Miracle-dz/UniFAR-FTUs huggingface dataset:
The interplay of DNA repair context with target sequence predictably biasses Cas9-generated mutations

The genome engineering capability of the CRISPR/Cas system depends on the DNA repair machinery to generate the final outcome. Several genes can have an impact on mutations created, but their exact function and contribution to the result of the repair are not completely characterised. This lack of knowledge has limited the ability to comprehend and regulate the editing outcomes. Here, we measure how the absence of 21 repair genes changes the mutation outcomes of Cas9-generated cuts at 2,812 synthetic target sequences in mouse embryonic stem cells. Absence of key non-homologous end joining genes Lig4, Xrcc4, and Xlf abolished small insertions and deletions, while disabling key microhomology-mediated repair genes Nbn and Polq reduced frequency of longer deletions. Complex alleles of combined insertion and deletions were preferentially generated in the absence of Xrcc6. We further discover finer structure in the outcome frequency changes for single nucleotide insertions and deletions between large microhomologies that are differentially modulated by the knockouts. We use the knowledge of the reproducible variation across repair milieus to build predictive models of Cas9 editing results that outperform the current standards. This work improves our understanding of DNA repair gene function, and provides avenues for more precise modulation of CRISPR/Cas9-generated mutations.
Identification of a recombinant human astrovirus type 5 strain from an acute gastroenteritis outbreak in Beijing, China

Two samples positive for the same strain (2103CP) of human astrovirus (HAstV) were identified during an acute gastroenteritis outbreak in Beijing, China. A full genomic analysis showed that for both samples, open reading frame 1a (ORF1a) clustered with HAstV-1, whereas ORF1b and ORF2 clustered with HAstV-5. The recombination site was detected upstream of the region where ORF1a and ORF1b overlapped. Recombinant HAstV-5 strains were reported previously in China in 2013 and 2021. Our results indicate the need for continuous surveillance of the prevalence and evolution of this recombinant HAstV-5 in China.
CLEP_A_400399 775..783

Background and Aims: Previous studies have not been able to determine whether non-selective beta-blockers (NSBB) reduce the risk of sepsis in cirrhosis. We aimed to examine this question with data from 1198 patients with cirrhosis and ascites included in clinical studies of satavaptan, a vasopressin receptor antagonist with no effect on infection risk. Methods: Risk of sepsis was estimated for NSBB users vs nonusers. Patients were examined every four weeks, or in relation to hospitalization, for the one-year duration of the trials. We computed the cumulative risk of sepsis for patients who did vs did not use NSBB at baseline. We used Cox regression to compare hazard rates of sepsis between current users and nonusers, accounting for changes in NSBB use over time. We adjusted for patient sex and age, MELD-Na score, albumin, use of antibiotics, use of proton pump inhibitors, cirrhosis etiology, history of variceal bleeding or SBP, severity of ascites and HE, HCC, other cancers, and diabetes, while stratifying on geographical region. Results: Of the 1198 patients, 54% used NSBB at some time. There were 56 sepsis episodes. The 1-year risk of sepsis was reduced to 5.7% (95% confidence interval [CI] 2.8–8.6) in baseline NSBB users vs 11.6% (95% CI 7.0–15.9) in baseline nonusers. The hazard ratio of sepsis for current NSBB users vs current nonusers was reduced to 0.5 (95% CI 0.3–0.8) and after adjustment to 0.7 (95% CI 0.4–1.3). Conclusion: NSBB use may reduce the risk of sepsis in patients with cirrhosis and ascites, but the precision of the estimate was limited by the number of episodes of sepsis.
High-Performance Staggered Top-Gate Thin-Film Transistors with Hybrid-Phase Microstructural ITO-Stabilized ZnO Channels

In this paper, the ITO-stabilized ZnO thin films with a hybrid-phase microstructure were introduced, where a number of nanocrystals were embedded in an amorphous matrix. The microstructural and optical properties of thin films were investigated. It was found that the grain boundary and native defect issues in the pristine polycrystalline ZnO could be well suppressed. Meanwhile, such thin films also possessed relatively smooth surface and high transmittance in the visible range. Afterwards, the corresponding staggered top-gate thin-film transistors (TFTs) were fabricated at a temperature of 300 °C and exhibited fairly high electrical characteristics, especially with a field-effect mobility of nearly 20 cm2 V−1 s−1 and a subthreshold swing as low as 0.115 V/decade. In addition, the electrical uniformity and the stability of devices were also examined to be excellent. It is expected that the staggered top-gate TFTs with hybrid-phase microstructural ITO-stabilized ZnO channels are promising in the next-genera.
C A T RAN : ULTRA - LIGHT NEURAL NETWORK FOR PREDICTING GENE - GENE INTERACTIONS FROM SINGLE - CELL DATA

Part of the difficulty of learning a gene-regulatory network from expression data is related to the fact that edges in such a network represent different interactions with a different effect size. Therefore modeling gene associations requires learning an individual function for each pair of interacting genes. This may greatly inflate the number of parameters in a model and lead to insufficient generalization. In this paper we propose a method for gene regulatory network inference, called CaTran (Causal Transformer), which avoids explicitly learning pairwise relation between genes, which allows it to significantly reduce the size of the model. The key feature of this approach is learning for each gene a low dimensional embedding and then using a self-attention mechanism to estimate its relation to other genes. Our method is applicable for both observational data and data with interventions. For the latter it implements a differentiable gene importance test and forces attention values to be in accordance with it. Because the gene regulatory network in CaTran is learned as a soft adjacency matrix, it allows sampling graphs with arbitrary number of edges based on a set threshold. Comparison of these graphs with the gene networks from databases showed that even for large graphs the edges are predicted with high precision.
Hard vs. Soft Contacts in Trajectory Optimization of Legged Robots: An Example with a Compliant Rubber Foot

In this contribution to the ICRA 2023 workshop "Embracing Contacts", we compare the use of soft and rigid contact models in the simulation and trajectory optimization of legged locomotion. Specifically, we introduce a soft contact model that closely resembles the compliance of our legged robotic hardware [1] and carefully fit the model parameters to experimentally recorded data. Motion simulations of the robot RAMbi were more accurate when computed with this soft model compared to a standard rigid model. Furthermore, we performed a simple gait optimization with both contact models for a one-legged hopper with 7 degrees of freedom, showing that the soft contact model produced results similar to those obtained with a rigid contact model. For this particular hardware example, our work clearly breaks with the common wisdom that rigid contact models are superior in simulation and optimization of legged robotic systems. The results presented in this contribution are taken from [2].
Anomalous Hall effect in Y2Fe17−xCox single crystals

We study experimentally the Hall resistivity of Y2Fe17−xCox single crystals with x 4 for wide temperature and applied magnetic field ranges and for various magnetic field orientations with respect to the easymagnetization axis. For small x, the anomalous Hall effect AHE is very anisotropic in these naturally layered compounds. For x 2, the AHE resistivity, measured with an applied magnetic field H c axis, is nearly 1 order of magnitude larger than the AHE resistivity for H along the hard-magnetization direction H c axis . Furthermore, the former is very large and varies linearly with the longitudinal resistivity , whereas the latter increases as 2. The behavior of the AHE for H c axis comes quite likely from the intrinsic effect related to the hopping between different Fe d orbitals. Such hopping is allowed for high-symmetry points at the crystallographic dumbbell sites in this configuration. On the other hand, interorbital hopping is not allowed for H c axis. However, a huge amplitude of the AHE scattering for this configuration, which follows from skew scattering, is puzzling. Both the AHE anisotropy and the large skew scattering vanish for sufficiently high Co content. We attribute this to variations in the electronic structure of the Y2Fe17−xCox system when Co atoms start to occupy the dumbbell crystallographic sites.
Combining Foundation Models and Symbolic AI for Automated Detection and Mitigation of Code Vulnerabilities

With the increasing reliance on collaborative and cloud-based systems, there is a drastic increase in attack surfaces and code vulnerabilities. Automation is key for fielding and defending software systems at scale. Researchers in Symbolic AI have had considerable success in finding flaws in human-created code. Also, run-time testing methods such as fuzzing do uncover numerous bugs. However, the major deficiency of both approaches is the inability of the methods to fix the discovered errors. They also do not scale and defy automation. Static analysis methods also suffer from the false positive problem – an overwhelming number of reported flaws are not real bugs. This brings up an interesting conun-drum: Symbolic approaches actually have a detrimental impact on programmer productivity, and therefore do not necessarily contribute to improved code quality. What is needed is a combination of automation of code generation using large language models (LLMs), with scalable defect elimination methods using symbolic AI, to create an environment for the automated generation of defect-free code.
Floating Carbon-Doped TiO2 Photocatalyst with Metallic Underlayers Investigation for Polluted Water Treatmentunder Visible-Light Irradiation

In the current study, we analysed the influence of metallic underlayers on carbon-doped TiO2 films for RhB decomposition and Salmonella typhimurium inactivation under visible-light irradiation. All the experiments were divided into two parts. First, layered M/C-doped-TiO2 film structures (M = Ni, Nb, Cu) were prepared by magnetron sputtering technique on borosilicate glass substrates in the two-step deposition process. The influence of metal underlayer on the formation of the carbondoped TiO2 films was characterised by X-ray diffractometer, scanning electron microscope, and atomic force microscope. The comparison between the visible-light assisted photocatalytic activity of M/C-doped TiO2 structures was performed by the photocatalytic bleaching tests of Rhodamine B dye aqueous solution. The best photocatalytic performance was observed for Ni/C-doped-TiO2 film combination. During the second part of the study, the Ni/C-doped-TiO2 film combination was deposited on high-density polyethylene beads which were selected as a floating substrate. The morphology and surface chemical analyses of the floating photocatalyst were performed. The viability and membrane permeability of Salmonella typhimurium were tested in cycling experiments under UV-B and visiblelight irradiation. Three consecutive photocatalytic treatments of fresh bacteria suspensions with the same set of floating photocatalyst showed promising results, as after the third 1 h-long treatment bacteria viability was still reduced by 90% and 50% for UV-B and visible-light irradiation, respectively. The membrane permeability and ethidium fluorescence results suggest that Ni underlayer might have direct and indirect effect on the bacteria inactivation process. Additionally, relatively low loss of the photocatalyst efficiency suggests that floating C-doped TiO2 photocatalyst with the Ni underlayer might be seen as the possible solution for the used photocatalyst recovery issue.
Advancements in Drug Repurposing: Examples in Psychiatric Medications

Because there are a limited number of animal models for psychiatric diseases that can be extrapolated to humans, drug repurposing has been actively pursued. This study was aimed at uncovering recent trends in drug repurposing approaches and new technologies that can predict efficacy on humans based on animal models used in psychiatric drug development. Psychiatric drugs that were approved by the FDA between 2002 and 2022 were listed, and the method of how the drug repurposing has been applied was analyzed. Drug repurposing has been increasingly applied to recently approved psychiatric drugs. The development concepts of psychiatric drugs that have been developed through drug repurposing over the past 20 years were found to be divided into six categories: new application exploration, reduction of side effects, improvement of symptom control, improvement of medication compliance, enhancement of drug efficacy, and reduction of drug–drug interactions. All repurposed drugs approved before 2016 used either prodrugs or active metabolites, while all drugs approved in 2021 and beyond used fixed-dose combinations with sophisticated ideas. SmartCube®, which uses artificial intelligence to predict human drug efficacy from animal phenotypes, was developed and produced novel drugs that show clinical efficacy. Well-designed drug repurposing approaches and new technologies for predicting human drug efficacy based off of animal models would contribute to novel psychiatric drug development.
Real-Time Physics-Based Object Pose Tracking during Contact-Based Manipulation

—We propose a method to track the 6D pose of an object over time, while the object is under contact-based manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera.
Real-Time Intrusion Detection of Insider Threats in Industrial Control System Workstations Through File Integrity Monitoring

—Industrial control systems (ICS) play a crucial role in various industries and ensuring their security is paramount for maintaining process continuity and reliability. In ICS, the most damaging cyber-attacks often come from trusted insiders rather than external threats or malware. Insiders have the advantage of bypassing security measures and staying undetected. This research focuses on developing a real-time intrusion detection system for ICS workstations that effectively detects insider threats while prioritizing user privacy. The approach employs file integrity monitoring to identify suspicious activities, particularly file violations such as data tampering and destruction. The model presented in this research demonstrates low system resource consumption by utilizing an event-triggered approach instead of continuous polling of file data. The model leverages built-in operating system functions, eliminating the need for third-party software installation. To minimize disruptions to the ICS network, the model operates at the supervisory level within the ICS architecture. Through extensive testing, the model achieves a high level of accuracy, detecting insider intrusions with a high true positive rate. This reliable detection capability contributes to enhancing the security of ICS and mitigating the risks associated with insider threats. By implementing this real-time intrusion detection system
Life History Traits and Longevity of the Invasive Asian Common Toad Duttaphrynus melanostictus (Schneider, 1799) in Madagascar

Simple Summary The Asian common toad Duttaphrynus melanostictus represents a serious threat to the biodiversity of Madagascar, where it was introduced in around 2010. Here, we study some traits of its life history, including its body size, age structure, and age at sexual maturity based on individuals sampled at different sites of Toamasina, on the east coast of Madagascar, in 2016, 2018, and 2019. The individuals studied here were larger, although no longer-living, with respect to individuals of the native populations in SE Asia. In the invasive population of Madagascar, the males were significantly smaller and younger than the females when data from different sites were pooled. However, when data are analyzed separately, this was true only for one site. The maximum age ascertained in this study is 3 years in males and 6 years in females. Males and females reach sexual maturity after the first and second year of age, respectively. Further studies on the life history traits of the population of D. melanostictus in Madagascar can be relevant for the management of this invasion as well as to better understand the biology of the species. Abstract We analyzed the body length, age structure, and age at sexual maturity of the invasive Asian common toad Duttaphrynus melanostictus from different sites in Toamasina, east Madagascar. We used skeletochronology as a proxy for age estimation, while gonads were histologically analyzed to determine the age of sexual maturity. The analysis of pooled age data from three sites investigated in 2016 showed that both sexes were larger, although not older, than those of native populations. For the individuals from Madagascar, the males were significantly smaller and younger (mean ± SD, SVL: 71.4 ± 1.6 mm; age: 1.8 ± 0.7 years) than the females (SVL: 78.42 ± 1.9 mm; age: 2.7 ± 1.3 years), when the data were pooled, but when the data were analyzed separately for each of the three sites, similar results were obtained only for one site. The oldest recorded male and female were 3 and 6 years old, respectively. Gonadal histology showed that the males and females reach sexual maturity after the first and second years of age, respectively. Further studies are needed to understand if the larger size and faster growth rates observed in the invasive population of D. melanostictus in Madagascar are a consequence of more favorable environmental conditions with respect to the native range (e.g., the availability of larger trophic niches, a lack of competitors, and lower predatory pressure), and we suggest to extend the monitoring of these life history traits to understand how they might influence the invasion.
Linking Subsidies for Agriculture and Food to Dietary Styles: Estimates for Switzerland

Agriculture contributes substantially to global greenhouse gas emissions and other environmental impacts. Current agricultural policies tolerate these impacts and sometimes even aggravate them through subsidies for unsustainable production systems and products. Against this background, we explore the resulting transfers to agriculture and food associated with different dietary styles. Using data from Switzerland, we consider both direct transfers through government subsidies and indirect transfers through toleration of environmental impacts and costs. Gross transfers were quantified based on existing studies that (i) define eight dietary styles in terms of the quantity of the different product groups, (ii) allocate subsidies for agriculture to product groups, (iii) allocate environmental impacts to product groups using life cycle assessment (LCA) methods, and (iv) estimate external costs of agriculture using politically endorsed expenditures for damage avoidance. To quantify net transfers associated with the dietary styles, we subtracted individual tax contributions from the subsidies received and average external costs individually suffered from external costs caused. We found that the gross transfers from government to agriculture range from about CHF 500 to about CHF 2000 annually per taxpayer/consumer depending on dietary styles. Net transfers per taxpayer/consumer range from about CHF −1000 to about CHF 1000 depending on dietary style and income. The estimates highlight that current policies strongly privilege and thereby also endorse environmentally problematic dietary styles over more environmentally friendly ones, contradicting official strategies and targets in the domains of environment and public health.
The State of Intent Detection in the Era of Large Autoregressive Language Models

In-context learning (ICL) using large pre-001 trained autoregressive language models (LLMs, 002 e.g. GPT-3) has demonstrated effective clas-003 sification performance at a variety of natural 004 language tasks. Using LLMs for intent detec-005 tion is challenging due to the large label space 006 and limited context window, such that it is diffi-007 cult to fit a sufficient number of examples in the 008 prompt to allow the use of in-context learning. 009 In this paper, dense retrieval is used to bypass 010 this limitation, giving the model only a par-011 tial view of the full label space. We show that 012 retriever-augmented large language models are 013 an effective way to tackle intent detection, by-014 passing context window limitations effectively 015 through the retrieval mechanism. Comparing 016 the LLaMA and OPT model families at differ-017 ent scales, we set new state of the art perfor-018 mance in the few-shot setting with zero training 019 for two of the three intent classification datasets 020 that we consider, while achieving competitive 021 results on the third one. This work demon-022 strates that the Retriever+ICL framework is a 023 strong zero-training competitor to fine-tuned in-024 tent detection approaches. In addition, a small 025 study on the number of examples provided at 026 different model scales is done, showing that 027 larger models are needed to make effective use 028 of more examples in-prompt. 029
Pretraining with Neural-Fly for Rapid Online Learning

—Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commodi-tization of uninhabited aerial vehicles (UAVs). However, since the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using traditional control de-sign methods. We present Neural-Fly, a learning-based approach that allows rapid online adaptation by incorporating pre-trained representations through deep learning. Neural-Fly builds on two key observations that aerodynamics in different wind conditions share a common representation and that the wind-specific part lies in a low-dimensional space. To that end, Neural-Fly uses a proposed learning algorithm, Domain Adversarially Invariant Meta-Learning (DAIML), to learn the shared representation, only using 12 minutes of flight data. This pretraining phase enables rapid online learning through a composite adaptation law, which only needs to update a set of linear coefficients for mixing the basis elements to effectively correct for the wind effects. When evaluated under challenging wind conditions generated with the Caltech Real Weather Wind Tunnel with wind speeds up to 43 . 6km / h ( 12 . 1m / s ), Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers. In addition to strong empirical performance, the exponential stability of Neural-Fly results in robustness guarantees. Finally, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only on-board sensors, and can transfer across drones with minimal performance degradation.