Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of hypoxia and uremia on oxidative stress on erythrocytes from hemodialysis patients

Oxidative stress (OS) is essential in uremia‐associated comorbidities, including renal anemia. Complications experienced by hemodialysis (HD) patients, such as hypoxemia and uremic toxins accumulation, induce OS and premature death of red blood cells (RBC). We aimed to characterize reactive oxygen species (ROS) production and antioxidant pathways in HD‐RBC and RBC from healthy controls (CON‐RBC) and evaluate the role of uremia and hypoxia in these pathways. ROS production, xanthine oxidase (XO) and superoxide dismutase (SOD) activities, glutathione (GSH), and heme oxygenase‐1 (HO‐1) levels were measured using flow cytometry or spectrophotometry in CON‐RBC and HD‐RBC (pre‐ and post‐HD), at baseline and after 24 h incubation with uremic serum (S‐HD) and/or under hypoxic conditions (5% O2). Ketoprofen was used to inhibit RBC uremic toxins uptake. HD‐RBC showed higher ROS levels and lower XO activity than CON‐RBC, particularly post‐HD. GSH levels were lower, while SOD activity and HO‐1 levels of HD‐RBC were higher than control. Hypoxia per se triggered ROS production in CON‐RBC and HD‐RBC. S‐HD, on top of hypoxia, increased ROS levels. Inhibition of uremic toxins uptake attenuated ROS of CON and HD‐RBC under hypoxia and uremia. CON‐RBC in uremia and hypoxia showed lower GSH levels than cells in normoxia and non‐uremic conditions. Redox mechanisms of HD‐RBC are altered and prone to oxidation. Uremic toxins and hypoxia play a role in unbalancing these systems. Hypoxia and uremia participate in the pathogenesis of OS in HD‐RBC and might induce RBC death and thus compound anemia.
Etiological Pathways to the Emergence of Preteen Problematic Sexual Behavior: An Exploratory Mediational Model

Studies examining the etiology of problematic sexual behavior (PSB) among pre-teen children often rely on identifying correlational relationships without examining potential causal mechanisms. This study describes an exploratory analysis of a potential mediational model where child sexual abuse (CSA) and child physical abuse (CPA) predict the onset of PSB through their impact on the emergence of posttraumatic stress (PTS) symptoms and self-dysregulation. The caregivers of 189 children between the ages of 3 and 11 years presenting for mental health treatment in the United States completed a battery of measures designed to assess each of the variables in the model. Cross-sectional, regression-based mediational analyses showed that the overall model performed adequately (R = 0.33, R 2 = 0.11, F = 3.07, p = .004). CSA exerted a direct effect on PSB that was not mediated through either PTS or self-dysregulation. However, no direct effect for CPA was found. Rather, CPA exerted a significant effect on the display of self-dysregulation, which in turn was associated with PSB. These results are discussed in light of clinical implications and directions for further research.
Prevalence and correlates of intimate partner violence following partner notification among index HIV clients attending primary healthcare facilities in Uganda

ABSTRACT Assisted partner notification (APN) program was adopted by Uganda to increase individuals testing for HIV through their partners who test HIV positive. Thus, early enrollment in treatment and ensuring prevention services for the affected couple. However, APN is associated with high levels of Intimate partner violence (IPV). We aimed at determining the prevalence of IPV following APN in a cross-sectional study of newly diagnosed HIV clients in southwestern Uganda. We used the modified version of the Conflict Tactics Scale to assess IPV. We also collected information on sociodemographics of the index clients and their sexual partners, and outcome of linkage to care of partner. Logistic regression was used to determine the factors associated with IPV. We enrolled 327 index clients, mean age was 39.1, 63.6% were female and 35.5 experienced IPV following APN. The likelihood of experiencing IPV was more than twice if a health worker/provider disclosed the status to the partner. However, if the partners turned out to be HIV positive, it was protective against experiencing IPV, adjusted odds ratio 0.39, 95% confidence interval 0.23–0.69, p = 0.001. We conclude that IPV is common following partner notification in rural Uganda and should be screened and addressed.
Colossolactone J, a highly modified lanostane triterpenoid from a natural fruiting body of Ganoderma colossus

Abstract Colossolactone J (1), an undescribed lanostane triterpenoid was isolated from a natural fruiting body of Ganoderma colossus using silica gel column chromatography and preparative HPLC. Its structure was elucidated on the basis of the spectroscopic method. The absolute configuration was determined by the combination of the modified Mosher’s method and detailed NMR data analysis. Graphical abstract
Statistical Learning of Language: A Meta-Analysis Into 25 Years of Research

Statistical learning is a key concept in our understanding of language acquisition. Ample work has highlighted its role in numerous linguistic functions-yet statistical learning is not a unitary construct, and its consistency across different language properties remains unclear. In a meta-analysis of auditory-linguistic statistical learning research spanning the last 25 years, we evaluated how learning varies across different language properties in infants, children, and adults and surveyed the methodological trends in the literature. We found robust learning across stimuli (syllables, words, etc.) in infants, and across stimuli and structures (adjacent dependencies, non-adjacent dependencies, etc.) in adults, with larger effect sizes when multiple cues were present. However, the analysis also showed significant publication bias and revealed a tendency toward using a narrow range of simplified language properties, including in the strength of the transitional probabilities used during training. Bayes factor analyses revealed prevalent data insensitivity of moderators commonly hypothesized to impact learning, such as the amount of exposure and transitional probability strength, which contradict core theoretical assumptions in the field. Methodological factors, such as the tasks used at test, also significantly impacted effect sizes in adults and children, suggesting that choice of task may critically constrain current theories of how statistical learning operates. Collectively, our results suggest that auditory-linguistic statistical learning has the kind of robustness needed to play a foundational role in language acquisition, but that more research is warranted to reveal its full potential.
Effects of physical exercise on the functionality of human nucleotidases: A systematic review

Nucleotidases contribute to the regulation of inflammation, coagulation, and cardiovascular activity. Exercise promotes biological adaptations, but its effects on nucleotidase activities and expression are unclear. The objective of this study was to review systematically the effects of exercise on nucleotidase functionality in healthy and unhealthy subjects. The MEDLINE, EMBASE, Cochrane Library, and Web of Science databases were searched to identify, randomized clinical trials, non‐randomized clinical trials, uncontrolled clinical trials, quasi‐experimental, pre‐, and post‐interventional studies that evaluated the effects of exercise on nucleotidases in humans, and was not limited by language and date. Two independent reviewers performed the study selection, data extraction, and assessment of risk of bias. Of the 203 articles identified, 12 were included in this review. Eight studies reported that acute exercise, in healthy and unhealthy subjects, elevated the activities or expression of nucleotidases. Four studies evaluated the effects of chronic training on nucleotidase activities in the platelets and lymphocytes of patients with metabolic syndrome, chronic kidney disease, and hypertension and found a decrease in nucleotidase activities in these conditions. Acute and chronic exercise was able to modify the blood plasma and serum levels of nucleotides and nucleosides. Our results suggest that short‐ and long‐term exercise modulate nucleotidase functionality. As such, purinergic signaling may represent a novel molecular adaptation in inflammatory, thrombotic, and vascular responses to exercise.
Wellness Program Participation and its Association With Reduction in Health Risks and Health Care Costs: One University’s Story

Purpose Evaluate if there is a(n): 1) reduction in health risk behaviors and 2) association between health and wellness initiative participation and claims costs decreases overtime. Design Health risk behavior change consisted of the analysis of pre (2009) – post (2019) measures of health questionnaire participants. The medical and pharmaceutical claims costs evaluation consisted of a time-series (2016-2019) design with a comparison group. Setting A large mid-western university with a health system. Sample The health risk behavior sample ranged from 5215-5399, depending on the variable of interest. The medical and pharmaceutical costs sample used a cohort of participants (participating all 4 years, n=11114) and non-participants (not participating all 4 years, n=4776). Intervention Comprehensive employee health and well-being initiative. Measures/analysis McNemar’s tests were used to identify bivariate associations between 2009 and 2019 health risks. The claims cost analysis used propensity score matching based on select demographics and linear mixed-effects regression modeling. Results Results show statistically significant (P > .001) improvements (ranging from −2.1% to −12.5%) in 7 of 8 health risk behaviors. 2016 and 2019 claims costs have a lower statistically significant (P > .001) percent increase for participants (32.6%) compared to non-participants (47.5%). Conclusion Employers, may consider implementing a comprehensive health and wellness program as part of their strategy to assist in health behavior risk reduction and health care cost containment.
COUNTING 3D-SPACES Classicality and probability in standard and many-worlds quantum mechanics from quantum-gravitational background-freedom

I explain that background freedom in quantum gravity automatically leads to a dissociation of the quantum state into states having a classical 3d-space. That is, interference is not completely well-deﬁned for states with diﬀerent 3d-space geometries, even if their linear combination is. The dissociation into 3d-space geometries still allows for interference at small scales, but pre-cludes it at macro scales. It grants the possibility of classical-looking macroscopic objects, including measuring devices. Counting the 3d-space geometries automatically gives the Born rule. But the wavefunction collapse turns out to be even more ad-hoc. Fortunately, the dissociation entails a kind of absolute decoherence, making the wavefunction collapse unnecessary. This naturally leads to a new version of the many-worlds interpretation, while solving its major problems: 1)
Application of initial data sequences to the study of black hole dynamical trapping horizons

Non‐continuous “jumps” of Apparent Horizons occur generically in 3+1 (binary) black hole evolutions. The dynamical trapping horizon framework suggests a spacetime picture in which these “Apparent Horizon jumps” are understood as spatial cuts of a single spacetime hypersurface foliated by (compact) marginally outer trapped surfaces. We present here some work in progress which makes use of uni‐parametric sequences of (axisymmetric) binary black hole initial data for exploring the plausibility of this spacetime picture. The modelling of Einstein evolutions by sequences of initial data has proved to be a successful methodological tool in other settings for the understanding of certain qualitative features of evolutions in restricted physical regimes.
ON CONCAVITY OF TAP FREE ENERGY IN THE SK MODEL

We analyse the Hessian of the Thouless-Anderson-Palmer (TAP) free energy for the Sherrington-Kirkpatrick model, below the de Almeida-Thouless line, evaluated in Bolthausen’s approximate solutions of the TAP equations. We show that the empirical spectral distribution weakly converges to a measure with negative support below the AT line, an that the support includes zero on the AT line. In this “macroscopic” sense, TAP free energy is concave in the order parameter of the theory, i.e
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network

With the outbreak of today's streaming data, the sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted item of a given user based on the historical item sequence. Some recent works tend to improve the sequential recommendation via random masking on the historical item so as to generate self-supervised signals. But such approaches will indeed result in sparser item sequence and unreliable signals. Besides, the existing sequential recommendation models are only user-centric, i.e., based on the historical items by chronological order to predict the probability of candidate items, which ignores whether the items from a provider can be successfully recommended. Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions. In this paper, we propose a novel Dual Contrastive Network (DCN) to achieve mutual harmony between user and item provider, generating ground-truth self-supervised signals for sequential recommendation by auxiliary user-sequence from an item-centric dimension. Specifically, we propose dual representation contrastive learning to refine the representation learning by minimizing the Euclidean distance between the representations of a given user/item and historical items/users of them. Before the second contrastive learning module, we perform the next user prediction to capture the trends of items preferred by certain types of users and provide personalized exploration opportunities for item providers. Finally, we further propose dual interest contrastive learning to self-supervise the dynamic interest from the next item/user prediction and static interest of matching probability. Experiments on four benchmark datasets verify the effectiveness of our proposed method.
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer

The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysis often fails on unseen patient data due to inherent domain biases. This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images. The proposed homogenizer is based on a U-Net architecture and can effectively reduce domain differences commonly seen with histology imaging data. We demonstrate our domain homogenizer's effectiveness by observing the reduction in domain differences between the preprocessed images. Using this homogenizer, along with a subsequent retina-net object detector, we were able to outperform the baselines of the 2021 MIDOG challenge in terms of average precision of the detected mitotic figures.
The Game of Life on a Magnetar Crust: From Gamma-Ray Flares to FRBs

This Letter presents a model to unify the diverse range of magnetar activity, through the building and release of elastic stress from the crust. A cellular automaton drives both local and global yielding of the crust, leading to braiding of coronal loops and energy release. The model behaves like a real magnetar in many ways: giant flares and small bursts both occur, as well as periods of quiescence whose typical duration is either ≲1 yr or ∼10–30 yr. The burst energy distribution broadly follows an earthquake-like power law over the energy range 1040–1045 erg. The local nature of coronal loops allows for the possibility of high-energy and fast radio bursts from the same magnetar. Within this paradigm, magnetar observations can be used to constrain the poorly understood mechanical properties of the neutron-star crust.
One Network, Many Robots: Generative Graphical Inverse Kinematics

Quickly and reliably ﬁnding accurate inverse kinematics (IK) solutions remains a challenging problem for robotic manipulation. Existing numerical solvers are broadly applicable, but rely on local search techniques to manage highly nonconvex objective functions. Recently, learning-based approaches have shown promise as a means to generate fast and accurate IK results; learned solvers can easily be integrated with other learning algorithms in end-to-end systems. However, learning-based methods have an Achilles’ heel: each robot of interest requires a specialized model which must be trained from scratch. To address this key short-coming, we investigate a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the ﬂexibility of graph neural networks (GNNs). We use this approach to train the ﬁrst learned generative graphical inverse kinematics (GGIK) solver that is, crucially, “robot-agnostic”— a single model is able to provide IK solutions for a variety of different robots. Additionally, the generative nature of GGIK allows the solver to produce a large number of diverse solutions in parallel with minimal additional computation time, making it appropriate for applications such as sampling-based motion planning. Finally, GGIK can complement local IK solvers by providing reliable initializa-tions. These advantages, as well as the ability to use task-relevant priors and to continuously improve with new data, suggest that GGIK has the potential to be a key component of ﬂexible, learning-based robotic manipulation systems.
Infrared: A Meta Bug Detector

The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors. Compared to the traditional static analysis tools, these bug detectors are directly learned from data, thus, easier to create. On the other hand, they are difficult to train, requiring a large amount of data which is not readily available. In this paper, we propose a new approach, called meta bug detection , which offers three crucial advantages over existing learning-based bug detectors: bug-type generic ( i.e., capable of catching the types of bugs that are totally unobserved during training), self-explainable ( i.e., capable of explaining its own prediction without any external interpretability methods) and sample efficient ( i.e., requiring substantially less training data than standard bug detectors). Our extensive evaluation shows our meta bug detector ( MBD ) is effective in catching a variety of bugs including null pointer dereference, array index out-of-bound, file handle leak, and even data races in concurrent programs; in the process MBD also significantly outperforms several noteworthy baselines including Facebook Infer, a prominent static analysis tool, and FICS, the latest anomaly detection method.
High-temperature electrical transport behaviors in textured Ca3Co4O9-based polycrystalline ceramics

Highly (00l) oriented Ca3Co4O9-based ceramics were fabricated by spark plasma sintering combined with a dynamic forging process. The grain orientation is effective in lowering the electrical resistivity and enhancing the thermoelectric power factor but with little influence on the Seebeck coefficient. A metallic-to-semiconducting transition temperature can be observed and the activation energy is almost independent of the La-doping. All of the Ca3Co4O9-based ceramic samples exhibit large thermoelectric power, and the figure of merit for La-doped Ca3Co4O9 sample can reach 0.26 at 975 K, which can be a promising candidate of p-type material for high-temperature thermoelectric application.
Characteristics of the solitary waves in multicomponent plasmas

We have investigated ion-acoustic solitary waves in a multicomponent plasma. The study shows an interaction of the negatively charged particles with the solitary waves, due to which interesting physical behaviour of the solitary waves is observed. Moreover, the isothermality and the non-isothermality of the plasma exhibit different soliton-type solutions and a transition of the soliton's behaviour can be shown through the changes of the non-isothermality in the plasma.
Learning to Weight Samples for Dynamic Early-exiting Networks

Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks allow easy samples to be output at early exits, removing the need for executing deeper layers. While existing works mainly focus on the architectural design of multi-exit networks, the training strategies for such models are largely left unexplored. The current state-of-the-art models treat all samples the same during training. However, the early-exiting behavior during testing has been ignored, leading to a gap between training and testing. In this paper, we propose to bridge this gap by sample weighting. Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers. The training of hard samples (mostly exit from deeper layers), however, should be emphasized by the late classifiers. Our work proposes to adopt a weight prediction network to weight the loss of different training samples at each exit. This weight prediction network and the backbone model are jointly optimized under a meta-learning framework with a novel optimization objective. By bringing the adaptive behavior during inference into the training phase, we show that the proposed weighting mechanism consistently improves the trade-off between classification accuracy and inference efficiency. Code is available at https://github.com/LeapLabTHU/L2W-DEN.
Unsupervised Lexical Substitution with Decontextualised Embeddings

We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method retrieves substitutes based on the similarity of contextualised and decontextualised word embeddings, i.e. the average contextual representation of a word in multiple contexts. We conduct experiments in English and Italian, and show that our method substantially outperforms strong baselines and establishes a new state-of-the-art without any explicit supervision or fine-tuning. We further show that our method performs particularly well at predicting low-frequency substitutes, and also generates a diverse list of substitute candidates, reducing morphophonetic or morphosyntactic biases induced by article-noun agreement.
Approximation results for Gradient Descent trained Shallow Neural Networks in 1d

Two aspects of neural networks that have been extensively studied in the recent literature are their function approximation properties and their training by gradient descent methods. The approximation problem seeks accurate approximations with a minimal number of weights. In most of the current literature these weights are fully or partially hand-crafted, showing the capabilities of neural networks but not necessarily their practical performance. In contrast, optimization theory for neural networks heavily relies on an abundance of weights in over-parametrized regimes. This paper balances these two demands and provides an approximation result for shallow networks in $1d$ with non-convex weight optimization by gradient descent. We consider finite width networks and infinite sample limits, which is the typical setup in approximation theory. Technically, this problem is not over-parametrized, however, some form of redundancy reappears as a loss in approximation rate compared to best possible rates.