Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial intelligence‐based analysis of body composition predicts outcome in patients receiving long‐term mechanical circulatory support

Abstract Background Obesity is a known cardiovascular risk factor and associated with higher postoperative complication rates in patients undergoing cardiac surgery. In heart failure (HF), conflicting evidence in terms of survival has been reported, whereas sarcopenia is associated with poor prognosis. An increasing number of HF patients require left ventricular assist device (LVAD) implantations. The postoperative mortality has improved in recent years but is still relatively high. The impact of body composition on outcome in this population remains unclear. The aim of this investigation was to examine the preoperative computed tomography (CT) body composition as a predictor of the postoperative outcome in advanced HF patients, who receive LVAD implantations. Methods Preoperative CT scans of 137 patients who received LVADs between 2015 and 2020 were retrospectively analysed using an artificial intelligence (AI)‐powered automated software tool based on a convolutional neural network, U‐net, developed for image segmentation (Visage Version 7.1, Visage Imaging GmbH, Berlin, Germany). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT and SAT), psoas and total abdominal muscle areas and sarcopenia (defined by lumbar skeletal muscle indexes). The body composition parameters were correlated with postoperative major complication rates, survival and postoperative 6‐min walk distance (6MWD) and quality of life (QoL). Results The mean age of patients was 58.21 ± 11.9 years; 122 (89.1%) were male. Most patients had severe HF requiring inotropes (Interagency Registry for Mechanically Assisted Circulatory Support [INTERMACS] profile I–III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). Forty‐four (32.1%) patients were obese (body mass index ≥ 30 kg/m2), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of postoperative infections (VAT 172.23 cm2 [54.96, 288.32 cm2] vs. 124.04 cm2 [56.57, 186.25 cm2], P = 0.022) and in‐hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2] vs. 135.42 cm2 [49.44, 227.91 cm2], P = 0.033; SAT 227.28 cm2 [139.38, 304.35 cm2] vs. 173.81 cm2 [97.65, 254.16 cm2], P = 0.009). Obese patients showed no improvement of 6MWD and QoL within 6 months postoperatively (obese: +0.94 ± 161.44 months, P = 0.982; non‐obese: +166.90 ± 139.00 months, P < 0.000; obese: +0.088 ± 0.421, P = 0.376; non‐obese: +0.199 ± 0.324, P = 0.002, respectively). Sarcopenia did not influence the postoperative outcome and survival within 1 year after LVAD implantation. Conclusions Preoperative AI‐based CT body composition identifies patients with poor outcome after LVAD implantation. Greater adipose tissue areas are associated with an increased risk for postoperative infections, in‐hospital mortality and impaired 6MWD and QoL within 6 months postoperatively.
Toxic metabolites and metabolic soft spots of celastrol based on glutathione metabolic capture and high-resolution mass spectrometry

ABSTRACT Background Celastrol is known as one of the most medicinally valuable compounds. However, the pharmaceutical application of celastrol is significantly limited due to high toxicity, while there are few reports on the mechanism of toxicity. Methods This study searched for possible toxic metabolites through phase I in vitro metabolism and glutathione capture experiments. Then in vivo metabolism experiments in mice and rats were conducted to look for metabolites in vivo. Finally, mice in vivo toxicity experiment was conducted to verify the toxicity of different doses of celastrol to mice. Results In the in vivo and in vitro metabolism experiments, we found 7 phase I metabolites in vitro, 9 glutathione conjugation metabolites in vitro, and 20 metabolites in vivo. The metabolic soft points of celastrol could be the quinone methyl structure at C3-OH and C6. In vivo toxicity experiments show that celastrol causes weight loss, diarrhea, gastrointestinal tract and liver inflammation in mice. Conclusions This study analyzed the metabolites and possible metabolic soft spots of celastrol, and its hepatotoxicity and gastrointestinal toxicity were demonstrated through in vivo studies for the first time. The results might provide an important basis for potential structural modification to increase the druggability of celastrol.
Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform

Objective A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening. Methods This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control. Results The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913). Conclusions The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.
The age‐related obesity paradigm: results from two large prospective cohort studies

Abstract Background The obesity paradigm has been a health concern globally for many years, its meaning is controversial. In this study, we assess the characteristics and causes of obesity paradigm and detail the mediation of obesity and inflammation on survival. Methods The original cohort included participants from the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, a prospective cohort of a nationally representative sample of adult participants; the oncology validation cohort included patients from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) from 2013 to 2021, a prospective cohort of Chinese patients with cancer. Survival analysis was performed using weighted (NHANES) or unweighted (INSCOC) Cox survival analyses. The normal BMI group was used as a reference for all comparisons. Systemic inflammation was defined as neutrophil‐to‐lymphocyte ratio (NLR) > 3. Model‐based causal mediation analysis was used to identify the mediators. Results A total of 52 270 (weighted population: 528506229) participants of the NHANES [mean follow‐up times: 10.2 years; mean age (SD): 47 (19.16) years] were included in the original cohort; and a total of 17 418 patients with cancer of INSCOC [mean follow‐up times: 2.9 years; mean age (SD): 57.37 (11.66) years] were included in the validation cohort. In the subgroups of all the participants, the obesity paradigm was more apparent in older participants and participants with disease [HR (95% CI): age ≥ 65 years, 0.84 (0.76, 0.93); with cancer, 0.84 (0.71, 0.99); with CVD, 0.74 (0.65, 0.85)]. As aged, the protective effect of a high BMI on survival gradually increased and a high BMI showed the effect of a protective factor on older participants [for obese II, HR (95% CI): young adults, 1.91 (1.40, 2.62); middle age, 1.56 (1.28, 1.91); old adults, 0.85 (0.76, 0.96]). The aged‐related obesity paradigm in patients with cancer from the NHANES was verified in the INSCOC cohorts [for obese, HR (95%CI): 0.65 (0.52, 0.81)]. The NLR is an important mediator of the effect of BMI on survival (proportion of mediation = 15.4%). Conclusions The obesity paradigm has a strong correlation with age. Relative to normal weight, obese in young people was association with higher all‐cause mortality, and obese in elderly people was not association with higher mortality. The protection of obesity is association with systemic inflammation.
Details of the gabbro‐to‐eclogite transition determined from microtextures and calculated chemical potential relationships

Permian‐aged metagabbros from the eclogite type‐locality in the eastern European Alps were partially to completely transformed to eclogite during Eoalpine intracontinental subduction. Microtextures developed along a preserved fluid infiltration and reaction front in the gabbros record the incipient gabbro‐to‐eclogite transition, allowing the details of the eclogitization process to be investigated. Original, anorthite‐rich igneous plagioclase is pervasively replaced by fine‐grained intergrowths of clinozoisite, kyanite and Na‐rich plagioclase. Where plagioclase was in contact with igneous orthopyroxene, 100–200 μm thick bimineralic coronae of symplectic kyanite and diopsidic clinopyroxene form along the edges of the grains. The rims of igneous orthopyroxene develop a complementary bimineralic corona of diopsidic clinopyroxene and garnet. Igneous clinopyroxene does not show any breakdown textures; however, jadeite content gradually increases towards the rims. In addition, exsolution lamellae inherited from the igneous clinopyroxene become progressively more jadeitic as eclogitization proceeds. Given that the igneous plagioclase is pervasively replaced by clinozoisite, kyanite and Na‐rich plagioclase, whereas kyanite–diopside symplectites are confined to narrow rim zones, we suggest that the development of these textures was controlled by the (im)mobility of different elements on different length scales. The presence of hydrous minerals in the core of anhydrous plagioclase indicates that H2O diffusivity occurred on a mm‐scale. By contrast, the size of the anhydrous diopside–kyanite and diopside–garnet symplectites indicate that Fe–Mg–Ca–Na diffusivity was limited to a 10s of μm scale. Chemical potential relations calculated in the idealized NCASH chemical system show that the clinozoisite–kyanite–albite intergrowths formed due to an increase of μH2O to plagioclase, whereas all other elements remained effectively immobile on the scale of this texture. Fluid conditions indicated by this texture span from virtually dry conditions ( aH2O∼ 0.15) to H2O‐saturation, and therefore does not imply that the rocks were ever fluid‐saturated. Calculations in the CMAS and NCFMAS systems show that the gabbro‐to‐eclogite transition is characterized by the growth of garnet, diopsidic clinopyroxene and kyanite due to diffusion of Ca (+ Na) and Mg (+ Fe) along a μCaO (+ Na2O)–μMgO (+ FeO) chemical potential gradient developed between orthopyroxene and plagioclase compositional domains. The anhydrous nature of the textures indicate that the gabbro‐to‐eclogite transition is not driven by hydration; however, increased μH2O acts as a catalyst that increases diffusivity of all elements and rates of dissolution–precipitation, allowing the overstepped metamorphic reactions to occur. Our results show that crustal eclogite formation requires low H2O content, confirming that true eclogites are dry rocks.
Impact of Network Topology on Neural Synchrony in a Model of the Subthalamic Nucleus-Globus Pallidus Circuit

Synchronous neural oscillations within the beta frequency range are observed across the parkinsonian basal ganglia network, including within the subthalamic nucleus (STN) - globus pallidus (GPe) subcircuit. The emergence of pathological synchrony in Parkinson’s disease is often attributed to changes in neural properties or connection strength, and less often to the network topology, i.e. the structural arrangement of connections between neurons. This study investigates the relationship between network structure and neural synchrony in a model of the STN-GPe circuit comprised of conductance-based spiking neurons. Changes in net synaptic input were controlled for through a synaptic scaling rule, which facilitated separation of the effects of network structure from net synaptic input. Five topologies were examined as structures for the STN-GPe circuit: Watts-Strogatz, preferential attachment, spatial, stochastic block, k-regular random. Beta band synchrony generally increased as the number of connections increased, however the exact relationship was topology specific. Varying the wiring pattern while maintaining a constant number of connections caused network synchrony to be enhanced or suppressed, demonstrating the ability of purely structural changes to alter synchrony. This relationship was well-captured by the algebraic connectivity of the network, the second smallest eigenvalue of the network’s Laplacian matrix. The structure-synchrony relationship was further investigated in a network model designed to emulate the action selection role of the STN-GPe circuit. It was found that increasing the number of connections and/or the overlap of action selection channels could lead to a rapid transition to synchrony, which was also predicted by the algebraic connectivity.
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning

Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such teaming performs compared to humans or AI agents only. In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment. In addition, we have developed a new simulator for critical infrastructure protection, focusing on a scenario where AI-powered drones and human teams collaborate to defend an airport against enemy drone attacks. We develop a user interface to allow humans to assist AI agents effectively. We demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents. Furthermore, human-AI collaboration requires lower mental and temporal demands, reduces human effort, and yields higher performance than if humans directly controlled all agents. In conclusion, we show that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting.
ENSO modulation of tropical Indian Ocean subseasonal variability

In this study, we use 30 years of retrospective climate model forecasts and observational estimates to show that El Niño/Southern Oscillation (ENSO) affects the amplitude of subseasonal variability of sea surface temperature (SST) in the southwest Indian Ocean, an important Tropical Intraseasonal Oscillation (TISO) onset region. The analysis shows that deeper background mixed‐layer depths and warmer upper ocean conditions during El Niño reduce the amplitude of the subseasonal SST variability over Seychelles‐Chagos Thermocline Ridge (SCTR), which may reduce SST‐wind coupling and the amplitude of TISO variability. The opposite holds for La Niña where the shallower mixed‐layer depth enhances SST variability over SCTR, which may increase SST‐wind coupling and the amplitude of TISO variability.
The log moments of smallest denominators

This addendum to [arXiv:2310.11251] examines the logarithmic moments of smallest denominators of rational numbers in shrinking sets, as well as higher dimensional variants (minimal resonance orders). This yields surprisingly simple formulas for the moments in dimension one, and answers questions raised by Meiss and Sander in their numerical study of torus maps with random rotation vectors [arXiv:2310.11600].
Box dimension of fractal interpolation surfaces with vertical scaling function

In this paper, we first present a simple lemma which allows us to estimate the box dimension of graphs of given functions by the associated oscillation sums and oscillation vectors. Then we define vertical scaling matrices of generalized affine fractal interpolation surfaces (FISs). By using these matrices, we establish relationships between oscillation vectors of different levels, which enables us to obtain the box dimension of generalized affine FISs under certain constraints.
Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks.

In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.
Ion Conductivity in Salt-Doped Polymers: Combined Effects of Temperature and Salt Concentration

We construct a coarse-grained molecular dynamics model based on poly(ethylene oxide) and lithium bis(trifluoromethane)sulfonimide salt to examine the combined effects of temperature and salt concentration on the transport properties. Salt doping notably slows the dynamics of polymer chains and reduces ion diffusivity, resulting in a glass transition temperature increase proportional to the salt concentration. The polymer diffusion is shown to be well represented by a modified Vogel–Fulcher–Tamman (M-VFT) equation that accounts for both the temperature and salt concentration dependence. Furthermore, we find that, at any temperature, the concentration dependence of the conductivity is well described by the product of its infinite dilution value and a correction factor accounting for the reduced segmental mobility with increasing salt concentration. These results highlight the important role of polymer segmental mobility in the salt concentration dependence of ion conductivity for temperatures near and above the glass transition.
A note on the PDE approach to the $L^\infty$ estimates for complex Hessian equations on transverse K\"ahler manifolds

We adapt the PDE approach of Guo-Phong-Tong and Guo-Phong-Tong-Wang [17, 18] to prove an $L^\infty$ estimate for transverse complex Monge-Amp\`ere equations on homologically orientable transverse K\"ahler manifolds. As an application, we obtain a purely PDE proof of the regularity of Calabi-Yau cone metrics on Q-Gorenstein T-varieties.
Probing Gauss-Bonnet-Corrected Inflation with Gravitational Waves

The low energy effective action of quantum gravity may include the higher curvature terms such as the Gauss-Bonnet term. The inflaton dynamics may be affected by the Gauss-Bonnet term if there is an inflaton-Gauss-Bonnet coupling. We show that an inflation model with a simple power law potential is made viable if it is coupled to the Gauss-Bonnet term since the prediction on the scalar spectral index and the tensor-to-scalar ratio are modified. We further point out that such a model predicts huge amount of gravitational waves at the high frequency range around 100GHz--100THz through the perturbative inflaton decay into gravitons induced by the Gauss-Bonnet term. Thus the spectrum of high frequency gravitational background is a unique feature of the inflation models with a Gauss-Bonnet correction.
Understanding normalization in contrastive representation learning and out-of-distribution detection

Contrastive representation learning has emerged as an outstanding approach for anomaly detection. In this work, we explore the $\ell_2$-norm of contrastive features and its applications in out-of-distribution detection. We propose a simple method based on contrastive learning, which incorporates out-of-distribution data by discriminating against normal samples in the contrastive layer space. Our approach can be applied flexibly as an outlier exposure (OE) approach, where the out-of-distribution data is a huge collective of random images, or as a fully self-supervised learning approach, where the out-of-distribution data is self-generated by applying distribution-shifting transformations. The ability to incorporate additional out-of-distribution samples enables a feasible solution for datasets where AD methods based on contrastive learning generally underperform, such as aerial images or microscopy images. Furthermore, the high-quality features learned through contrastive learning consistently enhance performance in OE scenarios, even when the available out-of-distribution dataset is not diverse enough. Our extensive experiments demonstrate the superiority of our proposed method under various scenarios, including unimodal and multimodal settings, with various image datasets.
Signature of BKT-like spin transport in a quasi-2D antiferromagnet BaNi$_2$V$_2$O$_8$

In two-dimensional (2D) spin systems, the augmentation of spin fluctuations gives rise to quasi-long-range order; however, how they manifest in spin transport remains unclear. Here we investigate the spin Seebeck effect (SSE) in a quasi-2D antiferromagnet, BaNi$_2$V$_2$O$_8$, which has been reported to exhibit the Berezinskii-Kosterlitz-Thouless (BKT) transition owing to its distinct 2D nature. We found that the SSE in Pt / BaNi$_2$V$_2$O$_8$ persists well above the N\'eel temperature, significantly different from the behavior of 3D ordered magnets. Our numerical analysis for a 2D microscopic spin model supports the hypothesis that the observed SSE is linked to strong magnetic correlations in the BKT-like phase.