Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of an operon and its regulator required for autoaggregation in Tetragenococcus halophilus

ABSTRACT Bacterial autoaggregation is associated with self- and non-self-recognition, surface colonization, and biofilm formation. Numerous aggregation factors have been cloned for basic and applied purposes. Snowflake Forming Collagen Binding Aggregation Factor (SFCBAF) is one such aggregation factor isolated from a few lactic acid bacteria species; however, the regulatory mechanisms of SFCBAF remain poorly understood. Here, we report the aggR and aggABC genes associated with autoaggregation in the halophilic lactic acid bacterium Tetragenococcus halophilus. A draft genome sequence analysis of five aggregating strains and subsequent comparative genomic analysis revealed that all of them have aggR and aggABC genes, which are possibly involved in autoaggregation. SFCBAF, a sortase, and a hypothetical protein are encoded by aggABC, which constitutes the agg operon. To analyze the function of these genes, phenotype- and genotype-based mutant screening was carried out. Mutants of either aggR or aggA lack autoaggregation and collagen binding abilities, indicating that they are both essential for these phenotypes. aggR was considered a positive regulator for the agg operon because aggR encodes a helix-turn-helix protein and aggR mutation resulted in diminished agg operon expression. Our results provide genetic bases underlying the mechanisms of aggregation and its regulation in T. halophilus. IMPORTANCE Tetragenococcus halophilus is a halophilic lactic acid bacterium generally used as a starter culture in fermenting soy and fish sauces. Aggregating strains can be useful in fermenting and obtaining clear soy sauce because cell clumps are trapped by the filter cake when the soy sauce mash is pressed. However, the genetic mechanisms of aggregation in T. halophilus are unknown. In this study, we identified genes encoding aggregation factor and its regulator. These findings may provide a foundation for developing improved T. halophilus starter cultures for soy sauce fermentation, leading to more efficient and consistent clear soy sauce production. Tetragenococcus halophilus is a halophilic lactic acid bacterium generally used as a starter culture in fermenting soy and fish sauces. Aggregating strains can be useful in fermenting and obtaining clear soy sauce because cell clumps are trapped by the filter cake when the soy sauce mash is pressed. However, the genetic mechanisms of aggregation in T. halophilus are unknown. In this study, we identified genes encoding aggregation factor and its regulator. These findings may provide a foundation for developing improved T. halophilus starter cultures for soy sauce fermentation, leading to more efficient and consistent clear soy sauce production.
Assessing left atrial intramyocardial fat infiltration from computerized tomography angiography in patients with atrial fibrillation

Abstract Aims Epicardial adipose tissue might promote atrial fibrillation (AF) in several ways, including infiltrating the underlying atrial myocardium. However, the role of this potential mechanism has been poorly investigated. The aim of this study is to evaluate the presence of left atrial (LA) infiltrated adipose tissue (inFAT) by analysing multi-detector computer tomography (MDCT)-derived three-dimensional (3D) fat infiltration maps and to compare the extent of LA inFAT between patients without AF history, with paroxysmal, and with persistent AF. Methods and results Sixty consecutive patients with AF diagnosis (30 persistent and 30 paroxysmal) were enrolled and compared with 20 age-matched control; MDCT-derived images were post-processed to obtain 3D LA inFAT maps for all patients. Volume (mL) and mean signal intensities [(Hounsfield Units (HU)] of inFAT (HU −194; −5), dense inFAT (HU −194; −50), and fat-myocardial admixture (HU −50; −5) were automatically computed by the software. inFAT volume was significantly different across the three groups (P = 0.009), with post-hoc pairwise comparisons showing a significant increase in inFAT volume in persistent AF compared to controls (P = 0.006). Dense inFAT retained a significant difference also after correcting for body mass index (P = 0.028). In addition, more negative inFAT radiodensity values were found in AF patients. Regional distribution analysis showed a significantly higher regional distribution of LA inFAT at left and right superior pulmonary vein antra in AF patients. Conclusion Persistent forms of AF are associated with greater degree of LA intramyocardial adipose infiltration, independently of body mass index. Compared to controls, AF patients present higher LA inFAT volume at left and right superior pulmonary vein antra.
CEST and nuclear Overhauser enhancement imaging with deep learning–extrapolated semisolid magnetization transfer reference: Scan‐rescan reproducibility and reliability studies

To develop a novel MR physics‐driven, deep‐learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR.
ControlVideo: Conditional Control for One-shot Text-driven Video Editing and Beyond

This paper presents \emph{ControlVideo} for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions (such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r. t. the source content, and temporal consistency, all while aligning with the text. By incorporating Low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing (e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous works achieved. The code is available at \href{https://github.com/thu-ml/controlvideo}{https://github.com/thu-ml/controlvideo} and the visualization results are available at \href{https://drive.google. com/file/d/1wEgc2io3UwmoC5vTPbkccFvTkwVqsZlK/view? usp=drive_link}{HERE}.
Decomposition of Symplectic Vector Bundles and Azumaya Algebras

Let $X$ be a connected CW complex. Let $\mathcal{V}$ be a symplectic vector bundle of rank $2mn$ over $X$, and let $\mathcal{A}$ be a topological Azumaya algebra of degree $2mn$ with a symplectic involution over a $X$. We give conditions for the positive integers $m$ and $n$, and the dimension of $X$ so that $\mathcal{V}$ can be decomposed as the tensor product of a symplectic vector bundle of rank $2m$ and an orthogonal vector bundle of rank $n$; and so that $\mathcal{A}$ can be decomposed as the tensor product of topological Azumaya algebras of degrees $2m$ and $n$ with involutions of the first kind.
Epitaxial two-dimensional membranes under intrinsic and extrinsic strains

Two-dimensional (2D) materials naturally form moir\'{e} patterns with other crystalline layers, such as other 2D material or the surface of a substrate. These patterns add a nanoscale characteristic length in the form of a superlattice: the moir\'{e} wavelength. Understanding the origin and characteristics of these patterns is crucial to design/interpret moir\'{e}-induced physical properties. Here, we use a mixed continuum mechanics + atomistic modeling to study two experimentally relevant epitaxial 2D materials -- graphene on Ir(111) and MoS$_2$ on Au(111) -- under extrinsic and intrinsic strain. We consider three different scenarios affecting substantially the lattice constant of the 2D materials, the wavelength and corrugation of the moir\'{e} pattern. (i) Under the influence of the interaction with the substrate, bending energy produces non trivial variations of the moir\'{e} properties, even when the strain is small; (ii) When locked on a progressively strained substrate via the valleys of the moir\'{e}, the membranes' nanorippling amplitude goes through several jumps related to relatively smaller jumps in the interatomic distance of the 2D materials; (iii) Finally, increasing the zero-deformation value of this interatomic distance (possibly controlable with temperature or illumination in experiments) the moir\'{e} wavelength can either increase or decrease.
SIDDHARTA-2 apparatus for kaonic atoms research on the DA$\Phi$NE collider

SIDDHARTA-2 represents a state-of-the-art experiment designed to perform dedicated measurements of kaonic atoms, which are particular exotic atom configurations composed of a negatively charged kaon and a nucleus. Investigating these atoms provides an exceptional tool to comprehend the strong interactions in the non-perturbative regime involving strangeness. The experiment is installed at the DA$\Phi$NE electron-positron collider, of the INFN National Laboratory of Frascati (INFN-LNF) in Italy, aiming to perform the first-ever measurement of the 2p$\rightarrow$1s X-ray transitions in kaonic deuterium, a crucial step towards determining the isospin-dependent antikaon-nucleon scattering lengths. Based on the experience gained with the previous SIDDHARTA experiment, which performed the most precise measurement of the kaonic hydrogen 2p$\rightarrow$1s X-ray transitions, the present apparatus has been upgraded with innovative Silicon Drift Detectors (SDDs), distributed around a cryogenic gaseous target placed in a vacuum chamber at a short distance above the interaction region of the collider. We present a comprehensive description of the SIDDHARTA-2 setup including the optimization of its various components during the commissioning phase of the collider.
Morphisms of Cuntz-Pimsner algebras from completely positive maps

We introduce positive correspondences as right C*-modules with left actions given by completely positive maps. Positive correspondences form a semi-category that contains the C*-correspondence (Enchilada) category as a"retract". Kasparov's KSGNS construction provides a semi-functor from this semi-category onto the C*-correspondence category. The need for left actions by completely positive maps appears naturally when we consider morphisms between Cuntz-Pimsner algebras, and we describe classes of examples arising from projections on C*-correspondences and Fock spaces, as well as examples from conjugation by bi-Hilbertian bimodules of finite index.
Superfluid fraction and Leggett bound in a density-modulated strongly interacting Fermi gas at zero temperature

We calculate the superfluid fraction of an interacting Fermi gas, in the presence of a one-dimensional periodic potential of strength $V_0$ and wave-vector $q$. Special focus is given to the unitary Fermi gas, characterized by the divergent behavior of the s-wave scattering length. Comparison with the Leggett's upper bound $(\langle n_{1D}\rangle<1/n_{1D}>)^{-1}$, with $n_{1D}$ the 1D column density, explicitly shows that, differently from the case of a dilute interacting Bose gas, the bound significantly overestimates the value of the superfluid fraction, except in the phonon regime of small $q$. Sum rule arguments show that the combined knowledge of the Leggett bound and of the actual value of the superfluid fraction allows for the determination of curvature effects providing the deviation of the dispersion of the Anderson-Bogoliubov mode from the linear phonon dependence. The comparison with the predictions of the weakly interacting BCS Fermi gas points out the crucial role of two-body interactions. The implications of our predictions on the anisotropic behavior of the sound velocity are also discussed.
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation

Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain. However, we find that retaining the spurious correlation between causal and non-causal factors plays a vital role in bridging the domain gap and improving target adaptation. Therefore, we propose to build a framework that disentangles and supports causal factor alignment by aligning the non-causal factors first. We also investigate and find that the strong shape bias of vision transformers, coupled with its multi-head attention, make it a suitable architecture for realizing our proposed disentanglement. Hence, we propose to build a Causality-enforcing Source-Free Transformer framework (C-SFTrans) to achieve disentanglement via a novel two-stage alignment approach: a) non-causal factor alignment: non-causal factors are aligned using a style classification task which leads to an overall global alignment, b) task-discriminative causal factor alignment: causal factors are aligned via target adaptation. We are the first to investigate the role of vision transformers (ViTs) in a privacy-preserving source-free setting. Our approach achieves state-of-the-art results in several DA benchmarks.
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts

Contrastive vision-language models, such as CLIP, have garnered considerable attention for various dowmsteam tasks, mainly due to the remarkable ability of the learned features for generalization. However, the features they learned often blend content and style information, which somewhat limits their generalization capabilities under distribution shifts. To address this limitation, we adopt a causal generative perspective for multimodal data and propose contrastive learning with data augmentation to disentangle content features from the original representations. To achieve this, we begins with exploring image augmentation techniques and develop a method to seamlessly integrate them into pre-trained CLIP-like models to extract pure content features. Taking a step further, recognizing the inherent semantic richness and logical structure of text data, we explore the use of text augmentation to isolate latent content from style features. This enables CLIP-like model's encoders to concentrate on latent content information, refining the learned representations by pre-trained CLIP-like models. Our extensive experiments across diverse datasets demonstrate significant improvements in zero-shot and few-shot classification tasks, alongside enhanced robustness to various perturbations. These results underscore the effectiveness of our proposed methods in refining vision-language representations and advancing the state-of-the-art in multimodal learning.
Target-Free Compound Activity Prediction via Few-Shot Learning

Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discovery. Existing few-shot learning approaches are limited to predicting binary labels (active/inactive). However, in real-world drug discovery, degrees of compound activity are highly relevant. We study Few-Shot Compound Activity Prediction (FS-CAP) and design a novel neural architecture to meta-learn continuous compound activities across large bioactivity datasets. Our model aggregates encodings generated from the known compounds and their activities to capture assay information. We also introduce a separate encoder for the unknown compound. We show that FS-CAP surpasses traditional similarity-based techniques as well as other state of the art few-shot learning methods on a variety of target-free drug discovery settings and datasets.
Investigation of Indian stock markets using topological data analysis and geometry-inspired network measures

Geometry-inspired measures (such as discrete Ricci curvatures) and topological data analysis (TDA) based methods (such as persistent homology) have become attractive tools for characterizing the higher-order structure of networks representing the financial systems. In this study, our goal is to perform a comparative analysis of both these approaches, especially by assessing the fragility and systemic risk in the Indian stock markets, which is known for its high volatility and risk. To achieve this goal, we analyze the time series of daily log-returns of stocks comprising the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). Specifically, our aim is to monitor the changes in standard network measures, edge-centric discrete Ricci curvatures, and persistent homology based topological measures computed from cross-correlation matrices of stocks. In this study, the edge-centric discrete Ricci curvatures have been employed for the first time in the analysis of the Indian stock markets. The Indian stock markets are known to be less diverse in comparison to the US market, and hence provides us an interesting example. Our results point that, among the persistent homology based topological measures, persistent entropy is simple and more robust than $L^1$-norm and $L^2$-norm of persistence landscape. In a broader comparison between network analysis and TDA, we highlight that the network analysis is sensitive to the way of constructing the networks (threshold or minimum spanning tree), as well as the threshold values used to construct the correlation-based threshold networks. On the other hand, the persistent homology is a more robust approach and is able to capture the higher-order interactions and eliminate noisy data in financial systems, since it does not take into account a single value of threshold but rather a range of values.
Unified-modal Salient Object Detection via Adaptive Prompt Learning

Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing completely different models for different tasks leads to labor and time consumption, as well as high computational and practical deployment costs. In this paper, we make the first attempt to address both single-modal and multi-modal SOD in a unified framework called UniSOD. Nevertheless, assigning appropriate strategies to modality variable inputs is challenging. To this end, UniSOD learns modality-aware prompts with task-specific hints through adaptive prompt learning, which are plugged into the proposed pre-trained baseline SOD model to handle corresponding tasks, while only requiring few learnable parameters compared to training the entire model. Each modality-aware prompt is generated from a switchable prompt generation block, which performs structural switching solely relied on single-modal and multi-modal inputs. UniSOD achieves consistent performance improvement on 14 benchmark datasets for RGB, RGB-D, and RGB-T SOD, which demonstrates that our method effectively and efficiently unifies single-modal and multi-modal SOD tasks.
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark

With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models. However, most benchmarks predominantly assess spatial understanding in the static image tasks, while overlooking temporal understanding in the dynamic video tasks. To alleviate this issue, we introduce a comprehensive Multi-modal Video understanding Benchmark, namely MVBench, which covers 20 challenging video tasks that cannot be effectively solved with a single frame. Specifically, we first introduce a novel static-to-dynamic method to define these temporal-related tasks. By transforming various static tasks into dynamic ones, we enable the systematic generation of video tasks that require a broad spectrum of temporal skills, ranging from perception to cognition. Then, guided by the task definition, we automatically convert public video annotations into multiple-choice QA to evaluate each task. On one hand, such a distinct paradigm allows us to build MVBench efficiently, without much manual intervention. On the other hand, it guarantees evaluation fairness with ground-truth video annotations, avoiding the biased scoring of LLMs. Moreover, we further develop a robust video MLLM baseline, i.e., VideoChat2, by progressive multi-modal training with diverse instruction-tuning data. The extensive results on our MVBench reveal that, the existing MLLMs are far from satisfactory in temporal understanding, while our VideoChat2 largely surpasses these leading models by over 15% on MVBench. All models and data are available at https://github.com/OpenGVLab/Ask-Anything.
Land-Use Intensification in Grazing Systems: Plant Trait Responses and Feedbacks to Ecosystem Functioning and Resilience

Land-use change is the single most important global driver of changes in biodiversity. Such changes in biodiversity, in turn, are expected to influence the functioning of ecosystems and their resilience to environmental perturbations and disturbances. It is widely recognised that the use of functional traits and functional diversity is best for understanding the causes and functional consequences of changes in biodiversity, but conceptual development has outpaced empirical applications. This thesis explores these ideas in grazing systems, which are expected to undergo rapid intensification of fertiliser use and grazing pressure to meet the growing global demand for livestock products. First, a flexible framework for measuring different facets of functional diversity is described, and a new multidimensional functional diversity index, called functional dispersion (FDis), is presented. Second, two vegetation sampling methods are compared with regard to their ability to detect changes in vegetation composition. Third, shifts in plant trait distributions following land-use changes are quantified and compared to null models, and a maximum entropy approach is used to quantify the direction and strength of selection on each trait. Fourth, it is shown that these shifts in trait distributions have cascading effects on primary production, litter decomposition, soil respiration, and ultimately soil carbon sequestration. Finally, data from 18 land-use intensity gradients are used to show that land-use intensification reduces functional redundancy and response diversity, two components of biodiversity that are thought to influence ecosystem resilience to future disturbances. This study illustrates (i) the importance of considering species functional differences to understand how plant communities react to changes in soil resource availability and grazing pressure, and (ii) how such changes directly, indirectly, and interactively control ecosystem functioning, as well as (iii) increasing the vulnerability of ecosystems to future disturbances.