Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Growth of Halophilic Bacteria in Saline Fracture Fluids from 2.4 km below Surface in the Deep Canadian Shield

Energy derived from water-rock interactions such as serpentinization and radiolysis, among others, can sustain microbial ecosystems deep within the continental crust, expanding the habitable biosphere kilometers below the earth’s surface. Here, we describe a viable microbial community including sulfate-reducing microorganisms from one such subsurface lithoautotrophic ecosystem hosted in fracture waters in the Canadian Shield, 2.4 km below the surface in the Kidd Creek Observatory in Timmins, Ontario. The ancient groundwater housed in fractures in this system was previously shown to be rich in abiotically produced hydrogen, sulfate, methane, and short-chain hydrocarbons. We have further investigated this system by collecting filtered water samples and deploying sterile in situ biosampler units into boreholes to provide an attachment surface for the actively growing fraction of the microbial community. Scanning electron microscopy, energy-dispersive X-ray spectroscopy, and DNA sequencing analyses were undertaken to classify the recovered microorganisms. Moderately halophilic taxa (e.g., Marinobacter, Idiomarina, Chromohalobacter, Thiobacillus, Hyphomonas, Seohaeicola) were recovered from all sampled boreholes, and those boreholes that had previously been sealed to equilibrate with the fracture water contained taxa consistent with sulfate reduction (e.g., Desulfotomaculum) and hydrogen-driven homoacetogenesis (e.g., Fuchsiella). In contrast to this “corked” borehole that has been isolated from the mine environment for approximately 7 years at the time of sampling, we sampled additional open boreholes. The waters flowing freely from these open boreholes differ from those of the long-sealed borehole. This work complements ongoing efforts to describe the microbial diversity in fracture waters at Kidd Creek in order to better understand the processes shaping life in the deep terrestrial subsurface. In particular, this work demonstrates that anaerobic bacteria and known halophilic taxa are present and viable in the fracture waters presently outflowing from existing boreholes. Major cations and anions found in the fracture waters at the 2.4 km level of the mine are also reported.
Locating the Origin of Femoral Maltorsion Using 3D Volumetric Technology—The Hockey Stick Theory

Background: The origin of femoral maltorsion is often unknown. However, defining the origin of the rotation of the femoral maltorsion can be useful for establishing the most suitable point to do an external derotational osteotomy. Previous studies have not considered the femoral diaphysis in their investigations of the origin of the deformity. The study of the whole morphology of the femur with 3D volumetric tools, including the femoral diaphysis can contribute to a better understanding of the behavior of femoral maltorsion. Methods: An atypical case of unilateral femoral anteversion was selected. Both femurs were used to obtain 3D bio-models. The mirror image of the asymptomatic side was obtained and overlapped with the symptomatic femur. The Hausdorff–Besicovitch method was used to evaluate the morphologic discrepancies (in mm) between the two femurs in three zones: (1) the femoral neck, (2) the proximal diaphysis, and (3) the distal diaphysis. The differences between the two femurs were analyzed and its correlation was statistically defined using a lineal regression model. Results: The deformity in the distal diaphysis increased from the supracondylar area until the apex of the antecurvatum angle (R2 = 0.91) and then decreased until the base of the femoral neck (R2 = (−0.83)), to finally increase significantly in the femoral neck area (R2 = 0.87). All of the correlations were statistically significant (p-value ˂ 0.001). Conclusion: The femoral maltorsion originates in the supracondylar area and its rotational axis is the longitudinal axis of the femoral diaphysis. Even though the deformity affects the femoral diaphysis, its clinical relevance is much higher in the femoral neck since the rotational axis passes through its base. Thus, the osteotomy can be conducted along all of the femoral diaphysis as long as it is done perpendicular to it.
Texture Synthesis Repair of RealSense D435i Depth Images with Object-Oriented RGB Image Segmentation

A depth camera is a kind of sensor that can directly collect distance information between an object and the camera. The RealSense D435i is a low-cost depth camera that is currently in widespread use. When collecting data, an RGB image and a depth image are acquired simultaneously. The quality of the RGB image is good, whereas the depth image typically has many holes. In a lot of applications using depth images, these holes can lead to serious problems. In this study, a repair method of depth images was proposed. The depth image is repaired using the texture synthesis algorithm with the RGB image, which is segmented through a multi-scale object-oriented method. The object difference parameter is added to the process of selecting the best sample block. In contrast with previous methods, the experimental results show that the proposed method avoids the error filling of holes, the edge of the filled holes is consistent with the edge of RGB images, and the repair accuracy is better. The root mean square error, peak signal-to-noise ratio, and structural similarity index measure from the repaired depth images and ground-truth image were better than those obtained by two other methods. We believe that the repair of the depth image can improve the effects of depth image applications.
Predicting Factors of Functional Outcome in Patients with Acute Ischemic Stroke Admitted to Neuro-Intensive Care Unit—A Prospective Cohort Study

Although thrombectomy is beneficial for most stroke patients with large vessel occlusion (LVO), it has added new issues in acute management due to intensive care support. In this prospective cohort study, we described the patients admitted to our neuro-intensive care unit (NICU) after thrombectomy in order to assess factors linked to functional outcomes. The outcome was independency assessed for stroke patients consecutively admitted to NICU for an ischemic stroke due to LVO of the anterior cerebral circulation that underwent intra-arterial mechanical thrombectomy (IAMT), either in combination with intravenous thrombolysis (IVT) in eligible patients or alone in patients with contraindications for IVT. Overall, 158 patients were enrolled. IVT (odds ratio (OR), 3.78; 95% confidence interval (CI), 1.20–11.90; p = 0.023) and early naso-gastric tube removal (OR, 3.32; 95% CI, 1.04–10.59 p = 0.042) were associated with good outcomes, whereas a high baseline National Institutes of Health Stroke Scale (NIHSS) score (OR, 0.72 for each point of increase; 95% CI, 0.61–0.85; p < 0.001) was a predictor of poor outcomes at 3 months. Older age (OR, 0.95 for each year of increase; 95% CI, 0.92–0.99; p = 0.020) and hemorrhagic transformation (OR, 0.31; 95% CI, 0.11–0.84; p = 0.022) were predictors of poor outcomes after IAMT, whereas a modified Treatment in Cerebral Infarction (mTICI) score of 2b/3 was a predictor of good outcomes (OR, 7.86; 95% CI, 1.65–37.39; p = 0.010) at 6 months. Our results show that acute stroke patients with LVO who require NICU management soon after IAMT may show specific clinical factors influencing short- and long-term neurologic independency.
Melatonin Rescues the Dendrite Collapse Induced by the Pro-Oxidant Toxin Okadaic Acid in Organotypic Cultures of Rat Hilar Hippocampus

The pro-oxidant compound okadaic acid (OKA) mimics alterations found in Alzheimer’s disease (AD) as oxidative stress and tau hyperphosphorylation, leading to neurodegeneration and cognitive decline. Although loss of dendrite complexity occurs in AD, the study of this post-synaptic domain in chemical-induced models remains unexplored. Moreover, there is a growing expectation for therapeutic adjuvants to counteract these brain dysfunctions. Melatonin, a free-radical scavenger, inhibits tau hyperphosphorylation, modulates phosphatases, and strengthens dendritic arbors. Thus, we determined if OKA alters the dendritic arbors of hilar hippocampal neurons and whether melatonin prevents, counteracts, or reverses these damages. Rat organotypic cultures were incubated with vehicle, OKA, melatonin, and combined treatments with melatonin either before, simultaneously, or after OKA. DNA breaks were assessed by TUNEL assay and nuclei were counterstained with DAPI. Additionally, MAP2 was immunostained to assess the dendritic arbor properties by the Sholl method. In hippocampal hilus, OKA increased DNA fragmentation and reduced the number of MAP2(+) cells, whereas melatonin protected against oxidation and apoptosis. Additionally, OKA decreased the dendritic arbor complexity and melatonin not only counteracted, but also prevented and reversed the dendritic arbor retraction, highlighting its role in post-synaptic domain integrity preservation against neurodegenerative events in hippocampal neurons.
Age, gender, and racial/ethnic differences in the association of triclocarban with adulthood obesity using NHANES 2013–2016

Abstract This study examined the association between triclocarban and obesity among US adults and compared the pattern of this association across age, gender, and racial/ethnic groups. Study found triclocarban to be associated with obesity (OR: OR:1.123 95% CI: 1.046, 1.205) and this association remained among women (OR:1.14 95% CI: 1.031, 1.261). Study participants aged 60 years and older were more likely to be overweight (OR:1.131 95% CI: 1.022 1.251) and obese (OR:1.192 95% CI: 1.079, 1.317) when compared to other age groups. Likewise, non-Hispanic whites (OR:1.126 95% CI: 1.003, 1.263) and “other race including multi-racial” (OR:1.431 95% CI: 1.219, 1.679) were more likely to be obese when compared to other racial/ethnic groups. In conclusion, triclocarban is associated with obesity among US adults and there is evidence of gender, age, and racial/ethnicity differences in the association.
Predicting Stress in Teens from Wearable Device Data Using Machine Learning Methods

Stress management is a pervasive issue in the modern high schooler's life. Despite many efforts to support adolescents' mental well-being, teenagers often fail to recognize signs of high stress and anxiety until their emotions have escalated. Being able to identify early signs of these intense emotional states and predict their onset using physiological signals collected passively in real-time could help teenagers improve their awareness of their emotional wellbeing and take a more proactive approach to managing their emotions. To evaluate the potential of this approach, we collected data from high schoolers with Empatica E4 wearable health monitors (wristband) while they were living their daily lives. The data consisted of stressful event reports and physiological markers over the course of 4 weeks. We developed a random forest model and a support vector machine model and systematically assessed their performance in terms of predicting the onset of stress events and identifying physiological signals of stress. The models showed strong performance in terms of these measures and provided insights on physiological indicators of adolescent stress.
Performance of Saliva Specimens for the Molecular Detection of SARS-CoV-2 in the Community Setting: Does Sample Collection Method Matter?

Data on the performance of saliva specimens for diagnosing coronavirus disease 2019 (COVID-19) in ambulatory patients are scarce and inconsistent. We assessed saliva-based specimens for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcriptase PCR (RT-PCR) in the community setting and compared three different collection methods. ABSTRACT Data on the performance of saliva specimens for diagnosing coronavirus disease 2019 (COVID-19) in ambulatory patients are scarce and inconsistent. We assessed saliva-based specimens for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcriptase PCR (RT-PCR) in the community setting and compared three different collection methods. This prospective study was conducted in three primary care centers. RT-PCR was performed on paired nasopharyngeal swabs (NPS) and saliva samples collected from outpatients with a broad clinical spectrum of illness. To assess differences in collection methods, saliva specimens were obtained in a different way in each of the participating centers: supervised collection (SVC), oropharyngeal washing (OPW), and self-collection (SC). Pairs of NPS and saliva samples from 577 patients (median age, 39 years; 44% men; 42% asymptomatic) were collected and tested, and 120 (20.8%) gave positive results. The overall agreement with NPS results and kappa coefficients (κ) for saliva samples obtained by SVC, OPW, and SC were 95% (κ = 0.85), 93.4% (κ = 0.76), and 93.3% (κ = 0.76), respectively. The sensitivities (95% confidence intervals [95% CI]) of the saliva specimens ranged from 86% (72.6% to 93.7%) for SVC to 66.7% (50.4% to 80%) for SC samples. Sensitivity was higher for samples with lower cycle threshold (CT) values. The best RT-PCR performance was observed for SVC, with sensitivities (95% CI) of 100% (85.9% to 100%) in symptomatic individuals and 88.9% (50.7% to 99.4%) in asymptomatic individuals at CT values of ≤30. We conclude that saliva is an acceptable specimen for the detection of SARS-CoV-2 in the community setting. Specimens collected under supervision perform comparably to NPS and can effectively identify individuals at higher risk of transmission under real-life conditions.
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages. Recent work has successfully trained neural IR systems using only supervised question answering (QA) examples from open-domain datasets. However, despite impressive performance on Wikipedia, neural IR lags behind traditional term matching approaches such as BM25 in more specific and specialized target domains such as COVID-19. Furthermore, given little or no labeled data, effective adaptation of QA systems can also be challenging in such target domains. In this work, we explore the application of synthetically generated QA examples to improve performance on closed-domain retrieval and MRC. We combine our neural IR and MRC systems and show significant improvements in end-to-end QA on the CORD-19 collection over a state-of-the-art open-domain QA baseline.
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning

Safeguard functions such as those provided by advanced emergency braking (AEB) can provide another layer of safety for autonomous vehicles (AV). A smart safeguard function should adapt the activation conditions to the driving policy, to avoid unnecessary interventions as well as improve vehicle safety. This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function. The collision avoidance strategy is designed in a reinforcement learning framework, obtained by Monte-Carlo Tree Search (MCTS). It can learn from past collisions and manipulate both braking and steering in stochastic traffics. The driving-policy adaptive activation function should dynamically assess current driving policy risk and kick in when an urgent threat is detected. To generate this activation function, MCTS' exploration and rollout modules are designed to fully evaluate the AV's current driving policy, and then explore other safer actions. In this study, the DPAS is validated with two typical highway-driving policies. The results are obtained through and 90,000 times in the stochastic and aggressive simulated traffic. The results are calibrated by naturalistic driving data and show that the proposed safeguard reduces the collision rate significantly without introducing more interventions, compared with the state-based benchmark safeguards. In summary, the proposed safeguard leverages the learning-based method in stochastic and emergent scenarios and imposes minimal influence on the driving policy.
Classification of Multimodal Hate Speech - The Winning Solution of Hateful Memes Challenge

Hateful Memes is a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. Difficult examples are added to the dataset to make it hard to rely on unimodal signals, which means only multimodal models can succeed. According to Kiela,the state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy) on Hateful Memes. I propose a new model that combined multimodal with rules, which achieve the first ranking of accuracy and AUROC of 86.8% and 0.923 respectively. These rules are extracted from training set, and focus on improving the classification accuracy of difficult samples.
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms

We provide new perspectives and inference algorithms for Maximum Entropy (MaxEnt) Inverse Reinforcement Learning (IRL), which provides a principled method to find a most non-committal reward function consistent with given expert demonstrations, among many consistent reward functions. We first present a generalized MaxEnt formulation based on minimizing a KL-divergence instead of maximizing an entropy. This improves the previous heuristic derivation of the MaxEnt IRL model (for stochastic MDPs), allows a unified view of MaxEnt IRL and Relative Entropy IRL, and leads to a model-free learning algorithm for the MaxEnt IRL model. Second, a careful review of existing inference algorithms and implementations showed that they approximately compute the marginals required for learning the model. We provide examples to illustrate this, and present an efficient and exact inference algorithm. Our algorithm can handle variable length demonstrations; in addition, while a basic version takes time quadratic in the maximum demonstration length L, an improved version of this algorithm reduces this to linear using a padding trick. Experiments show that our exact algorithm improves reward learning as compared to the approximate ones. Furthermore, our algorithm scales up to a large, real-world dataset involving driver behaviour forecasting. We provide an optimized implementation compatible with the OpenAI Gym interface. Our new insight and algorithms could possibly lead to further interest and exploration of the original MaxEnt IRL model.
CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction

Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has been made in estimating hand and object separately with deep learning methods, simultaneous HO pose estimation and contact modeling has not yet been fully explored. In this paper, we present an explicit contact representation namely Contact Potential Field (CPF), and a learning-fitting hybrid framework namely MIHO to Modeling the Interaction of Hand and Object. In CPF, we treat each contacting HO vertex pair as a spring-mass system. Hence the whole system forms a potential field with minimal elastic energy at the grasp position. Extensive experiments on the two commonly used benchmarks have demonstrated that our method can achieve state-of-the-art in several reconstruction metrics, and allow us to produce more physically plausible HO pose even when the ground-truth exhibits severe interpenetration or disjointedness. Our code is available at https://github.com/lixiny/CPF.
Refining Deep Generative Models via Wasserstein Gradient Flows

Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data. However, generation quality is generally inconsistent for any given model and can vary dramatically between samples. We introduce Discriminator Gradient flow (DGflow), a new technique that improves generated samples via the gradient flow of entropy-regularized f-divergences between the real and the generated data distributions. The gradient flow takes the form of a non-linear Fokker-Plank equation, which can be easily simulated by sampling from the equivalent McKean-Vlasov process. By refining inferior samples, our technique avoids wasteful sample rejection used by previous methods (DRS & MH-GAN). Compared to existing works that focus on specific GAN variants, we show our refinement approach can be applied to GANs with vector-valued critics and even other deep generative models such as VAEs and Normalizing Flows. Empirical results on multiple synthetic, image, and text datasets demonstrate that DGflow leads to significant improvement in the quality of generated samples for a variety of generative models, outperforming the state-of-the-art Discriminator Optimal Transport (DOT) and Discriminator Driven Latent Sampling (DDLS) methods.
Evolution of the Nonpotential Magnetic Field in the Solar Active Region 12673 Based on a Nonlinear Force-free Modeling

Active region (AR) 12673 has produced many M-class and several X-class flares, one of which being an X9.3 flare, which is recorded as the largest solar flare in solar cycle 24. We studied the evolution of the three-dimensional flare-productive magnetic field within AR 12673, using a time series of nonlinear force-free field extrapolations of every 12 hr from 2017 September 4 00:00 UT to 6 00:00 UT. Our analysis found that three magnetic flux ropes (MFRs) formed by September 4, one of which produced the X9.3 flare on September 6. One MFR has positive magnetic twist, which is a different sign from the other two MFRs. Since the temporal evolution of the magnetic flux of the MFR accumulating the positive twist is consistent with the profile of the GOES X-ray flux well observed from September 4 to 6, we suggest that the formation of the MFR having the positive twist is closely related to the occurrence of the M-class flares, including an M5.5 flare. We further found a magnetic null in the magnetic field surrounding the MFRs, in particular above the MFR having positive twist before the M5.5 flare, which is the largest M-flare observed during this period. By comparing with Atmospheric Imaging Assembly 1600 Å images, we found that the footpoints of the overlying field lines are anchored to the area where the brightening was initially observed. Therefore, we suggest that reconnection induced by the torus instability of the positively twisted MFR at the null possibly drove the M5.5 flare.