Miracle-dz/UniFAR-FTUs huggingface dataset:
ClaID: a Rapid Method of Clade-Level Identification of the Multidrug Resistant Human Fungal Pathogen Candida auris

C. auris was first isolated in Japan in 2009 as an antifungal drug-susceptible pathogen causing localized infections. Within a decade, it simultaneously evolved in different parts of the world as distinct clades exhibiting resistance to antifungal drugs at varying levels. ABSTRACT Candida auris, the multidrug-resistant human fungal pathogen, emerged as four major distinct geographical clades (clade 1–clade 4) in the past decade. Though isolates of the same species, C. auris clinical strains exhibit clade-specific properties associated with virulence and drug resistance. In this study, we report the identification of unique DNA sequence junctions by mapping clade-specific regions through comparative analysis of whole-genome sequences of strains belonging to different clades. These unique DNA sequence stretches are used to identify C. auris isolates at the clade level in subsequent in silico and experimental analyses. We develop a colony PCR-based clade-identification system (ClaID), which is rapid and specific. In summary, we demonstrate a proof-of-concept for using unique DNA sequence junctions conserved in a clade-specific manner for the rapid identification of each of the four major clades of C. auris. IMPORTANCE C. auris was first isolated in Japan in 2009 as an antifungal drug-susceptible pathogen causing localized infections. Within a decade, it simultaneously evolved in different parts of the world as distinct clades exhibiting resistance to antifungal drugs at varying levels. Recent studies hinted the mixing of isolates belonging to different geographical clades in a single location, suggesting that the area of isolation alone may not indicate the clade status of an isolate. In this study, we compared the genomes of representative strains of the four major clades to identify clade-specific sequences, which were then used to design clade-specific primers. We propose the utilization of whole genome sequence data to extract clade-specific sequences for clade-typing. The colony PCR-based method employed can rapidly distinguish between the four major clades of C. auris, with scope for expanding the panel by adding more primer pairs.
The influence of exposure approaches to in vitro lung epithelial barrier models to assess engineered nanomaterial hazard

Abstract Exposure to engineered nanomaterials (ENM) poses a potential health risk to humans through long-term, repetitive low-dose exposures. Currently, this is not commonplace within in vitro lung cell cultures. Therefore, the purpose of this study was to consider the optimal exposure approach toward determining the stability, sensitivity and validity of using in vitro lung cell mono- and co-cultures to determine ENM hazard. A range of exposure scenarios were conducted with DQ12 (previously established as a positive particle control) (historic and re-activated), TiO2 (JRC NM–105) and BaSO4 (JRC NM–220) on both monocultures of A549 cells as well as co-cultures of A549 cells and differentiated THP-1 cells. Cell cultures were exposed to either a single, or a repeated exposure over 24, 48- or 72-hours at in vivo extrapolated concentrations of 0–5.2 µg/cm2, 0-6 µg/cm2 and 0-1µg/cm2. The focus of this study was the pro-inflammatory, cytotoxic and genotoxic response elicited by these ENMs. Exposure to DQ12 caused pro-inflammatory responses after 48 hours repeat exposures, as well as increases in micronucleus frequency. Neither TiO2 nor BaSO4 elicited a pro-inflammatory response at this time point. However, there was induction of IL-6 after 24 hours TiO2 exposure. In conclusion, it is important to consider the appropriateness of the positive control implemented, the cell culture model, the time of exposure as well as the type of exposure (bolus or fractionated) before establishing if an in vitro model is appropriate to determine the level of response to the specific ENM of interest.
Contrast With Compression Therapy Enhances Muscle Function Recovery and Attenuates Glycogen Disruption After Exercise

Background: Exercise-associated muscle damage (EAMD) temporally impairs muscle function and intramuscular glycogen storage. Contrast with compression (CwC) therapy provides localized EAMD treatment with minimal changes in core/tissue temperature that can impair glycogen resynthesis. Hypothesis: CwC will enhance the recovery of strength, power, and joint mobility, reduce markers of EAMD, and attenuate the disruption of glycogen storage observed after damaging exercise. Study Design: Randomized controlled trial with crossover design. Level of Evidence: Level 2. Methods: Ten men completed 2 bouts of eccentric elbow flexor exercise, separated by 1 week, using contralateral arms. After each bout, participants received either CwC therapy (at 0, 24, and 48 h postexercise) or no therapy with intervention order and limb randomly assigned. Prior to (pre-exercise) and 1, 24, 48, and 72 h after each exercise bout, muscular strength, muscular power, intramuscular glycogen, creatine kinase, muscle thickness, muscle soreness, pressure pain threshold, active elbow flexion, passive elbow extension, and dietary intake were assessed. Comparisons were made between conditions over time (interaction effects) using separate repeated-measures analyses of variance/multivariate analyses of variance and effect sizes (Cohen d) to describe treatment effect at each time point. Results: Significant interaction effects were observed for muscular strength (d = 0.67-1.12), muscular power (d = 0.20-0.65), intramuscular glycogen (d = 0.29-0.81), creatine kinase (d = 0.01-0.96), muscle thickness (d = 0.35-0.70), muscle soreness (d = 0.18-0.85), and active elbow flexion (d = 0.65-1.17) indicating a beneficial effect of CwC over time (P ≤ 0.05). In contrast, no significant interaction effect was observed for pressure pain threshold or passive elbow extension (P > 0.05). Conclusion: These results support the use of CwC for the recovery of muscle function after damaging exercise in male patients and indicate that CwC attenuates, but does not remove, the disruption of intramuscular glycogen stores observed after intense eccentric exercise. Clinical Relevance: Glycolysis-dependent athletes may benefit from CwC therapy after training/competition that causes EAMD.
“We’re in the same book, but we’re in different parts of the book”: Dominant and sub-group narratives of life following a Down syndrome determination

Parents of children with Down syndrome (DS) were interviewed about if their experiences raising their children matched their initial assumptions about parenting a child with DS. A dominant narrative was identified, wherein most parents described initially having negative assumptions, which did not come to fruition; parenting their child was not very different from parenting a typical child. There was also a sub-group of participants who disputed the dominant narrative; parenting their child was challenging and the dominant narrative marginalizes that experience. The findings indicate that although for many parents, having a child with DS is like “taking the scenic route” (i.e., it involves a few more hurdles, but often more rewards), this framing is not always applicable. Therefore, health care providers and support organizations should promote a narrative that encompasses the diversity of parenting a child with DS.
Computational profiling of natural compounds as promising inhibitors against the spike proteins of SARS‐CoV‐2 wild‐type and the variants of concern, viral cell‐entry process, and cytokine storm in COVID‐19

The continuous spread and evolution of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), and the rapid surge in infection cases in the coronavirus disease 2019 (COVID‐19) evoke a dire need for effective therapeutics. In this study, we explored the inhibitory potential of a library of 605 phytocompounds, selected from Indian medicinal plants with reported antiviral and anti‐inflammatory activities, against the receptor‐binding domain of spike proteins of the SARS‐CoV‐2 wild‐type and the variants of concern, including variants B.1. 1.7 (Alpha), B.1. 351 (Beta), P.1 (Gamma), B.1. 617.2 (Delta), and B.1. 1.529 (Omicron). Our approach was based on extensive molecular docking, assessment of drug‐likeness, and robust molecular dynamics simulations. We also identified promising inhibitory candidates against the host (human) proteins associated with SARS‐CoV‐2 spike activation and attachment, namely, ACE2 receptor, proteases TMPRSS2 and CTSL, and the endocytic regulator AAK1. In addition, we screened promising inhibitory compounds against the human proinflammatory cytokines‐ IL‐6, IL‐1β, TNF‐α, and IFN‐γ, that are associated with the adverse cytokine storm in COVID‐19 patients. Our analysis returned an encouraging list of promising inhibitory candidates that includes: abietatriene against the spike proteins of the SARS‐CoV‐2 wild‐type and the variants of concern; taraxerol against the human ACE2, CTSL and TNF‐α; β‐amyrin against the human TMPRSS2; cynaroside against the human AAK1 and IL‐1β; and friedelin against the human IL‐6 and IFN‐γ. Our findings provide substantial evidence for the inhibitory potential of these compounds and encourage further in vitro and in vivo studies to validate their use as safe and effective therapeutics against COVID‐19.
Reduced asthma morbidity during COVID-19 in minority children: is medication adherence a reason?

Abstract Objectives Asthma control improved during the COVID-19 pandemic. This study examined objectively measured medication adherence, asthma morbidity and quality of life (QoL) outcomes in Black and Latinx children by month for January-June 2019 (pre-COVID) compared to January-June 2020 (including first peak of COVID). Methods Secondary analyses of 94 children with asthma (ages 10–17 years, 64% Latinx, 36% Black) and their caregivers assigned to the comparison group of a longitudinal RCT intervention trial. Outcomes included mean aggregate electronic adherence for controller medications, oral steroid bursts, acute healthcare utilization, caregiver asthma QoL, and the Asthma Control Test. Repeated measures analyses were conducted due to multiple observations. Results Adherence to controller medications declined 48% from 2019 to 2020 (LS Mean = 33.9% vs. 17.6%, p=.0004, f=.92) with levels reaching a low in May 2020. A reduction in steroid bursts was observed over the same timeframe, 1.29 vs. 0.61, p = 0.006, f=. 63. Caregiver QoL increased from 2019 to 2020 on total score (5.18 vs. 5.85, p = 0.002, f=.72), activity limitations (5.04 vs. 5.95), and emotional functioning (5.26 vs. 5.80). Although not statistically significant, a clinically meaningful 62% reduction in acute healthcare visits (p = 0.15) was reported in 2020. Children reported better asthma control (OR = 1.47, 95% CI 1.24, 1.73, p < 0.0001) in 2020 versus 2019 driven by improvements from May to June 2020. Conclusions Decreased asthma morbidity in minority children during COVID was coupled with decreased adherence to controller medications. This observed decrease in morbidity is not explained by improvements in adherence.
Caricatured facial movements enhance perception of emotional facial expressions

Although faces “in the wild” constantly undergo complicated movements, humans adeptly perceive facial identity and expression. Previous studies, focusing mainly on identity, used photographic caricature to show that distinctive form increases perceived dissimilarity. We tested whether distinctive facial movements showed similar effects, and we focussed on both perception of expression and identity. We caricatured the movements of an animated computer head, using physical motion metrics extracted from videos. We verified that these “ground truth” metrics showed the expected effects: Caricature increased physical dissimilarity between faces differing in expression and those differing in identity. Like the ground truth dissimilarity, participants’ dissimilarity perception was increased by caricature when faces differed in expression. We found these perceived dissimilarities to reflect the “representational geometry” of the ground truth. However, neither of these findings held for faces differing in identity. These findings replicated across two paradigms: pairwise ratings and multiarrangement. In a final study, motion caricature did not improve recognition memory for identity, whether manipulated at study or test. We report several forms of converging evidence for spatiotemporal caricature effects on dissimilarity perception of different expressions. However, more work needs to be done to discover what identity-specific movements can enhance face identification.
Concept Embedding Analysis: A Review

Deep neural networks (DNNs) have found their way into many applications with potential impact on the safety, security, and fairness of human-machine-systems. Such require basic understanding and sufficient trust by the users. This motivated the research field of explainable artificial intelligence (XAI), i.e. finding methods for opening the"black-boxes"DNNs represent. For the computer vision domain in specific, practical assessment of DNNs requires a globally valid association of human interpretable concepts with internals of the model. The research field of concept (embedding) analysis (CA) tackles this problem: CA aims to find global, assessable associations of humanly interpretable semantic concepts (e.g., eye, bearded) with internal representations of a DNN. This work establishes a general definition of CA and a taxonomy for CA methods, uniting several ideas from literature. That allows to easily position and compare CA approaches. Guided by the defined notions, the current state-of-the-art research regarding CA methods and interesting applications are reviewed. More than thirty relevant methods are discussed, compared, and categorized. Finally, for practitioners, a survey of fifteen datasets is provided that have been used for supervised concept analysis. Open challenges and research directions are pointed out at the end.
Towards Implicit Text-Guided 3D Shape Generation

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable of producing high-fidelity shapes with colors that match the given text description. This work has several technical contributions. First, we decouple the shape and color predictions for learning features in both texts and shapes, and propose the word-level spatial transformer to correlate word features from text with spatial features from shape. Also, we design a cyclic loss to encourage consistency between text and shape, and introduce the shape IMLE to diversify the generated shapes. Further, we extend the framework to enable text-guided shape manipulation. Extensive experiments on the largest existing text-shape benchmark [10] manifest the superiority of this work. The code and the models are available at https://github.com/liuzhengzhe/Towards-Implicit-Text-Guided-Shape-Generation.
Dynamics and Stability of Flow through Abdominal Aortic Aneurysms

The main objective of this thesis is to characterise the flow fields observed in an ab- dominal aortic aneurysm (AAA) under different physiological conditions during its progressive enlargement. An axisymmetric AAA, modeled as an inflation of gaus- sian shape on a vessel of circular cross-section, is considered in the present study. This means that the results are more significant for the early stages of growth of an AAA. The model AAA is characterized by a maximum height H and width W , made dimensionless by the upstream vessel diameter. To begin with, the flow char- acteristics in AAAs are investigated using steady flows. The global linear stability of the base flows is analysed by determining the eigenfrequencies and eigenfunctions of small-amplitude perturbations. In order to understand the instability mechanisms, the energy transfer between the base flow and the perturbations is computed. The flow in relatively shallow aneurysms (of relatively large width) become unstable by the lift-up mechanism and have a perturbation flow which is characterized by sta- tionary, growing modes. More localized aneurysms (with relatively small width) become unstable at larger Reynolds numbers, presumably by an elliptic instability mechanism; in this case the perturbation flow is characterized by oscillatory modes. For the case of pulsatile flows, two types of physiological flowrate waveforms are considered in our study, corresponding to rest and exercise conditions. The flows are observed to remain attached to the walls during the systolic phase, with flow separation generally observed during the deceleration after the peak systole. Dur- ing this phase, the vorticity is found to roll-up into a vortex at the proximal end. This vortex enlarges with time and impinges at the downstream end of the AAA, resulting in large spatial gradients of wall shear stress (WSS) along the wall, which are not found in the healthy case. The flow conditions are observed to vary signifi- cantly with Womersley (Wo) and Reynolds (Re) numbers, with the flow remaining attached to the walls for longer times, as the Womersley number Wo increases. The principal effect of increasing Re is that the primary vortex formed after peak systole is stronger. Clinically relevant flow characteristics of aneurysmal flow, i.e. detache- ment of flow and impingement on the distal end, the presence of low oscillatory WSS within the AAA, are observed even for very shallow aneurysms. For deep aneurysms, multiple vortices are observed throughout the cycle within the AAA cavity. Stability analysis of pulsatile base flows reveals that the maximum values of the perturbations are observed near the distal end of the AAA. However, they are not entirely confined to the AAA cavity and extend downstream, implying that once an AAA is formed, the disturbed flow conditions spread even to the unde- formed arterial walls downstream of the AAA. Finally, by considering two equiva- lent AAA shapes modeled by a sinusoidal and a gaussian function, the sensitivity of these results with respect to the details of the geometry has been established.
Comparative performance of novel nodal-to-edge finite elements over conventional nodal element for electromagnetic analysis

In nodal-based finite element method (FEM), degrees of freedom are associated with the nodes of the element whereas, for edge FEM, degrees of freedom are assigned to the edges of the element. Edge element is constructed based on Whitney spaces. Nodal elements impose both tangential and normal continuity of vector or scalar fields across interface boundaries. But in edge elements, only tangential continuity is imposed across interface boundaries, which is consistent with electromagnetic field problems. Therefore the required continuities in the electromagnetic analysis are directly obtained with edge elements whereas in nodal elements they are attained through potential formulations. Hence, while using edge elements, field variables are directly calculated but with nodal elements, post-processing is required to obtain the field variables from the potentials. Here, we present the finite element formulations with the edge element as well as with nodal elements. Thereafter, we have demonstrated the relative performances of different nodal and edge elements through a series of examples. All possible complexities like curved boundaries, non-convex domains, sharp corners, non-homogeneous domains have been addressed in those examples. The robustness of edge elements in predicting the singular eigen values for the domains with sharp edges and corners is evident in the analysis. A better coarse mesh accuracy has been observed for all the edge elements as compared to the respective nodal elements. Edge elements are also not susceptible to mesh distortion.
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100

Training convolutional neural networks (CNNs) with a strict Lipschitz constraint under the l2 norm is useful for provable adversarial robustness, interpretable gradients and stable training. While 1-Lipschitz CNNs can be designed by enforcing a 1-Lipschitz constraint on each layer, training such networks requires each layer to have an orthogonal Jacobian matrix (for all inputs) to prevent the gradients from vanishing during backpropagation. A layer with this property is said to be Gradient Norm Preserving (GNP). In this work, we introduce a procedure to certify the robustness of 1-Lipschitz CNNs by relaxing the orthogonalization of the last linear layer of the network that significantly advances the state of the art for both standard and provable robust accuracies on CIFAR-100 (gains of 4.80% and 4.71%, respectively). We further boost their robustness by introducing (i) a novel Gradient Norm preserving activation function called the Householder activation function (that includes every GroupSort activation) and (ii) a certificate regularization. On CIFAR-10, we achieve significant improvements over prior works in provable robust accuracy (5.81%) with only a minor drop in standard accuracy (−0.29%). Code for reproducing all experiments in the paper is available at https://github.com/singlasahil14/SOC.
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism to select a possibly clean subset of data. Next, an off-the-shelf semi-supervised learning method is used for training where rejected samples are treated as unlabeled data. Our comprehensive analysis shows that current selection methods disproportionately select samples from easy (fast learnable) classes while rejecting those from relatively harder ones. This creates class imbalance in the selected clean set and in turn, deteriorates performance under high label noise. In this work, we propose UNICON, a simple yet effective sample selection method which is robust to high label noise. To address the disproportionate selection of easy and hard samples, we introduce a Jensen-Shannon divergence based uniform selection mechanism which does not require any probabilistic modeling and hyperparameter tuning. We complement our selection method with contrastive learning to further combat the memorization of noisy labels. Extensive experimentation on multiple benchmark datasets demonstrates the effectiveness of UNICON; we obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate. Our code is publicly available.11https://github.com/nazmul-karim170/UNICON-Noisy-Label
Optimistic Online Convex Optimization in Dynamic Environments

In this paper, we study the optimistic online convex optimization problem in dynamic environments. Existing works have shown that Ader enjoys an $O\left(\sqrt{\left(1+P_T\right)T}\right)$ dynamic regret upper bound, where $T$ is the number of rounds, and $P_T$ is the path length of the reference strategy sequence. However, Ader is not environment-adaptive. Based on the fact that optimism provides a framework for implementing environment-adaptive, we replace Greedy Projection (GP) and Normalized Exponentiated Subgradient (NES) in Ader with Optimistic-GP and Optimistic-NES respectively, and name the corresponding algorithm ONES-OGP. We also extend the doubling trick to the adaptive trick, and introduce three characteristic terms naturally arise from optimism, namely $M_T$, $\widetilde{M}_T$ and $V_T+1_{L^2\rho\left(\rho+2 P_T\right)\leqslant\varrho^2 V_T}D_T$, to replace the dependence of the dynamic regret upper bound on $T$. We elaborate ONES-OGP with adaptive trick and its subgradient variation version, all of which are environment-adaptive.