Epithelial hypoxia maintains colonization resistance against Candida albicans.

Antibiotic treatment promotes the outgrowth of intestinal Candida albicans, but the mechanisms driving this fungal bloom remain incompletely understood. We identify oxygen as a resource required for post-antibiotic C. albicans expansion. C. albicans depleted simple sugars in the ceca of gnotobiotic mice but required oxygen to grow on these resources in vitro, pointing to anaerobiosis as a potential factor limiting growth in the gut. Clostridia species limit oxygen availability in the large intestine by producing butyrate, which activates peroxisome proliferator-activated receptor gamma (PPAR-γ) signaling to maintain epithelial hypoxia. Streptomycin treatment depleted Clostridia-derived butyrate to increase epithelial oxygenation, but the PPAR-γ agonist 5-aminosalicylic acid (5-ASA) functionally replaced Clostridia species to restore epithelial hypoxia and colonization resistance against C. albicans. Additionally, probiotic Escherichia coli required oxygen respiration to prevent a post-antibiotic bloom of C. albicans, further supporting the role of oxygen in colonization resistance. We conclude that limited access to oxygen maintains colonization resistance against C. albicans.

Ultralow Catalytic Loading for Optimised Electrocatalytic Performance of AuPt Nanoparticles to Produce Hydrogen and Ammonia.

The hydrogen evolution and nitrite reduction reactions are key to producing green hydrogen and ammonia. Antenna-reactor nanoparticles hold promise to improve the performances of these transformations under visible-light excitation, by combining plasmonic and catalytic materials. However, current materials involve compromising either on the catalytic activity or the plasmonic enhancement and also lack control of reaction selectivity. Here, we demonstrate that ultralow loadings and non-uniform surface segregation of the catalytic component optimize catalytic activity and selectivity under visible-light irradiation. Taking Pt-Au as an example we find that fine-tuning the Pt content produces a 6-fold increase in the hydrogen evolution compared to commercial Pt/C as well as a 6.5-fold increase in the nitrite reduction and a 2.5-fold increase in the selectivity for producing ammonia under visible light excitation relative to dark conditions. Density functional theory suggests that the catalytic reactions are accelerated by the intimate contact between nanoscale Pt-rich and Au-rich regions at the surface, which facilitates the formation of electron-rich hot-carrier puddles associated with the Pt-based active sites. The results provide exciting opportunities to design new materials with improved photocatalytic performance for sustainable energy applications.

Reliability and Validity of Maximal Respiratory Pressures.

BACKGROUND: Maximal respiratory pressure is used to assess the inspiratory and expiratory muscles strength by using maximal inspiratory pressure (PImax) and maximal expiratory pressure (PEmax). This study aimed to summarize and evaluate the reliability and validity of maximal respiratory pressure measurements. METHODS: This systematic review followed the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) recommendations and was reported by using the PRISMA checklist. Studies published before March 2023 were searched in PubMed and EMBASE databases. RESULTS: A total of 642 studies were identified by using the online search strategy and manual search (602 and 40, respectively). Twenty-three studies were included. The level of evidence for test-retest reliability was moderate for PImax and PEmax (intraclass correlation coefficient > 0.70 for both), inter-rater reliability was low for PImax and very low for PEmax (intraclass correlation coefficient > 0.70 for both), and the measurement error was very low for PImax and PEmax. In addition, concurrent validity presented a high level of evidence for PImax and PEmax (r > 0.80). CONCLUSIONS: Only concurrent validity of maximal respiratory pressure measured with the manometers evaluated in this review presented a high level of evidence. The quality of clinical studies by using maximal respiratory pressure would be improved if more high-quality studies on measurement properties, by following well established guidelines and the COSMIN initiative, were available.

High fat intake sustains sorbitol intolerance after antibiotic-mediated Clostridia depletion from the gut microbiota.

Carbohydrate intolerance, commonly linked to the consumption of lactose, fructose, or sorbitol, affects up to 30% of the population in high-income countries. Although sorbitol intolerance is attributed to malabsorption, the underlying mechanism remains unresolved. Here, we show that a history of antibiotic exposure combined with high fat intake triggered long-lasting sorbitol intolerance in mice by reducing Clostridia abundance, which impaired microbial sorbitol catabolism. The restoration of sorbitol catabolism by inoculation with probiotic Escherichia coli protected mice against sorbitol intolerance but did not restore Clostridia abundance. Inoculation with the butyrate producer Anaerostipes caccae restored a normal Clostridia abundance, which protected mice against sorbitol-induced diarrhea even when the probiotic was cleared. Butyrate restored Clostridia abundance by stimulating epithelial peroxisome proliferator-activated receptor-gamma (PPAR-γ) signaling to restore epithelial hypoxia in the colon. Collectively, these mechanistic insights identify microbial sorbitol catabolism as a potential target for approaches for the diagnosis, treatment, and prevention of sorbitol intolerance.

A dataset for predicting cloud cover over Europe.

Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections difficult. In this paper, we present the European Cloud Cover dataset, which can be used to learn statistical relations between cloud cover and other environmental variables, to potentially improve future climate projections. The dataset was created using a novel technique called Area Weighting Regridding Scheme to map satellite observations to cloud fractional cover on the same grid as the other variables in the dataset. Baseline experiments using autoregressive models document that it is possible to use the dataset to predict cloud fractional cover.

Triple Play of Band Gap, Interband, and Plasmonic Excitations for Enhanced Catalytic Activity in Pd/HxMoO3 Nanoparticles in the Visible Region.

Plasmonic photocatalysis has been limited by the high cost and scalability of plasmonic materials, such as Ag and Au. By focusing on earth-abundant photocatalyst/plasmonic materials (HxMoO3) and Pd as a catalyst, we addressed these challenges by developing a solventless mechanochemical synthesis of Pd/HxMoO3 and optimizing photocatalytic activities in the visible range. We investigated the effect of HxMoO3 band gap excitation (at 427 nm), Pd interband transitions (at 427 nm), and HxMoO3 localized surface plasmon resonance (LSPR) excitation (at 640 nm) over photocatalytic activities toward the hydrogen evolution and phenylacetylene hydrogenation as model reactions. Although both excitation wavelengths led to comparable photoenhancements, a 110% increase was achieved under dual excitation conditions (427 + 640 nm). This was assigned to a synergistic effect of optical excitations that optimized the generation of energetic electrons at the catalytic sites. These results are important for the development of visible-light photocatalysts based on earth-abundant components.

Three-dimensional fluid-structure interaction simulation of the Wheatley aortic valve.

Valvular heart diseases (such as stenosis and regurgitation) are recognized as a rapidly growing cause of global deaths and major contributors to disability. The most effective treatment for these pathologies is the replacement of the natural valve with a prosthetic one. Our work considers an innovative design for prosthetic aortic valves that combines the reliability and durability of artificial valves with the flexibility of tissue valves. It consists of a rigid support and three polymer leaflets which can be cut from an extruded flat sheet, and is referred to hereafter as the Wheatley aortic valve (WAV). As a first step towards the understanding of the mechanical behavior of the WAV, we report here on the implementation of a numerical model built with the ICFD multi-physics solver of the LS-DYNA software. The model is calibrated and validated using data from a basic pulsatile-flow experiment in a water-filled straight tube. Sensitivity to model parameters (contact parameters, mesh size, etc.) and to design parameters (height, material constants) is studied. The numerical data allow us to describe the leaflet motion and the liquid flow in great detail, and to investigate the possible failure modes in cases of unfavorable operational conditions (in particular, if the leaflet height is inadequate). In future work the numerical model developed here will be used to assess the thrombogenic properties of the valve under physiological conditions.

A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering.

An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.

Design and Characterization of pMyc/pMax Peptide-Coupled Gold Nanosystems for Targeting Myc in Prostate Cancer Cell Lines.

Myc and Max are essential proteins in the development of prostate cancer. They act by dimerizing and binding to E-box sequences. Disrupting the Myc:Max heterodimer interaction or its binding to E-box sequences to interrupt gene transcription represent promising strategies for treating cancer. We designed novel pMyc and pMax peptides from reference sequences, and we evaluated their ability to bind specifically to E-box sequences using an electrophoretic mobility shift assay (EMSA). Then, we assembled nanosystems (NSs) by coupling pMyc and pMax peptides to AuNPs, and determined peptide conjugation using UV-Vis spectroscopy. After that, we characterized the NS to obtain the nanoparticle's size, hydrodynamic diameter, and zeta potential. Finally, we evaluated hemocompatibility and cytotoxic effects in three different prostate adenocarcinoma cell lines (LNCaP, PC-3, and DU145) and a non-cancerous cell line (Vero CCL-81). EMSA results suggests peptide-nucleic acid interactions between the pMyc:pMax dimer and the E-box. The hemolysis test showed little hemolytic activity for the NS at the concentrations (5, 0.5, and 0.05 ng/µL) we evaluated. Cell viability assays showed NS cytotoxicity. Overall, results suggest that the NS with pMyc and pMax peptides might be suitable for further research regarding Myc-driven prostate adenocarcinomas.

The DEAD-box RNA helicase RhlB is required for efficient RNA processing at low temperature in Caulobacter.

IMPORTANCE: One of the most important control points in gene regulation is RNA stability, which determines the half-life of a transcript from its transcription until its degradation. Bacteria have evolved a sophisticated multi-enzymatic complex, the RNA degradosome, which is dedicated mostly to RNA turnover. The combined activity of RNase E and the other RNA degradosome enzymes provides an efficient pipeline for the complete degradation of RNAs. The DEAD-box RNA helicases are very often found in RNA degradosomes from phylogenetically distant bacteria, confirming their importance in unwinding structured RNA for subsequent degradation. This work showed that the absence of the RNA helicase RhlB in the free-living Alphaproteobacterium Caulobacter crescentus causes important changes in gene expression and cell physiology. These are probably due, at least in part, to inefficient RNA processing by the RNA degradosome, particularly at low-temperature conditions.

Graphene as an inhomogeneously broadened two-level saturable absorber.

We show that the inter-band optical conductivity of graphene follows a dependence on intensity that is characteristic of inhomogeneously broadened saturable absorbers, and we obtain a simple formula for the saturation intensity. We compare our results with those from more exact numerical calculations and selected sets of experimental data, and obtain good agreement for photon energies much larger than twice the chemical potential.

5-ASA can functionally replace Clostridia to prevent a post-antibiotic bloom of Candida albicans by maintaining epithelial hypoxia.

Antibiotic prophylaxis sets the stage for an intestinal bloom of Candida albicans , which can progress to invasive candidiasis in patients with hematologic malignancies. Commensal bacteria can reestablish microbiota-mediated colonization resistance after completion of antibiotic therapy, but they cannot engraft during antibiotic prophylaxis. Here we use a mouse model to provide a proof of concept for an alternative approach, which replaces commensal bacteria functionally with drugs to restore colonization resistance against C. albicans . Streptomycin treatment, which depletes Clostridia from the gut microbiota, disrupted colonization resistance against C. albicans and increased epithelial oxygenation in the large intestine. Inoculating mice with a defined community of commensal Clostridia species reestablished colonization resistance and restored epithelial hypoxia. Notably, these functions of commensal Clostridia species could be replaced functionally with the drug 5-aminosalicylic acid (5-ASA), which activates mitochondrial oxygen consumption in the epithelium of the large intestine. When streptomycin-treated mice received 5-ASA, the drug reestablished colonization resistance against C. albicans and restored physiological hypoxia in the epithelium of the large intestine. We conclude that 5-ASA treatment is a non-biotic intervention that restores colonization resistance against C. albicans without requiring the administration of live bacteria.

VISEM-Tracking, a human spermatozoa tracking dataset.

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

The patients' experience of neuroimaging of primary brain tumors: a cross-sectional survey study.

PURPOSE: To gain insight into how patients with primary brain tumors experience MRI, follow-up protocols, and gadolinium-based contrast agent (GBCA) use. METHODS: Primary brain tumor patients answered a survey after their MRI exam. Questions were analyzed to determine trends in patients' experience regarding the scan itself, follow-up frequency, and the use of GBCAs. Subgroup analysis was performed on sex, lesion grade, age, and the number of scans. Subgroup comparison was made using the Pearson chi-square test and the Mann-Whitney U-test for categorical and ordinal questions, respectively. RESULTS: Of the 100 patients, 93 had a histopathologically confirmed diagnosis, and seven were considered to have a slow-growing low-grade tumor after multidisciplinary assessment and follow-up. 61/100 patients were male, with a mean age ± standard deviation of 44 ± 14 years and 46 ± 13 years for the females. Fifty-nine patients had low-grade tumors. Patients consistently underestimated the number of their previous scans. 92% of primary brain tumor patients did not experience the MRI as bothering and 78% would not change the number of follow-up MRIs. 63% of the patients would prefer GBCA-free MRI scans if diagnostically equally accurate. Women found the MRI and receiving intravenous cannulas significantly more uncomfortable than men (p = 0.003). Age, diagnosis, and the number of previous scans had no relevant impact on the patient experience. CONCLUSION: Patients with primary brain tumors experienced current neuro-oncological MRI practice as positive. Especially women would, however, prefer GBCA-free imaging if diagnostically equally accurate. Patient knowledge of GBCAs was limited, indicating improvable patient information.

Bile acids as modulators of gut microbiota composition and function.

Changes in the composition of gut-associated microbial communities are associated with many human illnesses, but the factors driving dysbiosis remain incompletely understood. One factor governing the microbiota composition in the gut is bile. Bile acids shape the microbiota composition through their antimicrobial activity and by activating host signaling pathways that maintain gut homeostasis. Although bile acids are host-derived, their functions are integrally linked to bacterial metabolism, which shapes the composition of the intestinal bile acid pool. Conditions that change the size or composition of the bile acid pool can trigger alterations in the microbiota composition that exacerbate inflammation or favor infection with opportunistic pathogens. Therefore, manipulating the composition or size of the bile acid pool might be a promising strategy to remediate dysbiosis.

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models.

Introduction: A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models. Methods: In this work, we propose a technique for handling such varied numbers of EEG channels by splitting the EEG montages into distinct regions and merge the channels within the same region to a region representation. The solution is termed Region Based Pooling (RBP). The procedure of splitting the montage into regions is performed repeatedly with different region configurations, to minimize potential loss of information. As RBP maps a varied number of EEG channels to a fixed number of region representations, both current and future DL architectures may apply RBP with ease. To demonstrate and evaluate the adequacy of RBP to handle a varied number of EEG channels, sex classification based solely on EEG was used as a test example. The DL models were trained on 129 channels, and tested on 32, 65, and 129-channels versions of the data using the same channel positions scheme. The baselines for comparison were zero-filling the missing channels and applying spherical spline interpolation. The performances were estimated using 5-fold cross validation. Results: For the 32-channel system version, the mean AUC values across the folds were: RBP (93.34%), spherical spline interpolation (93.36%), and zero-filling (76.82%). Similarly, on the 65-channel system version, the performances were: RBP (93.66%), spherical spline interpolation (93.50%), and zero-filling (85.58%). Finally, the 129-channel system version produced the following results: RBP (94.68%), spherical spline interpolation (93.86%), and zero-filling (91.92%). Conclusion: In conclusion, RBP obtained similar results to spherical spline interpolation, and superior results to zero-filling. We encourage further research and development of DL models in the cross-dataset setting, including the use of methods such as RBP and spherical spline interpolation to handle a varied number of EEG channels.

Phosphate and IL-10 concentration as predictors of long-covid in hemodialysis patients: A Brazilian study.

Background: The global burden of persistent COVID-19 in hemodialysis (HD) patients is a worrisome scenario worth of investigation for the critical care of chronic kidney disease (CKD). We performed an exploratory post-hoc study from the trial U1111-1237-8231 with two specific aims: i) to investigate the prevalence of COVID-19 infection and long COVID symptoms from our Cohort of 178 Brazilians HD patients. ii) to identify whether baseline characteristics should predict long COVID in this sample. Methods: 247 community-dwelling older (>60 years) patients (Men and women) undergoing HD (glomerular filtration rate < 15 mL/min/1.73m2) with arteriovenous fistula volunteered for this study. All patients presented hypertension and diabetes. Patients were divided in two groups: without long-COVID and with long-COVID. Body composition, handgrip strength, functional performance, iron metabolism, phosphate, and inflammatory profile were assessed. Patients were screened for 11-months after COVID-19 infection. Results were considered significant at P < 0.05. Results: We found that more than 85% of the COVID-19 infected patients presented a severe condition during the infection. In our sample, the mortality rate over 11-month follow was relatively low (8.4%) when compared to worldwide (approximately 36%). Long COVID was highly prevalent in COVID-19 survivors representing more than 80% of all cases. Phosphate and IL-10 were higher in the long COVID group, but only phosphate higher than 5.35 mg/dL appears to present an increased prevalence of long COVID, dyspnea, and fatigue. Conclusion: There was a high prevalence of COVID-19 infection and long COVID in HD patients from the Brazilian trial 'U1111-1237-8231'. HD clinics should be aware with phosphate range in HD patients as a possible target for adverse post-COVID events.

Mitochondrial genetics of Ophiderini, with a new species from the Eudocima phalonia species group (Lepidoptera: Noctuidae: Calpinae).

We evaluate species, species group, and generic concepts for Ophiderini and Phyllodini (Lepidoptera: Calpinae) with a cladistic analysis of COI 5 mitochondrial DNA sequences. Species recognized by current taxonomy formed monophyletic clades with three exceptions: Eudocima phalonia (L.), E. cocalus (Cramer) and E. hypermnestra (Cramer). Eudocima phalonia formed two allopatric clades, an African clade sister to E. lequeuxi Brou Zilli, and another clade sister to E. euryzona (Hampson). Each of these four clades comprises a separate taxon diagnosable by unique combinations of discrete genitalic characters, and the African clade previously lumped under E. phalonia is described herein as E. afrikana sp. n. Eudocima cocalus and E. hypermnestra phenotypes overlap in COI 5 haplotypes. Eleven Eudocima species groups delimited from morphology are independently supported as monophyletic with the molecular analysis. Unique combinations of COI 5 characters diagnosing species and species groups are provided. Eudocima is largely supported as monophyletic, except E. formosa is excluded from the Eudocima clade, and Graphigona regina, Tetrisia florigera, and Ferenta stolliana are embedded within it. Structural morphology of E. formosa also suggests it does not belong in Eudocima. Adult images are shown for most species of Ophiderini, including many DNA sequence vouchers, and their diagnoses and general distributions are provided.

SinGAN-Seg: Synthetic training data generation for medical image segmentation.

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources.

Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.