Miracle-dz/UniFAR-FTUs huggingface dataset:
Restricting a single amino acid cross-protects Drosophila melanogaster from nicotine poisoning through mTORC1 and GCN2 signalling

Dietary interventions that restrict protein intake have repeatedly been shown to offer beneficial health outcomes to the consumer. Benefits such as increased stress tolerance can be observed when individual amino acids are restricted, thus mimicking dietary protein restriction. Here, we sought to further understand the relationship between dietary amino acids and stress tolerance using Drosophila melanogaster. Using a chemically defined medium for Drosophila, we found that transiently restricting adult flies of a single essential amino acid generally protects against a lethal dose of the naturally occurring insecticide, nicotine. This protection varied with the identity of the focal amino acid and depended on the duration and intensity of its restriction. To understand the molecular basis of these effects, we modified the signalling of two cellular sensors of amino acids, GCN2 and mTORC1, in combination with amino acid restriction. We found that GCN2 was necessary for diets to protect against nicotine, whereas the suppression of mTORC1 was sufficient to induce nicotine resistance. This finding implies that amino acid restriction acts via amino acid signalling to cross-protect against seemingly unrelated stressors. Altogether, our study offers new insights into the physiological responses to restriction of individual amino acids that confer stress tolerance.
A tunable electromagnetic metagrating

We explore electromagnetic (EM) wave incidence upon gratings of reconfigurable metamaterial cylinders, which collectively act as a metagrating, to identify their potential as reconfigurable subwavelength surfaces. The metacylinders are created by a closely spaced, microstructured array of thin plates that, in the limit of small inter-plate spacing, are described by a semi-analytical continuum model. We build upon metacylinder analysis in water waves, translating this to EM for TE polarization (longitudinal magnetic field) for which the metacylinders exhibit anisotropic scattering; this is exploited for the multiple scattering of light by an infinite metagrating of uniform cylinder radius and angle, for which we retrieve the far-field reflection and transmission spectra for plane-wave incidence. These spectra reveal unusual effects including perfect reflection and a negative Goos–Hänchen shift in the transmitted field, as well as perfect symmetry in the far-field scattering coefficients. The metagrating also hosts Rayleigh–Bloch surface waves, whose dispersion is contingent on the uniform cylinder angle, shifting under rotation towards the light-line as the cylinder angle approaches the horizontal. For both plane-wave scattering and the calculation of the array-guided modes, the cylinder angle is the principal variable in determining the wave interaction, and the metagrating is tunable simply through rotation of the constituent metacylinders.
Characteristics of the ErmK Protein of Bacillus halodurans C-125

Erm proteins confer MLSB antibiotic resistance (minimal inhibitory concentration [MIC] value up to 4,096 μg/mL) on microorganisms ranging from antibiotic producers to pathogens, imposing one of the most pressing threats to clinics. Therefore, Erm proteins have long been speculated to be plausible targets for developing inhibitor(s). ABSTRACT Bacillus halodurans C-125 is an alkaliphilic microorganism that grows best at pH 10 to 10.5. B. halodurans C-125 harbors the erm (erythromycin resistance methylase) gene as well as the mphB (macrolide phosphotransferase) and putative mef (macrolide efflux) genes, which confer resistance to macrolide, lincosamide, and streptogramin B (MLSB) antibiotics. The Erm protein expressed in B. halodurans C-125 could be classified as ErmK because it shares 66.2% and 61.2% amino acid sequence identity with the closest ErmD and Erm(34), respectively. ErmK can be regarded as a dimethylase, as evidenced by reverse transcriptase analysis and the antibiotic resistance profile exhibited by E. coli expressing ermK. Although ErmK showed one-third or less in vitro methylating activity compared to ErmC′, E. coli cells expressing ErmK exhibited comparable resistance to erythromycin and tylosin, and a similar dimethylation proportion of 23S rRNA due to the higher expression rate in a T7 promoter-mediated expression system. The less efficient methylation activity of ErmK might reflect an adaption to mitigate the fitness cost caused by dimethylation through the Erm protein presumably because B. halodurans C-125 has less probability to encounter the antibiotics in its favorable growth conditions and grows retardedly in neutral environments. IMPORTANCE Erm proteins confer MLSB antibiotic resistance (minimal inhibitory concentration [MIC] value up to 4,096 μg/mL) on microorganisms ranging from antibiotic producers to pathogens, imposing one of the most pressing threats to clinics. Therefore, Erm proteins have long been speculated to be plausible targets for developing inhibitor(s). In our laboratory, it has been noticed that there are variations in enzymatic activity among the Erm proteins, Erm in antibiotic producers being better than that in pathogens. In this study, it has been observed that Erm protein in B. halodurans C-125 extremophile is a novel member of Erm protein and acts more laggardly, compared to that in pathogen. While this sluggishness of Erm protein in extremophile might be evolved to reduce the fitness cost incurred by Erm activity adapting to its environments, this feature could be exploited to develop the more potent and/or efficacious drug to combat formidably problematic antibiotic-resistant pathogens.
The approximate deconvolution model for large-eddy simulations of compressible flows and its application to shock-turbulent-boundary-layer interaction

A formulation of the approximate deconvolution model (ADM) for the large-eddy simulation (LES) of compressible flows in complex geometries is detailed. The model is applied to supersonic compression ramp flow where shock-turbulence interaction occurs. With the ADM approach an approximation to the unfiltered solution is obtained from the filtered solution by a series expansion involving repeated filtering. Given a sufficiently good approximation of the unfiltered solution at a time instant, the flux terms of the underlying filtered transport equations can be computed directly, avoiding the need to explicitly compute subgrid-scale closures. The effect of nonrepresented scales is modeled by a relaxation regularization involving a secondary filter operation and a dynamically estimated relaxation parameter. Results of the large-eddy simulation of the turbulent supersonic boundary layer along a compression ramp compare well with filtered DNS data. The filtered shock solution is correctly predicted by the ADM pr.
From darkness to brightness: the cholangioscopy-guided selective biliary cannulation with the help of transparent cap during ERCP

Endoscopic retrograde cholangiopancreatography (ERCP) is recommended as the primary treatment for choledocholithiasis [1]. Selective biliary cannulation is the most challenging step for ERCP since only an indirect image under X-ray can be obtained, and this process largely determines the occurrence of postoperative pancreatitis [2, 3]. Simultaneous repeated X-ray irradiation adversely affects the health of operators and patients [4]. To solve these problems, we have developed a novel biliary cannulating technique under direct vision of the cholangioscope, whose tip is covered by a conical transparent cap (▶Video 1). A 68-year-old man was diagnosed with choledocholithiasis by magnetic resonance cholangiopancreatography (MRCP) and endoscopic ultrasound (EUS) and was therefore referred for ERCP treatment. A cholangioscope fitted with a tapered transparent cap was inserted into the duodenal papilla (▶Fig. 1). With the help of the tapered transparent cap pushing aside the inner folds of the papilla, we clearly observed the petal-shaped biliary orifice using a regular cholangioscope (▶Fig. 1). Under the direct vision of the cholangioscope, a guidewire was precisely inserted into the common bile duct (CBD) (▶Fig. 2). With the guidance of the guidewire and the expansion of the transparent cap, the cholangioscope was squeezed into the CBD successfully and showed a gallstone with a diameter of about 10mm (▶Fig. 3). After the papilla was moderately incised, the stone was completely extracted by a regular basket (▶Fig. 4). The CBD was repeatedly flushed with saline, and a clean cavity without residual stones was confirmed under direct visualization of the cholangioscope (▶Fig. 5). In this case, we performed a visualized selective biliary cannulation assisted by a transparent cap-covered cholangioscope. Different from blind CBD cannulation under X-ray during traditional ERCP [5], the cholangioscopy-aided biliary cannulation is a visible operation, thus it is much easier and safer. To the best of our knowledge, this is the first report that the opening anatomy of CBD has been retrogradely observed during ERCP.
Data-driven model discovery for plasma turbulence modelling

An important problem in nuclear fusion plasmas is the prediction and control of turbulence which drives the cross-field transport, thus leading to energy loss from the system and deteriorating confinement. Turbulence, being a highly nonlinear and multiscale process, is challenging to theoretically describe and computationally model. Most advanced computational models fall into one of the two categories: fluid or gyro-kinetic. They both come at a high computational cost and cannot be applied for routine simulation of plasma discharge evolution and control. Development of reduced models based on (physics informed) artificial neural networks could potentially fulfil the need for affordable simulations of plasma turbulence. However, the training requires an extensive data base and the obtained models lack extrapolation capability to scenarios not originally encountered during training. This leads to reduced models of limited validity which may not prove adequate for predicting scenarios in future machines. In contrast, we explore a data-driven model discovery approach based on sparse regression to infer governing nonlinear partial differential equations directly from the data. Our input data are generated by simulations of drift-wave turbulence according to the Hasegawa–Wakatani and modified Hasegawa–Wakatani models. Balancing model accuracy and complexity enables the reconstruction of the systems of partial differential equations accurately describing the dynamics simulated in the input data sets. Sparse regression is not data hungry and can be extrapolated to unexplored parameter ranges. We explore and demonstrate the potential of this approach for fusion plasma turbulence modelling. The findings show that the methodology is promising for the development of reduced and computationally efficient turbulence models as well as for existing model cross-validation.
Neural Bidirectional Texture Function Compression and Rendering

The recent success of Machine Learning encouraged research using artificial neural networks (NNs) in computer graphics. A good example is the bidirectional texture function (BTF), a data-driven representation of surface materials that can encapsulate complex behaviors that would otherwise be too expensive to calculate for real-time applications, such as self-shadowing and interreflections. We propose two changes to the state-of-the-art using neural networks for BTFs, specifically NeuMIP. These changes, suggested by recent work in neural scene representation and rendering, aim to improve baseline quality, memory footprint, and performance. We conduct an ablation study to evaluate the impact of each change. We test both synthetic and real data, and provide a working implementation within the Mitsuba 2 rendering framework. Our results show that our method outperforms the baseline in all these metrics and that neural BTF is part of the broader field of neural scene representation. Project website: https://traverse-research.github. io/NeuBTF/.
The study of the L-fuzzy concept lattice

The L-Fuzzy concept theory that we have developed sets up classifications from the objects and attributes of a context through L-Fuzzy relations. This theory generalizes the formal concept theory of R. Wille. In this paper we begin with the L-Fuzzy concept definition that generalizes the definitions of the formal concept theory, and we study the lattice structure of the L-Fuzzy concept set, giving a constructive method for calculating this lattice. At the end, we apply this constructive method to an example that has been studied by other methods.
Prometheus: A mobile telepresence system connecting the 1st person and 3rd person perspectives continuously

Figure 1: System overview. A remote worker captures the area with a camera and streams the image (we call it rst-person view) to our system. Then, the system estimates the camera’s position and direction from the image and the pre-scanned 3D geometry. Finally, our system generates a third-person view by overlapping the captured image and the location information on the geometry. The receiver can switch these views freely. (a) First-person view: The image captured by remote worker is displayed in the center, with the pre-scanned geometry around it. This view moves with the movement of the remote worker. (b) Switching between rst-person and third-person view: The receiver can switch between the two views. (c) Third-person view: The user can move the viewpoint freely in the geometry model. The rst-person view and the camera’s position are also displayed so that the receiver can know the location of the remote worker.
Investigating the Effects of Synchronized Visuo-Tactile Stimuli for Inducing Kinesthetic Illusion in Observational Learning of Whole-Body Movements

Skill improvement in sports is an essential factor in keeping the motivation to continue. Previous studies showed that kinesthetic illusion enhances observational learning. However, such studies have only dealt with the learning of movements using a single part of the body, and whether kinesthetic illusion can be induced in observational learning of whole-body movements has not been clarified. In this study, we conducted an experiment involving human subjects and confirmed that synchronized visuo-tactile stimuli can induce kinesthetic illusion even in whole-body movements. Moreover, we also demonstrated a complete mediation model in which the synchronization of visuo-tactile stimuli influences kinesthetic illusion mediated by body ownership.
Motion In-betweening for Physically Simulated Characters

We present a motion in-betweening framework to generate high quality, physically plausible character animation when we are given temporally sparse keyframes as soft animation constraints. More specifically, we learn imitation policies for physically simulated characters by using deep reinforcement learning where the policies can access limited information only. Once learned, the physically simulated characters are capable of adapting to external perturbations while following given sparse input keyframes. We demonstrate the performance of our framework on two different motion datasets and also compare our results with the the results generated by a baseline imitation policy.
Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects

Adversarial attacks against supervised learninga algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects. We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to six attacks related to supervised learning algorithms. Using Log Assistant to conduct Postmortems for Supervised Learning (LOPSUL), we quantify the frequency of the identified log-related coding patterns with 278 open-source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category.
A Method of Real-time Data Access and Storage in Urban Rail Based on Containers

This paper proposed a method of real-time data access and storage in urban rail based on containers. It deploys real-time data access and application services in containers to get operation and management ability from native containers; besides, it builds real-time data computing engines in the container to improve data access performance. In addition, it creates a special data storage structure to enhance query efficiency. API gateway and subscribe channel are dynamically published and updated to increase data query flexibility. Finally, we tested real-time data access and query performance in containers on a containerized platform. The test results show that the method of real-time data access and storage is satisfied with the performance requirements in the urban rail.
Efficient Drone Exploration in Real Unknown Environments

We propose an autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Recent studies have applied saliency prediction to drone exploration. However, these studies are not sufficiently mature. The ADES system proposes a smaller and faster saliency prediction model and adopts a novel drone exploration approach based on visual-inertial odometry (VIO) to solve the practical problems encountered during exploration, i.e., exploring salient objects without colliding with them and not repeatedly exploring salient objects. The system not only has a performance comparable to that of the state-of-the-art multiple-discontinuous-image saliency prediction network (TA-MSNet) but also enables drones to explore unknown environments more efficiently.
Punctuation Prediction in Bangla Text

Punctuation prediction is critical as it can enhance the readability of machine-transcribed speeches or texts significantly by adding appropriate punctuation. Furthermore, systems like Automatic Speech Recognizer (ASR) produce texts that are unpunctuated, making the readability difficult for humans and also hampers the performance of various natural language processing (NLP) tasks. Such NLP related tasks have been investigated thoroughly for English; however, very limited work is done for punctuation prediction in the Bangla language. In this study, we train a bidirectional recurrent neural network (BRNN) along with Attention model with a plausibly large Bangla dataset. Afterwards, we apply extensive postprocessing techniques for predicting punctuation more accurately with the employed model. Initially, we perform experimentation with a relatively imbalanced dataset, and our model shows promising results F1=56.9 for Period) in punctuation prediction. Later, we also investigate the model’s performance using a balanced Bangla dataset to achieve higher performance scores (F1=62.2 for Question). Thus, the goal of this study is to propose an efficient approach that can predict punctuation in Bangla texts effectively. Our study also includes investigation on how our postprocessing techniques affect the prediction performance. Being an early attempt for the punctuation prediction in Bangla text, our work is expected to significantly contribute in the NLP field for the Bangla language, and will pave the way for future work with the Bangla language in this direction.
Improved Residual Connection Network for Diabetic Foot Ulcers Classification

Most existing image classification methods have achieved remarkable progress in the field of natural images. However, in the data-scarce and data-complex field of diabetic foot ulcer (DFU), accurate classification of data remains a thorny problem. In this paper, we propose an end-to-end network model for the multi-class (4-class) classification task of DFU. Specifically, in order to keep the network at a certain depth and avoid the gradient disappearing during the training process, we define the identity residual block as the basic module for building the model. For the purpose of improving the expressive ability of the network, we design an asymmetric convolution module to guide the network to focus on the central region that contains more information in the image. By alternately connecting identity residual blocks with asymmetric convolution modules, our model enables the network to better extract important features of the image, while ensuring that the network does not have gradients dissipating at a certain depth. The model is validated on the DFUC2021 test set, and the F1-score and AUC value are 0.585 and 0.850, respectively, which greatly outperforms the baseline results and can provide a reference for the doctor's diagnosis.
Robust and Efficient Structure-from-Motion Method for Ambiguous Large-Scale Indoor Scene

Figure 1: Comparison of reconstruction results of large-scale indoor scene with high ambiguity and repeated structures. (a) Result of COLMAP [Schönberger and Frahm 2016], (b) result of [Su et al. 2020], and (c) result of our method. Both (a) and (b) indicate failure to reconstruct some ambiguous areas and floor collapse. By contrast, our method could successfully construct a complete model, and all parts were connected correctly. ACM Reference Format: Chia-Hung Yu and Kuan-Wen Chen. 2022. Robust and Efficient Structurefrom-Motion Method for Ambiguous Large-Scale Indoor Scene. In SIGGRAPH Asia 2022 Posters (SA ’22 Posters), December 06-09, 2022. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3550082.3564199
CLASSIFICATION OF ACTIVE GALAXIES AND THE PROSPECT OF A UNIFIED PHENOMENOLOGY.

The observational classification of active galaxies is reviewed and its likely meaning discussed. There is only one kind of active galactic nucleus (AGN). The observed variety arises from three degrees of freedom: (1) Dust opacity, which produces the distinction between Type 1 and Type 2 AGN. (2) Viewing angle of a relativistic jet, which produces the distinction between blazars and Type 1 AGN. (3) Duty cycle of activity (i.e., fraction of time spent "on") which produces the distinction between radio-loud and radio-quiet AGN. The duty cycle may be related to the mass of the spheroidal component of the parent galaxy. A fourth degree of freedom is, of course, the overall luminosity. For theories of AGN one needs a clear qualitative phenomenology and one needs to formulate a quantitative phenomenology.