Raschfinch6114

Fusarium causes significant post-harvest quality losses and mycotoxin contamination in stored wheat but the colonisation dynamics of the grain and how this may be affected by the initial inoculum position in the grain mass is poorly understood. This study examined the 3D growth kinetics and mycotoxin production (deoxynivalenol and zearalenone) by F. graminearum during hyphal colonisation from different initial inoculum positions in wheat microcosms (top-centre, bottom-centre, and bottom-side) maintained at two water activities (aw; 0.95 and 0.97). Clear jars were used to visually follow the colonisation dynamics. Fungal respiration and associated dry matter loss (DML) and ergosterol were also quantified. Colonisation dynamics was shown to be affected by the inoculation position. selleck chemicals At the end of the colonisation process, fungal respiration and DML were driven by the inoculation position, and the latter also by the prevailing aw. Fungal biomass (ergosterol) was mainly affected by the aw. The initial inoculum position did not affect the relative mycotoxin production. There was a positive correlation between respiration and ergosterol, and between mycotoxin production and colonisation indicators. We suggest that spatially explicit predictive models can be used to better understand the colonisation patterns and mycotoxin contamination of stored cereal commodities and to aid more effective post-harvest management.Obstructive sleep apnea (OSA) is a chronic and prevalent disorder, strongly associated with cardiovascular disease (CVD). The apnea-hypopnea index (AHI), or respiratory event index (REI), and the oxygen desaturation index (ODI) are the clinical metrics of sleep apnea in terms of diagnosis and severity. However, AHI, or REI, does not quantify OSA-related hypoxemia and poorly predicts the consequences of sleep apnea in cardiometabolic diseases. Moreover, it is unclear whether ODI correlates with CVD in OSA. Our study aimed to examine the possible associations between respiratory sleep indices and CVD in OSA, in a non-clinic-based population in Cyprus. We screened 344 subjects of a stratified, total sample of 4118 eligible responders. All participants were adults (age 18+), residing in Cyprus. Each patient answered with a detailed clinical history in terms of CVD. A type III sleep test was performed on 282 subjects (81.97%). OSA (REI ≥ 15) was diagnosed in 92 patients (32.62%, Group A). REI less then 15 was observed in the remaining 190 subjects (67.37%, Group B). In OSA group A, 40 individuals (43%) reported hypertension, 17 (18.5%) arrhythmias, 10 (11%) heart failure, 9 (9.8%) ischemic heart disease and 2 (2%) previous stroke, versus 46 (24%), 21 (11%), 7 (3.7%), 12 (6.3%) and 6 (3%), in Group B, respectively. Hypertension correlated with REI (p = 0.001), ODI (p = 0.003) and mean SaO2 (p less then 0.001). Arrhythmias correlated with mean SaO2 (p = 0.001) and time spent under 90% oxygen saturation (p = 0.040). Heart failure correlated with REI (p = 0.043), especially in the supine position (0.036). No statistically significant correlations were observed between ischemic heart disease or stroke and REI, ODI and mean SaO2. The pathogenesis underlying CVD in OSA is variable. According to our data, hypertension correlated with REI, ODI and mean SaO2. Arrhythmias correlated only with hypoxemia (mean SaO2), whereas heart failure correlated only with REI, especially in the supine position.The worldwide emergence of microbial resistance to available antibiotics presents a global threat to public health and health systems. This special issue aimed to gather papers describing novel antibiotics, originating form chemical synthesis, repurposing of existent drugs, or from natural sources like plant extracts, herbs and spices. A total of 13 papers were published, covering a wide range of topic, including antimicrobial resistance surveillance studies; synthesis of novel molecules with antimicrobial activities; modification or repurposing of already existing molecules, plant-derived active extracts, and molecules; the effects of antimicrobial therapy on microbiota; and the investigation of novel formulations for human and veterinary uses. After decades of antibiotics discovery decline, antibiotics discovery is boosting. Recent developments of post genomics approaches and bioinformatics tools will most certainly turn the tide in the discovery and development of antimicrobials in this exciting field.Wild-type (WT) zebrafish are commonly used in behavioral tests, however, the term WT corresponds to many different strains, such as AB, Tübingen long fin (TL), and Wild Indian Karyotype (WIK). Since these strains are widely used, there has to be at least one study to demonstrate the behavioral differences between them. In our study, six zebrafish strains were used, which are AB, absolute, TL, golden, pet store-purchased (PET), and WIK zebrafishes. The behavior of these fishes was tested in a set of behavioral tests, including novel tank, mirror-biting, predator avoidance, social interaction, and shoaling tests. From the results, the differences were observed for all behavioral tests, and each strain displayed particular behavior depending on the tests. In addition, from the heatmap and PCA (principal component analysis) results, two major clusters were displayed, separating the AB and TL zebrafishes with other strains in another cluster. Furthermore, after the coefficient of variation of each strain in every behavioral test was calculated, the AB and TL zebrafishes were found to possess a low percentage of the coefficient of variation, highlighting the strong reproducibility and the robustness of the behaviors tested in both fishes. Each zebrafish strain tested in this experiment showed specifically different behaviors from each other, thus, strain-specific zebrafish behavior should be considered when designing experiments using zebrafish behavior.Foods, food ingredients, and their balanced consumption are recognized to have an important role in achieving or maintaining a state of wellbeing by acting as carriers of functional components and bioactive molecules. However, the potential contribution of foods to consumers' health has so far only been partially exploited. The rapidly evolving scenario of the coronavirus disease 2019 (COVID-19) pandemic is stimulating profound reflection on the relationships between food and the etiological agent, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, the status of knowledge regarding food as a possible defense/co-therapeutic strategy against the SARS-CoV-2 coronavirus is considered through the discussion of two main current lines of research. One line of research relates to the role of micronutrients, food components, and diets in the strengthening of the immune system through clinical trials; formulations could be developed as immune system enhancers or as co-adjuvants in therapies. The other line of research relates to investigation of the chemical interactions that specific food compounds can have with host or virus targets so as to interfere with the viral infective cycle of SARS-CoV-2. This line requires, as a first step, an in silico evaluation to discover lead compounds, which may be further developed through drug-design studies, in vitro and in vivo tests, and, finally, clinical trials to obtain therapeutic molecules. All of these promising strategies promote the role of food in preventive/co-therapeutic strategies to tackle the COVID-19 pandemic.There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of "fresh", "likely spoiled", and "spoiled" based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.A direct relation between antibiotic use and resistance has been shown at country level. We aim to investigate the association between antibiotic prescribing for patients from individual Dutch primary care practices and antibiotic resistance of bacterial isolates from routinely submitted urine samples from their patient populations. Practices' antibiotic prescribing data were obtained from the Julius Network and related to numbers of registered patients. Practices were classified as low-, middle- or high-prescribers and from each group size-matching practices were chosen. Culture and susceptibility data from submitted urine samples were obtained from the microbiology laboratory. Percentages of resistant isolates, and resistant isolates per 1000 registered patients per year (population resistance) were calculated and compared between the groups. The percentages of resistant Escherichia coli varied considerably between individual practices, but the three prescribing groups' means were very similar. However, as the higher-prescribing practices requested more urine cultures per 1000 registered patients, population resistance was markedly higher in the higher-prescribing groups. This study showed that the highly variable resistance percentages for individual practices were unrelated to antibiotic prescribing levels. However, population resistance (resistant strains per practice population) was related to antibiotic prescribing levels, which was shown to coincide with numbers of urine culture requests. Whether more urine culture requests in the higher-prescribing groups were related to treatment failures, more complex patient populations, or to general practitioners' testing behaviour needs further investigation.