Genetic analysis of feed efficiency and novel feeding behavior traits measured in group-housed broilers using electronic feeders.

This study aimed to estimate genetic parameters for feeding behavior (FB) traits and to assess their genetic relationship with performance traits in group-housed broilers. In total, 99,472,151 visits were recorded for 95,711 birds between 2017 and 2022 using electronic feeders. The visits were first clustered into 2,667,617 daily observations for ten FB traits: daily feed intake (DFI), daily number of visits (NVIS), time spent at the feeders (TSF), number of visited feeders (NVF), visiting activity interval (VAI), feeding rate (FR), daily number of meals (NMEAL), average intake per meal (INTMEAL), number of visits per meal (VISMEAL) and interval between meals (MEALIVL). All FB traits were then considered as the average per bird across the feeding test period. Three growth traits (body weight at the start - SBW and at the end of the feeding test - FBW, and weight gain over the test period - BWG), and 2 feed efficiency (FE) traits (Feed Conversion Rate - FCR and Residual Feed Intake - RFI) were also recorded. The (co)variance components were estimated using multitrait animal mixed models. For growth and FE, the heritability (h2) estimates were moderate, ranging from 0.20 ± 0.01 (BWG) to 0.32 ± 0.02 (RFI). Overall, the h2 estimates for FB traits were higher than for productive traits, ranging from 0.31 ± 0.01 (DFI) to 0.56 ± 0.02 (TSF). DFI presented high genetic correlations (0.53-0.86) with all performance traits. Conversely, the remaining FB traits presented null to moderate genetic correlations with these traits, ranging from -0.38 to 0.42 for growth traits and between -0.14 and 0.25 for FE traits. Genetic selection for favorable feeding behavior is expected to exhibit a fast genetic response. The results suggest that it is possible to consider different feeding strategies without compromising the genetic progress of FE. Conversely, breeding strategies prioritizing a higher bird activity might result in lighter broiler lines in the long term, given the negative genetic correlations between visit-related traits (NV, NVF, and NMEAL) and growth traits (SBW and FBW).

Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.

Critical exponents of master-node network model.

The dynamics of competing opinions in social network plays an important role in society, with many applications in diverse social contexts such as consensus, election, morality, and so on. Here, we study a model of interacting agents connected in networks in order to analyze their decision stochastic process. We consider a first-neighbor interaction between agents in a one-dimensional network with the shape of ring topology. Moreover, some agents are also connected to a hub, or master node, who has preferential choice or bias. Such connections are quenched. As the main results, we observed a continuous nonequilibrium phase transition to an absorbing state as a function of control parameters. By using the finite-size scaling method we analyzed the static and dynamic critical exponents to show that this model probably cannot match any universality class already known.

Enhancing emission control and analyzing the performance and combustion attributes of vehicular engines with spirulina microalgae diesel Ce2O3 nanoparticles blends.

The current research investigates the utilization of spirulina microalgae biodiesel blends in a naturally aspirated constant speed compression ignition engine with Ce2O3 nanoparticles at the concentration of 50 ppm under diverse engine loading conditions. Blends of microalgae dispersed with neat diesel at the volume of 20% and 40%. A series of tests were conducted to evaluate the combined effects of microalgae and nanoparticles on engine performance, combustion efficiency, and emission characteristics. The study revealed that increasing the microalgae concentration in the diesel fuel resulted in reduced brake thermal efficiency due to less effective atomization and lower calorific value. Surprisingly, the 20% biodiesel blend with nanoparticles exhibited the highest brake thermal efficiency across various engine loads, while the 40% blend showed higher brake specific fuel consumption compared to both the 20% blend and neat diesel, primarily because of its lower heating value necessitating increased fuel consumption. Furthermore, the biodiesel blends led to lower in-cylinder pressure than pure diesel, mainly attributable to suboptimal atomization. In terms of emissions, the utilization of microalgae-based fuel led to a significant reduction in NOx, CO, and smoke emissions, attributed to the lower cylinder temperatures associated with these blends. In conclusion, this study underscores the potential of spirulina microalgae, particularly when combined with nanoparticles at an optimal concentration, as a promising and environmentally friendly alternative for compression ignition engines.

Treatment of evolving cancers will require dynamic decision support.

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

'It was called a grab bag and nobody wanted to grab them': Teachers' perceptions of school lunches during the COVID-19 pandemic - a regional case study.

Background: The COVID-19 pandemic brought changes to primary school lunches leading to concerns over nutritional quality and uptake of lunches by vulnerable children. Regional data from Tayside, Scotland, showed that only 55% of children who were eligible for free school meals took these (normal uptake pre-pandemic was 66%). The current work aimed to identify teachers' perceptions of meal provisioning in primary schools during the first year of the COVID-19 pandemic. Design and methods: A cross-sectional online survey was carried out among primary school teachers across Tayside, Scotland. Using an online survey (21 questions combining multiple choice formats and open text) and interviews, primary school teachers shared their views on food quality, quantity, meal choices and factors influencing uptake of primary school lunches. Interviews were recorded, transcribed and thematically analysed with respect to factors influencing consumption. Results: The survey was completed by 41 teachers and 8 participated in a follow up interview. Around one-third (29%) of primary school teachers believed the quality of lunches had decreased and cited poor appearance of food, use of takeaway containers and food wastage. The lunch format was viewed negatively principally relating to the substitution of hot lunches with cold sandwiches, portion sizes, choice and perceived value for money. Concerns were expressed about acceptability and how far the meals contributed to food security. Conclusions: Further work on food provisioning is needed in order to identify ways to provide a nutritional safety net for vulnerable children.

Daytime Sleepiness Is Associated with Lower Cognitive Scores: The Look AHEAD Study.

Background: Daytime sleepiness is common in older adults and may result from poor nighttime sleep due to sleep disordered breathing, fragmented sleep, or other sleep disorders. Daytime sleepiness may be associated with cognition in older adults. Objectives: We investigated the association between self-reported daytime sleepiness and cognitive function in the Look AHEAD clinical trial. Design: Observational follow-up of a randomized clinical trial of an intensive lifestyle intervention. Setting: Clinic. Participants: Participants (n=1,778) aged 45-76 years at baseline with type 2 diabetes and overweight or obesity. Interventions: Participants were randomized to an intensive lifestyle intervention for weight loss or a control condition of diabetes support and education. Measurements: Participants provided self-reported levels of daytime sleepiness at baseline and years 12-13. Cognitive function was assessed with a neurocognitive battery at years 12-13 and 18-20. Results: Participants who reported having frequent daytime sleepiness (often or always) performed significantly worse than others on the cognitive composite (-0.35; p-value=0.014) after controlling for covariates. When stratified by intervention arm, participants assigned to the intensive lifestyle intervention who reported often/always having daytime sleepiness performed worse on Digit Symbol Coding (-0.63; p-value=0.05) and Trail Making Part-B (-0.56; p-value=0.02) after controlling for covariates. Statistical interactions revealed associations between daytime sleepiness and the following covariates: race and ethnicity, APOE ε4 carrier status, baseline history of cardiovascular disease, and depression. Conclusions: Daytime sleepiness over ~13 years predicted poorer cognitive performance in older individuals who, by virtue of having diabetes and overweight/obesity, are at high risk for sleep disorders and cognitive impairment.

Exploratory Multisite MR Spectroscopic Imaging Shows White Matter Neuroaxonal Loss Associated with Complications of Type 1 Diabetes in Children.

BACKGROUND AND PURPOSE: Type 1 diabetes affects over 200,000 children in the United States and is associated with an increased risk of cognitive dysfunction. Prior single-site, single-voxel MRS case reports and studies have identified associations between reduced NAA/Cr, a marker of neuroaxonal loss, and type 1 diabetes. However, NAA/Cr differences among children with various disease complications or across different brain tissues remain unclear. To better understand this phenomenon and the role of MRS in characterizing it, we conducted a multisite pilot study. MATERIALS AND METHODS: In 25 children, 6-14 years of age, with type 1 diabetes across 3 sites, we acquired T1WI and axial 2D MRSI along with phantom studies to calibrate scanner effects. We quantified tissue-weighted NAA/Cr in WM and deep GM and modeled them against study covariates. RESULTS: We found that MRSI differentiated WM and deep GM by NAA/Cr on the individual level. On the population level, we found significant negative associations of WM NAA/Cr with chronic hyperglycemia quantified by hemoglobin A1c (P < .005) and a history of diabetic ketoacidosis at disease onset (P < .05). We found a statistical interaction (P < .05) between A1c and ketoacidosis, suggesting that neuroaxonal loss from ketoacidosis may outweigh that from poor glucose control. These associations were not present in deep GM. CONCLUSIONS: Our pilot study suggests that MRSI differentiates GM and WM by NAA/Cr in this population, disease complications may lead to neuroaxonal loss in WM in children, and deeper investigation is warranted to further untangle how diabetic ketoacidosis and chronic hyperglycemia affect brain health and cognition in type 1 diabetes.

Bioluminescence resonance energy transfer biosensor for measuring activity of a protease secreted by Pseudomonas fluorescens growing in milk.

Bacterial proteases are sporadic contributors to milk spoilage, reducing the quality of ultra-heat treated (UHT) milk and other dairy products. Current methods for measuring bacterial protease activity in milk are insensitive and too slow to be used in routine testing in dairy processing plants. We have designed a novel bioluminescence resonance energy transfer (BRET)-based biosensor to measure the activity of proteases secreted by bacteria in milk. The BRET-based biosensor is highly selective for bacterial protease activity compared with other proteases tested, notably including plasmin, which is abundant in milk. It incorporates a novel peptide linker that is selectively cleaved by P. fluorescens AprX proteases. The peptide linker is flanked by green fluorescent protein (GFP2) at the N-terminus and a variant Renilla luciferase (RLuc2) at the C-terminus. Complete cleavage of the linker by bacterial proteases from Pseudomonas fluorescens strain 65, leads to a 95% decrease in the BRET ratio. We applied an azocasein-based calibration method to the AprX biosensor using standard international enzyme activity units. In a 10-min assay, the detection limit for AprX protease activity in buffer was equivalent to 40 pg/mL (≈0.8 pM, 22 µU/mL) and 100 pg/mL (≈2pM, 54 µU/mL) in 50% (v/v) full fat milk. The EC50 values were 1.1 ± 0.3 ng/mL (87 µU/mL) and 6.8 ± 0.2 ng/mL (540 µU/mL), respectively. The biosensor was approximately 800x more sensitive than the established FITC-Casein method in a 2-h assay, the shortest feasible time for the latter method. The protease biosensor is sensitive and fast enough to be used in production settings. It is suitable for measuring bacterial protease activity in raw and processed milk, to inform efforts to mitigate the effects of heat-stable bacterial proteases and maximise the shelf-life of dairy products.

Phytochemical profiling, anti-hyperglycemic, antifungal, and radicals scavenging potential of crude extracts of Athyrium asplenioides- an in-vitro approach.

This research was aimed to evaluate the phytochemical profile, antifungal, anti-hyperglycemic, as well as antioxidant activity competence of different extracts of Athyrium asplenioides through in-vitro approach. The A. asplenioides crude methanol extract contained considerable quantity of pharmaceutically precious phytochemicals (saponins, tannins, quinones, flavonoid, phenols, steroid, and terpenoids) than others (acetone, ethyl acetate, and chloroform). Interestingly, the crude methanol extract showed remarkable antifungal activity against Candida species (C. krusei: 19.3 ± 2 mm > C. tropicalis: 18.4 ± 1 mm > C. albicans: 16.5 ± 1 mm > C. parapsilosis: 15.5 ± 2 mm > C. glabrate: 13.5 ± 2 mm > C. auris: 7.6 ± 1 mm) at a concentration of 20 mg mL-1. The crude methanol extract also showed remarkable anti-hyperglycemic activity on concentration basis. Surprisingly, remarkable free radicals scavenging potential against DPPH (76.38%) and ABTS (76.28%) free radicals at a concentration of 20 mg mL-1. According to the findings, the A. asplenioides crude methanol extract contains pharmaceutically valuable phytochemicals and may be useful for drug discovery.

Utilization of the Nannochloropsis microalgae biochar prepared via microwave assisted pyrolysis on the mixed biomass fuel pellets.

Nannochloropsis microalgae biochar has become increasingly attractive due to its potential as a component of microalgae-based biodiesel blends. This biochar is a by-product of the pyrolysis process, but its use in the energy sector has been limited. In this study, pellets were formed using microalgae residues and their physiochemical properties were analyzed to assess the feasibility of using microalgae biochar as a fuel source. Three types of biomasses, namely date seed dust, coconut shell waste, and microalgae biochar, were utilized to produce fuel pellets. These pellets were categorized into three types, B1, B2, and B3, based on the composition of the biomass. The inclusion of microalgae biochar in the pellets resulted in enhanced calorific value, as well as improved heating value and bulk density. Moreover, the mechanical strength of microalgae-based pellets was higher due to their high lignin content compared to another biomass. The moisture absorption test results showed that the use of mixed biomass reduced the moisture content over an extended period. Microalgae pellets exhibited higher young's modulus and greater impact resistance, indicating greater mechanical strength. Furthermore, due to their higher calorific value, the combustion time of microalgae pellets was greater than that of other biomass. In conclusion, the results of this study suggest that microalgae biochar can be a promising alternative fuel source for the energy sector.

Effects of adaptation diet and exogenous enzymes on true metabolizable energy and cecal microbial ecology, short-chain fatty acid profile, and enzyme activity in roosters fed barley and rye diets.

Three experiments evaluated effects of adaptation diet and exogenous ß-glucanase and xylanase on TMEn of barley and rye. Single Comb White Leghorn roosters were fed adaptation diets based on corn/soybean meal (SBM), barley/SBM with and without ß-glucanase, or rye/corn/SBM with and without xylanase for 4 wk. In Experiments 1 and 2, after the adaptation period, TMEn was determined using a 48 h precision-fed rooster assay for 100% barley or 100% rye diets with or without ß-glucanase or xylanase, respectively. Experiment 3 consisted only of feeding adaptation diets for 4 wk. Cecal samples were collected at the end of experiments for microbial ecology, short-chain fatty acid (SCFA) profiles, and enzyme activity analyses. In Experiments 1 and 2, ß-glucanase increased (P < 0.05) TMEn of barley, and there was no significant effect of adaptation diet on TMEn values. Total cecal Eubacteria and Ruminococcaceae were decreased (P < 0.05) and Escherichia coli were increased (P < 0.05) at the end of the TMEn assay compared with the end of the adaptation period (with no TMEn assay). There was a large decrease (P < 0.05) for most cecal SCFA at the end of the TMEn assay compared with the end of the adaptation period. Both cecal ß-glucanase and xylanase activity were increased for birds fed adaptation diets containing the respective enzyme. In Experiment 3, there were no consistent effects of adaptation diet on cecal microbial profiles or SCFA but cecal ß-glucanase activity was increased (P < 0.05) by exogenous ß-glucanase for barley and cecal xylanase activity was increased (P < 0.05) by exogenous xylanase for rye. Overall, the results indicated that TMEn of barley was increased by exogenous ß-glucanase, adaptation diet did not significantly influence the TMEn response to the dietary enzymes, and cecal fermentation (based on cecal SCFA) was greatly reduced by the TMEn assay. Cecal ß-glucanase and xylanase activity, however, were often increased by feeding high barley and high rye diets containing exogenous enzymes.

The oral microbiota is a reservoir for antimicrobial resistance: resistome and phenotypic resistance characteristics of oral biofilm in health, caries, and periodontitis.

BACKGROUND: Antimicrobial resistance (AMR) is an ever-growing threat to modern medicine and, according to the latest reports, it causes nearly twice as many deaths globally as AIDS or malaria. Elucidating reservoirs and dissemination routes of antimicrobial resistance genes (ARGs) are essential in fighting AMR. Human commensals represent an important reservoir, which is underexplored for the oral microbiota. Here, we set out to investigate the resistome and phenotypic resistance of oral biofilm microbiota from 179 orally healthy (H), caries active (C), and periodontally diseased (P) individuals (TRN: DRKS00013119, Registration date: 22.10.2022). The samples were analysed using shotgun metagenomic sequencing combined, for the first time, with culture technique. A selection of 997 isolates was tested for resistance to relevant antibiotics. RESULTS: The shotgun metagenomics sequencing resulted in 2,069,295,923 reads classified into 4856 species-level OTUs. PERMANOVA analysis of beta-diversity revealed significant differences between the groups regarding their microbiota composition and their ARG profile. The samples were clustered into three ecotypes based on their microbial composition. The bacterial composition of H and C samples greatly overlapped and was based on ecotypes 1 and 2 whereas ecotype 3 was only detected in periodontitis. We found 64 ARGs conveying resistance to 36 antibiotics, particularly to tetracycline, macrolide-lincosamide-streptogramin, and beta-lactam antibiotics, and a correspondingly high prevalence of phenotypic resistance. Based on the microbiota composition, these ARGs cluster in different resistotypes, and a higher prevalence is found in healthy and caries active than in periodontally diseased individuals. There was a significant association between the resistotypes and the ecotypes. Although numerous associations were found between specific antibiotic resistance and bacterial taxa, only a few taxa showed matching associations with both genotypic and phenotypic analyses. CONCLUSIONS: Our findings show the importance of the oral microbiota from different niches within the oral cavity as a reservoir for antibiotic resistance. Additionally, the present study showed the need for using more than one method to reveal antibiotic resistance within the total oral biofilm, as a clear mismatch between the shotgun metagenomics method and the phenotypic resistance characterization was shown.

Selective NO2 Detection of CaCu3Ti4O12 Ceramic Prepared by the Sol-Gel Technique and DRIFT Measurements to Elucidate the Gas Sensing Mechanism.

NO2 is one of the main greenhouse gases, which is mainly generated by the combustion of fossil fuels. In addition to its contribution to global warming, this gas is also directly dangerous to humans. The present work reports the structural and gas sensing properties of the CaCu3Ti4O12 compound prepared by the sol-gel technique. Rietveld refinement confirmed the formation of the pseudo-cubic CaCu3Ti4O12 compound, with less than 4 wt% of the secondary phases. The microstructural and elemental composition analysis were carried out using scanning electron microscopy and X-ray energy dispersive spectroscopy, respectively, while the elemental oxidation states of the samples were determined by X-ray photoelectron spectroscopy. The gas sensing response of the samples was performed for different concentrations of NO2, H2, CO, C2H2 and C2H4 at temperatures between 100 and 300 °C. The materials exhibited selectivity for NO2, showing a greater sensor signal at 250 °C, which was correlated with the highest concentration of nitrite and nitrate species on the CCTO surface using DRIFT spectroscopy.

Optimizing engine performance and reducing emissions of greenhouse gases through spirulina microalgae and nano-additive blends.

The shift in focus towards biofuels has led to the attention towards fourth-generation fuels, particularly microalgae, due to its high oil productivity and simple cultivation processes. The current study aimed to examine the effects of spirulina microalgae blends in a naturally aspirated diesel engine by testing two blend percentages (15% and 30%) and incorporating Fe2O3 nanoparticles (75 ppm). A series of test conducted in a single-cylinder engine with an optimum compression ratio of 17.5. The fuels tested include 100% diesel (D0), diesel with Fe2O3 nanoparticles (DF), diesel with 15% microalgae blends (B15), diesel with 15% microalgae blends and Fe2O3 nanoparticles (B15F), diesel with 30% microalgae blends (B30), and diesel with 30% microalgae blends and Fe2O3 nanoparticles (B30F). The results showed that the addition of microalgae blends led to a marginal increase in engine performance, while the addition of Fe2O3 nanoparticles led to a significant increase in brake thermal efficiency and decreased fuel consumption. The emissions rate was also lower compared to diesel, but the addition of Fe2O3 nanoparticles increased the oxygen content in the fuel, thereby improving the combustion rates. By ensuring the complete combustion the formation of CO2, HC and smoke intensity was also found to be significantly lower compared to diesel fuel. On the contrary, NOx increased due to the cylinder temperatures. This research highlights the potential of using microalgae as a sustainable source of biofuel, and the positive effects of adding Fe2O3 nanoparticles to enhance the fuel's efficiency.

Are Previous Joint Injuries Associated with Muscle Performance in Volleyball Athletes?

Objective The aim of the present study is to determine whether previous shoulder and knee injuries were associated with isokinetic fatigue index and agonist/antagonist ratio of shoulder internal/external rotators and knee flexors/extensors in male volleyball athletes. Methods The current study is a cross-sectional investigation of 49 male elite volleyball players competing at a high level in Brazil. Isokinetic fatigue index and agonist/antagonist profiles were assessed during the preseason. Additionally, in order to record previous injuries, the athletes answered a standardized questionnaire. We conducted a receiver operating characteristic (ROC) curve analysis to determine the association strength and the clinically relevant cut-off point for variables presenting statistical significance for the area under the curve (AUC) (α = 0.05). An independent t-test was used to compare isokinetic variables between athletes with and without previous injury (α = 0.05). Results The results of the ROC curve analysis indicated that hamstring fatigue index values at 300 o /s were associated with the presence of previous knee injury (area under the curve [AUC] = 73%, p = 0.004), and shoulder external rotators fatigue index values at 360°/s were not associated with the presence of previous shoulder injury (AUC = 68%, p = 0.053). Conclusions Elite volleyball athletes who reported previous knee injuries were prone to a higher fatigue index than those reporting no injuries. Knee flexor resistance training might be useful for those athletes who reported knee injuries in the previous season.

COVID-19 and Pulmonary Angiogenesis: The Possible Role of Hypoxia and Hyperinflammation in the Overexpression of Proteins Involved in Alveolar Vascular Dysfunction.

COVID-19 has been considered a vascular disease, and inflammation, intravascular coagulation, and consequent thrombosis may be associated with endothelial dysfunction. These changes, in addition to hypoxia, may be responsible for pathological angiogenesis. This research investigated the impact of COVID-19 on vascular function by analyzing post-mortem lung samples from 24 COVID-19 patients, 10 H1N1pdm09 patients, and 11 controls. We evaluated, through the immunohistochemistry technique, the tissue immunoexpressions of biomarkers involved in endothelial dysfunction, microthrombosis, and angiogenesis (ICAM-1, ANGPT-2, and IL-6, IL-1ß, vWF, PAI-1, CTNNB-1, GJA-1, VEGF, VEGFR-1, NF-kB, TNF-α and HIF-1α), along with the histopathological presence of microthrombosis, endothelial activation, and vascular layer hypertrophy. Clinical data from patients were also observed. The results showed that COVID-19 was associated with increased immunoexpression of biomarkers involved in endothelial dysfunction, microthrombosis, and angiogenesis compared to the H1N1 and CONTROL groups. Microthrombosis and vascular layer hypertrophy were found to be more prevalent in COVID-19 patients. This study concluded that immunothrombosis and angiogenesis might play a key role in COVID-19 progression and outcome, particularly in patients who die from the disease.

The global Cenozoic diversification process of tortoises (Testudinidae).

Great environmental changes may affect the survival capability of a variety of organisms. Testudinidae is the most diverse family of terrestrial chelonians within the whole order (Testudines). Interestingly, however, the number of extinct species overcome the extant ones. In order to understand better how the diversification process of this family occurred, this work used the PyRate software, which estimates both the preservation and diversification processes in a continuous time interval. For such, the software used a list of fossil occurrences obtained from the Paleobiology Database whereas the extant species list was obtained from Catalogue of Life. This way, the software was able to infer the probability of the ancestral clade having resulted in these species during its evolutionary history. The analyses generated graphs containing the diversification, extinction and speciation curves and their respective associated 95% credibility intervals. A great rise in the extinction rate was observed starting 6 million years ago. This rise is believed to be related to the drop of atmospheric CO2 all over the globe at the end of the Miocene, about 8-6 million years ago. This event led to a turnover of the vegetation composition on the warmer areas of the planet, with plants that used C3 metabolism giving way to C4 plants. In terms of landscape, grasses and herbal vegetation, such as savannas, started dominating. As for other animal groups, those herbivores with grazing habits were more successful than those used to only browsing or that did not have enough flexibility of choice.

Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep.

This study investigated the feasibility of using easy-to-measure phenotypic traits to predict sheep resistant, resilient, and susceptible to gastrointestinal nematodes, compared the classification performance of multinomial logistic regression (MLR), linear discriminant analysis (LDA), random forest (RF), and artificial neural network (ANN) methods, and evaluated the applicability of the best classification model on each farm. The database comprised 3654 records of 1250 Santa Inês sheep from 6 farms. The animals were classified into resistant (2605 records), resilient (939 records), and susceptible (110 records) according to fecal egg count and packed cell volume. A random oversampling method was performed to balance the dataset. The classification methods were fitted using the information of age class, the month of record, farm, sex, Famacha© degree, body weight, and body condition score as predictors, and the resistance, resilience, and susceptibility to gastrointestinal nematodes as the target classes to be predicted considering data from all farms randomly. An additional leave-one-farm-out cross-validation technique was used to assess prediction quality across farms. The MLR and LDA models presented good performances in predicting susceptible and resistant animals. The results suggest that the use of readily available records and easily measurable traits may provide useful information for supporting management decisions at the farm level.

Genetic diversity of Trypanosoma cruzi infecting raccoons (Procyon lotor) in 2 metropolitan areas of southern Louisiana: implications for parasite transmission networks.

Trypanosoma cruzi, the aetiological agent of Chagas disease, exists as an anthropozoonosis in Louisiana. Raccoons are an important reservoir, as they demonstrate high prevalence and maintain high parasitaemia longer than other mammals. Given the complex nature of parasite transmission networks and importance of raccoons as reservoirs that move between sylvatic and domestic environments, detailing the genetic diversity of T. cruzi in raccoons is crucial to assess risk to human health. Using a next-generation sequencing approach targeting the mini-exon, parasite diversity was assessed in 2 metropolitan areas of Louisiana. Sequences were analysed along with those previously identified in other mammals and vectors to determine if any association exists between ecoregion and parasite diversity. Parasites were identified from discrete typing units (DTUs) TcI, TcII, TcIV, TcV and TcVI. DTUs TcII, TcV and TcVI are previously unreported in raccoons in the United States (US). TcI was the most abundant DTU, comprising nearly 80% of all sequences. All but 1 raccoon harboured multiple haplotypes, some demonstrating mixed infections of different DTUs. Furthermore, there is significant association between DTU distribution and level III ecoregion in Louisiana. Finally, while certain sequences were distributed across multiple tissues, others appeared to have tissue-specific tropism. Taken together, these findings indicate that ongoing surveillance of T. cruzi in the US should be undertaken across ecoregions to fully assess risk to human health. Given potential connections between parasite diversity and clinical outcomes, deep sequencing technologies are crucial and interventions targeting raccoons may prove useful in mitigating human health risk.