Carane-3,4-diol Derivatives as Potential Water-Based Herbicides.

Twelve novel carane-3,4-diol derivatives were designed, synthesized, and evaluated for their herbicidal activities against Lolium multiflorum Lam. and Brassica campestris for the first time. The relationships between the chemical structural factors, including types, the number or the carbon chain length of functional groups, associated with the lipophilicity and the herbicidal activity of the tested compounds were also discussed. The results showed that most of newly synthesized compounds had a dose-dependent, herbicidal activity against the root and shoot growths of Lolium multiflorum Lam. and Brassica campestris. Compared to carane-3,4-diol, most of the target derivatives possessed improved lipophilicity and certain solubilities in representative solvents with different polarities. Particularly, ester derivatives 3a-3b and 3e can be dissolved or dispersed in water, but also displayed higher herbicidal activity against Lolium multiflorum Lam. and Brassica campestris than other ester derivatives. The 50 % inhibitory concentration (IC50) value of compound 3e against shoot growth of Brassica campestris (0.485 mmol/L) was superior to that of commercial herbicide glyphosate (1.14 mmol/L), indicating that the potential application as a water-based herbicide for Brassica campestris control.

Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients.

OBJECTIVE: To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model, and further to predict the kidney failure (KF) status and mortality of heart failure (HF) patients. MATERIALS AND METHODS: The time-invariant EMR data included demographic information and comorbidities, and the time-varying EMR data were lab tests. We used a Transformer encoder module to represent the time-invariant data, and refined a long short-term memory (LSTM) with a Transformer encoder attached to the top to represent the time-varying data, taking the original measured values and their corresponding embedding vectors, masking vectors, and two types of time intervals as inputs. The proposed representations of patients with time-invariant and time-varying data were used to predict KF status (949 out of 5268 HF patients diagnosed with KF) and mortality (463 in-hospital deaths) for HF patients. Comparative experiments were conducted between the proposed model and some representative machine learning models. Ablation experiments were also performed around the time-varying data representation, including replacing the refined LSTM with the standard LSTM, GRU-D and T-LSTM, respectively, and removing the Transformer encoder and the time-varying data representation module, respectively. The visualization of the attention weights of the time-invariant and time-varying features was used to clinically interpret the predictive performance. We used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the F1-score to evaluate the predictive performance of the models. RESULTS: The proposed model achieved superior performance, with average AUROCs, AUPRCs and F1-scores of 0.960, 0.610 and 0.759 for KF prediction and 0.937, 0.353 and 0.537 for mortality prediction, respectively. Predictive performance improved with the addition of time-varying data from longer time periods. The proposed model outperformed the comparison and ablation references in both prediction tasks. CONCLUSIONS: Both time-invariant and time-varying EMR data of patients could be efficiently represented by the proposed unified deep learning model, which shows higher performance in clinical prediction tasks. The way to use time-varying data in the current study is hopeful to be used in other kinds of time-varying data and other clinical tasks.

Interplay between zinc and cell proliferation and implications for the growth of livestock.

Zinc (Zn) plays a critical role in the growth of livestock, which depends on cell proliferation. In addition to modifying the growth associated with its effects on food intake, mitogenic hormones, signal transduction and gene transcription, Zn also regulates body weight gain through mediating cell proliferation. Zn deficiency in animals leads to growth inhibition, along with an arrest of cell cycle progression at G0/G1 and S phase due to depression in the expression of cyclin D/E and DNA synthesis. Therefore, in the present study, the interplay between Zn and cell proliferation and implications for the growth of livestock were reviewed, in which Zn regulates cell proliferation in several ways, especially cell cycle progression at the G0/G1 phase DNA synthesis and mitosis. During the cell cycle, the Zn transporters and major Zn binding proteins such as metallothioneins are altered with the requirements of cellular Zn level and nuclear translocation of Zn. In addition, calcium signaling, MAPK pathway and PI3K/Akt cascades are also involved in the process of Zn-interfering cell proliferation. The evidence collected over the last decade highlights the necessity of Zn for normal cell proliferation, which suggests Zn supplementation should be considered for the growth and health of poultry.

Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study.

BACKGROUND: The widespread secondary use of electronic medical records (EMRs) promotes health care quality improvement. Representation learning that can automatically extract hidden information from EMR data has gained increasing attention. OBJECTIVE: We aimed to propose a patient representation with more feature associations and task-specific feature importance to improve the outcome prediction performance for inpatients with acute myocardial infarction (AMI). METHODS: Medical concepts, including patients' age, gender, disease diagnoses, laboratory tests, structured radiological features, procedures, and medications, were first embedded into real-value vectors using the improved skip-gram algorithm, where concepts in the context windows were selected by feature association strengths measured by association rule confidence. Then, each patient was represented as the sum of the feature embeddings weighted by the task-specific feature importance, which was applied to facilitate predictive model prediction from global and local perspectives. We finally applied the proposed patient representation into mortality risk prediction for 3010 and 1671 AMI inpatients from a public data set and a private data set, respectively, and compared it with several reference representation methods in terms of the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. RESULTS: Compared with the reference methods, the proposed embedding-based representation showed consistently superior predictive performance on the 2 data sets, achieving mean AUROCs of 0.878 and 0.973, AUPRCs of 0.220 and 0.505, and F1-scores of 0.376 and 0.674 for the public and private data sets, respectively, while the greatest AUROCs, AUPRCs, and F1-scores among the reference methods were 0.847 and 0.939, 0.196 and 0.283, and 0.344 and 0.361 for the public and private data sets, respectively. Feature importance integrated in patient representation reflected features that were also critical in prediction tasks and clinical practice. CONCLUSIONS: The introduction of feature associations and feature importance facilitated an effective patient representation and contributed to prediction performance improvement and model interpretation.

The influence of brightness combinations and background colour on legibility and subjective preference under negative polarity.

This study explores the influence of colour combinations on legibility and aesthetic feelings for the currently popular negative polarity interface design. Legibility was measured in two different ways in two tasks: time threshold (Task I) and a 9-point subjective rating (Task II). In Task I, we combined an adaptive program to measure 37 participants' recognition thresholds and online pseudo-word recognition tasks; in Task II, 44 participants' subjective preferences were measured using a scale. We found that higher brightness contrasts led to better legibility; different background colours with identical brightness and saturation did not cause significant differences; brighter texts produced better subjective preference for aesthetic appearance, legibility, and visual comfort; and different background colours had no significant effect on subjective preference. These findings have implications for digital interface design. Practitioner summary: In display design under negative polarity, experimental results show that higher brightness contrast leads to higher legibility, while background colour has no such significant effect; background brightness and hue have no significant effect on subjective preference, but text brightness and background colour have significant interaction effect on subjective preference. Abbreviations: OLED: organic light-emitting diode; LCD: liquid crystal display; ANOVA: analysis of variance; VDT: visual displsy terminal; CET-4: college english test band 4; ISO: International Organization for Standardization; HSB: hues saturation brightness.

Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework.

BACKGROUND: Liver cancer is a substantial disease burden in China. As one of the primary diagnostic tools for detecting liver cancer, dynamic contrast-enhanced computed tomography provides detailed evidences for diagnosis that are recorded in free-text radiology reports. OBJECTIVE: The aim of our study was to apply a deep learning model and rule-based natural language processing (NLP) method to identify evidences for liver cancer diagnosis automatically. METHODS: We proposed a pretrained, fine-tuned BERT (Bidirectional Encoder Representations from Transformers)-based BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Field) model to recognize the phrases of APHE (hyperintense enhancement in the arterial phase) and PDPH (hypointense in the portal and delayed phases). To identify more essential diagnostic evidences, we used the traditional rule-based NLP methods for the extraction of radiological features. APHE, PDPH, and other extracted radiological features were used to design a computer-aided liver cancer diagnosis framework by random forest. RESULTS: The BERT-BiLSTM-CRF predicted the phrases of APHE and PDPH with an F1 score of 98.40% and 90.67%, respectively. The prediction model using combined features had a higher performance (F1 score, 88.55%) than those using APHE and PDPH (84.88%) or other extracted radiological features (83.52%). APHE and PDPH were the top 2 essential features for liver cancer diagnosis. CONCLUSIONS: This work was a comprehensive NLP study, wherein we identified evidences for the diagnosis of liver cancer from Chinese radiology reports, considering both clinical knowledge and radiology findings. The BERT-based deep learning method for the extraction of diagnostic evidence achieved state-of-the-art performance. The high performance proves the feasibility of the BERT-BiLSTM-CRF model in information extraction from Chinese radiology reports. The findings of our study suggest that the deep learning-based method for automatically identifying evidences for diagnosis can be extended to other types of Chinese clinical texts.

Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.

BACKGROUND: A new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data. METHODS: We first calculated feature-level similarities according to the features' attributes. A domain expert provided patient similarity scores of 30 randomly selected patients. These similarity scores and feature-level similarities for 30 patients comprised the labeled sample set, which was used for the semi-supervised learning algorithm to learn the patient-level similarities for all patients. Then we used the k-nearest neighbor (kNN) classifier to predict four liver conditions. The predictive performances were compared in four different situations. We also compared the performances between personalized kNN models and other machine learning models. We assessed the predictive performances by the area under the receiver operating characteristic curve (AUC), F1-score, and cross-entropy (CE) loss. RESULTS: As the size of the random training samples increased, the kNN models using the learned patient similarity to select near neighbors consistently outperformed those using the Euclidean distance to select near neighbors (all P values < 0.001). The kNN models using the learned patient similarity to identify the top k nearest neighbors from the random training samples also had a higher best-performance (AUC: 0.95 vs. 0.89, F1-score: 0.84 vs. 0.67, and CE loss: 1.22 vs. 1.82) than those using the Euclidean distance. As the size of the similar training samples increased, which composed the most similar samples determined by the learned patient similarity, the performance of kNN models using the simple Euclidean distance to select the near neighbors degraded gradually. When exchanging the role of the Euclidean distance, and the learned patient similarity in selecting the near neighbors and similar training samples, the performance of the kNN models gradually increased. These two kinds of kNN models had the same best-performance of AUC 0.95, F1-score 0.84, and CE loss 1.22. Among the four reference models, the highest AUC and F1-score were 0.94 and 0.80, separately, which were both lower than those for the simple and similarity-based kNN models. CONCLUSIONS: This learning-based method opened an opportunity for similarity measurement based on heterogeneous EMR data and supported the secondary use of EMR data.

Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records.

BACKGROUND: Conventional risk prediction techniques may not be the most suitable approach for personalized prediction for individual patients. Therefore, individualized predictive modeling based on similar patients has emerged. This study aimed to propose a comprehensive measurement of patient similarity using real-world electronic medical records data, and evaluate the effectiveness of the individualized prediction of a patient's diabetes status based on the patient similarity. RESULTS: When using no more than 30% of the whole training sample, the personalized predictive models outperformed corresponding traditional models built on randomly selected training samples of the same size as the personalized models (P < 0.001 for all). With only the top 1000 (10%), 700 (7%) and 1400 (14%) similar samples, personalized random forest, k-nearest neighbor and logistic regression models reached the globally optimal performance with the area under the receiver-operating characteristic (ROC) curve of 0.90, 0.82 and 0.89, respectively. CONCLUSIONS: The proposed patient similarity measurement was effective when developing personalized predictive models. The successful application of patient similarity in predicting a patient's diabetes status provided useful references for diagnostic decision-making support by investigating the evidence on similar patients.

Gene structure and spatio-temporal expression of chicken LPIN2.

LPIN2 is one of the members of the Lipin family, which acts as a phosphatidate phosphatase enzyme. In this study, we identified the cDNA sequence and exonic variants of chicken LPIN2, and evaluated its spatio-temporal expression patterns. It indicated that chicken LPIN2 cDNA contained a 2,664-bp open reading frame flanked by a 176-bp 5' untranslated region and a 429-bp 3' untranslated region, predicted encoding one protein of 886 amino acids. Fourteen variants (three missense mutations) were detected from the coding region of chicken LPIN2. W265L was predicted to affect the gene function (p < 0.01) and eight synonymous mutations were predicted to affect the binding sites of SR proteins, which suggested the important functions of these variants. Real-time quantitative PCR revealed that LPIN2 in two genotypic chickens (LD and HB chickens, with difference in growth rate) presented similar tissue expression patterns, which was liver and ovary enriched with low abundance in skeleton muscles. Chicken LPIN2 exhibited tissue-specific temporal-expression patterns during postnatal development (0-16 weeks). Chicken cutaneous LPIN2 was in steady-state mRNA levels during postnatal development; chicken LPIN2 mRNA in pectoralis major had a prominent level at 0 week-old, then dropped dramatically at 4 week-old and maintained a relatively low level through 4-16 weeks; while chicken hepatic LPIN2 had a relatively high expression at 0 week-old, with a relatively low level through 4-12 weeks and a slight increase at 16 week-old. The studies about the basic gene features of chicken LPIN2 would lay the foundation for further exploring its biological function.

High bacterial loads of Ureaplasma may be associated with non-specific cervicitis.

BACKGROUND: Ureaplasma parvum and Ureaplasma urealyticum are commonly found in the cervix of women with non-chlamydial and non-gonococcal cervicitis or non-specific cervicitis (NSC). However their contribution to the aetiology of NSC is controversial. METHODS: U. parvum and U. urealyticum were identified and quantified in cervical swabs collected from 155 women with NSC and 312 controls without NSC, using real-time PCR. The relative bacterial quantification was then calculated using the Ureaplasma copy number divided by the number of host cells; this is important for the correction of bias linked to the number of cells harvested in different swabs. RESULTS: Ureaplasma was detected in 58.7% (91/155) of NSC patients: U. parvum in 30.3%, U. urealyticum in 16.1%, and mixed infection in 12.3%. It was also detected in 54.5% (170/312) of controls: U. parvum in 33.0%, U. urealyticum in 11.5%, and mixed infection in 9.9%. There were no significant differences for U. parvum, U. urealyticum, or mixed infection between the 2 groups (p > 0.05). However, both biovars were present at higher concentrations in NSC patients than in controls (p < 0.05). Using >10 copies/1000 cells as a reference, the positive rate of U. parvum in NSC patients was 16.1%, significantly higher than that in controls at 5.1% (relative risk 3.145, p < 0.05); positive rates of U. urealyticum in NSC patients and controls were 28.4% and 8.7%, respectively, with a statistically significant difference (relative risk 3.131, p < 0.05). CONCLUSIONS: Ureaplasma can adhere to host cells, colonize, internalize, and subsequently produce pathological lesions. A high density of Ureaplasma in the cervix may be associated with the aetiology of NSC.

The effect of music tempo on movement flow.

There has been much controversy over the effects of music tempo on movement flow. In this study, a single-factor repeated-measurement design was used to explore the effect of music tempo (fast, slow, and no music control) on movement flow by measuring both subjective experiences and objective electroencephalographic (EEG) characteristics during brisk walking. In the experiment, 20 college students walked briskly on a treadmill using EEG equipment. Each participant walked for 10 min on three different days. Their brain waves were recorded during brisk walking on a treadmill. After each walk, the participants completed a form of short flow state scale-2 (S FSS-2), which covered nine major aspects of flow. The results showed that music tempo had a significant effect on subjective experiences and objective physiological characteristics; that is, a higher subjective flow level for fast-tempo music in brisk walking and a significant enhancement of mean power values in the subconscious brain waves of the delta, theta, alpha, and beta bands for fast tempo music were observed. A fast tempo facilitated the movement flow. The findings of this study can be instructive for the use of music in exercises to improve sports training outcomes.

Prediction of the mechanical properties of TPMS structures based on Back propagation neural network.

Triply Periodic Minimal Surface (TPMS) has the characteristics of high porosity, a highly interconnected network, and a smooth surface, making it an ideal candidate for bone tissue engineering applications. However, due to the complex relationship between multiple parameters of the TPMS structure and mechanical properties, it is a challenging task to optimize the properties of TPMS structures with different parameters. In this study, a Back-Propagation Neural Network (BPNN) was utilized to construct the relationship between TPMS parameters. Its mechanical performance and the TPMS structure were optimized using the BPNN. Results indicated that after training the correlation coefficient (R) between the BPNN prediction and the experimental results is 0.955475, it shows that our BPNN model has an adequate accuracy in describing the TPMS structures properties. Result of TPMS structure optimization shows that after optimization the yield strength of Hybridized Gyroid-Diamond Structure (HGDS) is 6.20 MPa, which is increased by 102.61% when compared with the original Hybridized Gyroid-Diamond Structure (3.06 MPa). Result of topological morphology indicates the effective bearing area of the optimized model was increased by 12.92% compared with the original model, which ascribe the increase in yield strength of the optimization model.

Effects of varying wheat levels on growth performance, intestinal barrier, and cecal microbiota of broilers.

Introduction: The study aimed to investigate the potential effects of varying wheat levels in broiler diets on growth performance, intestinal barrier, and cecal microbiota. Methods: Day-old male broilers were fed the same diet until 10 d of age. Then they were randomly assigned to 1) the low-level wheat group, where inclusion of 15.0% and 25.0% wheat in the grower and finisher diet, respectively, 2) the medium-level wheat group with 30.0% and 40.0% of wheat in the grower and finisher periods; and 3) the high-level wheat dietary group, in which the grower and finisher diets contained 55.77% and 62.38% of wheat, respectively. Results: Dietary treatments unaffected the body weight at 39 d, whereas incorporating high wheat in diets significantly increased the feed intake and reduced the feed conversion ratio from 10 to 39 d (p < 0.05). Except for increased phosphorus digestibility in the high wheat group, dietary treatments had no significant effect on the apparent digestibility of dry matter, crude protein, and ether extract. Meanwhile, the broilers that consumed the medium and high content of wheat presented a higher villus height and the ratio of villus height to crypt depth than those fed the low-level wheat birds. Feeding the medium-level wheat enhanced ileal integrity and depressed the expression of proinflammatory cytokines in the ileum. The addition of high levels of wheat reduced the Chao1 index and the abundance of Lactobacillaceae, Bacteroidaceae, and Ruminococcacea in cecal content, which probably decreased the metabolism of histidine, sulfur-containing amino acids, and the biosynthesis of lysine. Discussion: These results support the medium-level wheat diet improved intestinal barrier function and had no deleterious effects on the growth performance of broiler; dietary inclusion of high wheat reduced the feed conversion rate, which might be associated with the disturbed gut microbiota and decreased metabolism of amino acids.

Alteration of growth performance, glycolipid metabolism, and bone characteristics of broiler chickens in response to different inclusion levels of dietary wheat.

Objective: The aim of this study was to evaluate the effects of different levels of wheat inclusion on growth performance, glycolipid metabolism, and tibial properties of broiler chickens. Methods: A total of 480 1-d-old male broiler chickens were initially fed identical starter diets until d 10. Subsequently, they were divided into 3 treatments consisting of 8 replicates with 20 birds per replicate, i.e., 1) low-level wheat addition group, with wheat ratios of 15% and 25% during the grower and finisher periods, respectively; 2) medium-level wheat inclusion group, incorporating 30% and 40% wheat in the grower and finisher diets, respectively; and 3) high-level wheat addition group, containing 55.8% and 62.4% wheat in the grower and finisher diets, until d 39. Results: When compared to the low- and medium-level wheat diet, the high-level wheat inclusion in the diet increased feed intake and reduced the feed conversion ratio (both p<0.01), which was accompanied by a longer jejunum (p=0.031). Meanwhile, the high-level addition of wheat displayed a decreased abundance of Ruminococcin, Bacteroidetes, and Lactobacillus than the low-wheat group. With the increase of the proportion of wheat treatment, the contents of cholesterol, triglyceride, and high-density lipoprotein cholesterol were elevated in serum, whereas the concentration of serum C-terminal cross-linked telopeptide of type I collagen, a bone resorption marker, was decreased. In addition, the diet with medium and high levels of wheat improved the yield load of tibia, along with comparable bone dimension and weight. Conclusion: The medium- and high-level wheat additions increased serum glycolipid deposition and enhanced tibial mechanical properties, whereas the high-level wheat diet compromised the growth performance of broiler chickens, which might be associated with the alteration of gut microbiota.

Analyzing Comorbidity Patterns in Patients With Thyroid Disease Using Large-Scale Electronic Medical Records: Network-Based Retrospective Observational Study.

BACKGROUND: Thyroid disease (TD) is a prominent endocrine disorder that raises global health concerns; however, its comorbidity patterns remain unclear. OBJECTIVE: This study aims to apply a network-based method to comprehensively analyze the comorbidity patterns of TD using large-scale real-world health data. METHODS: In this retrospective observational study, we extracted the comorbidities of adult patients with TD from both private and public data sets. All comorbidities were identified using ICD-10 (International Classification of Diseases, 10th Revision) codes at the 3-digit level, and those with a prevalence greater than 2% were analyzed. Patients were categorized into several subgroups based on sex, age, and disease type. A phenotypic comorbidity network (PCN) was constructed, where comorbidities served as nodes and their significant correlations were represented as edges, encompassing all patients with TD and various subgroups. The associations and differences in comorbidities within the PCN of each subgroup were analyzed and compared. The PageRank algorithm was used to identify key comorbidities. RESULTS: The final cohorts included 18,311 and 50,242 patients with TD in the private and public data sets, respectively. Patients with TD demonstrated complex comorbidity patterns, with coexistence relationships differing by sex, age, and type of TD. The number of comorbidities increased with age. The most prevalent TDs were nontoxic goiter, hypothyroidism, hyperthyroidism, and thyroid cancer, while hypertension, diabetes, and lipoprotein metabolism disorders had the highest prevalence and PageRank values among comorbidities. Males and patients with benign TD exhibited a greater number of comorbidities, increased disease diversity, and stronger comorbidity associations compared with females and patients with thyroid cancer. CONCLUSIONS: Patients with TD exhibited complex comorbidity patterns, particularly with cardiocerebrovascular diseases and diabetes. The associations among comorbidities varied across different TD subgroups. This study aims to enhance the understanding of comorbidity patterns in patients with TD and improve the integrated management of these individuals.

Longifolene-Derived Primary Amine Carboxylates for Sustainable Weed Management: Synthesis and Herbicidal Activity.

Twelve novel longifolene-derived primary amine carboxylates were synthesized and evaluated for herbicidal activity. The structures of title compounds were confirmed by Fourier-transform infrared spectroscopy, 1H nuclear magnetic resonance (NMR), 13C NMR, and high-resolution mass spectrometry. The results showed that all the synthesized compounds exhibited higher herbicidal activity than the corresponding carboxylic acids involved in the reaction and the commercial herbicide glyphosate; some of them even possessed inhibition rates of 100% against Lolium multiflorum Lam. and Brassica campestris at low concentrations (0.039-0.313 mmol/L). Moreover, structural factors, including types of carboxylates and carbon chain length, had a great influence on the herbicidal performance. The herbicidal activity of dicarboxylates was similar to or much higher than that of corresponding monocarboxylates and glyphosate. Furthermore, compound 5l was found to be the most active candidate against the root and shoot growth of L. multiflorum Lam. and B. campestris with half maximal inhibitory concentrations (IC50) of around 0.010 and 0.023 mmol/L. The present work indicated that those prepared compounds have great potential to serve as high-performance botanical herbicides used at low doses.

Replacement of Corn with Different Levels of Wheat Impacted the Growth Performance, Intestinal Development, and Cecal Microbiota of Broilers.

Replacing corn with different levels of wheat in the iso-energy and -protein diet of broilers and the impacts on growth performance and intestinal homeostasis of broilers under the condition of supplying the multienzyme complex were evaluated in this study. A total of 480 10-day-old male broilers were assigned randomly to the low-level wheat group (15% wheat and 35.18% corn), the medium-level wheat group (30% and 22.27%), and the high-level wheat group (55.77% wheat without corn) until 21 d. The different levels of wheat supplementation did not affect hepatic function, serum glycolipid profile, or bone turnover. The replacement of corn with 55% wheat in the diet of broilers increased the body weight at 21 d and feed intake during 10 to 21 d (both p < 0.05), with a comparable feed conversion ratio. Compared with the low-wheat group, the dietary addition of medium or high wheat levels notably increased the ratio of villus height to crypt depth in the duodenum (p < 0.05) and the ileal villus height (p < 0.05). Meanwhile, the supplementation of medium and high wheat in the diet increased the proportion of Bacteroidetes, and a diet with high wheat proportion elevated the content of Firmicutes when compared to the low-level wheat group (both p < 0.05). In addition, the diet containing 30-55% wheat enhanced the anti-inflammatory capability in both the ileum and the serum. These findings suggest that the replacement of corn with 55% wheat in the diet improved the growth performance of 21-day-old broilers, which might be linked to the alteration in intestinal morphology and cecal microbiota.

Fermented calcium butyrate supplementation in post-peak laying hens improved ovarian function and tibia quality through the "gut-bone" axis.

The compromised egg quality and leg abnormality during the end of the laying cycle (after 40 weeks) have been leading to poor animal welfare and substantial economic losses. Therefore, the effects of fermented calcium (Ca) butyrate, produced by fermentation by Clostridium butyricum, on production, eggshell quality, and tibial property of hens were explored. A total of 192 Hy-line brown laying hens at 50-week-old were assigned to a basal diet or the basal diet with 300 mg/kg of the fermented Ca butyrate from 50 to 58 weeks of age. Each treatment had 6 replicates with 16 hens each. The diet supplemented with 300 mg/kg fermented Ca butyrate notably increased egg weight, ovarian follicle number, and eggshell strength (P = 0.072) as compared to the basal diet, which were associated with cytokine secretion, toll-like receptor signaling pathways, and intestinal immunity based on the RNA-seq data from the granulosa. Dietary Ca butyrate inclusion decreased the expression of ileal tumor necrosis factor-alpha and serum pro-inflammatory cytokine concentration, as well as increased the content of serum immunoglobulin A when compared to the basal diet (both P < 0.05). The birds that received fermented Ca butyrate diets exhibited higher villus height (P < 0.05) and upregulated expression of tight junction proteins, whereas it did not alter the composition of cecal microbiota (P > 0.05). In addition, the diet with fermented Ca butyrate reduced the number of osteoclasts in the proximal tibia and the level of C-terminal cross-linked telopeptide of type I collagen, a bone resorption marker (P < 0.05), whereas it tended to increase the concentration of the procollagen type I N-terminal propeptide that reflects bone formation marker in serum. Moreover, the layers fed fermented Ca butyrate diets possessed higher (P < 0.05) bone area and trabecular number of the proximal tibia, yield load, and ultimate load than those that consumed basal diets. Collectively, dietary fermented Ca butyrate supplementation in post-peak layer diets improved the ovarian function and tibia quality, which might be related to enhancing intestinal integrity and consequently decreasing inflammation mediated bone resorption.

Enhancing aggression in Henan gamecocks via augmentation of serotonergic-dopaminergic signaling and attenuation of neuroimmune response.

Animal aggression is one of the most conserved behaviors. Excessive and inappropriate aggression was a serious social concern across species. After long-term selection under strict stress conditions, Henan gamecock serves as a good model for studying aggressive behavior. In this research, we constructed a Henan game chicken backcross population containing 25% Rhode Island Red (RIR), and conducted brain transcriptomics and serum metabolomics analyses on Henan gamecock (HGR) through its comparison with its female encounters (HGH) and the male backcross birds (BGR). The study revealed that seven differential metabolites in serum and 172 differentially expressed genes in the brain were commonly shared in both HGR vs. HGH and HGR vs. BGR comparisons. They exhibited the same patterns of modulation in Henan gamecocks, following either HGH < HGR > BGR or HGH > HGR < BGR style. Therein, some neurological genes involving in serotonergic and dopaminergic signaling were upregulated, while the levels of many genes related with neuro-immune function were decreased in Henan gamecock. In addition, many unknown genes specifically or highly expressed in the brain of the Henan gamecock were identified. These genes are potentially key candidates for enhancing the bird's aggression. Multi-omics joint analysis revealed that tyrosine metabolism and neuroactive ligand-receptor interaction were commonly affected. Overall, our results propose that the aggressiveness of Henan gamecocks can be heightened by the activation of the serotonergic-dopaminergic metabolic process in the brain, which concurrently impairs the neuroimmune system. Further research is needed to identify the function of these unknown genes on the bird's aggressive behavior.

Highly Effective and Broad-Spectrum Antimicrobial Quaternary Ammonium Salts Containing Camphene Structure: Preparation, Surface-Active Properties, and Bioassay.

A series of novel quaternary ammonium salts (QASs) (4a-4n) comprising a camphene moiety were synthesized for the first time. Fourteen examples were prepared from camphene through Prins reaction, halogenation, and quaternarization, successively. The structures of the synthesized QASs were analyzed by Fourier transform infrared spectroscopy, 1H NMR, 13C NMR and high-resolution mass spectrometry. Surface-active properties, emulsifying abilities, and foaming properties of the investigated compounds were then studied. The antimicrobial activities of these QASs against Gram-positive bacteria (Staphylococcus aureus and Bacillus subtilis), Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae), and fungi species (Candida albicans, Candida tropicalis, and Aspergillus niger) were determined by the microdilution method. The results showed that the chemical structural factors, including types of substitutes and alkyl chain length, might be correlated with the lipid-water partition coefficient (cLog P), which played a critical role in the antimicrobial process. Compounds with alkyl chain lengths (N) in the range of 10-14 were relatively more active, while compounds bearing pyridinium, benzyl, methylimidazolium groups, or varied alkyl chain lengths (N < 5 and N > 16) were almost inactive. Compound 4k possessing a dodecyl group exhibited the most effective and broad-spectrum antimicrobial activity against almost all tested bacteria and fungi with the minimal inhibitory concentration values ranging from 0.24 to 0.98 µg/mL.