Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired functions of human monocyte-derived dendritic cells and induction of regulatory T cells by pathogenic Leptospira

Leptospirosis is a global zoonosis caused by pathogenic Leptospira. The disease outcome is influenced by the interplay between innate and adaptive immune responses. Dendritic cells (DCs) play a crucial role in shaping the adaptive immune response. A recent study revealed that pathogenic Leptospira limited the activation of human monocyte-derived dendritic cells (MoDCs) compared to non-pathogenic Leptospira, but their impact on T-cell responses has not been investigated. Our study is the first to explore how viable pathogenic and non-pathogenic Leptospira affect the interaction between human MoDCs and T cells. We found that MoDCs infected with pathogenic leptospires (L. interrogans serovar Pomona and a clinical isolate, MoDCs-P) exhibited lower levels of CD80 and CD83 expression, suggesting partially impaired MoDC maturation, induced regulatory T cells (Tregs) while failing to induce CD4+ T cell proliferation, compared to MoDCs infected with non-pathogenic leptospires (L. biflexa serovar Patoc and L. meyeri serovar Ranarum, MoDCs-NP). In contrast, non-pathogenic leptospires enhanced MoDC maturation and induced higher T cell proliferation including IFN-γ-producing CD4+ T cells, indicative of a Th1-type response. Furthermore, pathogenic leptospires induced higher MoDC apoptosis through a cysteine aspartic acid-specific protease-3 (caspase-3)-dependent pathway and upregulated expression of the prostaglandin-endoperoxide synthase 2 (PTGS2) gene. Notably, prostaglandin E2 (PGE2), a product of the PTGS2 pathway, was found at higher levels in the sera of patients with acute leptospirosis and in the supernatant of MoDCs-P, possibly contributing to Treg induction, compared to those of healthy donors and MoDCs-NP, respectively. In conclusion, this study reveals a novel immunosuppressive strategy employed by pathogenic Leptospira to evade host immunity by partially impairing MoDC maturation and inducing Tregs. These findings deepen our understanding of leptospirosis pathogenesis in humans and may provide a novel strategy to modulate DCs for the prevention and treatment of the disease.
ROB-ME: a tool for assessing risk of bias due to missing evidence in systematic reviews with meta-analysis

Various methods are available to help users assess whether selective non-publication of studies or selective non-reporting of study results has occurred, but not its impact on a meta-analysis. This limitation of existing methods leaves users to decide their own approach for judging the risk of bias in a meta-analysis result. In this paper, Page and colleagues describe the ROB-ME (risk of bias due to missing evidence) tool, a structured approach for assessing the risk of bias that arises when entire studies, or particular results within studies, are missing from a meta-analysis because of the P value, magnitude, or direction of the study results. The tool is anticipated to help authors and users of systematic reviews identify meta-analyses at high risk of bias and interpret results appropriately.
Study on the Estimation of Regional Oxygen Production:A Case Study in Harbin

With the recent advancement in climate change research,it is imperative to examine the dynamic of oxygen concentration in the atmosphere.This research,therefore,attempted to estimate the regional oxygen production by simulating various levels of plant productivities in Harbin,Heilongjiang Province,China,using the photosynthesis principle.In particular,the total amount and spatial distribution of oxygen production within Harbin were estimated using the net primary productivity(NPP) and net ecosystem productivity(NEP). The NPP and NEP,in turn,were estimated employing the C-FIX model with the help of MODIS Net Primary Production(NPP) product.Several conclusions can be drawn as follows.Firstly,in 2008,the total net oxygen production in Harbin was 394423.95 tons.For oxygen consumption,autotrophic respiration accounted for 30.787% of the initial oxygen production,and heterotrophic respiration consumption was negligible(0.001%). We also found that the more initial oxygen production was,the more consumption through plant respiration.Secondly,oxygen production in Harbin has distinct seasonal variations.The production is near zero in winter(from December to February),medium in autumn and spring(slightly higher in autumn),and highest in summer.Finally,oxygen production also has apparent spatial patterns.Within Harbin City,Acheng District has the highest monthly and annual oxygen production,followed by Hulan and Songbei districts,while the old districts,including Nangang,Daoli,Daowai,Xiangfang and Pingfang,have the lowest level.In summary,this paper estimated the daily,monthly,and annual net oxygen production and examined their spatial distribution dynamics by applying the photosynthesis principle with the help of remote sensing(RS) and geographic information system(GIS) technologies.This research proposed a new idea for regional oxygen production estimation,thus providing a basis for studying the dynamics of regional oxygen concentration,and evaluating regional environmental quality for supporting human life.
Epigenetic regulation of asymmetric cell division by the LIBR-BRD4 axis

Abstract Asymmetric cell division (ACD) is a mechanism used by stem cells to maintain the number of progeny. However, the epigenetic mechanisms regulating ACD remain elusive. Here we show that BRD4, a BET domain protein that binds to acetylated histone, is segregated in daughter cells together with H3K56Ac and regulates ACD. ITGB1 is regulated by BRD4 to regulate ACD. A long noncoding RNA (lncRNA), LIBR (LncRNA Inhibiting BRD4), decreases the percentage of stem cells going through ACD through interacting with the BRD4 mRNAs. LIBR inhibits the translation of BRD4 through recruiting a translation repressor, RCK, and inhibiting the binding of BRD4 mRNAs to polysomes. These results identify the epigenetic regulatory modules (BRD4, lncRNA LIBR) that regulate ACD. The regulation of ACD by BRD4 suggests the therapeutic limitation of using BRD4 inhibitors to treat cancer due to the ability of these inhibitors to promote symmetric cell division that may lead to tumor progression and treatment resistance.
Ophiolites in the Non-volcanic Banda Outer Arc of East Indonesia: Field Occurrence and Petrological Variety of the World's Youngest Ophiolite

The western Pacific region, where the Eurasia, Australia, and Pacific plates currently interact, has been recognized as an important site for constraining the origins and emplacement of ophiolites (particularly for island-arc or supra-subduction zone types), because the spatial distribution of oceanic micro-plates and numerous ophiolitic rocks along their convergent margins infers possible genetic linkages among them. Mafic-ultramafic rocks distributed in the Timor-Tanimbar island chain, eastern Indonesia may be a good example of the on-going emplacement of the marginal basin lithosphere on the continental margin in the arc-continent collision zone, and are recognized as a possible modern analogue for Mesozoic Tethyan-type ophiolites (e.g. Troodos and Oman) in the Alpine-Himalayan orogenic system. Geological occurrence suggests that the buoyant subduction of the Australian continent uplifted fragments of newly formed mantle-crust section, which extends to neighboring pre-emplaced forearc marginal basins. However, from petrological and geochemical points of view, young pillowed basalt, dolerite, and gabbroic cumulate commonly possess island-arc signatures, whereas structurally underlying peridotites are mostly fertile (lherzolitic) in composition. This suggests that the crustal section is not linked to the underlying mantle by a genetic melt-and-residua relationship, as inferred from the lack of complete succession and the presence of abundant crosscutting structures. This inconsistency leads to the emergence of two contrasting models accounting for the unusual occurrence of a fertile mantle in the forearc setting of the Timor-Tanimbar region: (1) thrust-stacked fragments of the subcontinental mantle originally exhumed in the rifting stage of Australia; (2) depth-related heterogeneities in the lithospheric part of the mantle wedge. We note that the current debates on the origins of fertile lherzolites found throughout the Tethyan sutures and western Pacific regions can be settled through a better understanding of Timor-Tanimbar peridotite masses by age-dating studies employing several radiogenic isotope systematics.
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction

Adversarial patch-based attacks have shown to be a major deterrent towards the reliable use of machine learning models. These attacks involve the strategic modification of localized patches or specific image areas to deceive trained machine learning models. In this paper, we propose \textit{DefensiveDR}, a practical mechanism using a dimensionality reduction technique to thwart such patch-based attacks. Our method involves projecting the sample images onto a lower-dimensional space while retaining essential information or variability for effective machine learning tasks. We perform this using two techniques, Singular Value Decomposition and t-Distributed Stochastic Neighbor Embedding. We experimentally tune the variability to be preserved for optimal performance as a hyper-parameter. This dimension reduction substantially mitigates adversarial perturbations, thereby enhancing the robustness of the given machine learning model. Our defense is model-agnostic and operates without assumptions about access to model decisions or model architectures, making it effective in both black-box and white-box settings. Furthermore, it maintains accuracy across various models and remains robust against several unseen patch-based attacks. The proposed defensive approach improves the accuracy from 38.8\% (without defense) to 66.2\% (with defense) when performing LaVAN and GoogleAp attacks, which supersedes that of the prominent state-of-the-art like LGS (53.86\%) and Jujutsu (60\%).
Ecology and Diversity of Diatoms in Kuttanadu Paddy Fields in Relation to Soil Regions, Seasons and Paddy-Growth-Stages

This assessment of ecology and diversity of Diatoms in Kuttanadu , is continuation of the previous publications of Green-algae and Blue-green-algae of the same region; all the three investigations were carried out simultaneously. The unique Kuttanadu wetlands (90°17'N to 90°40'N; 760°19'E to 760°33'E), a well known ‘Rice bowl’ of Kerala, spread over 53,639 hectares, is located in Alapuzha District of Kerala, India. This wetland was once known for its high biodiversity, but currently severely affected by intensive green-revolution activities of past few decades. Main objective of the present report is to explain the diversity and ecological status of Diatoms in relation to the environment conditions currently existing in the region. Duration of the study was from December 2009 to October 2010. Soil samples of three different soil-types in two different crop-seasons, at two diverse crop-growth-stages are analyzed and compared. Altogether 120 composite soil samples randomly collected from the broad wetlands are analyzed. A rich community of Diatoms, of 40 species is found out. The order Naviculales is observed as the dominant Diatom (40%) in these paddy wetlands. Ecological parameters of Diatoms were found highest in the Lower Kuttanadu soil region, during Virippu season, at the seedling- stage of the crop, whereas the lowest value for most of the parameters were observed in Kayal soils during Puncha season at the seedling-stage. Apart from the specific soil factors influencing Diatom population characteristics in the region, crop seasons and soil phosphorus are found to have significant influence on Diatoms in this wetland paddy soils.
Interpretation of the Transformer and Improvement of the Extractor

It has been over six years since the Transformer architecture was put forward. Surprisingly, the vanilla Transformer architecture is still widely used today. One reason is that the lack of deep understanding and comprehensive interpretation of the Transformer architecture makes it more challenging to improve the Transformer architecture. In this paper, we first interpret the Transformer architecture comprehensively in plain words based on our understanding and experiences. The interpretations are further proved and verified. These interpretations also cover the Extractor, a family of drop-in replacements for the multi-head self-attention in the Transformer architecture. Then, we propose an improvement on a type of the Extractor that outperforms the self-attention, without introducing additional trainable parameters. Experimental results demonstrate that the improved Extractor performs even better, showing a way to improve the Transformer architecture.
Efficient Reinforcement Learning from Partial Observability

In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior performance for algorithms that conflate observations with state. Partially Observable Markov Decision Processes (POMDPs), on the other hand, provide a general framework that allows for partial observability to be accounted for in learning, exploration and planning, but presents significant computational and statistical challenges. To address these difficulties, we develop a representation-based perspective that leads to a coherent framework and tractable algorithmic approach for practical reinforcement learning from partial observations. We provide a theoretical analysis for justifying the statistical efficiency of the proposed algorithm, and also empirically demonstrate the proposed algorithm can surpass state-of-the-art performance with partial observations across various benchmarks, advancing reliable reinforcement learning towards more practical applications.
Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases

—In this paper, we propose the neural Born iteration method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iteration method (TBIM). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions with their corresponding residuals. Two different schemes of NeuralBIMs are presented in this paper including supervised and unsupervised learning schemes. With the data set generated by method of moments (MoM), supervised NeuralBIMs are trained with the knowledge of total ﬁelds and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded loss functions founding on the governing equations of ISPs, which results in no requirements of total ﬁelds and contrasts for training. Representative numerical results further validate the effectiveness and competitiveness of both supervised and unsupervised NeuralBIMs.
Light bipolarons in a system of electrons coupled to dispersive optical phonons

We investigate the ground state properties of the bipolaron coupled to quantum dispersive optical phonons in the one-dimensional Holstein-Hubbard model. We concentrate on the interplay between the phonon dispersion and the Coulomb repulsion and their mutual effect on the bipolaron effective mass, the binding energy, and the phase diagram. Most surprisingly, the sign of the curvature of the optical phonon dispersion plays a decisive role on the bipolaron binding energy in the presence of the Coulomb repulsion $U$. In particular, when the sign of the phonon dispersion curvature matches the sign of the electron dispersion curvature, the bipolaron remains bound in the strong coupling limit even when $U\to \infty$ and the binding emanates from the exchange of phonons between two electrons residing on adjacent sites. At moderate electron-phonon coupling a light bipolaron exists up to large values of $U$. Finally, an intuitive explanation of the role of the phonon dispersion on the bipolaron binding energy is derived using the strong coupling limit where the binding emanates from the exchange of phonons between two electrons residing on adjacent sites which leads to enhanced stability of bipolarons at elevated Coulomb repulsion.
"HoVer-UNet": Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation

We present HoVer-UNet, an approach to distill the knowledge of the multi-branch HoVerNet framework for nuclei instance segmentation and classification in histopathology. We propose a compact, streamlined single UNet network with a Mix Vision Transformer backbone, and equip it with a custom loss function to optimally encode the distilled knowledge of HoVerNet, reducing computational requirements without compromising performances. We show that our model achieved results comparable to HoVerNet on the public PanNuke and Consep datasets with a three-fold reduction in inference time. We make the code of our model publicly available at https://github.com/DIAGNijmegen/HoVer-UNet.
Design for Assurance: Employing Functional Verification Tools for Thwarting Hardware Trojan Threat in 3PIPs

Third-party intellectual property cores are essential building blocks of modern system-on-chip and integrated circuit designs. However, these design components usually come from vendors of different trust levels and may contain undocumented design functionality. Distinguishing such stealthy lightweight malicious design modification can be a challenging task due to the lack of a golden reference. In this work, we make a step towards design for assurance by developing a method for identifying and preventing hardware Trojans, employing functional verification tools and languages familiar to hardware designers. We dump synthesized design netlist mapped to a field programmable gate array technology library and perform switching as well as coverage analysis at the granularity of look-up-tables (LUTs) in order to identify specious signals and cells. We automatically extract and formally prove properties related to switching and coverage, which allows us to retrieve Trojan trigger condition. We further provide a solution to preventing Trojan from activation by reconfiguring the confirmed malicious LUTs. Experimental results have demonstrated that our method can detect and mitigate Trust-Hub as well as recently reported don't care Trojans.
Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications

The advent of Large Language Models (LLMs) heralds a pivotal shift in online user interactions with information. Traditional Information Retrieval (IR) systems primarily relied on query-document matching, whereas LLMs excel in comprehending and generating human-like text, thereby enriching the IR experience significantly. While LLMs are often associated with chatbot functionalities, this paper extends the discussion to their explicit application in information retrieval. We explore methodologies to optimize the retrieval process, select optimal models, and effectively scale and orchestrate LLMs, aiming for cost-efficiency and enhanced result accuracy. A notable challenge, model hallucination-where the model yields inaccurate or misinterpreted data-is addressed alongside other model-specific hurdles. Our discourse extends to crucial considerations including user privacy, data optimization, and the necessity for system clarity and interpretability. Through a comprehensive examination, we unveil not only innovative strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems, but also the consequential considerations that underline the need for a balanced approach aligned with user-centric principles.
cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia

In this paper, we provide an affirmative answer to the long-standing question: Are GPUs useful in solving linear programming? We present cuPDLP.jl, a GPU implementation of restarted primal-dual hybrid gradient (PDHG) for solving linear programming (LP). We show that this prototype implementation in Julia has comparable numerical performance on standard LP benchmark sets as Gurobi, a highly optimized implementation of the simplex and interior-point methods. Furthermore, we present the superior performance of cuPDLP.jl with its CPU counterpart. This demonstrates the power of using GPUs in the optimization solvers.
Constraints on Hairy Kerr black hole with quasi-periodic oscillations

The Hairy Kerr black hole is a novel black hole solution that depicts a rotating space-time encompassed by an axisymmetric fluid. It has significant observational importance and is an excellent candidate for an astrophysical black hole. Our study investigates the impact of the hairy charge on the quasi-periodic oscillations (QPOs) of X-ray binaries in the Hairy Kerr black hole (HKBH) space-time. The relativistic precession model is employed to compute the three principal frequencies of the accretion disk encircling the HKBH. We compare our outcomes with the observations of five X-ray binaries and employ a Markov chain Monte Carlo (MCMC) simulation for restricting the hairy charge parameters. There is no substantial evidence for the existence of hairy charge in the HKBH space-time. Therefore, we are placing observational constraints on the deformation parameters with $0<\alpha<0.07697$ and hairy charge values ranging from $0.27182
An Optimisation Approach to Non-Separable Quaternion-Valued Wavelet Constructions

We formulate the construction of quaternion-valued wavelets on the plane as a feasibility problem. We refer to this as the quaternionic wavelet feasibility problem. The constraint sets arise from the standard requirements of smoothness, compact support and orthonormality. We solve the resulting feasibility problems by employing the Douglas-Rachford algorithm. From the solutions of the quaternionic wavelet feasibility problem, we derive novel examples of compactly supported, smooth and orthonormal quaternion-valued wavelets on the plane. We also illustrate how a symmetry condition can be added to produce symmetric quaternion-valued scaling functions on the plane.
Uncertainty Estimation in Contrast-Enhanced MR Image Translation with Multi-Axis Fusion

In recent years, deep learning has been applied to a wide range of medical imaging and image processing tasks. In this work, we focus on the estimation of epistemic uncertainty for 3D medical image-to-image translation. We propose a novel model uncertainty quantification method, Multi-Axis Fusion (MAF), which relies on the integration of complementary information derived from multiple views on volumetric image data. The proposed approach is applied to the task of synthesizing contrast enhanced T1-weighted images based on native T1, T2 and T2-FLAIR scans. The quantitative findings indicate a strong correlation ($\rho_{\text healthy} = 0.89$) between the mean absolute image synthetization error and the mean uncertainty score for our MAF method. Hence, we consider MAF as a promising approach to solve the highly relevant task of detecting synthetization failures at inference time.