Ohjama5671

able genetics for this species while mitigating diseases like brucellosis.This study evaluated the applicability of intrauterine artificial insemination (IUAI) in gilts and the impact of age at insemination and different body characteristics of gilts on the success rate for cannula insertion. Additionally, reproductive performance was evaluated for IUAI and cervical artificial insemination (CAI), considering different semen dose sizes. A total of 636 gilts were assigned in a 2 × 2 factorial design two artificial insemination techniques (CAI and IUAI) and two semen dose sizes (1.5 × 109 sperm cells/50 mL or 2.5 × 109 sperm cells/80 mL). In those gilts assigned to IUAI (n = 319) the success rate for intrauterine cannula insertion was evaluated according to weight at first detected estrus, body condition score (BCS), and age at insemination. Reproductive performance, occurrence of bleeding, and semen backflow during all inseminations were compared among groups (2 × 2). Two subgroups were evaluated regarding the time expended to perform insemination (n = 380), and the semen backflow cops (P less then 0.01). In conclusion, higher weight, BCS, and age increased the success rate for cannula insertion. However, IUAI did not optimize the insemination procedure, and remains limited for gilts due to the low success rate for cannula insertion. Reproductive performance was not affected by IUAI or CAI using 1.5 or 2.5 billion sperm cells in 50 or 80 mL, respectively, suggesting the possibility of using CAI with 1.5 billion sperm cells/50 mL in gilts.The present study investigates whether predictions during language comprehension are generated by engaging the language production system. Previous studies investigating either prediction or production highlighted M/EEG desynchronization (power decrease) in the alpha (8-10 Hz) and beta (13-30 Hz) frequency bands preceding the target. However, it is unclear whether this electrophysiological modulation underlies common mechanisms. We recorded EEG from participants performing both a comprehension and a production task in two separate blocks. Participants listened to high and low constraint incomplete sentences and were asked either to name a picture to complete them (production) or to simply listen to the final word (comprehension). We found that in a silent gap before the final stimulus, predictable stimuli elicited alpha and beta desynchronization in both tasks, signaling the pre-activation of linguistic information. Source estimation highlighted the involvement of left-lateralized language areas and temporo-parietal areas in the right hemisphere. Furthermore, correlations between the desynchronizations in comprehension and production showed spatiotemporal commonalities in language-relevant areas of the left hemisphere. As proposed by prediction-by-production models, our results suggest that comprehenders engage the production system while predicting upcoming words.Aerobic granular sludge as a promising technology showed great resistance to adverse conditions. However, the interaction between oxytetracycline (OTC) and granular sludge was not studied sufficiently. This study therefore investigated OTC-tolerance ability of incomplete and complete granulation sludge from aspects of simultaneous nutrients removal, sludge characteristics, microbial activity, community changes, and vice versa OTC removal performance. Incomplete granulation sludge showed better denitrification performance and resistance. Whereas, denitrification and phosphorus removal of complete granulation sludge suffered a permanent collapse under 5 mg/L OTC. OTC could be removed by rapid adsorption and slow biodegradation via granular sludge. The EPS, especially TB-PS, played a significant role during the operational period subjected to OTC. The major genera of Lysobacter and Candidatus_Competibacter laid the biological basis for stability and functionality of granules, which acted as the putative contributors for resisting and removing OTC. This study showed that incomplete-granulated sludge qualified more promising application prospect.Surfactants are multipurpose products found in most sectors of contemporary industry. Their large-scale manufacturing has been mainly carried out using traditional chemical processes. Some of the chemical species involved in their production are considered hazardous and some industrial processes employing them categorised as "having potential negative impact on the environment". Biological surfactants have therefore been generally accepted worldwide as suitable sustainable greener alternatives. Biosurfactants exhibit the same functionalities of synthetic analogues while having the ability to synergize with other molecules improving performances; this strengthens the possibility of reaching different markets via innovative formulations. Recently, their use was suggested to help combat Covid-19. In this review, an analysis of recent bibliography is presented with descriptions, statistics, classifications, applications, advantages, and challenges; evincing the reasons why biosurfactants can be considered as the chemical specialities of the future. Finally, the uses of the solid-state fermentation as a production technology for biosurfactants is presented.Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. this website Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes.