Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of partial substitution of soybean meal with high protein sunflower meal in broiler diets

Gerzilov, V. & Petrov, P. B. (2022). Effects of partial substitution of soybean meal with high protein sunflower meal in broiler diets. Bulg. J. Agric. Sci., 28 (1), 151–157 A total of 210 one day-old male Ross-308 broiler chicks allotted in 7 dietary treatment groups of 30 birds each, balanced for body weight, were reared to 49 days of age and fed with diets consisting different HiSFM (CP–45.4%) and SBM (CP– 46.0%) levels. During the starter period (1-10 d) diets contained HiSFM from 5 to 15% and SBM from 20 to 29.9%; in the grower period (11-24 d) diets contained HiSFM from 10 to 25% and SBM from 5.5 to 20% and finally in the finishing (25-49 d) period – HiSFM was from 15 to 26.5% and SBM was from 0 to 11.3%. The results showed that at 49 days of age, broilers attained a body weight of 3010±91.9 g (group 5) and 3180±112 g (group 6), with nonsignificant differences among groups (P>0.05). The inclusion of higher dietary HiSFM levels in rations of broiler chickens balanced for L-lysine did not pose risk for the growth performance of birds. The inclusion of HiSFM in broiler diets could provide about or more than half of necessary crude protein during the grower (52.79%) and finisher period (53.55 – 61.70%). Recommended dietary HiSFM level for the starter period is up to 10%, for the grower period – up to 20% and for the finisher period – up to 23%. The replacement of 10% SBM with an equal amount of HiSFM reduces the costs of diets by 3.74% to 4.61% depending on the feeding period.
DB111360 2369..2374

Polycystic ovary syndrome (PCOS) recently has been identified as a risk factor associated with type 2 diabetes. However, the evidence derives from cross-sectional observational studies, retrospective studies, or short-term prospective studies. This long-term prospective study of a large cohort of women with PCOS, followed from youth to middle age, aimed at estimating, for the first time, the incidence and potential predictors of type 2 diabetes in this population. A total of 255 women with PCOS were followed for at least 10 years (mean follow-up 16.9 years). Six women were patients with diabetes at baseline, and another 42 women developed type 2 diabetes during the follow-up. The incidence rate of type 2 diabetes in the study population was 1.05 per 100 person-years. The agestandardized prevalence of diabetes at the end of follow-up was 39.3%, which is significantly higher with respect to that of the general Italian female population of a similar age (5.8%). The likelihood of developing type 2 diabetes significantly increased as BMI, fasting glucose, and glucose area under the curve at baseline increased and significantly decreased as sex hormone–binding globulin (SHBG) levels at follow-up increased. This study demonstrates that the risk of type 2 diabetes is markedly elevated in middle-aged women with PCOS and suggests including BMI, glucose, and SHBG-circulating levels in the risk stratification. Diabetes 61:2369–2374, 2012
Marrying Work and Technical Artifact within the Healthcare Organization: A Work Network Approach

Despite the widespread belief that IT innovations bring beneficial change in clinical practices, healthcare practitioners struggle to realize the innovation potential of electronic medical records (EMR). This paper reports on two in-depth case studies of clinical EMR implementation based on an 11 month field study with the goal of developing a richer understanding as to why, when, and how IT innovation and organizational change occur. We propose the work network as an approach for analyzing the processes of configuration, implementation and use of the system over time. The work network offers a flexible formalism that allows researchers to analyze the evolving relationships between work, actors, institutions, and technology as the EMR artifact is inscribed in the work system. By linking up specific aspects of infrastructure and organizing over time, the work network clarifies how tensions emerge around complex IT implementation and describes the evolving fitting work so important for successful innovation with IT.
ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model

When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy.
ROBUST FACE PRESENTATION ATTACK DETECTION WITH MULTI-CHANNEL NEURAL NETWORKS

Vulnerability against presentation attacks remains a challenging issue limiting the reliable use of face recognition systems. Though several methods have been proposed in the literature for the detection of presentation attacks, the majority of these methods fail in generalizing to unseen attacks and environments. Since the quality of attack instruments keeps getting better, the difference between bonaﬁde and attack samples is diminishing making it harder to distinguish them using the visible spectrum alone. In this context, multi-channel presentation attack detection methods have been proposed as a solution to secure face recognition systems. Even with multiple channels, special care needs to be taken to ensure that the model generalizes well in challenging scenarios. In this chapter, we present three different strategies to use multi-channel information for presentation attack detection. Speciﬁcally, we present different architecture choices for fusion, along with ad-hoc loss functions as opposed to standard classiﬁcation objective. We conduct an extensive set of experiments in the HQ-WMCA dataset, which contains a wide variety of attacks and sensing channels together with challenging unseen attack evaluation protocols. We make the protocol, source codes, and data publicly available to enable further extensions of the work.
Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving – a retrospective clinical study to early detect spontaneous disease appearance

The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30’340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time, eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time, as well as of eating-and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews, ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals’ health around calving.
Drug delivery from a solid formulation during breastfeeding—A feasibility study with mothers and infants

Background Breastfeeding is critical to health outcomes, particularly in low-resource settings where there is little access to clean water. For infants in their first twelve months of life, the delivery of medications is challenging, and use of oral syringes to deliver liquid formulations can pose both practical and emotional challenges. Objective To explore the potential to deliver medicine to infants via a solid formulation during breastfeeding. Methods Single center feasibility study within a tertiary level neonatal unit in the UK, involving twenty-six breastfeeding mother-infant dyads. A solid formulation of Vitamin B12 was delivered to infants during breastfeeding. Outcomes included the quantitative change in serum vitamin B12 and assessment of maternal expectations and experiences. Results Delivery of Vitamin B12 through a solid formulation that dissolved in human milk did not impair breastfeeding, and Vitamin B12 levels rose in all infants from a mean baseline (range) 533 pg/mL (236–925 pg/mL) to 1871 pg/mL (610–4981 pg/mL) at 6–8 hours post-delivery. Mothers described the surprising ease of ‘drug’ delivery, with 85% reporting a preference over the use of syringes. Conclusions Solid drug formulations can be delivered during breastfeeding and were preferred by mothers over the delivery of liquid formulations via a syringe.
Predicting metabolizable energy from digestible energy for growing and finishing beef cattle and relationships to the prediction of methane

Abstract Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r2 = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH4, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r2 = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.
Risk factors for depression and anxiety in pregnant women during the COVID-19 pandemic: Evidence from meta-analysis

Background The prevalence of anxiety and depression in pregnant women has significantly increased after the spread of COVID-19 throughout the world. We carried out this meta-analysis to reveal the information about risk factors for depression and anxiety in pregnant women during the COVID-19 pandemic. Methods We searched the PubMed, Embase and CNKI (China National Knowledge Infrastructure) databases for all articles. The odds ratio (OR) corresponding to the 95% confidence interval (95% CI) was used to assess the risk factors for mental health. The statistical heterogeneity among studies was assessed with the Q-test and I2 statistics. Results We collected 17 studies including 15,050 pregnant women during the COVID-19 pandemic. Our results found that factors including decrease in the perception of general support and difficulties in household finances have damage effects on anxiety, and factors including undereducated, unemployed during pregnancy, with a chronic physical illness before pregnancy, decrease in the perception of general support, difficulties in household finances, disobey the isolation rules, and smoking during pregnancy have increased risk of depression. Conclusion Our meta-analysis revealed some risk factors for mental health in pregnant women during COVID-19 pandemic. Mental health interventions in pregnant women may involve targeted methods individually.
Skeletal muscle atrophy and myosteatosis are not related to long-term aneurysmal subarachnoid hemorrhage outcome

The prognosis of aneurysmal subarachnoid hemorrhage (aSAH) is highly variable. This study aims to investigate whether skeletal muscle atrophy and myosteatosis are associated with poor outcome after aSAH. In this study, a cohort of 293 consecutive aSAH-patients admitted during a 4-year period was retrospectively analyzed. Cross-sectional muscle measurements were obtained at the level of the third cervical vertebra. Muscle atrophy was defined by a sex-specific cutoff value. Myosteatosis was defined by a BMI-specific cutoff value. Poor neurological outcome was defined as modified Rankin Scale 4–6 at 2 and 6-month follow-up. Patient survival state was checked until January 2021. Generalized estimating equation was performed to assess the effect of muscle atrophy / myosteatosis on poor neurological outcome after aSAH. Cox regression was performed to analyze the impact of muscle atrophy and myosteatosis on overall survival. The study found that myosteatosis was associated with poor neurological condition (WFNS 4–5) at admission after adjusting for covariates (odds ratio [OR] 2.01; 95%CI 1.05,3.83; P = .03). It was not associated with overall survival (P = .89) or with poor neurological outcomes (P = .18) when adjusted for other prognostic markers. Muscle atrophy was not associated with overall survival (P = .58) or neurological outcome (P = .32) after aSAH. In conclusion, myosteatosis was found to be associated with poor physical condition directly after onset of aSAH. Skeletal muscle atrophy and myosteatosis were however irrelevant to outcome in the Western-European aSAH patient. Future studies are needed to validate these finding.
Cortical oscillatory dysfunction in Parkinson disease during movement activation and inhibition

Response activation and inhibition are functions fundamental to executive control that are disrupted in Parkinson disease (PD). We used magnetoencephalography to examine event related changes in oscillatory power amplitude, peak latency and frequency in cortical networks subserving these functions and identified abnormalities associated with PD. Participants (N = 18 PD, 18 control) performed a cue/target task that required initiation of an un-cued movement (activation) or inhibition of a cued movement. Reaction times were variable but similar across groups. Task related responses in gamma, alpha, and beta power were found across cortical networks including motor cortex, supplementary and pre- supplementary motor cortex, posterior parietal cortex, prefrontal cortex and anterior cingulate. PD-related changes in power and latency were noted most frequently in the beta band, however, abnormal power and delayed peak latency in the alpha band in the pre-supplementary motor area was suggestive of a compensatory mechanism. PD peak power was delayed in pre-supplementary motor area, motor cortex, and medial frontal gyrus only for activation, which is consistent with deficits in un-cued (as opposed to cued) movement initiation characteristic of PD.
Developmental Stage, Solid Food Introduction, and Suckling Cessation Differentially Influence the Comaturation of the Gut Microbiota and Intestinal Epithelium in Rabbits

ABSTRACT Background In mammals, the establishment around weaning of a symbiotic relationship between the gut microbiota and its host determines long-term health. Objectives The aim of this study was to identify the factors driving the comaturation of the gut microbiota and intestinal epithelium at the suckling-to-weaning transition. We hypothesized that the developmental stage, solid food ingestion, and suckling cessation contribute to this process. Methods From birth to day 18, Hyplus rabbits were exclusively suckling. From day 18 to day 25, rabbits were 1) exclusively suckling; 2) suckling and ingesting solid food; or 3) exclusively ingesting solid food. The microbiota (16S amplicon sequencing), metabolome (nuclear magnetic resonance), and epithelial gene expression (high-throughput qPCR) were analyzed in the cecum at days 18 and 25. Results The microbiota structure and metabolic activity were modified with age when rabbits remained exclusively suckling. The epithelial gene expression of nutrient transporters, proliferation markers, and innate immune factors were also regulated with age (e.g., 1.5-fold decrease of TLR5). Solid food ingestion by suckling rabbits had a major effect on the gut microbiota by increasing its α diversity, remodeling its structure (e.g., 6.3-fold increase of Ruminococcaceae), and metabolic activity (e.g., 4.6-fold increase of butyrate). Solid food introduction also regulated the gene expression of nutrient transporters, differentiation markers, and innate immune factors in the epithelium (e.g., 3-fold increase of nitric oxide synthase). Suckling cessation had no effect on the microbiota, while it regulated the expression of genes involved in epithelial differentiation and immunoglobulin transport (e.g., 2.5-increase of the polymeric immunoglobulin receptor). Conclusions In rabbits, the maturation of the microbiota at the suckling-to-weaning transition is driven by the introduction of solid food and, to a lesser extent, by the developmental stage. In contrast, the maturation of the intestinal epithelium at the suckling-to-weaning transition is under the influence of the developmental stage, solid food introduction, and suckling cessation.
The protective effects of butorphanol tartrate against homocysteine-induced blood-brain barrier dysfunction

ABSTRACT A high concentration of homocysteine (Hcy) has been recently reported to be closely associated with the development of stroke, which is related to the Hcy-induced blood-brain barrier (BBB) dysfunction. Butorphanol tartrate is a promising analgesic agent that targets the opiate receptor and shows promising protective effects on ischemia/reperfusion injury. The present research proposes to investigate the protective effect of butorphanol tartrate on Hcy-induced BBB disruption to explore the potential application of butorphanol tartrate in treating Hcy-induced stroke. Hcy was utilized to establish both an in vivo animal model and in vitro human brain vascular endothelial cells (HBVECs) injury model. We found that the increased diffusion of sodium fluorescein and Evan’s blue, declined expression of Claudin-5, and increased production of interleukin- 6 (IL-6) and tumor necrosis factor-α (TNF-α) were observed in Hcy-treated mice, which were all significantly reversed by butorphanol tartrate. In Hcy-stimulated HBVECs, increased endothelial permeability and reduced expression levels of Claudin-5 and Krüppel-like factor 5 (KLF5) were observed, all of which were dramatically rescued by 2 and 5 µM butorphanol tartrate. Lastly, the protective function of butorphanol tartrate in Hcy-stimulated HBVECs was dramatically abolished by the knockdown of KLF5. Collectively, butorphanol tartrate showed protective effects on Hcy-induced BBB disruption by upregulating the KLF5/Claudin-5 axis. Graphical Abstract
ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome.

BACKGROUND Deep convolutional neural network (CNN) and its derivatives have recently shown great promise in the prediction of brain disorders using brain connectome data. Existing deep CNN methods using single global row and column convolutional filters have limited ability to extract discriminative information from brain connectome for prediction tasks. PURPOSE This paper presents a novel deep Connectome-Inception CNN (ConCeptCNN) model, which is developed based on multiple convolutional filters. The proposed model is used to extract topological features from brain connectome data for neurological disorders classification and analysis. METHODS The ConCeptCNN uses multiple vector-shaped filters extract topological information from the brain connectome at different levels for complementary feature embeddings of brain connectome. The proposed model is validated using two datasets: the Neuro Bureau ADHD-200 dataset and the Cincinnati Early Prediction Study (CINEPS) dataset. RESULTS In a cross-validation experiment, the ConCeptCNN achieved a prediction accuracy of 78.7% for the detection of ADHD in adolescents and an accuracy of 81.6% for the prediction of cognitive deficits at 2-year corrected age in very preterm infants. In addition to the classification tasks, the ConCeptCNN identified several brain regions that are discriminative to neurodevelopmental disorders. CONCLUSIONS We compared the ConCeptCNN with several peer CNN methods. The results demonstrated that proposed model improves overall classification performance of neurodevelopmental disorders prediction tasks. This article is protected by copyright. All rights reserved.