Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest Fire Detection Algorithm Based on DetNet-FPN Feature Fusion Network

The occurrence of forest fire has caused a large area of forest damage, casualties and economic losses, and forest fire detection is the key to the timely warning of fire. The problem of small target loss still exists in the forest fire detection algorithm using FPN network. In order to solve the problem of poor definition of object edge and loss of small target flame semantic information caused by 32-fold downsampling in FPN multi-scale feature fusion network, a forest fire detection algorithm based on DetNet-FPN feature fusion network was proposed. The backbone network of the algorithm adopts DetNet59, which is specially designed for target detection task. The network is improved on the basis of ResNet50, and the sixth stage is added. In order to maintain the resolution of high-level feature map, downsampling is abandoned in the fifth and sixth stages. Furthermore, dilated convolution is used to replace the original bottleneck structure with 3x3 convolution to enlarge the receptive field of feature map, thus improving the detection ability of small scale targets. Experimental results show that compared with FPN algorithm, the average accuracy of the proposed algorithm is improved by 2.70%, and the accuracy of small target is improved by 2.3%, which has good detection effect in various scenarios.
An hardware accelerator design of Mobile-Net model on FPGA

Domain specific hardware architectures and hardware accelerators have been a vital part of modern system design. Especially for math intensive applications involving tasks related to machine perception, incorporating hardware accelerators that work in tandem with general purpose micro-processors can prove to be energy efficient both at server and edge scenarios. FPGAs, due to their reconfigurability makes it possible to have customized hardware designed as per the computational and memory requirements specific to that application. This work proposes an optimized low latency hardware accelerator implementation of Mobile-net V2 CNN on an FPGA. This paper presents an implementation of Mobile-net-V2 inference on a Xilinx Ultrascale+ MPSOC platform incorporating solely half precision floating point arithmetic for both parameters and activations of the network. The proposed implementation is also optimized by merging all batch-norm layers with its preceding convolutional layers. For applications which cannot compromise on performance of the algorithm for execution speed and efficiency, an optimized floating point inference is proposed. The current implementation offers an overall performance improvement of at-least 20X with moderate resource utilization with minimal variance in inference latency, as compared to performing inference on the processor alone with almost no degradation in the model accuracy.
Temporal control of Num1 contact site formation reveals a relationship between mitochondrial division and mitochondria-plasma membrane tethering

Mitochondrial division is critical for maintenance of mitochondrial morphology and cellular homeostasis. Previous work has suggested that the mitochondria-ER-cortex anchor (MECA), a tripartite membrane contact site between mitochondria, the ER, and the plasma membrane, is involved in mitochondrial division. However, its role is poorly understood. We developed a system to control MECA formation and depletion, which allowed us to investigate the relationship between MECA-mediated contact sites and mitochondrial division. Num1 is the protein that mediates mitochondria-ER-plasma membrane tethering at MECA sites. Using both rapamycin-inducible dimerization and auxin-inducible degradation components coupled with Num1, we developed systems to temporally control the formation and depletion of the native contact site. Additionally, we designed a regulatable Num1-independant mitochondria-PM tether. We found that mitochondria-PM tethering alone is not sufficient to rescue mitochondrial division and that a specific feature of Num1-mediated tethering is required. This study demonstrates the utility of systems that regulate contact site formation and depletion in studying the biological functions of membrane contact sites.
REPIC — an ensemble learning methodology for cryo-EM particle picking

Cryo-EM (cryogenic electron microscopy) particle identification from micrographs (i.e., picking) is challenging due to the low signal-to-noise ratio and lack of ground truth for particle locations. Moreover, current computational methods (“pickers”) identify different particle sets, complicating the selection of the best-suited picker for a protein of interest. Here, we present REPIC, an ensemble learning methodology that uses multiple pickers to find consensus particles. REPIC identifies consensus particles by framing its task as a graph problem and using integer linear programming to select particles. REPIC picks high-quality particles when the best picker is not known a priori and for known difficult-to-pick particles (e.g., TRPV1). Reconstructions using consensus particles achieve resolutions comparable to those from particles picked by experts, without the need for downstream particle filtering. Overall, our results show REPIC requires minimal (often no) manual picking and significantly reduces the burden on cryo-EM users for picker selection and particle picking. Availability https://github.com/ccameron/REPIC
WINDING ROADS TO SUCCESS: AN EXAMINATION OF ELONGATED PATHWAYS TO DEGREE COMPLETION IN MATHEMATICS

iv List of Tables ix List of Figures x Chapter One: Introduction 1 The “Nontraditional” Student 1 Defining Path Types 2 Elaborating the Issue 4 Overview of the Thesis 5 Chapter Two: Frameworks 6 Mathematical Identity 6 Self-efficacy 7 Efficacy in Mathematics 7 Productive Disposition in Mathematics 8 Predicting Persistence 8 Influences on Choice of STEM Major 10 Summary 10 Chapter Three: Methods 12 Research Questions 12 Potential for Bias 13
Characterizing the microbial metagenome of calcareous stromatolite formations in the San Felipe Creek in Anza Borrego Desert

Here we describe the metagenome composition, community functional annotation, and diversity of prokaryotic microbial species derived from calcareous stromatolite formations discovered in the dry stream bed of the open-canopy, ephemeral San Felipe Creek in the Anza Borrego Desert. In this environment, resident microbes must be able to adapt to the harsh conditions of extreme heat, high UV light, desiccation and fluctuating solubilization/precipitation and hydration/evaporation. Metagenomic analysis revealed a community capable of carrying out complete nitrogen fixation and assimilatory nitrate reduction, forming biofilms and quorum sensing, and potentially forming thick-walled akinetes as desiccation-resistant stages. Nitrogen cycling is likely to play a fundamental role in mediating both the structure of the stromatolite microbial community and the mineral precipitation/dissolution. The viruses present in the stromatolites, particularly Nodularia and Mycobacterium phages are also likely to impact community population dynamics and activity. Stromatolite community members possess different morphological and physiological strategies to cope with desiccation stress. Metagenomic signatures were found for scytonemin, carotenoids, synthesis of potential microsporine-like amino acids; genes involved in microalgal desiccation tolerance, including those encoding aquaporins, chaperones, antioxidants; and enzymes responsible for the synthesis of trehalose, sucrose, and polyamines. The stromatolite ecosystem provides a diverse array of microniches where different functional guilds can develop complex metabolite exchange with the substrate supporting their life in extreme conditions. Metagenome analyses revealed several genes that might enable a specialized and unique group of endolithic cyanobacteria including Chroococcidiopsis, Hyella, Myxosarcina, and Pleurocapsa to derive metals and important nutrients from rocks, being potentially destructive for the calcareous formations. Our study revealed environmental adaptations of freshwater microbial communities in desert stream stromatolites which may provide valuable insights into Precambrian paleoenvironments, which are little known.
Deep learning for rapid and reproducible histology scoring of lung injury in a porcine model

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Although in vitro models replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for highly-trained pathologists. Standardized scoring simplifies the task and allows better comparisons between research groups and across different injury models, but it is time-consuming, and interobserver variability remains a significant concern. Convolutional neural networks (CNNs), which have emerged as a key tool in image analysis, could automate this process, potentially enabling faster and more reproducible analysis. Here we explored the reproducibility of human standardized scoring for an animal model of ARDS and its suitability for training CNNs for automated scoring at the whole slide level. We found large variations between human scorers, even for pre-clinical experts and board-certified pathologies in evaluating ARDS animal models. We demonstrate that CNNs (VGG16, EfficientNetB4) are suitable for automated scoring and achieve up to 83% F1-score and 78% accuracy. Thus, CNNs for histopathological classification of acute lung injury could help reduce human variability and eliminate a time-consuming manual research task with acceptable performance.
The wake of two staggered square cylinders

This work aims to provide a systematic experimental study of the wake behind two staggered square cylinders at a Reynolds number $Re=1.3\times 10^{4}$ . Four distinct flow regimes, i.e. two single-street modes S-I and S-II and two double-street modes T-I and T-II, are identified based on extensive data, including Strouhal numbers $(St)$ , flow structures and their downstream evolution. S-I, S-II and T-II are each further subdivided into two types. The flow characteristics in each regime are presented in terms of shear layer reattachment and impingement, vortex impingement, gap flow behaviour, interaction between cylinders and downstream evolution of the wake. A detailed discussion is made regarding how the physical aspects of the flow are connected to the initial conditions and the $St$ number.
Use of Epivolve phage display to generate a monoclonal antibody with opsonic activity directed against a subdominant epitope on extracellular loop 4 of Treponema pallidum BamA (TP0326)

Syphilis, a sexually transmitted infection caused by the spirochete Treponema pallidum (Tp), is resurging globally. Opsonic antibodies (Abs) targeting surface-exposed epitopes of the spirochete’s outer membrane proteins (OMPs) are believed to promote macrophage-mediated clearance of the bacterium during infection and are presumed to be key to vaccine development. Tp’s repertoire of outer membrane proteins includes BamA (β-barrel assembly machinery subunit A/TP0326), the central component of the molecular machine that inserts newly exported OMP precursors into the OM lipid bilayer. BamA is a bipartite protein consisting of an 18-stranded β-barrel with nine extracellular loops (ECLs) and five periplasmic POTRA (polypeptide transport-associated) domains. Antisera directed against BamA ECL4 promote internalization of Tp by rabbit peritoneal macrophages. Herein, we employed a novel two-stage, phage display strategy, termed “Epivolve” (for epitope evolution), to generate five site-directed murine monoclonal Abs (mAbs) targeting a centrally located peptide (S2) of BamA ECL4. Each of the five mAbs demonstrated reactivity by immunoblotting and ELISA to nanogram amounts of BamA ECL4 displayed by a Pyrococcus furiosus thioredoxin (PfTrx) scaffold (PfTrxBamA/ECL4). One mAb containing a unique amino acid sequence in both light and heavy chains showed activity in an opsonophagocytosis assay employing murine bone marrow-derived macrophages. Mice and rabbits hyperimmunized with PfTrxBamA/ECL4 produced opsonic antisera that strongly recognized the ECL presented in a heterologous scaffold and overlapping ECL4 peptides including S2. In contrast, Abs generated during Tp infection of mice and rabbits poorly recognized the peptides, indicating that S2 contains a subdominant epitope. Epivolve, which circumvents the natural immune response, can be utilized for the generation of mAbs that target subdominant opsonic epitopes in ECLs of Tp OMPs.
Cactus: a user-friendly and reproducible ATAC-Seq and mRNA-Seq analysis pipeline for data preprocessing, differential analysis, and enrichment analysis

The widespread use of ATAC-Seq and mRNA-Seq generates a need for methods to thoroughly analyze the resulting data. Here we introduce Cactus (Chromatin accessibility and transcriptomics unifying software), a pipeline that streamlines the individual or integrated analysis of ATAC-Seq and mRNA-Seq data. The pipeline is written in Nextflow for efficient scaling, caching, and parallelization, and all tools are packaged in Singularity/Docker containers or conda/Mamba virtual environments for simple installation and high reproducibility. Cactus conducts preprocessing on raw sequencing reads, followed by differential analysis between conditions. Results are split into Differential Analysis Subsets (DASs) based on significances thresholds, direction of change, annotated genomic regions, and experiment type. Then, Cactus computes enrichments of entries from internal (i.e., DASs) and external (i.e., ontologies, pathways, DNA binding motifs, ChIP-Seq (Chromatin immunoprecipitation sequencing) binding sites, and chromatin states) databases in DASs and presents results in barplots and customizable heatmaps. The visualization of the results is simplified by the generation of merged PDFs, merged tables, and formatted Excel tables. In conclusion, Cactus can assist researchers in gaining comprehensive insights from chromatin accessibility and gene expression data in a quick, user-friendly, and reproducible manner.
Intermediate basal cell population in prostate homeostasis and cancer initiation

Many glandular epithelia are mainly composed of basal cells and luminal cells, including the prostate gland. Adult prostate basal and luminal cells are independently self-sustained by unipotent stem cells that can reactivate multipotency under prostate inflammation and carcinogenesis contexts. However, the defined basal stem cell populations responsible for prostate regeneration and their cell fates in prostate homeostasis, inflammation and carcinogenesis remain unclear. Using a genetic proliferation tracer (ProTracer) system, we found that basal cells exhibited extensive cell loss and proliferation during androgen-mediated prostate regression and regeneration, respectively. A rare intermediate basal cell population that expresses luminal cell markers (Nkx3.1 and Pbsn) (termed Basal-B) and a large basal cell population (termed Basal-A) were identified in mouse prostates by single-cell RNA sequencing. Basal-B cells exhibited a greater capacity for organoid formation and luminal cell differentiation in vitro. Genetic lineage tracing using dual recombinases showed that prostate homeostasis and regeneration are not driven by specific basal cell types. Fate-mapping results showed that Basal-B cells had a greater tendency to generate luminal cells under bacteria-induced prostate inflammation. Deletion of Pten in basal cells resulted in Basal-A-to-Basal-B-to-luminal transition and prostatic intraepithelial neoplasia. Moreover, the human Basal-B-cell population was significantly increased in human benign prostate hyperplasia and prostatic intraepithelial neoplasia samples compared with normal prostate samples. This study identifies intermediate Basal-B cells as a potential stem cell population and provides genetic evidence of prostate basal cell lineage plasticity under physiological and pathological contexts.
Optical nanosensor passivation enables highly sensitive detection of the inflammatory cytokine IL-6

Interleukin-6 (IL-6) is known to a play critical role in the progression of inflammatory diseases such as cardiovascular disease, cancer, sepsis, viral infection, neurological disease, and autoimmune diseases. Emerging diagnostic and prognostic tools, such as optical nanosensors, experience challenges in successful clinical application in part due to protein corona formation dampening their selectivity and sensitivity. To address this problem, we explored the rational screening of several classes of biomolecules to be employed as agents in non-covalent surface passivation as a strategy to screen interference from non-specific proteins. Findings from this screening were applied to the detection of IL-6 by a fluorescent antibody-conjugated single-walled carbon nanotube (SWCNT)-based nanosensor. The IL-6 nanosensor exhibited highly sensitive and specific detection after passivation with a polymer, poly-L-lysine, as demonstrated by IL-6 detection in human serum within a clinically relevant range of 25 pg/mL to 25,000 pg/mL, exhibiting a limit of detection over three orders of magnitude lower than prior antibody-conjugated SWCNT sensors. This work holds the potential for rapid and highly sensitive detection of IL-6 in clinical settings with future application to other cytokines or disease-specific biomarkers.
BiATNovo: A Self-Attention based Bidirectional Peptide Sequencing Method

Traditional database-based peptide sequencing methods have shortcomings in discoverability and universality, while de novo sequencing is the essential way to analyze unknown proteins and discover new peptides and proteins. Most existing de novo sequencing algorithms have the problem of accumulated deviation and unbalanced output. At the same time, some algorithms could be more suitable for Data-Independent Acquisition Mass Spectrometry (DIA-MS) data. This paper designed a bidirectional peptide sequencing method to alleviate the problems of unbalanced output and deviation accumulation in the sequencing process. The self-attention mechanism was applied to de novo sequencing to increase the interaction within the peptide sequence and the interaction between the MS/MS spectra and the peptide sequence. On the DIA-MS dataset, the peptide prediction accuracy improved by an average of 15.6% compared with the state-of-the-art method. On the DDA-MS dataset, our method achieved the best performance on partial datasets, the amino acid accuracy improved by an average of 3%. At the same time, two new evaluation scores, Position-BLEU and Alignment score, were proposed to evaluate the misalignment between the predicted sequence and the reference sequence, and the partial absence of fragment ions.
Intrinsic Priors for Objective Bayesian Model Selection

Abstract We put forward the idea that for model selection the intrinsic priors are becoming a center of a cluster of a dominant group of methodologies for objective Bayesian Model Selection. The intrinsic method and its applications have been developed in the last two decades, and has stimulated closely related methods. The intrinsic methodology can be thought of as the long searched approach for objective Bayesian model selection and hypothesis testing. In this paper we review the foundations of the intrinsic priors, their general properties, and some of their applications.
The kinetics and basal levels of the transcriptome response during Effector-Triggered Immunity in Arabidopsis are mainly controlled by four immune signaling sectors

To observe the transcriptome response during Effector-Triggered Immunity (ETI) without complications from any other pathogen factors or heterogeneously responding cell populations, we transgenically and conditionally expressed the Pseudomonas syringae effector AvrRpt2 in Arabidopsis leaves. We studied this ETI-specific, cell-autonomous transcriptome response in 16 exhaustively combinatorial genetic backgrounds for the jasmonate (JA), ethylene (ET), PAD4, and salicylate (SA) immune signaling sectors. Removal of some or all four sectors had relatively small impacts on the intensity of the overall ETI transcriptome response (1972 upregulated and 1290 downregulated genes). Yet, we found that the four signaling sectors strongly affect the kinetics of the ETI transcriptome response based on analysis of individual genes via time-course modeling and of the collective behaviors of the genes via a PCA-based method: the PAD4 sector alone and the JA;SA sector interaction (defined by the averaging model) accelerated the response, while the ET;SA sector interaction delayed it. The response acceleration by the PAD4 sector or the JA;SA sector interaction was consistent with their positive contributions to ETI measured by pathogen growth inhibition. The responsive genes overlapping between ETI and Pattern-Triggered Immunity (PTI) had distinct regulatory trends regarding the four sectors, indicating different regulatory circuits in upstream parts of ETI and PTI signaling. The basal mRNA levels of most ETI-upregulated genes, but not downregulated genes, were predominantly positively regulated by the PAD4;SA sector interaction. This detailed mechanistic decomposition of the roles of four signaling sectors allowed us to propose a potential regulatory network involved in ETI signaling.
Efficient framework for operating on data sketches

We study the problem of analyzing massive data streams based on concise data sketches. Recently, a number of papers have investigated how to estimate the results of set-theory operations based on sketches. In this paper we present a framework that allows to estimate the result of any sequence of set-theory operations. The starting point for our solution is the solution from 2021. Compared to this solution, the newly presented sketching algorithm is much more computationally efficient as it requires on average O (log n ) rather than O ( n ) comparisons for n stream elements. We also show that the estimator dedicated to sketches proposed in that reference solution is, in fact, a maximum likelihood estimator.
Revisiting the past to understand the present and future of soil health in Brazil

As Soil Scientists, we are gathering important and valuable knowledge about the chemical, physical and biological processes in soil, and with the increasing effects of climate change, this knowledge may play a pivotal role in the future of our planet. However, we must revisit crucial points in our past to understand how humanity’s evolution has shaped the current state of soil health. Furthermore, we must also consider that we are funded and supported by the society in which we live, and therefore social and political factors will inevitably play a part in the future of soil health. In this review, we address important historical aspects of crop development and soil microbiome combined with the provision of key ecosystem services to ensure soil sustainability. In addition, we provide a brief overview of key concepts related to soil health, including the criteria of the selection of indicators for soil health assessment, whilst focusing on the role of soil biology. Moreover, we provide an overview of research conducted across diverse biomes in Brazil, highlighting approaches to assess soil health in both agroecosystems and natural ecosystems. We also emphasize the significance of harnessing beneficial plant-microorganism interactions as an ecologically sustainable strategy for enhancing soil health. Finally, we conclude the review by discussing potential advancements in soil health assessment in Brazil, and their potential application in broader agricultural and forestry contexts.