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. To address these challenges, we propose Focused Active Learning (FocAL), which combines a Bayesian Neural Network with Out-of-Distribution detection to estimate different uncertainties for the acquisition function. Specifically, the weighted epistemic uncertainty accounts for the class imbalance, aleatoric uncertainty for ambiguous images, and an OoD score for artifacts. We perform extensive experiments to validate our method on MNIST and the real-world Panda dataset for the classification of prostate cancer. The results confirm that other AL methods are 'distracted' by ambiguities and artifacts which harm the performance. FocAL effectively focuses on the most informative images, avoiding ambiguities and artifacts during acquisition. For both experiments, FocAL outperforms existing AL approaches, reaching a Cohen's kappa of 0.764 with only 0.69% of the labeled Panda data.

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. RT-PCR and sequencing of the partial H gene were used to detect and analyse CDV in tissues. The total positive percentage was 20.22% (18/89), comprising 13 red foxes (44.8%), 4 European badgers (28.6%), and 1 American mink (4.5%), while 24 Eurasian otters tested negative. Phylogenetic analysis indicated that all of the CDV strains belong to the European lineage. Geographically distant individuals and different species shared the same viral strain, suggesting a strong capacity of CDV for interspecies and long-distance transmission. This calls for further research, particularly focusing on potential impacts of CDV on endangered carnivores.

Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study.

BACKGROUND: Frailty resulting from the loss of muscle quality can potentially be delayed through early detection and physical exercise interventions. There is a demand for cost-effective tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessments. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional, and microstructural muscle properties, while biological assays derived from blood samples are associated with functional information. OBJECTIVE: This study aims to assess multiparametric combinations of ultrasound and blood-based biomarkers to offer a cross-sectional evaluation of the patient frailty phenotype and to track changes in muscle quality associated with supervised exercise programs. METHODS: This prospective observational multicenter study will include patients aged 70 years and older who are capable of providing informed consent. We aim to recruit 100 patients from hospital environments and 100 from primary care facilities. Each patient will undergo at least two examinations (baseline and follow-up), totaling a minimum of 400 examinations. In hospital environments, 50 patients will be measured before/after a 16-week individualized and supervised exercise program, while another 50 patients will be followed up after the same period without intervention. Primary care patients will undergo a 1-year follow-up evaluation. The primary objective is to compare cross-sectional evaluations of physical performance, functional capacity, body composition, and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based assays. We will analyze ultrasound raw data obtained with a point-of-care device, along with a set of biomarkers previously associated with frailty, using quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Additionally, we will examine the sensitivity of these biomarkers to detect short-term muscle quality changes and functional improvement after a supervised exercise intervention compared with usual care. RESULTS: At the time of manuscript submission, the enrollment of volunteers is ongoing. Recruitment started on March 1, 2022, and ends on June 30, 2024. CONCLUSIONS: The outlined study protocol will integrate portable technologies, using quantitative muscle ultrasound and blood biomarkers, to facilitate an objective cross-sectional assessment of muscle quality in both hospital and primary care settings. The primary objective is to generate data that can be used to explore associations between biomarker combinations and the cross-sectional clinical assessment of frailty and sarcopenia. Additionally, the study aims to investigate musculoskeletal changes following multicomponent physical exercise programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT05294757; https://clinicaltrials.gov/ct2/show/NCT05294757. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50325.

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. Extracting knowledge in this scenario is challenging, as it involves noisy annotations. Jointly learning the underlying (expert) segmentation and the annotators' expertise is currently a commonly used approach. Unfortunately, this approach is frequently carried out by learning a different neural network for each annotator, which scales poorly when the number of annotators grows. For this reason, this strategy cannot be easily applied to real-world CPATH segmentation. This paper proposes a new family of methods for CR segmentation of histopathological images. Our approach consists of two coupled networks: a segmentation network (for learning the expert segmentation) and an annotator network (for learning the annotators' expertise). We propose to estimate the annotators' behavior with only one network that receives the annotator ID as input, achieving scalability on the number of annotators. Our family is composed of three different models for the annotator network. Within this family, we propose a novel modeling of the annotator network in the CR segmentation literature, which considers the global features of the image. We validate our methods on a real-world dataset of Triple Negative Breast Cancer images labeled by several medical students. Our new CR modeling achieves a Dice coefficient of 0.7827, outperforming the well-known STAPLE (0.7039) and being competitive with the supervised method with expert labels (0.7723). The code is available at https://github.com/wizmik12/CRowd_Seg.

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. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.

Structural characterization of PaaX, the main repressor of the phenylacetate degradation pathway in Escherichia coli W: A novel fold of transcription regulator proteins.

PaaX is a transcriptional repressor of the phenylacetic acid (PAA) catabolic pathway, a central route for bacterial aerobic degradation of aromatic compounds. Induction of the route is achieved through the release of PaaX from its promoter sequences by the first compound of the pathway, phenylacetyl-coenzyme A (PA-CoA). We report the crystal structure of PaaX from Escherichia coli W. PaaX displays a novel type of fold for transcription regulators, showing a dimeric conformation where the monomers present a three-domain structure: an N-terminal winged helix-turn-helix domain, a dimerization domain similar to the Cas2 protein and a C-terminal domain without structural homologs. The domains are separated by a crevice amenable to harbour a PA-CoA molecule. The biophysical characterization of the protein in solution confirmed several hints predicted from the structure, i.e. its dimeric conformation, a modest importance of cysteines and a high dependence of solubility and thermostability on ionic strength. At a moderately acidic pH, the protein formed a stable folding intermediate with remaining α-helical structure, a disrupted tertiary structure and exposed hydrophobic patches. Our results provide valuable information to understand the stability and mechanism of PaaX and pave the way for further analysis of other regulators with similar structural configurations.

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. However, creating labeled datasets in histopathology is laborious and time-consuming. We have developed a crowdsourcing-multiple instance labeling/learning protocol that is applied to the creation and use of the CR-AI4SkIN dataset.2 CR-AI4SkIN contains 271 WSIs of 7 Cutaneous Spindle Cell (CSC) neoplasms with expert and non-expert labels at region and WSI levels. It is the first dataset of these types of neoplasms made available. The regions selected by the experts are used to learn an automatic extractor of Regions of Interest (ROIs) from WSIs. To produce the embedding of each WSI, the representations of patches within the ROIs are obtained using a contrastive learning method, and then combined. Finally, they are fed to a Gaussian process-based crowdsourcing classifier, which utilizes the noisy non-expert WSI labels. We validate our crowdsourcing-multiple instance learning method in the CR-AI4SkIN dataset, addressing a binary classification problem (malign vs. benign). The proposed method obtains an F1 score of 0.7911 on the test set, outperforming three widely used aggregation methods for crowdsourcing tasks. Furthermore, our crowdsourcing method also outperforms the supervised model with expert labels on the test set (F1-score = 0.6035). The promising results support the proposed crowdsourcing multiple instance learning annotation protocol. It also validates the automatic extraction of interest regions and the use of contrastive embedding and Gaussian process classification to perform crowdsourcing classification tasks.

LC-MS Fingerprinting Development for Standardized Precipitate from Agastache mexicana, Which Induces Antihypertensive Effect through NO Production and Calcium Channel Blockade.

The aim of this work was to evaluate the vasorelaxant and antihypertensive effects of a standardized precipitate of the hydroalcoholic extract from Agastache mexicana (PPAm), comprising ursolic acid, oleanolic acid, acacetin, luteolin and tilianin, among others. In the ex vivo experiments, preincubation with L-NAME (nonspecific inhibitor of nitric oxide synthases) reduced the relaxation induced by PPAm; nevertheless, preincubation with indomethacin (nonspecific inhibitor of cyclooxygenases) did not generate any change in the vasorelaxation, and an opposed effect was observed to the contraction generated by CaCl2 addition. Oral administration of 100 mg/kg of PPAm induced a significant acute decrease in diastolic (DBP) and systolic (SBP) blood pressure in spontaneously hypertensive rats, without changes in heart rate. Additionally, PPAm showed a sustained antihypertensive subacute effect on both DBP and SBP for 10 days compared to the control group. On the other hand, human umbilical vein cells treated with 10 µg/mL of PPAm showed a significant reduction (p < 0.05) in intracellular adhesion molecule-1, compared to the control, but not on vascular cell adhesion molecule-1. In conclusion, PPAm induces a significant antihypertensive effect in acute- and subacute-period treatments, due to its direct vasorelaxant action on rat aortic rings through NO production and Ca2+ channel blockade.

Ex Vivo and In Silico Approaches of Tracheal Relaxation through Calcium Channel Blockade of 6-Aminoflavone and Its Toxicological Studies in Murine Models.

Asthma is a condition in which a person's airways become inflamed, narrowed, and produce greater amounts of mucus than normal. It can cause shortness of breath, chest pain, coughing, or wheezing. In some cases, symptoms may be exacerbated. Thus, the current study was designed to determine the mechanism of action of 6-aminoflavone (6-NH2F) in ex vivo experiments, as well as to determine its toxicity in acute and sub-chronic murine models. Tissues were pre-incubated with 6-NH2F, and concentration-response curves to carbachol-induced contraction were constructed. Therefore, tracheal rings pre-treated with glibenclamide, 2-aminopyridine, or isoproterenol were contracted with carbachol (1 µM), then 6-NH2F relaxation curves were obtained. In other sets of experiments, to explore the calcium channel role in the 6-NH2F relaxant action, tissues were contracted with KCl (80 mM), and 6-NH2F was cumulatively added to induce relaxation. On the other hand, tissues were pre-incubated with the test sample, and after that, CaCl2 concentration-response curves were developed. In this context, 6-NH2F induced significant relaxation in ex vivo assays, and the effect showed a non-competitive antagonism pattern. In addition, 6-NH2F significantly relaxed the contraction induced by KCl and CaCl2, suggesting a potential calcium channel blockade, which was corroborated by in silico molecular docking that was used to approximate the mode of interaction with the L-type Ca2+ channel, where 6-NH2F showed lower affinity energy when compared with nifedipine. Finally, toxicological studies revealed that 6-NH2F possesses pharmacological safety, since it did not produce any toxic effect in both acute and sub-acute murine models. In conclusion, 6-aminoflavone exerted significant relaxation through calcium channel blockade, and the compound seems to be safe.

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. Structural and cellular studies notably reveal that the temporal and spatial localization of LytB is governed by the interaction between specific modules of LytB and the final PASTA domain of StkP. Our data collectively provide a comprehensive understanding of how LytB performs final separation of daughter cells and highlights the regulatory role of eukaryotic-like kinases on lytic machineries in the last step of cell division in streptococci.

Δ9-Tetrahydrocannabinol Treatment Modifies Insulin Secretion in Pancreatic Islets from Prediabetic Mice Under Hypercaloric Diet.

Background: The endocannabinoid system over-activation is associated with type-2 diabetes mellitus onset, involving physiological, metabolic, and genetic alterations in pancreatic islets. The use of Δ9-Tetrahydrocannabinol (THC) as treatment is still controversial since its effects and mechanisms on insulin secretion are unclear. The aim of this study was to evaluate the effects of THC treatment in pancreatic islets from prediabetic mice. Methods: Prediabetes was induced in mice by hypercaloric diet, and then treated with THC for 3 weeks. Blood glucose and body weight were determined, after behavior tests. Histological changes were evaluated in whole pancreas; in isolated islets we analyzed the effect of THC exposure in glucose-stimulated insulin secretion (GSIS), gene expression, intracellular cyclic adenosine monophosphate (cAMP), and cytosolic calcium changes. Results: THC treatment in prediabetic mice enhanced anxiety and antidepressive behavior without changes in food ingestion, decreased oral-glucose tolerance test, plasma insulin and weight, with small alterations on pancreatic histology. In isolated islets from healthy mice THC increased GSIS, cAMP, and CB1 receptor (CB1r) expression, meanwhile calcium release was diminished. Small changes were observed in islets from prediabetic mice. Conclusions: THC treatment improves some clinical parameters in prediabetic mice, however, in isolated islets, modifies GSIS, intracellular calcium and gene expression, suggesting specific effects related to diabetes evolution.

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. In this sense, this document sets forth the experimental design that allows to carry out the study under operando DRIFT-MS conditions through time for the OSRE reaction. Selection criteria for parameters, materials, configuration, and experimental conditions are included, particularly optimizing the parameters of particle size and the dilution factor with KBr as well as the temperature and flow conditions for carrying out the reaction.•Clear signals of the adsorbed species in IR that do not present interference by water in the reaction atmosphere.•Simple assembly and online product detection by MS that allow to follow the change in the products of the OSRE reaction according to the temperature.•Controlled entry of gases and quantification by loop injection.

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. In this work, we introduce the attention Gaussian process (AGP) model, a novel probabilistic attention mechanism based on Gaussian processes (GPs) for deep MIL. AGP provides accurate bag-level predictions as well as instance-level explainability and can be trained end-to-end. Moreover, its probabilistic nature guarantees robustness to overfit on small datasets and uncertainty estimations for the predictions. The latter is especially important in medical applications, where decisions have a direct impact on the patient's health. The proposed model is validated experimentally as follows. First, its behavior is illustrated in two synthetic MIL experiments based on the well-known MNIST and CIFAR-10 datasets, respectively. Then, it is evaluated in three different real-world cancer detection experiments. AGP outperforms state-of-the-art MIL approaches, including deterministic deep learning ones. It shows a strong performance even on a small dataset with less than 100 labels and generalizes better than competing methods on an external test set. Moreover, we experimentally show that predictive uncertainty correlates with the risk of wrong predictions, and therefore it is a good indicator of reliability in practice. Our code is publicly available.

Glycemic status and health-related quality of life (HRQOL) in populations at risk of diabetes in two Latin American cities.

PURPOSE: To estimate the health-related quality of life (HRQOL) according to glycemic status, and its relationship with sociodemographic and clinical factors in a population at risk of developing type 2 diabetes (T2D). METHODS: Cross-sectional study, using cluster sampling. Data were collected from 1135 participants over 30 years of age, at risk of developing T2D from the PREDICOL project. Participants' glycemic status was defined using an oral glucose tolerance test (OGTT). Participants were divided into normoglycemic subjects (NGT), prediabetes and diabetics do not know they have diabetes (UT2D). HRQOL was assessed using the EQ-5D-3L questionnaire of the EuroQol group. Logistic regression and Tobit models were used to examine factors associated with EQ-5D scores for each glycemic group. RESULTS: The mean age of participants was 55.6 ± 12.1 years, 76.4% were female, and one in four participants had prediabetes or unknown diabetes. Participants reported problems most frequently on the dimensions of Pain/Discomfort and Anxiety/Depression in the different glycemic groups. The mean EQ-5D score in NGT was 0.80 (95% CI 0.79-0.81), in prediabetes, 0.81 (95% CI 0.79-0.83), and in participants with UT2D of 0.79 (95% CI 0.76-0.82), respectively. Female sex, older age, city of residence, lower education, receiving treatment for hypertension, and marital status were significantly associated with lower levels of HRQOL in the Tobit regression analysis. CONCLUSIONS: HRQOL of NGT, prediabetes, and UT2D participants was statistically similar. However, factors such as gender, age. and place of residence were found to be significant predictors of HRQOL for each glycemic group.

Insulin Sensitization by PPARγ and GLUT-4 Overexpression/Translocation Mediates the Antidiabetic Effect of Plantago australis.

Plantago australis Lam. Subsp. hirtella (Kunth) Rahn is a medicinal plant used as a diuretic, anti-inflammatory, antibacterial, throat cancer treatment and for the control of diabetes. P. australis was collected in the state of Morelos, México. The hydroalcoholic extract (HAEPa) of P. australis was obtained by maceration and concentrated in vacuo. Once dry, it was evaluated through an oral glucose tolerance test (OGTT) in normoglycemic mice and in a non-insulin-dependent diabetic mice model. The expression of PPARγ and GLUT-4 mRNA was determined by rt-PCR, and GLUT-4 translocation was confirmed by confocal microscopy. The toxicological studies were conducted in accordance with the guidelines suggested by the OECD, sections 423 and 407, with some modifications. HAEPa significantly decreased glycemia in OGTT curves, as well as in the experimental diabetes model compared to the vehicle group. In vitro tests showed that HAEPa induced an α-glucosidase inhibition and increased PPARγ and GLUT-4 expression in cell culture. The LD50 of HAEPa was greater than 2000 mg/kg, and sub-chronic toxicity studies revealed that 100 mg/kg/day for 28 days did not generate toxicity. Finally, LC-MS analysis led to the identification of verbascoside, caffeic acid and geniposidic acid, and phytochemical approaches allowed for the isolation of ursolic acid, which showed significant PPARγ overexpression and augmented GLUT-4 translocation. In conclusion, HAEPa induced significant antidiabetic action by insulin sensitization through PPARγ/GLUT-4 overexpression.

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., sodium phosphate, potassium phosphate, or calcium phosphate), whereas organic phosphates are ester compounds (e.g., the phospholipids found in cell membranes). In this sense, the meat industry remains active in its efforts to improve formulations for processed meat products with the use of natural ingredients. Despite efforts to improve formulations, many processed meat products still contain inorganic phosphates, which are used for their technological contributions to meat chemistry including improvements in water-holding capacity and protein solubilization. This review provides a thorough evaluation of phosphate substitutes in meat formulations and other processing technologies that can help eliminate phosphates from the formulations of processed meat products. In general, several ingredients have been evaluated as replacements for inorganic phosphates with varying degrees of success such as plant-based ingredients (e.g., starches, fibers, or seeds), fungi ingredients (e.g., mushrooms and mushroom extracts), algae ingredients, animal-based ingredients (e.g., meat/seafood, dairy, or egg materials), and inorganic compounds (i.e., minerals). Although these ingredients have shown some favorable effects in certain meat products, none have exactly matched the many functions of inorganic phosphates, so the support of extrinsic technologies, such as tumbling, ultrasound, high-pressure processing (HPP), and pulsed electric field (PEF), may be necessary to achieve similar physiochemical properties as conventional products. The meat industry should continue to investigate ways to scientifically innovate the formulations of, and the technologies used in, processed meat products while also listening to (and acting upon) the feedback from consumers.

Resting metabolic rate in relation to incident disability and mobility decline among older adults: the modifying role of frailty.

BACKGROUND: Alterations in resting metabolic rate (RMR), the largest component of daily total energy expenditure, with aging have been shown in various studies. However, little is known about the associations between RMR and health outcomes in later life. AIMS: To analyze whether RMR is associated with incident disability and mobility decline in a 10-year longitudinal study, as well as the moderating role of frailty in these associations. METHODS: Data from 298 older adults aged 70 and over from the Frailty and Dependence in Albacete (FRADEA) study in Spain were used, including a baseline measurement in 2007-2009 and a follow-up measurement 10 years later. RMR was measured by indirect calorimetry. Outcomes were incident disability in basic activities of daily living (BADL, Barthel Index), incident disability in instrumental ADL (IADL, Lawton index), and mobility decline (Functional Ambulation Categories scores). Fried's frailty phenotype was used as an indicator of frailty. Logistic regression analyses were conducted. RESULTS: Fully adjusted and stratified analyses revealed that only in the pre-frail/frail group, a higher RMR was associated with a lower risk of incident BADL disability (OR = 0.47, 95% CI = 0.23-0.96, p = 0.037), incident IADL disability (OR = 0.39, 95% CI = 0.18-0.84, p = 0.017), and mobility decline (OR = 0.30, 95% CI = 0.14-0.64, p = 0.002). CONCLUSIONS: To our knowledge, this is the first study looking at the associations between RMR and functional health using a longitudinal research design. The results suggest that RMR could be used as an early identifier of a specific resilient group within the pre-frail and frail older population, with a lower risk of further health decline.

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. The particularities of predicted models are inherently addressed in the new predicted_model mode, rendering preliminary treatment superfluous but also harmless. B-value conversion from predicted LDDT or error estimates, the removal of unstructured polypeptide, hierarchical decomposition of structural units from domains to local folds and systematically probing the model against the experimental data will ensure the optimal use of the model in phasing. Concomitantly, the exhaustive use of models and stereochemistry in phasing, refinement and validation raises the concern of crystallographic model bias and the need to critically establish the information contributed by the experiment. Therefore, in its predicted_model mode ARCIMBOLDO_SHREDDER will first determine whether the input model already constitutes a solution or provides a straightforward solution with Phaser. If not, extracted fragments will be located. If the landscape of solutions reveals numerous, clearly discriminated and consistent probes or if the input model already constitutes a solution, model-free verification will be activated. Expansions with SHELXE will omit the partial solution seeding phases and all traces outside their respective masks will be combined in ALIXE, as far as consistent. This procedure completely eliminates the molecular replacement search model in favour of the inferences derived from this model. In the case of fragments, an incorrect starting hypothesis impedes expansion. The predicted_model mode has been tested in different scenarios.

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. Here, we determine the structure of the apo and substrate bound states of the Sis0455 enzyme, revealing an insertion at the C-terminal region of the CARF domain, which plays a key role closing the catalytic site upon substrate binding. Our analysis reveals the key residues of Sis0455 during cleavage and the coupling of the active site closing with their positioning to proceed with cA4 phosphodiester hydrolysis. A time course comparison of cA4 cleavage between the short, Sis0455, and long ring nucleases, Sis0811, shows the slower cleavage kinetics of the former, suggesting that the combination of these two types of enzymes with the same function in a genome could be an evolutionary strategy to regulate the levels of the second messenger in different infection scenarios.

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. While the complete molecular details of such mechanism are not yet known, evidence has been recently reported that clarify essential aspects of this complex system. In this chapter we will present recent structural advances on this topic. The three-dimensional structure of FtsE, FtsX, and some of the lytic enzymes or their cognate regulators revealed an unexpected scenario in which a delicate set of intermolecular interactions, conserved among different bacterial genera, could be at the core of this regulatory mechanism providing exquisite control in both space and time of this central process to assist bacterial survival.