized using a structured light scanner. The extremely water-repellent variant (dry_V) showed distinct differences, expressed primarily in the number of ejected particles, which was 2.5 times higher than in the four soils with lower levels of hydrophobicity. It was also observed that as a result of the drop impact onto an extremely hydrophobic soil surface, a form known as liquid marble was created inside the crater. Soil moisture content determined the manner, scale and dynamics of the splash erosion. In the case of wet soils, the phenomenon proceeded up to five times faster, and as a result of the drop impact, a large number of fine particles were ejected, which reached nearly twice the velocities and three times the displacement distances compared to the dry soil group. However, the particles and/or aggregate splashed on the dry samples were larger, which also translated into the formation of craters up to twice as extensive as those in the wet soils.
A COMMENT ON "LEARNING, MUTATION, AND LONG-RUN EQUILIBRIA IN GAMES"

mutation. ) Kandori, Mailath, and Rob (henceforth KMR) first provide a useful general theorem concerning the stationary distribution of strategies under Darwinian dynamics. They then divide the analysis of the 2 x 2 game into three cases: dominant strategy games (e.g., prisoners' dilemma), coordination games, and games with no symmetric pure strategy equilibrium (e.g., battle of the sexes). We refer to these as DS, C, and NP games. In each case, KMR claim that, as the rate of mutation vanishes, the stationary distribution of strategies converges to a symmetric Nash equilibrium. They emphasize C games, which have two symmetric Nash equilibria, and characterize the conditions under which the distribution converges to the risk dominant equilibrium. In this note, we argue that while their formal conclusions for C games are correct, their results for DS and NP games are valid only for large populations of players. In small populations, Darwinian dynamics may produce non-Nash outcomes in these two cases. Section 1 summarizes the KMR model, and Section 2 provides examples of 2 x 2 games in which Darwinian dynamics generate non-Nash outcomes. A theorem in Section 3 describes the Darwinian equilibrium of any 2 x 2 game.
ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://github.com/nchucvml/ADMM-SRNet.