The CrowdGleason dataset: Learning the Gleason grade from crowds and experts.

Background: Currently, prostate cancer (PCa) diagnosis relies on the human analysis of prostate biopsy Whole Slide Images (WSIs) using the Gleason score. Since this process is error-prone and time-consuming, recent advances in machine learning have promoted the use of automated systems to assist pathologists. Unfortunately, labeled datasets for training and validation are scarce due to the need for expert pathologists to provide ground-truth labels.

Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD.

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure. In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information.

The association between sociodemographic characteristics, clinical indicators and body mass index in a population at risk of type 2 diabetes: A cross-sectional study in two Colombian cities.

Aims: To assess the association between sociodemographic and clinical factors with body mass index (BMI) in a population at risk of type 2 diabetes (T2D) in Bogotá and Barranquilla, Colombia.

Methods: This cross-sectional study used data from the PREDICOL Study. Participants with a FINDRISC ≥ 12 who underwent an Oral Glucose Tolerance Test (OGTT) were included in the study (n=1166).

Knowing Our Enemy in the Antimicrobial Resistance Era: Dissecting the Molecular Basis of Bacterial Defense Systems.

Bacteria and their phage adversaries are engaged in an ongoing arms race, resulting in the development of a broad antiphage arsenal and corresponding viral countermeasures. In recent years, the identification and utilization of CRISPR-Cas systems have driven a renewed interest in discovering and characterizing antiphage mechanisms, revealing a richer diversity than initially anticipated. Currently, these defense systems can be categorized based on the bacteria's strategy associated with the infection cycle stage.

Focused active learning for histopathological image classification.

Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.

Study of Canine Distemper Virus Presence in Catalonia's Wild Carnivores through H Gene Amplification and Sequencing.

Canine distemper virus (CDV) is recognised worldwide as an important pathogen in both domestic and wild carnivores. Few data are available on its impact and spread on the wildlife/wildlife-domestic animal-environment interface. This study, aimed at developing a conservation-oriented control strategy, analysed 89 sick or deceased animals from 2019 to 2023 at the Wildlife Rehabilitation Centre in Torreferrussa.

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images.

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions.

Learning from crowds for automated histopathological image segmentation.

Automated semantic segmentation of histopathological images is an essential task in Computational Pathology (CPATH). The main limitation of Deep Learning (DL) to address this task is the scarcity of expert annotations. Crowdsourcing (CR) has emerged as a promising solution to reduce the individual (expert) annotation cost by distributing the labeling effort among a group of (non-expert) annotators.

Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset.

Digital Pathology (DP) has experienced a significant growth in recent years and has become an essential tool for diagnosing and prognosis of tumors. The availability of Whole Slide Images (WSIs) and the implementation of Deep Learning (DL) algorithms have paved the way for the appearance of Artificial Intelligence (AI) systems that support the diagnosis process. These systems require extensive and varied data for their training to be successful.

Molecular basis of the final step of cell division in Streptococcus pneumoniae.

Bacterial cell-wall hydrolases must be tightly regulated during bacterial cell division to prevent aberrant cell lysis and to allow final separation of viable daughter cells. In a multidisciplinary work, we disclose the molecular dialogue between the cell-wall hydrolase LytB, wall teichoic acids, and the eukaryotic-like protein kinase StkP in Streptococcus pneumoniae. After characterizing the peptidoglycan recognition mode by the catalytic domain of LytB, we further demonstrate that LytB possesses a modular organization allowing the specific binding to wall teichoic acids and to the protein kinase StkP.

Operando DRIFT-MS for studying the oxidative steam reforming of ethanol (OSRE) reaction.

An operando DRIFT-MS system (Diffuse Reflectance Infrared Fourier Transform Spectroscopy coupled with Mass Spectrometry) was designed and set up to study the oxidative steam reforming of ethanol reaction (OSRE). This reaction involves the mixture of water, ethanol and oxygen to produce mainly hydrogen, which is a rather attractive energy carrier. Spectroscopic monitoring of the process is a key tool to contribute to the understanding of: i) the dynamics on the catalyst surface, ii) the reaction mechanism and iii) the effect of the solid's properties on the catalytic process.

Probabilistic Attention Based on Gaussian Processes for Deep Multiple Instance Learning.

Multiple instance learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised methods. This is especially interesting for areas where the creation of large annotated datasets remains challenging, as in medicine. Although recent deep learning MIL approaches have obtained state-of-the-art results, they are fully deterministic and do not provide uncertainty estimations for the predictions.

Phosphate alternatives for meat processing and challenges for the industry: A critical review.

Meat and meat products provide high levels of nutrition and many health benefits to consumers, yet a controversy exists regarding the use of non-meat additives, such as the inorganic phosphates that are commonly used in meat processing, and particularly their relationship to cardiovascular health and kidney complications. Inorganic phosphates are salts of phosphoric acid (e.g.

Verification: model-free phasing with enhanced predicted models in ARCIMBOLDO_SHREDDER.

Structure predictions have matched the accuracy of experimental structures from close homologues, providing suitable models for molecular replacement phasing. Even in predictions that present large differences due to the relative movement of domains or poorly predicted areas, very accurate regions tend to be present. These are suitable for successful fragment-based phasing as implemented in ARCIMBOLDO.

Molecular basis of cyclic tetra-oligoadenylate processing by small standalone CRISPR-Cas ring nucleases.

Standalone ring nucleases are CRISPR ancillary proteins, which downregulate the immune response of Type III CRISPR-Cas systems by cleaving cyclic oligoadenylates (cA) second messengers. Two genes with this function have been found within the Sulfolobus islandicus (Sis) genome. They code for a long polypeptide composed by a CARF domain fused to an HTH domain and a short polypeptide constituted by a CARF domain with a 40 residue C-terminal insertion.

Regulation of Lytic Machineries by the FtsEX Complex in the Bacterial Divisome.

The essential membrane complex FtsE/FtsX (FtsEX), belonging to the ABC transporter superfamily and widespread among bacteria, plays a relevant function in some crucial cell wall remodeling processes such as cell division, elongation, or sporulation. FtsEX plays a double role by recruiting proteins to the divisome apparatus and by regulating lytic activity of the cell wall hydrolases required for daughter cell separation. Interestingly, FtsEX does not act as a transporter but uses the ATPase activity of FtsE to mechanically transmit a signal from the cytosol, through the membrane, to the periplasm that activates the attached hydrolases.

Proportion constrained weakly supervised histopathology image classification.

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis because it alleviates the burden of labeling all cancerous regions of large Whole Slide Images (WSIs) in detail. However, these methods require large datasets to perform properly, and many approaches only focus on simple binary classification.

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

Background And Objective: Intracranial hemorrhage (ICH) is a life-threatening emergency that can lead to brain damage or death, with high rates of mortality and morbidity. The fast and accurate detection of ICH is important for the patient to get an early and efficient treatment. To improve this diagnostic process, the application of Deep Learning (DL) models on head CT scans is an active area of research.

Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification.

Stain variation between images is a main issue in the analysis of histological images. These color variations, produced by different staining protocols and scanners in each laboratory, hamper the performance of computer-aided diagnosis (CAD) systems that are usually unable to generalize to unseen color distributions. Blind color deconvolution techniques separate multi-stained images into single stained bands that can then be used to reduce the generalization error of CAD systems through stain color normalization and/or stain color augmentation.

Predictors of diabetes risk in urban and rural areas in Colombia.

Background: Nutritional habits low in fruits and vegetables and sedentary lifestyle are associated with a higher risk of developing Type 2 Diabetes (T2D). However, it is important to assess differences between urban and rural areas. This study aimed to analyze the associations between the risk of developing T2D and setting in the Colombian north coast in 2017.

Structural basis of cyclic oligoadenylate degradation by ancillary Type III CRISPR-Cas ring nucleases.

Type III CRISPR-Cas effector systems detect foreign RNA triggering DNA and RNA cleavage and synthesizing cyclic oligoadenylate molecules (cA) in their Cas10 subunit. cAs act as a second messenger activating auxiliary nucleases, leading to an indiscriminate RNA degradation that can end in cell dormancy or death. Standalone ring nucleases are CRISPR ancillary proteins which downregulate the strong immune response of Type III systems by degrading cA.

Integrative structural biology of the penicillin-binding protein-1 from , an essential component of the divisome machinery.

The penicillin-binding proteins are the enzyme catalysts of the critical transpeptidation crosslinking polymerization reaction of bacterial peptidoglycan synthesis and the molecular targets of the penicillin antibiotics. Here, we report a combined crystallographic, small-angle X-ray scattering (SAXS) in-solution structure, computational and biophysical analysis of PBP1 of PBP1), providing mechanistic clues about its function and regulation during cell division. The structure reveals the pedestal domain, the transpeptidase domain, and most of the linker connecting to the "penicillin-binding protein and serine/threonine kinase associated" (PASTA) domains, but not its two PASTA domains, despite their presence in the construct.

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference.

Background And Objective: Color variations in digital histopathology severely impact the performance of computer-aided diagnosis systems. They are due to differences in the staining process and acquisition system, among other reasons. Blind color deconvolution techniques separate multi-stained images into single stained bands which, once normalized, can be used to eliminate these negative color variations and improve the performance of machine learning tasks.