Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship of Subcutaneous Adipose Tissue Inflammation–Related Gene Expression With Ectopic Lipid Deposition in Persons With HIV

Supplemental Digital Content is Available in the Text. Objective: Fat redistribution from subcutaneous adipose tissue (SAT) to the abdominal viscera, pericardium, liver, and skeletal muscle contributes to the rising burden of cardiometabolic disease among persons with HIV (PWH). Previous studies found SAT inflammation in PWH impairs lipid storage and persists despite plasma viral suppression on antiretroviral therapy (ART). In this study, we identified SAT immune-related genes associated with ectopic fat deposition in PWH on long-term ART. Design and methods: A total of 92 PWH with well-controlled viremia underwent computed tomography imaging and abdominal SAT biopsy for gene expression analysis. SAT gene expression was measured using a NanoString panel of 255 immune-related genes. Associations between gene expression and computed tomography measurements of the volume and attenuation (radiodensity) of metabolically relevant ectopic fat depots were assessed using multivariable linear regression and network analysis. Results: Greater SAT volume was associated with higher visceral and pericardial adipose tissue volume, but lower skeletal muscle attenuation. Lower SAT attenuation, a measure of lipid content, was associated with lower visceral adipose tissue attenuation. Hierarchical clustering identified a subset of macrophage-related genes in SAT, including CCL2, CCL22, CCL13, CCR1, CD86, CD163, IL-6, IL-10, MRC1, and TREM2, which were associated with an increased lipid deposition in multiple ectopic depots. Conclusion: Altered expression of macrophage-related genes in SAT is associated with differences in ectopic fat depot morphometrics among PWH on long-term ART, including in the pericardial and visceral compartments. These findings provide basis for future studies to assess host, virus, and treatment factors shaping the SAT immune environment and its effects on morphometric changes and metabolic comorbidities in PWH.
Olfactory camouflage and communication in birds

Smell is a sensory modality that is rarely considered in birds, but evidence is mounting that olfaction is an important aspect of avian behaviour and ecology. The uropygial gland produces an odoriferous secretion (preen oil) that can differ seasonally and between the sexes. These differences are hypothesized to function in olfactory camouflage, i.e. minimizing detection by nest predators (olfactory crypsis hypothesis), and/or intraspecific olfactory communication, particularly during breeding (sex semiochemical hypothesis). However, evidence for seasonal and sex differences in preen oil is mixed, with some studies finding differences and others not, and direct evidence for the putative function(s) of seasonal variation and sex differences in preen oil remains limited. We conducted a systematic review of the evidence for such changes in preen oil chemical composition, finding seasonal differences in 95% of species (57/60 species in 35 studies) and sex differences in 47% of species (28/59 species in 46 studies). We then conducted phylogenetic comparative analyses using data from 59 bird species to evaluate evidence for both the olfactory crypsis and sex semiochemical hypotheses. Seasonal differences were more likely in the incubating than non‐incubating sex in ground‐nesting species, but were equally likely regardless of incubation strategy in non‐ground‐nesting species. This result supports the olfactory crypsis hypothesis, if ground nesters are more vulnerable to olfactorily searching predators than non‐ground nesters. Sex differences were more likely in species with uniparental than biparental incubation and during breeding than non‐breeding, consistent with both the olfactory crypsis and sex semiochemical hypotheses. At present, the data do not allow us to disentangle these two hypotheses, but we provide recommendations that will enable researchers to do so.
Wood density and hydraulic traits influence species’ growth response to drought across biomes

Tree species display a wide variety of water‐use strategies, growth rates and capacity to tolerate drought. However, if we want to forecast species capacity to cope with increasing aridity and drought, we need to identify which measurable traits confer resilience to drought across species. Here, we use a global tree ring network (65 species; 1931 site series of ring‐width indices—RWI) to evaluate the relationship of long‐term growth‐drought sensitivity (RWI‐SPEI drought index relationship) and short‐term growth response to extreme drought episodes (resistance, recovery and resilience indices) with functional traits related to leaf, wood and hydraulic properties. Furthermore, we assess the influence of climate (temperature, precipitation and climatic water deficit) on these trait‐growth relationships. We found a close correspondence between the long‐term relationship between RWI and SPEI and resistance and recovery of tree growth to severe drought episodes. Species displaying a stronger RWI‐SPEI relationship to drought and low resistance and high recovery to extreme drought episodes tended to have a higher wood density (WD) and more negative leaf minimum water potential (Ψmin). Such associations were largely maintained when accounting for direct climate effects. Our results indicate that, at a cross‐species level and global scale, wood and hydraulic functional traits explain species’ growth responses to drought at short‐ and long‐term scales. These trait‐growth response relationships can improve our understanding of the cross‐species capacity to withstand climate change and inform models to better predict drought effects on forest ecosystem dynamics.
The Impact of Spinopelvic Parameters on Hip Degeneration After Spinal Fusion

Study Design. Retrospective cohort study. Objective. This study aimed to investigate the effects of spinopelvic alignment parameters after spinal fusion on the rate of joint space narrowing in nonarthritic hips. Summary of Background Data. Spinal fusion affects the forces on the adjacent hip joint. Therefore, hip joint narrowing may be increased after spinal fusion surgery. However, the relationship between spinal alignment and hip degeneration remains unclarified. Methods. We retrospectively reviewed data from patients who underwent lumbar spinal fusion from 2011 to 2018 at our institute. Patients with hip osteoarthritis (Kellgren-Lawrence grade ≥II) or hip dysplasia were excluded. The rate of hip joint space narrowing after spinal fusion was measured in 191 patients (382 hips). We assessed the effects of the following spinopelvic alignment parameters on the joint narrowing rate: pelvic tilt, sacral slope (SS), pelvic incidence (PI), lumbar lordosis (LL), PI-LL, sagittal vertical axis, and distance between the C7 plumb line, and the central sacral vertical line (C7-CSVL). Results. The hip joint narrowing rate was greater when four or more levels were fused compared with single-level fusion. After adjusting for the effects of patient-related factors, the alignment parameters significantly associated with the hip joint narrowing rate were the PI (P = 0.0002), SS (P = 0.047), and PI-LL (P = 0.0022). A subgroup analysis of patients who underwent long fusion (four or more levels combined with iliac screws) also indicated that the PI (P = 0.013), SS (P = 0.0054), and PI-LL (P = 0.046) were associated with the hip joint narrowing rate. Conclusion. The PI, SS, and PI-LL were associated with the progression of hip joint narrowing after spinal fusion, especially after fusion of four or more levels. Surgeons need to be aware of the risk of increased hip joint narrowing in patients with a large PI, SS, and PI-LL after fusion surgery.
Factors Associated with Active Trachoma among Children in Ebinat District, South Gondar Zone, North West Ethiopia: a community-based cross-sectional study

Background: Despite the availability of effective prevention strategies and treatments, trachoma is still the major cause of infectious blindness worldwide. The majority of blindness from trachoma is currently reported from sub-Saharan Africa, with the greatest burden in Ethiopia. This study aimed to determine the prevalence and factors associated with active trachoma among children aged 1-9 years in Ebinat District. Methods: A community-based cross-sectional study design was conducted in Ebinat District, South Gondar Zone, North West Ethiopia from March 1-30 2018. A multi-stage random cluster sampling technique was employed and all children 1-9 years old from the selected households were clinically examined for trachoma by ophthalmic nurses using the World Health Organization (WHO) simplified clinical grading scheme. A total of 565 children were included. Data were analyzed using SPSS. Multivariable logistic regression was used to identify factors associated with active trachoma among children. Result: The overall prevalence of active trachoma was 36.1% (95% CI: 32.0% - 40.0%). Of all active trachoma cases, 26.5% and 9.6% were trachomatous follicular and trachomatous follicular-intense respectively. Discharge on the eye (AOR=8.23; 95% CI: 4.27-15.87), presence of liquid waste around the main house (AOR=2.31; 95% CI: 1.47-3.61), presence of human feces around the main house (AOR=2.64; 95% CI: 1.61- 4.35), unclean faces (AOR= 5.71; 95% CI: 2.18 - 14.96), not washing face and hands frequently (AOR= 2.28; 95% CI: 1.33-3.90), not using soap while washing face and hands (AOR=1.95; 95% CI: 1.21-3.34), and having more flies on childrens face (AOR=1.74; 95% CI: 1.12-2.73) were significantly associated with active trachoma. Conclusion and recommendation: Active trachoma is a major public health problem among children in the Ebinat District. Surgery for trichiasis, antibiotics for active disease, facial hygiene, and environmental improvement (SAFE) strategy at school and community are recommended to lower the burden of trachoma in the Ebinat District, Northwest Ethiopia.
Introduction: Design and Creativity

Design and other creative industries not only shape our lives in numerous ways, providing 'cultural' goods such as films, music and magazines, but also shape the look and feel of everyday objects and spaces. The creative industries are also important economically; governments and businesses now make considerable efforts to manage creativity for a range of political and economic ends. Does the management of design conflict with traditional ideas of creative freedom and autonomy? How do government policies and business priorities influence the day-to-day practices of designers? And how far have the processes and purpose of creative work been changed by its new centrality to business and government? Bringing together case studies and material from a range of industries and contexts, as well as a series of interviews with practitioners, Design and Creativity provides a cutting-edge account of key trends in the creative industries at the start of the twenty-first century.
Evaluation of 2D and 3D Displays to Validate the Integration of Human Performance Analysis Methods into Submarine Acquisitions

When deciding to incorporate advanced display hardware into Navy submarines, acquisition personnel must consider its physical form and fit. Human performance concerns are often neglected, however, since no such methodology exists within this process. In this effort, we developed a toolkit to enable basic, low-resource human performance screenings of display hardware and to integrate results into acquisition decisions. As a test case, we applied the toolkit toward three display types being considered for Navy submarines: standard 2D, high-fidelity autostereoscopic 3D, and low-fidelity autostereoscopic 3D. Using minimal resources, the toolkit produced useful results that would enable acquisition personnel to gain insight into potential human performance advantages and risks that would otherwise not be considered at the acquisition stage of system development. An overview of the test case results is presented.
Diversify and Disambiguate: Learning From Underspecified Data

Many datasets are underspecified: there exist multiple equally viable solutions to a given task. Underspecification can be problematic for methods that learn a single hypothesis because different functions that achieve low training loss can focus on different predictive features and thus produce widely varying predictions on out-of-distribution data. We propose DivDis, a simple two-stage framework that first learns a diverse collection of hypotheses for a task by leveraging unlabeled data from the test distribution. We then disambiguate by selecting one of the discovered hypotheses using minimal additional supervision, in the form of additional labels or inspection of function visualization. We demonstrate the ability of DivDis to find hypotheses that use robust features in image classification and natural language processing problems with underspecification.
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine

Graph Neural Networks (GNNs) have emerged as a powerful model for ML over graph-structured data. Yet, scalability remains a major challenge for using GNNs over billion-edge inputs. The creation of mini-batches used for training incurs computational and data movement costs that grow exponentially with the number of GNN layers as state-of-the-art models aggregate information from the multi-hop neighborhood of each input node. In this paper, we focus on scalable training of GNNs with emphasis on resource efficiency. We show that out-of-core pipelined mini-batch training in a single machine outperforms resource-hungry multi-GPU solutions. We introduce Marius++, a system for training GNNs over billion-scale graphs. Marius++ provides disk-optimized training for GNNs and introduces a series of data organization and algorithmic contributions that 1) minimize the memory-footprint and end-to-end time required for training and 2) ensure that models learned with disk-based training exhibit accuracy similar to those fully trained in mixed CPU/GPU settings. We evaluate Marius++ against PyTorch Geometric and Deep Graph Library using seven benchmark (model, data set) settings and find that Marius++ with one GPU can achieve the same level of model accuracy up to 8 × faster than these systems when they are using up to eight GPUs. For these experiments, disk-based training allows Marius++ deployments to be up to 64 × cheaper in monetary cost than those of the competing systems.
Reducing the Vertex Cover Number via Edge Contractions

The CONTRACTION(vc) problem takes as input a graph $G$ on $n$ vertices and two integers $k$ and $d$, and asks whether one can contract at most $k$ edges to reduce the size of a minimum vertex cover of $G$ by at least $d$. Recently, Lima et al. [JCSS 2021] proved, among other results, that unlike most of the so-called blocker problems, CONTRACTION(vc) admits an XP algorithm running in time $f(d) \cdot n^{O(d)}$. They left open the question of whether this problem is FPT under this parameterization. In this article, we continue this line of research and prove the following results: 1. CONTRACTION(vc) is W[1]-hard parameterized by $k + d$. Moreover, unless the ETH fails, the problem does not admit an algorithm running in time $f(k + d) \cdot n^{o(k + d)}$ for any function $f$. In particular, this answers the open question stated in Lima et al. [JCSS 2021] in the negative. 2. It is NP-hard to decide whether an instance $(G, k, d)$ of CONTRACTION(vc) is a yes-instance even when $k = d$, hence enhancing our understanding of the classical complexity of the problem. 3. CONTRACTION(vc) can be solved in time $2^{O(d)} \cdot n^{k - d + O(1)}$. This XP algorithm improves the one of Lima et al. [JCSS 2021], which uses Courcelle's theorem as a subroutine and hence, the $f(d)$-factor in the running time is non-explicit and probably very large. On the other hard, it shows that when $k=d$, the problem is FPT parameterized by $d$ (or by $k$).
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

Large Language Models have been successful in a wide variety of Natural Language Processing tasks by capturing the compositionality of the text representations. In spite of their great success, these vector representations fail to capture meaning of idiomatic multi-word expressions (MWEs). In this paper, we focus on the detection of idiomatic expressions by using binary classification. We use a dataset consisting of the literal and idiomatic usage of MWEs in English and Portuguese. Thereafter, we perform the classification in two different settings: zero shot and one shot, to determine if a given sentence contains an idiom or not. N shot classification for this task is defined by N number of common idioms between the training and testing sets. In this paper, we train multiple Large Language Models in both the settings and achieve an F1 score (macro) of 0.73 for the zero shot setting and an F1 score (macro) of 0.85 for the one shot setting. An implementation of our work can be found at https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_Shot_Learning.
On simultaneous fitting of nonlinear and linear rheology data: Preventing a false sense of certainty

Uncertainty propagates through calculations, down to molecular scales to infer microstructural features, and up to macroscopic scales with predictive flow simulations. Here we study uncertainty quantification for sequential (two-step) versus simultaneous (all at once) fitting methods with linear and weakly-nonlinear rheological data. Using an example of a combined dataset on smallamplitude oscillatory shear (SAOS) and medium-amplitude oscillatory shear (MAOS) for a linear entangled polymer melt (cis-1,4-polyisoprene), we demonstrate with a multi-mode Giesekus model how the fit parameter uncertainties are significantly under-estimated with the sequential fit because of the neglect of model parameter correlations. These results are surprising because weakly-nonlinear data is only an asymptotic step away from the linear data, yet it has significant impact on calibrating the linear model parameters. Similarly, the nonlinear parameter estimates and uncertainties are impacted by considering the linear data in a simultaneous fit. To compare multi-mode spectra of nonlinear parameters (mobility parameters αi from the Giesekus model), we derive new average measures based on moments of the spectra related to the high-frequency MAOS limit. The spectral averages are also sensitive to sequential versus simultaneous fitting. Our results reveal the importance of using simultaneous fitting for honest uncertainty quantification, even with weakly-nonlinear data. * Current affiliation: Department of Chemical and Biomolecular Engineering, University of Illinois UrbanaChampaign, Urbana, IL 61801, USA †Corresponding author: ewoldt@illinois.edu 2
Path Planning for the Dynamic UAV-Aided Wireless Systems Using Monte Carlo Tree Search

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, for the first time, we propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed time-saving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.
Dark matter in galaxy clusters: Parametric strong-lensing approach

Context. We present a parametric strong-lensing analysis of three massive galaxy clusters for which Hubble Space Telescope imaging is available, as well as spectroscopy of multiply imaged systems and galaxy cluster members. Our aim is to probe the inner shape of dark matter haloes, in particular the existence of a core. We adopted the following working hypothesis: any group- or cluster-scale dark matter clump introduced in the modelling should be associated with a luminous counterpart. We also adopted some additional well-motivated priors in the analysis, even when this degraded the quality of the ﬁt, quantiﬁed using the root mean square between the observed and model-generated images. In particular, in order to alleviate the degeneracy between the smooth underlying component and the galaxy-scale perturbers, we used the results from previous spectroscopic campaigns, which allowed us to ﬁx the mass of the galaxy-scale component. In the unimodal galaxy cluster AS1063, a core mass model is favoured over a non-core mass model, and this is also the case in the multimodal cluster MACSJ0416. In the unimodal cluster MACSJ1206, we fail to reproduce the strong-lensing constraints using a parametric approach within the adopted working hypothesis. We then successfully added a mild perturbation in the form of a superposition of B-spline potentials, which allowed us to obtain a decent ﬁt (root mean square = 0.5 ′′ ), and ﬁnally ﬁnd that a core mass model is favoured. Overall, our analysis suggest evidence for core cluster-scale dark matter haloes in these three clusters. These ﬁndings may be useful for the interpretation within alternative dark matter scenario, such as self-interacting dark matter. We propose a working hypothesis for parametric strong-lensing modelling in which the quest for the best-ﬁt model is balanced by the quest for presenting a physically motivated mass model, in particular by imposing priors.
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics

: The advent of autonomous driving and advanced driver assistance systems necessitates continuous develop-ments in computer vision for 3D scene understanding. Self-supervised monocular depth estimation, a method for pixel-wise distance estimation of objects from a single camera without the use of ground truth labels, is an important task in 3D scene understanding. However, existing methods for this task are limited to convolutional neural network (CNN) architectures. In contrast with CNNs that use localized linear operations and lose feature resolution across the layers, vision transformers process at constant resolution with a global receptive ﬁeld at every stage. While recent works have compared transformers against their CNN counterparts for tasks such as image classiﬁcation, no study exists that investigates the impact of using transformers for self-supervised monocular depth estimation. Here, we ﬁrst demonstrate how to adapt vision transformers for self-supervised monocular depth estimation. Thereafter, we compare the transformer and CNN-based architectures for their performance on KITTI depth prediction benchmarks, as well as their robustness to natural corruptions and adversarial attacks, including when the camera intrinsics are unknown. Our study demonstrates how transformer-based architecture, though lower in run-time efﬁciency, achieves comparable performance while being more robust and generalizable.