Miracle-dz/UniFAR-FTUs huggingface dataset:
Cholesterol metabolism in aging simultaneously altered in liver and nervous system

In humans, aging, triggers increased plasma concentrations of triglycerides, cholesterol, low-density lipoproteins and lower capacity of high-density lipoproteins to remove cellular cholesterol. Studies in rodents showed that aging led to cholesterol accumulation in the liver and decrease in the brain with reduced cholesterol synthesis and increased levels of cholesterol 24-hydroxylase, an enzyme responsible for removing cholesterol from the brain. Liver diseases are also related to brain aging, inducing changes in cholesterol metabolism in the brain and liver of rats. It has been suggested that late onset Alzheimer’s disease is associated with metabolic syndrome. Non-alcoholic fatty liver is associated with lower total brain volume in the Framingham Heart Study offspring cohort study. Furthermore, disorders of cholesterol homeostasis in the adult brain are associated with neurological diseases such as Niemann-Pick, Alzheimer, Parkinson, Huntington and epilepsy. Apolipoprotein E (apoE) is important in transporting cholesterol from astrocytes to neurons in the etiology of sporadic Alzheimer’s disease, an aging-related dementia. Desmosterol and 24S-hydroxycholesterol are reduced in ApoE KO hypercholesterolemic mice. ApoE KO mice have synaptic loss, cognitive dysfunction, and elevated plasma lipid levels that can affect brain function. In contrast to cholesterol itself, there is a continuous uptake of 27- hydroxycholesterol in the brain as it crosses the blood-brain barrier and this flow can be an important link between intra- and extracerebral cholesterol homeostasis. Not surprisingly, changes in cholesterol metabolism occur simultaneously in the liver and nervous tissues and may be considered possible biomarkers of the liver and nervous system aging.
Impact of metformin on tyrosine kinase inhibitor response in chronic myeloid leukemia

Objective Oral tyrosine kinase inhibitors (TKIs) are first line therapy for chronic myeloid leukemia (CML). A complete cytogenetic response (CCyR) correlates with increased overall survival, however only 66%–88% of patients achieve CCyR after one year of TKI treatment. Because TKI therapy alone cannot eliminate CML stem cells, strategies aimed at achieving faster and deeper responses are needed to improve long-term survival. Metformin is a widely prescribed glucose-lowering agent for patients with diabetes and in preclinical studies, has been shown to suppress cell viability, induce apoptosis, and downregulate the mTORC1 signaling pathway in imatinib resistant CML cell lines (K562R). This study aims to investigate the utility of metformin added to TKI therapy in patients with CML. Data Sources An observational study at an academic medical center (Salt Lake City, UT) was performed for adults with newly diagnosed, chronic-phase CML to evaluate attainment of CCyR from TKI therapy with or without concomitant metformin use. Descriptive analyses were used to describe baseline characteristics and attainment of response to TKI therapy. Data Summary Fifty-nine patients were evaluated. One hundred percent (5 of 5) in the metformin group and 73.6% (39 of 54) in the non-metformin group achieved CCyR. Approximately 20% of patients in both groups relapsed (defined by a loss of CCyR during study) after a median 34.5 months of follow-up. Conclusions Future research is warranted to validate these findings and determine the utility of metformin added to TKI therapy.
The Tuberculous Granuloma and Preexisting Immunity.

Pulmonary granulomas are widely considered the epicenters of the immune response to Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). Recent animal studies have revealed factors that either promote or restrict TB immunity within granulomas. These models, however, typically ignore the impact of preexisting immunity on cellular organization and function, an important consideration because most TB probably occurs through reinfection of previously exposed individuals. Human postmortem research from the pre-antibiotic era showed that infections in Mtb-naïve individuals (primary TB) versus those with prior Mtb exposure (postprimary TB) have distinct pathologic features. We review recent animal findings in TB granuloma biology, which largely reflect primary TB. We also discuss our current understanding of postprimary TB lesions, about which much less is known. Many knowledge gaps remain, particularly regarding how preexisting immunity shapes granuloma structure and local immune responses at Mtb infection sites. Expected final online publication date for the Annual Review of Immunology, Volume 40 is April 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Clinical and Economic Impact of COVID-19 on Plantation Workers: Preliminary Results from the Guatemala Agricultural Workers and Respiratory Illness Impact (AGRI) Study

We evaluated the clinical and socioeconomic burdens of respiratory disease in a cohort of Guatemalan banana plantation workers. All eligible workers were offered enrollment from June 15 to December 30, 2020, and annually, then followed for influenza-like illnesses (ILI) through: 1) self-reporting to study nurses, 2) sentinel surveillance at health posts, and 3) absenteeism. Workers with ILI submitted nasopharyngeal swabs for influenza, RSV, and SARS-CoV-2 testing, then completed surveys at days 0, 7, and 28. Through October 10, 2021, 1,833 workers developed 169 ILIs (12.0/100 person-years) and 43 (25.4%) of these ILIs were laboratory-confirmed SARS-CoV-2 (3.1/100 person-years). Workers with SARS-CoV-2-positive ILI reported more anosmia (p<0.01), dysgeusia (p<0.01), difficulty concentrating (p=0.01), and irritability (p=0.01), and greater clinical and well-being severity scores (Flu-iiQ) than test-negative ILIs; they also had greater absenteeism (p<0.01) and lost income (median US$127.1, p<0.01). These results support the prioritization of Guatemalan farm workers for COVID-19 vaccination.
Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions

Understanding the cumulative effect of multiple fairness-enhancing interventions at different stages of the machine learning (ML) pipeline is a critical and underexplored facet of the fairness literature. Such knowledge can be valuable to data scientists/ML practitioners in designing fair ML pipelines. This paper takes the first step in exploring this area by undertaking an extensive empirical study comprising 60 combinations of interventions, 9 fairness metrics, 2 utility metrics (Accuracy and F1 Score) across 4 benchmark datasets. We quantitatively analyze the experimental data to measure the impact of multiple interventions on fairness, utility and population groups. We found that applying multiple interventions results in better fairness and lower utility than individual interventions on aggregate. However, adding more interventions do no always result in better fairness or worse utility. The likelihood of achieving high performance (F1 Score) along with high fairness increases with larger number of interventions. On the downside, we found that fairness-enhancing interventions can negatively impact different population groups, especially the privileged group. This study highlights the need for new fairness metrics that account for the impact on different population groups apart from just the disparity between groups. Lastly, we offer a list of combinations of interventions that perform best for different fairness and utility metrics to aid the design of fair ML pipelines.
Scalar Bound States of $D^{\ast}\bar{D}^{\ast}$ and $B^{\ast}\bar{B}^{\ast}$ in the Bethe-Salpeter Formalism

We study the scalar bound states of D∗D̄∗ and B∗B̄∗ in the Bethe-Salpeter formalism, with the effective interaction kernel extracted from the chiral perturbative theory and the heavy quark effective theory in the ladder approximation and the covariant instantaneous approximation. The results show that, in the scalar case (J = 0), there can only exist I = 0 bound states for parameters in proper range, while there cannot exist the I = 1 bound states in the whole reasonable parameter range, due to more constraints arising from our definition of the Bethe-Salpeter wavefunction. PACS numbers 11.10. St, 12.39. Fe, 12.40. Yx, 13.20. Jf, 13.30. Eg
Accelerometer-Based Bed Occupancy Detection for Automatic, Non-Invasive Long-Term Cough Monitoring

We present a new machine learning based bed occupancy detection system that uses only the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed occupancy detection is necessary for automatic long-term cough monitoring since the time that the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost-effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture achieved an AUC of 0.94. To demonstrate the application of this bed occupancy detection system to a complete cough monitoring system, the daily cough rates along with the corresponding laboratory indicators of a patient undergoing TB treatment were estimated over a period of 14 days. This provides a preliminary indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring the long-term recovery of patients suffering from respiratory diseases such as TB and COVID-19.
Uncertainty Modeling for Out-of-Distribution Generalization

Though remarkable progress has been achieved in various vision tasks, deep neural networks still suffer obvious performance degradation when tested in out-of-distribution scenarios. We argue that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features and do not explicitly consider the uncertain statistics discrepancy caused by potential domain shifts during testing. In this paper, we improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. Hence, each feature statistic is no longer a deterministic value, but a probabilistic point with diverse distribution possibilities. With the uncertain feature statistics, the models can be trained to alleviate the domain perturbations and achieve better robustness against potential domain shifts. Our method can be readily integrated into networks without additional parameters. Extensive experiments demonstrate that our proposed method consistently improves the network generalization ability on multiple vision tasks, including image classification, semantic segmentation, and instance retrieval. The code can be available at https://github.com/lixiaotong97/DSU.
Hidden Kac-Moody structures in the fermionic sector of five-dimensional supergravity

We study the supersymmetric quantum dynamics of the cosmological models obtained by reducing D = 5 supergravity to one timelike dimension. This consistent truncation has fourteen bosonic degrees of freedom, while the quantization of the homogeneous gravitino field leads to a 2–dimensional fermionic Hilbert space. We construct a consistent quantization of the model in which the wave function of the Universe is a 2–component spinor depending on fourteen continuous coordinates, which satisfies eight Dirac-like wave equations (supersymmetry constraints) and one Klein-Gordon-like equation (Hamiltonian constraint). The fermionic part of the quantum Hamiltonian is built from operators that generate a 2-dimensional representation of the (infinitedimensional) maximally compact sub-algebra K(G 2 ) of the rank-4 hyperbolic Kac–Moody algebra G 2 . The (quartic-in-fermions) squared-mass term μ̂ 2 entering the Klein-Gordon-like equation has several remarkable properties: (i) it commutes with the generators of K(G 2 ); and (ii) it is a quadratic polynomial in the fermion number NF ∼ ΨΨ, and a symplectic fermion bilinear CF ∼ ΨCΨ. Some aspects of the structure of the solutions of our model are discussed, and notably the Kac-Moody meaning of the operators describing the reflection of the wave function on the fermion-dependent potential walls (“quantum fermionic Kac-Moody billiard”).
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space

Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to kNN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS.
Weak commutativity, virtually nilpotent groups, and Dehn functions

The group $\mathfrak{X}(G)$ is obtained from $G\ast G$ by forcing each element $g$ in the first free factor to commute with the copy of $g$ in the second free factor. We make significant additions to the list of properties that the functor $\mathfrak{X}$ is known to preserve. We also investigate the geometry and complexity of the word problem for $\mathfrak{X}(G)$. Subtle features of $\mathfrak{X}$ are encoded in a normal abelian subgroup $W<\mathfrak{X}(G)$ that is a module over $\mathbb{Z} Q$, where $Q= H_1(G,\mathbb{Z})$. We establish a structural result for this module and illustrate its utility by proving that $\mathfrak{X}$ preserves virtual nilpotence, the Engel condition, and growth type -- polynomial, exponential, or intermediate. We also use it to establish isoperimetric inequalities for $\mathfrak{X}(G)$ when $G$ lies in a class that includes Thompson's group $F$ and all non-fibered K\"ahler groups. The word problem is solvable in $\mathfrak{X}(G)$ if and only if it is solvable in $G$. The Dehn function of $\mathfrak{X}(G)$ is bounded below by a cubic polynomial if $G$ maps onto a non-abelian free group.
Biases in Estimates of Black Hole Kicks from the Spin Distribution of Binary Black Holes

A population of more than 50 binary black hole mergers has now been observed by the LIGO and Virgo gravitational-wave observatories. While neutron stars are known to have large velocities associated with impulsive kicks imparted to them at birth in supernovae, whether black holes receive similar kicks, and of what magnitude, remains an open question. Recently, Callister et al. analyzed the binary black hole population under the hypothesis that they were all formed through isolated binary evolution and claimed that large black hole kicks (greater than 260 km s−1 at 99% confidence) were required for the spin distribution of merging binary black holes to match observations. Here we highlight that a key assumption made by Callister et al.—that all secondary black holes can be tidally spun up—is not motivated by physical models and may lead to a bias in their estimate of the magnitudes of black hole kicks. We make only minor changes to the Callister et al. model, accounting for a population of wider merging binaries where tidal synchronization is ineffective. We show that this naturally produces a bimodal spin distribution for secondary black holes and that the spin–orbit misalignments observed in the binary black hole population can be explained by more typical black hole kicks of order 100 km s−1, consistent with kicks inferred from Galactic X-ray binaries containing black holes. We conclude that the majority of the binary black hole population is consistent with forming through isolated binary evolution.
Physical Properties of the Host Galaxies of Ca-rich Transients

Calcium-rich (Ca-rich) transients are a new class of supernovae (SNe) that are known for their comparatively rapid evolution, modest peak luminosities, and strong nebular calcium emission lines. Currently, the progenitor systems of Ca-rich transients remain unknown. Although they exhibit spectroscopic properties not unlike core-collapse Type Ib/c SNe, nearly half are found in the outskirts of their host galaxies, which are predominantly elliptical, suggesting a closer connection to the older stellar populations of SNe Ia. In this paper, we present a compilation of publicly available multiwavelength observations of all known and/or suspected host galaxies of Ca-rich transients ranging from far-UV to IR, and use these data to characterize their stellar populations with prospector. We estimate several galaxy parameters including integrated star formation rate, stellar mass, metallicity, and age. For nine host galaxies, the observations are sensitive enough to obtain nonparametric star formation histories, from which we recover SN rates and estimate probabilities that the Ca-rich transients in each of these host galaxies originated from a core-collapse versus Type Ia-like explosion. Our work supports the notion that the population of Ca-rich transients do not come exclusively from core-collapse explosions, and must either be only from white dwarf stars or a mixed population of white dwarf stars with other channels, potentially including massive star explosions. Additional photometry and explosion site spectroscopy of larger samples of Ca-rich host galaxies will improve these estimates and better constrain the ratio of white dwarf versus massive star progenitors of Ca-rich transients.
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms

Sparse Principal Component Analysis (PCA) is a prevalent tool across a plethora of subfields of applied statistics. While several results have characterized the recovery error of the principal eigenvectors, these are typically in spectral or Frobenius norms. In this paper, we provide entrywise $\ell_{2,\infty}$ bounds for Sparse PCA under a general high-dimensional subgaussian design. In particular, our results hold for any algorithm that selects the correct support with high probability, those that are sparsistent. Our bound improves upon known results by providing a finer characterization of the estimation error, and our proof uses techniques recently developed for entrywise subspace perturbation theory.
Compaction band localization in geomaterials: a mechanically consistent failure criterion

Compaction bands play a key role in the deformation processes of porous rocks and explain different aspects of physical processes in geological formations. The state-of-the-art description of the localized strains that lead to compaction banding has limitations from the mechanical point of view. Thus, we describe the phenomenon using a consistent axiomatic formulation. We build a viscoplastic model using minimal assumptions; we base our model on six principles to study compaction band localization triggered by viscous effects. We analyze different stress states to determine the conditions that trigger compaction bands. Laboratory experiments show that a material undergoes different localizations depending on the confinement pressure; thus, we perform a series of numerical experiments that reproduce these phenomena under varying triaxial compression conditions. These simulations use a simple viscoplastic constitutive model for creep based on Perzyna's viscoplasticity and show how confinement changes the localization type for different triaxial tests. Our analysis allows us to describe this transition, band periodicity and spacing, and their dependence on the material parameters.
Structured Time Series Prediction without Structural Prior

Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.). In the case of multivariate time series, the key to good performances is to properly capture the dependencies between the variates. Often, these variates are structured, i.e. they are localised in an abstract space, usually representing an aspect of the physical world, and prediction amounts to a form of diffusion of the information across that space over time. Several neural network models of diffusion have been proposed in the literature. However, most of the existing proposals rely on some a priori knowledge on the structure of the space, usually in the form of a graph weighing the pairwise diffusion capacity of its points. We argue that this piece of information can often be dispensed with, since data already contains the diffusion capacity information, and in a more reliable form than that obtained from the usually largely hand-crafted graphs. We propose instead a fully data-driven model which does not rely on such a graph, nor any other prior structural information. We conduct a first set of experiments to measure the impact on performance of a structural prior, as used in baseline models, and show that, except at very low data levels, it remains negligible, and beyond a threshold, it may even become detrimental. We then investigate, through a second set of experiments, the capacity of our model in two respects: treatment of missing data and domain adaptation.