Miracle-dz/UniFAR-FTUs huggingface dataset:
Inactivated Poliovirus Vaccine Induces Antibodies that Inhibit RNA Synthesis of SARS-CoV-2: An open-label, pre-post vaccine clinical trial

Background: Poliovirus vaccination induces an adaptive humoral immune response; in vitro experiments show polio-immune sera contain antibodies against the poliovirus RNA transcriptase that cross-react with SARS-CoV-2. While structural similarities between poliovirus and SARS-CoV-2 could have major implications for the COVID-19 response worldwide, polio-induced immune responses against SARS-CoV-2 have not been confirmed in prospective clinical trials. Objective: To evaluate whether immune sera from adults who recently received inactivated poliovirus vaccination (IPV) can block SARS-CoV-2's ability to synthesize RNA. Intervention: IPV intramuscular injection. Measurements: Pre-inoculation and 4-weeks post-inoculation sera were tested for anti-3Dpol (RNA-dependent RNA polymerase, RdRp) antibodies using enzyme-linked immunosorbent assays (ELISA). To assess IPV's ability to induce antibodies that inhibit SARS-CoV-2 RNA synthesis, immune-based detection assays tested RdRp enzymatic activity in polio-immune sera. Results: 298 of the 300 enrolled participants completed both on-site visits. Comparing pre-inoculation to 4-week samples, 85.2% of participants demonstrated an increase in anti-3Dpol antibodies against RdRp proteins. Among tested post-inoculation samples, 94.4% demonstrated inhibition of SARS-CoV-2 RNA synthesis. Few inoculation-related side effects were reported (2.0%), all were minor. Limitations: Participants were not systematically tested for COVID-19, though known exposures were reported and positive results (1.7%) were documented. Conclusion: IPV can induce antibodies that inhibit SARS-CoV-2 RNA synthesis, minimizing the risk of viral replication in infected individuals. This finding has practical implications for resource-deficient areas that may have limited access to newly developed COVID-19 vaccines and/or areas with low COVID-19 vaccination rates due to hesitancy. Funding Source: Private donors. Registration: ClinicalTrials.gov: NCT04639375.
The consequences of a year of the COVID-19 pandemic for the mental health of young adult twins in England and Wales

Background The COVID-19 pandemic has affected all our lives, not only through the infection itself but also through the measures taken to control the spread of the virus (e.g. lockdown). Aims Here, we investigated how the COVID-19 pandemic and unprecedented lockdown affected the mental health of young adults in England and Wales. Method We compared the mental health symptoms of up to 4773 twins in their mid-20s in 2018 prior to the COVID-19 pandemic (T1) and during four-wave longitudinal data collection during the pandemic in April, July and October 2020, and in March 2021 (T2–T5) using phenotypic and genetic longitudinal designs. Results The average changes in mental health were small to medium and mainly occurred from T1 to T2 (average Cohen d = 0.14). Despite the expectation of catastrophic effects of the pandemic on mental health, we did not observe trends in worsening mental health during the pandemic (T3–T5). Young people with pre-existing mental health problems were disproportionately affected at the beginning of the pandemic, but their increased problems largely subsided as the pandemic persisted. Twin analyses indicated that the aetiology of individual differences in mental health symptoms did not change during the lockdown (average heritability 33%); the average genetic correlation between T1 and T2–T5 was 0.95, indicating that genetic effects before the pandemic were substantially correlated with genetic effects up to a year later. Conclusions We conclude that on average the mental health of young adults in England and Wales has been remarkably resilient to the effects of the pandemic and associated lockdown.
The potential of CYP2D6 phenotyping to improve opioid dosing strategies

Background: Prescription opioids account for more than 40% of opioid induced mortalities. With the trend towards increased opioid prescribing set to continue rising, opioid-based adverse drug reactions, (ADRs) and their associated human and financial cost present a major global public health concern. The review examined the relationship between CYP2D6 phenotypes and opioid metabolising. The aim was to establish whether screening for CYP2D6 phenotypes would improve existing opioid dosing strategies and reduce ADRs. Method: A systematic review was conducted using the online Web of Science database. Selected key words and Boolean operator combinations were used to search the relevant literature. Identified studies were screened against pre-defined inclusion/exclusion criteria. Eligible studies were subject to full review and quality assessment. A narrative analysis was performed to synthesise data from the studies included in the final review. Results: The review yielded five studies that met the eligibility criteria and were subject to full review. Four of the five studies reported significant effects of CYP2D6 phenotypes on opioid metabolising or opioid based ADRs. Three studies focused exclusively on pharmacokinetics, two studies focused exclusively on ADRs, and one study considered pharmacokinetics and ADRs. All pharmacokinetic studies reported a significant association between CYP2D6 phenotypes and opioid metabolising. Only one of the studies reported a significant association between CYP2D6 phenotypes and ADRs. Conclusion: The majority of evidence considered in the review supports the role of CYP2D6 in the metabolising of opioids and opioid based ADRs. Consequently, CYP2D6 screening should be considered as a potential mechanism for improving existing opioid dosing strategies and reducing ADRs. There is a need for further higher quality primary data studies focusing specifically on CYP2D6 phenotyping in the context of dosage strategies and exploring impact in longitudinal designs. Future studies should also seek to develop cost effective CYP2D6 screening methods to help support the clinical significance of CYP2D6 phenotyping.
Comparative study of Fe(Se,Te) thin films on flexible coated conductor templates and single-crystal substrates

In this paper, we compare epitaxial Fe(Se,Te) thin films grown by pulsed laser deposition on CaF2, SrTiO3, MgO single crystals as well as on different metallic templates having a CeO2 based top surface. In particular, we performed a detailed structural and superconducting analysis. X-ray diffraction studies showed highly textured films on all templates. The superconducting transition temperatures are between 21 K and 14 K for Fe(Se,Te) films on CaF2 and on MgO single-crystal substrates, respectively, whereas films on the metal templates show T c values up to 18 K. The critical current density (J c) was determined from magnetization loops in fields up to 7 T. Calculations in the framework of the extended critical state model showed J c values over 2 MA cm−2 and 0.9 MA cm−2 at 5 K in self-field on single-crystal substrates and metal templates substrates, respectively. Detailed transmission electron microscopy studies reveal smooth layers on all templates. Whereas small defects were found in films on single crystals, pronounced grain boundaries with higher misorientation angle were visible in the layers on metallic templates.
Environmental flow requirements largely reshape global surface water scarcity assessment

The inclusion of environmental flow requirements (EFRs) in global water scarcity assessments is essential to obtain a reasonable representation of the water scarcity status. However, at a global scale, the quantification of EFRs is subject to large uncertainties resulting from various methods. So far, it is unclear to what extent the uncertainties in EFRs affect global water scarcity assessments. In this study, we examined the differences between EFR estimation methods and quantified their effects on spatially explicit water scarcity assessments, based on reconstructed global water withdrawal data and naturalized streamflow simulations. The global mean EFRs estimated by different methods ranged from 129 m3 s−1 to 572 m3 s−1. Consequently, with the fulfillment of the EFRs, the area under water scarcity ranged between 8% and 52% of the total global land area, and the affected population ranged between 28% and 60% of the total population. In India and Northern China, 44%–66% and 22%–58% of the country’s land area, respectively, is affected by water scarcity; this percentage is higher than that found in other countries. The effects of different EFRs on water scarcity assessment are large in many regions, but relatively small in regions that experience intensive water use due to anthropological activities (such as Northern China and India). Through this study, we have put forth the need for the reconciliation of the estimates of EFRs to produce more reasonable and consistent water scarcity assessments.
How Soil Bacterial Communities with Seasonal Variation Respond Differently to Long-Term Fertilization and Plastic Film Mulching

Although the fertilization effect on soil microbial communities have been studied extensively, its influence combined with plastic-film mulching on seasonal variations of bacterial communities remains unknown. High throughput sequencing was used to explore seasonal bacterial communities in a longterm fertilization experiment that commenced in 1987. Non-fertilization and the application of manure combined with nitrogen fertilization significantly (P<0.05) increased bacterial abundance at phylum level without film mulching in July and with film mulching in October. Also, the relative abundance of dominant bacterial groups in October exceeded those in July. Nitrogen fertilization (N) significantly (P<0.05) decreased soil bacterial diversity and richness with or without film mulching compared to nonfertilization controls during July and October. Plastic film mulching and long-term manure fertilization significantly (P<0.05) increased bacterial diversity and richness as well as the relative abundance of dominant bacterial groups. Statistical analysis revealed that soil pH was the main driving force influencing bacterial groups. In conclusion, our results highlight the relative importance of long-term application of manure fertilization and plastic film mulching on seasonal variations of bacterial diversity and structure. This finding also highlights the relevance of soil pH as the main deterministic abiotic factor for soil bacterial community structure regardless of film mulching.
Cross-Domain Imitation Learning via Optimal Transport

Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajectories and stationary distributions between the expert and imitation agents is challenging because they live on different systems that may not even have the same dimensionality. We propose Gromov-Wasserstein Imitation Learning (GWIL), a method for cross-domain imitation that uses the Gromov-Wasserstein distance to align and compare states between the different spaces of the agents. Our theory formally characterizes the scenarios where GWIL preserves optimality, revealing its possibilities and limitations. We demonstrate the effectiveness of GWIL in non-trivial continuous control domains ranging from simple rigid transformation of the expert domain to arbitrary transformation of the state-action space.
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits

In this paper, we propose a novel neural exploration strategy in contextual bandits, EE-Net, distinct from the standard UCB-based and TS-based approaches. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration tradeoff in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB). In recent literature, linear contextual bandits have adopted ridge regression to estimate the reward function and combine it with TS or UCB strategies for exploration. However, this line of works explicitly assumes the reward is based on a linear function of arm vectors, which may not be true in real-world datasets. To overcome this challenge, a series of neural bandit algorithms have been proposed, where a neural network is used to learn the underlying reward function and TS or UCB are adapted for exploration. Instead of calculating a large-deviation based statistical bound for exploration like previous methods, we propose"EE-Net", a novel neural-based exploration strategy. In addition to using a neural network (Exploitation network) to learn the reward function, EE-Net uses another neural network (Exploration network) to adaptively learn potential gains compared to the currently estimated reward for exploration. Then, a decision-maker is constructed to combine the outputs from the Exploitation and Exploration networks. We prove that EE-Net can achieve $\mathcal{O}(\sqrt{T\log T})$ regret and show that EE-Net outperforms existing linear and neural contextual bandit baselines on real-world datasets.
Neural Tangent Kernel Empowered Federated Learning

Federated learning (FL) is a privacy-preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distributed learning, a unique characteristic of FL is statistical heterogeneity, namely, data distributions across participants are different from each other. Meanwhile, recent advances in the interpretation of neural networks have seen a wide use of neural tangent ker-nels (NTKs) for convergence analyses. In this pa-per, we propose a novel FL paradigm empowered by the NTK framework. The paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the conventional FL paradigms. Speciﬁ-cally, sample-wise Jacobian matrices, rather than model weights/gradients, are uploaded by participants. The server then constructs an empirical kernel matrix to update a global model without explicitly performing gradient descent. We further develop a variant with improved communication efﬁciency and enhanced privacy. Numerical results show that the proposed paradigm can achieve the same accuracy while reducing the number of communication rounds by an order of magnitude compared to federated averaging.
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver

Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to couple tightly with the model-learning procedure. Hence the learned models may lack the ability of being re-used with more specialized planners. In this paper we address this issue and provide approaches to learn an RL model efficiently without the guidance of a reward signal. In particular, we take a plug-in solver approach, where we focus on learning a model in the exploration phase and demand that \emph{any planning algorithm} on the learned model can give a near-optimal policy. Specicially, we focus on the linear mixture MDP setting, where the probability transition matrix is a (unknown) convex combination of a set of existing models. We show that, by establishing a novel exploration algorithm, the plug-in approach learns a model by taking $\tilde{O}(d^2H^3/\epsilon^2)$ interactions with the environment and \emph{any} $\epsilon$-optimal planner on the model gives an $O(\epsilon)$-optimal policy on the original model. This sample complexity matches lower bounds for non-plug-in approaches and is \emph{statistically optimal}. We achieve this result by leveraging a careful maximum total-variance bound using Bernstein inequality and properties specified to linear mixture MDP.
Decoding ECoG signal into 3D hand translation using deep learning

Objective. Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship. Approach. In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity. Main results. Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively. Significance. This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.
Towards Accurate Cross-Domain in-Bed Human Pose Estimation

Human behavioral monitoring during sleep is essential for various medical applications. Majority of the contactless human pose estimation algorithms are based on RGB modality, causing ineffectiveness in in-bed pose estimation due to occlusions by blankets and varying illumination conditions. Long-wavelength infrared (LWIR) modality based pose estimation algorithms overcome the aforementioned challenges; however, ground truth pose generations by a human annotator under such conditions are not feasible. A feasible solution to address this issue is to transfer the knowledge learned from images with pose labels and no occlusions, and adapt it towards real world conditions (occlusions due to blankets). In this paper, we propose a novel learning strategy comprises of two-fold data augmentation to reduce the cross-domain discrepancy and knowledge distillation to learn the distribution of unlabeled images in real world conditions. Our experiments and analysis show the effectiveness of our approach over multiple standard human pose estimation baselines 1.
Differential Calculi on Quantum Principal Bundles over Projective Bases

We propose a sheaf-theoretic approach to the theory of differential calculi on quantum principal bundles over non-affine bases. After recalling the affine case we define differential calculi on sheaves of comodule algebras as sheaves of covariant bimodules together with a morphism of sheaves -- the differential -- such that the Leibniz rule and surjectivity hold locally. The main class of examples is given by covariant calculi over quantum flag manifolds, which we provide via an explicit Ore extension construction. In a second step we introduce principal covariant calculi by requiring a local compatibility of the calculi on the total sheaf, base sheaf and the structure Hopf algebra in terms of exact sequences. In this case Hopf--Galois extensions of algebras lift to Hopf--Galois extensions of exterior algebras with compatible differentials. In particular, the examples of principal (covariant) calculi on the quantum principal bundles $\mathcal{O}_q(\mathrm{SL}_2(\mathbb{C}))$ and $\mathcal{O}_q(\mathrm{GL}_2(\mathbb{C}))$ over the projective space $\mathrm{P}^1(\mathbb{C})$ are discussed in detail.
STRUCTURAL CHARACTERIZATION OF THE INTERFACIAL SELF-ASSEMBLY OF POLYELECTROLYTES

Figure 1: a: We performed a long term membrane formation for a single case (0.1% w/w chitosan, 0.1% w/w PFacid , f= 0.5 Hz, γ=0.03%) in order to perform a strain sweep on a thick membrane and define the limits of linear elastic regime. B: We observed that for long term formed membranes (>24 hours) the linear elastic regime was limited to surprisingly low values of strain (<0.1 % ). The direct microscopic observation of the membrane decorated with tracing particles during the strain sweep experiments showed that this highly non-linear behaviour was caused by wrinkling instability of the membrane. The deformation of ≈ 20 % lead to the rapid decline in Gi. The direct observation showed that it was caused by the loss of connectivity between the membrane and the bicone.
CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability

What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a lifelong manner entails a plethora of additional challenges with respect to set-up and evaluation. The latter have recently sparked a growing amount of critiques on prominent algorithm-centric perspectives and evaluation protocols being too narrow, resulting in several attempts at constructing guidelines in favor of specific desiderata or arguing against the validity of prevalent assumptions. In this work, we depart from this mindset and argue that the goal of a precise formulation of desiderata is an ill-posed one, as diverse applications may always warrant distinct scenarios. Instead, we introduce the Continual Learning EValuation Assessment Compass: the CLEVA-Compass. The compass provides the visual means to both identify how approaches are practically reported and how works can simultaneously be contextualized in the broader literature landscape. In addition to promoting compact specification in the spirit of recent replication trends, it thus provides an intuitive chart to understand the priorities of individual systems, where they resemble each other, and what elements are missing towards a fair comparison.