Effects of 3-nitrooxypropanol (3-NOP, Bovaer®10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions

The objective of this study was to determine the potential effect and interaction of 3- nitrooxypropanol (3-NOP; Bovaer®) and whole cottonseed (WCS) on lactational performance, and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS) [224 ± 36 d in milk, 26 ± 3.7 kg milk yield], were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin Square design with 2 × 2 factorial arrangements of treatments with 4, 24-d periods. The experimental treatments were: 1) Control (basal TMR), 2) 3-NOP (60 mg/kg TMR DM), 3) WCS (5% TMR DM), and 4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake (DMI) and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed system) during the last 3 d of each 24-d experimental period when animals were housed in tie stalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. There was no interaction of 3-NOP and WCS for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13, 14 and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18 vs. 8%), CH4 intensity (g/kg MY; 19% vs. 3%) and CH4 intensity (g/kg ECM; 19 vs. 4%). Hydrogen production was increased by 2.85 folds in HF and 1.53 folds in BS cows receiving 3-NOP. Further, there was a 3-NOP ' Time interaction for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at around 4 h after morning feeding but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high fiber diet resulted in 18 to 19% reduction in enteric CH4 emission in Swiss Holstein Friesian cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to low number of cows per breed. Lastly, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance CH4 inhibition effect of 3-NOP of dairy cows.

Exhalomics as a noninvasive method for assessing rumen fermentation in dairy cows: Can exhaled-breath metabolomics replace rumen sampling?

Previously, we used secondary electrospray ionization-mass spectrometry (SESI-MS) to investigate the diurnal patterns and signal intensities of exhaled (EX) volatile fatty acids (VFA) of dairy cows. The current study aimed to validate the potential of an exhalomics approach for evaluating rumen fermentation. The experiment was conducted in a switchback design, with 3 periods of 9 d each, including 7 d for adaptation and 2 d for sampling. Four rumen-cannulated original Swiss Brown (Braunvieh) cows were randomly assigned to 1 of 2 diet sequences (ABA or BAB): (A) low starch (LS; 6.31% starch on a dry matter basis) and (B) high starch (HS; 16.2% starch on a dry matter basis). Feeding was once per day at 0830 h. Exhalome (with the GreenFeed System), and rumen samples were collected 8 times to represent every 3 h of a day, and EX-VFA and ruminal (RM)-VFA were analyzed using SESI-MS and HPLC, respectively. Furthermore, the VFA concentration in the gas phase (HR-VFA) was predicted based on RM-VFA and Henry's Law (HR) constants. No interactions were identified between the types of diets (HS vs. LS) and the measurement methods on daily average VFA profiles (RM vs. EX or HR vs. EX), suggesting a consistent performance among the methods. Additionally, when the 3-h interval VFA data from HS and LS diets were analyzed separately, no interactions were observed between methods and time of day, indicating that the relative daily pattern of VFA molar proportions was similar regardless of the VFA measurement method used. The results revealed that the levels of acetate sharply increased immediately after feeding, trailed by an increase in the acetate:propionate ratio and a steady increase for propionate (2 h after feeding the HS diet, 4 h for LS), and butyrate. This change was more pronounced for the HS diet than the LS diet. However, there was no overall diet effect on the VFA molar proportions, although the measurement methods affected the molar proportions. Furthermore, we observed a strong positive correlation between the levels of RM and EX acetate for both diets (HS: r = 0.84; LS: r = 0.85), RM and EX propionate (r = 0.74), and RM and EX acetate:propionate ratio (r = 0.80). Both EX-VFA and RM-VFA exhibited similar responses to feeding and dietary treatments, suggesting that EX-VFA could serve as a useful proxy for characterizing RM-VFA molar proportions to evaluate rumen fermentation. Similar relationships were observed between RM-VFA and HR-VFA. In conclusion, this study underscores the potential of exhalomics as a reliable approach for assessing rumen fermentation. Moving forward, research should further explore the depth of exhalomics in ruminant studies to provide a comprehensive insight into rumen fermentation metabolites, especially across diverse dietary conditions.

iTTCA-MVL: A multi-view learning model based on physicochemical information and sequence statistical information for tumor T cell antigens identification

Immunotherapy is an emerging treatment method aimed at activating the human immune system and relying on its own immune function to kill cancer cells and tumor tissues. It has the advantages of wide applicability and minimal side effects. Effective identification of tumor T cell antigens (TTCAs) will help researchers understand their functions and mechanisms and carry out research on anti-tumor vaccine development. Considering that using biological experimental technology to identify TTCAs can be costly and time-consuming, it is necessary to develop a robust bioinformatics computing tool. At present, different machine learning models have been proposed for identifying TTCAs, but there is still room for further improvement in their performance. To establish a TTCA predictor with better prediction performance, we propose a prediction model called iTTCA-MVL in this paper. We extracted three sets of features from the views of physicochemical information and sequence statistics, namely the distribution descriptor of composition, transition, and distribution (CTDD), TF-IDF, and LSA topic. Then, we used least squares support vector machines (LSSVMs) as submodels and Hilbert‒Schmidt independence criteria (HSIC) as constraints to establish an independent and complementary multi-view learning model. The prediction accuracy of iTTCA-MVL on the independent test set is 0.873, and Matthew's correlation coefficient is 0.747, which is significantly better than those of existing methods. Therefore, iTTCA-MVL is an excellent prediction tool that researchers can use to accurately identify TTCAs.

Dietary supplementation of vitamin D3 and calcium partially recover the compromised time budget and circadian rhythm of lying behavior in lactating cows under heat stress

Heat stress (HS) impedes cattle behavior and performance and is an animal comfort and welfare issue. The objective of this study was to characterize the time budget and circadian rhythm of lying behavior in dairy cows during HS and to assess the effect of dietary supplementation of vitamin D3 and Ca. Twelve multiparous Holstein cows (42.2 ± 5.6 kg milk/d; 83 ± 27 d in milk) housed in tiestalls were used in a split-plot design with the concentration of dietary vitamin E and Se as main plots (LESe: 11.1 IU/kg and 0.55 mg/kg, and HESe: 223 IU/kg and 1.8 mg/kg, respectively). Within each plot cows were randomly assigned to (1) HS with low concentrations of vitamin D3 and Ca (HS, 1,012 IU/kg and 0.73%, respectively), (2) HS with high concentrations of vitamin D3 and Ca (HS+D3/Ca; 3,764 IU/kg and 0.97%, respectively), or (3) thermoneutral pair-fed (TNPF) with low concentrations of vitamin D3 and Ca (1,012 IU/kg and 0.73%, respectively) in a Latin square design with 14-d periods and 7-d washouts. Lying behavior was measured with HOBO Loggers in 15-min intervals. Overall, cows in HS spent less time lying per day relative to TNPF from d 7 to 14. Daily lying time was positively correlated with milk yield, energy-corrected milk yield, and feed efficiency, and was negatively correlated with rectal temperature, respiratory rate, fecal calprotectin, tumor necrosis factor-α, and C-reactive protein. A treatment by time interaction was observed for lying behavior: the time spent lying was lesser for cows in HS than in TNPF in the early morning (0000-0600 h) and in the night (1800-2400 h). The circadian rhythm of lying behavior was characterized by fitting a cosine function of time into linear mixed model. Daily rhythmicity of lying was detected for cows in TNPF and HS+D3/Ca, whereas only a tendency in HS cows was observed. Cows in TNPF had the highest mesor (the average level of diurnal fluctuations; 34.2 min/h) and amplitude (the distance between the peak and mesor; 17.9 min/h). Both the mesor and amplitude were higher in HS+D3/Ca relative to HS (26.6 vs. 25.2 min/h and 3.91 min/h vs. 2.18 min/h, respectively). The acrophase (time of the peak) of lying time in TNPF, HS, and HS+D3/Ca were 0028, 0152, and 0054 h, respectively. Lastly, a continuous increase in daily lying time in TNPF was observed during the first 4 d of the experimental period in which DMI was gradually restricted, suggesting that intake restrictions may shift feeding behavior and introduce biases in the behavior of animals. In conclusion, lying behavior was compromised in dairy cows under HS, characterizing reduced daily lying time and disrupted circadian rhythms, and the compromised lying behavior can be partially restored by supplementation of vitamin D3 and Ca. Further research may be required for a more suitable model to study behavior of cows under HS.

CircRNA identification and feature interpretability analysis

BACKGROUND

Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation.

RESULTS

We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification.

CONCLUSIONS

CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .

A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation

BACKGROUND

Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA.

RESULTS

CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs.

CONCLUSIONS

This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.

Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

[HVPG minimally invasive era: exploration based on forearm venous approach]

Objective: The transjugular or transfemoral approach is used as a common method for hepatic venous pressure gradient (HVPG) measurement in current practice. This study aims to confirm the safety and effectiveness of measuring HVPG via the forearm venous approach. Methods: Prospective recruitment was conducted for patients with cirrhosis who underwent HVPG measurement via the forearm venous approach at six hospitals in China and Japan from September 2020 to December 2020. Patients' clinical baseline information and HVPG measurement data were collected. The right median cubital vein or basilic vein approach for all enrolled patients was selected. The HVPG standard process was used to measure pressure. Research data were analyzed using SPSS 22.0 statistical software. Quantitative data were used to represent medians (interquartile ranges), while qualitative data were used to represent frequency and rates. The correlation between two sets of data was analyzed using Pearson correlation analysis. Results: A total of 43 cases were enrolled in this study. Of these, 41 (95.3%) successfully underwent HVPG measurement via the forearm venous approach. None of the patients had any serious complications. The median operation time for HVPG detection via forearm vein was 18.0 minutes (12.3~38.8 minutes). This study confirmed that HVPG was positively closely related to Child-Pugh score (r = 0.47, P = 0.002), albumin-bilirubin score (r = 0.37, P = 0.001), Lok index (r = 0.36, P = 0.02), liver stiffness (r = 0.58, P = 0.01), and spleen stiffness (r = 0.77, P = 0.01), while negatively correlated with albumin (r = -0.42, P = 0.006). Conclusion: The results of this multi-centre retrospective study suggest that HVPG measurement via the forearm venous approach is safe and feasible.

Exhaled volatile fatty acids, ruminal methane emission, and their diurnal patterns in lactating dairy cows

To date, the commonly used methods to assess rumen fermentation are invasive. Exhaled breath contains hundreds of volatile organic compounds (VOC) that can reflect animal physiological processes. In the present study, for the first time, we aimed to use a noninvasive metabolomics approach based on high-resolution mass spectrometry to identify rumen fermentation parameters in dairy cows. Enteric methane (CH4) production from 7 lactating cows was measured 8 times over 3 consecutive days using the GreenFeed system (C-Lock Technology Inc.). Simultaneously, exhalome samples were collected in Tedlar gas sampling bags and analyzed offline using a secondary electrospray ionization high-resolution mass spectrometry system. In total, 1,298 features were detected, among them targeted exhaled volatile fatty acids (eVFA; i.e., acetate, propionate, butyrate), which were putatively annotated using their exact mass-to-charge ratio. The intensity of eVFA, in particular acetate, increased immediately after feeding and followed a similar pattern to that observed for ruminal CH4 production. The average total eVFA concentration was 35.5 count per second (CPS), and among the individual eVFA, acetate had the greatest concentration, averaging 21.3 CPS, followed by propionate at 11.5 CPS, and butyrate at 2.67 CPS. Further, exhaled acetate was on average the most abundant of the individual eVFA at around 59.3%, followed by 32.5 and 7.9% of the total eVFA for propionate and butyrate, respectively. This corresponds well with the previously reported proportions of these VFA in the rumen. The diurnal patterns of ruminal CH4 emission and individual eVFA were characterized using a linear mixed model with cosine function fit. The model characterized similar diurnal patterns for eVFA and ruminal CH4 and H2 production. Regarding the diurnal patterns of eVFA, the phase (time of peak) of butyrate occurred first, followed by that of acetate and propionate. Importantly, the phase of total eVFA occurred around 1 h before that of ruminal CH4. This corresponds well with existing data on the relationship between rumen VFA production and CH4 formation. Results from the present study revealed a great potential to assess the rumen fermentation of dairy cows using exhaled metabolites as a noninvasive proxy for rumen VFA. Further validation, with comparisons to rumen fluid, and establishment of the proposed method are required.

[Research progress in anti-reflux reconstructions and mechanism after proximal gastrectomy]

The electrophysiological activity of the gastrointestinal tract and the mechanical anti-reflux structure of the gastroesophageal junction are the basis of the anti-reflux function of the stomach. Proximal gastrectomy destroys the mechanical structure and normal electrophysiological channels of the anti-reflux. Therefore, the residual gastric function is disordered. Moreover, gastroesophageal reflux is one of the most serious complications. The emergence of various types of anti-reflux surgery through the mechanism of reconstructing mechanical anti-reflux barrier and establishing buffer zone, and the preservation of, the pacing area and vagus nerve of the stomach, the continuity of the jejunal bowel, the original gastroenteric electrophysiological activity of the gastrointestinal tract, and the physiological function of the pyloric sphincter, are all important measures for gastric conservative operations. There are many types of reconstructive approaches after proximal gastrectomy. The design based on the anti-reflux mechanism and the functional reconstruction of mechanical barrier, and the protection of gastrointestinal electrophysiological activities are important considerations for the selected of reconstructive approaches after proximal gastrectomy. In clinical practice, we should consider the principle of individualization and the safety of radical resection of tumor to select a rational reconstructive approaches after proximal gastrectomy.

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

SgRNA-RF: Identification of SgRNA On-Target Activity With Imbalanced Datasets

Single-guide RNA is a guide RNA (gRNA), which guides the insertion or deletion of uridine residues into kinetoplastid during RNA editing. It is a small non-coding RNA that can be combined with pre -mRNA pairing. SgRNA is a critical component of the CRISPR/Cas9 gene knockout system and play an important role in gene editing and gene regulation. It is important to accurately and quickly identify highly on-target activity sgRNAs. Due to its importance, several computational predictors have been proposed to predict sgRNAs on-target activity. All these methods have clearly contributed to the development of this very important field. However, they also have certain limitations. In the paper, we developed a new classifier SgRNA-RF, which extracts the features of nucleic acid composition and structure of on-target activity sgRNA sequence and identified by random forest algorithm. In addition to solving an imbalanced dataset, this paper proposed a new method called CS-Smote. We compared sgRNA-RF with state-of-the-art predictors on the five datasets, and found SgRNA-RF significantly improved the identification accuracy, with accuracies of 0.8636,0.9161,0.894,0.938,0.965,0.77,0.979,0.973, respectively. The user-friendly web server that implements sgRNA-RF is freely available at http://server.malab.cn/sgRNA-RF/.

[Status of HVPG clinical application in China in 2021]

Objective: The investigation and research on the application status of Hepatic Venous Pressure Gradient (HVPG) is very important to understand the real situation and future development of this technology in China. Methods: This study comprehensively investigated the basic situation of HVPG technology in China, including hospital distribution, hospital level, annual number of cases, catheters used, average cost, indications and existing problems. Results: According to the survey, there were 70 hospitals in China carrying out HVPG technology in 2021, distributed in 28 provinces (autonomous regions and municipalities directly under the central Government). A total of 4 398 cases of HVPG were performed in all the surveyed hospitals in 2021, of which 2 291 cases (52.1%) were tested by HVPG alone. The average cost of HVPG detection was (5 617.2±2 079.4) yuan. 96.3% of the teams completed HVPG detection with balloon method, and most of the teams used thrombectomy balloon catheter (80.3%). Conclusion: Through this investigation, the status of domestic clinical application of HVPG has been clarified, and it has been confirmed that many domestic medical institutions have mastered this technology, but it still needs to continue to promote and popularize HVPG technology in the future.

[QUADAS-C-A tool for assessing risk of bias regarding Quality Assessment of Diagnostic Accuracy Studies-Comparative]

This paper introduced the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C), illustrated the comparison with the QUADAS-2, and using QUADAS-C together with QUADAS-2 to present QUADAS-C results through systematic reviews. Like the domain for QUADAS-2, QUADAS-C retained four domains, including patient selection, index test, reference standard, flow, and timing, and comprised additional questions for each QUADAS-2 part. Unlike the QUADAS-2 tool, the starting question of each domain for QUADAS-C was designed to summarize the risk of biased information captured by QUADAS-2. QUADAS-C only dealt with the risk of bias but did not include the part of concerns regarding applicability. The answers to signaling questions for each domain of QUADAS-C would lead to a 'low''high' or 'unclear' risk of biased judgment for the original study.

GMNN2CD: identification of circRNA-disease associations based on variational inference and graph Markov neural networks

MOTIVATION

With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for searching the etiopathogenesis and treatment of diseases. Nevertheless, it is inefficient to learn new associations only through biotechnology.

RESULTS

Consequently, we present a computational method, GMNN2CD, which employs a graph Markov neural network (GMNN) algorithm to predict unknown circRNA-disease associations. First, used verified associations, we calculate semantic similarity and Gaussian interactive profile kernel similarity (GIPs) of the disease and the GIPs of circRNA and then merge them to form a unified descriptor. After that, GMNN2CD uses a fusion feature variational map autoencoder to learn deep features and uses a label propagation map autoencoder to propagate tags based on known associations. Based on variational inference, GMNN alternate training enhances the ability of GMNN2CD to obtain high-efficiency high-dimensional features from low-dimensional representations. Finally, 5-fold cross-validation of five benchmark datasets shows that GMNN2CD is superior to the state-of-the-art methods. Furthermore, case studies have shown that GMNN2CD can detect potential associations.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/nmt315320/GMNN2CD.git.

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.

More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments.

Characterizing viral circRNAs and their application in identifying circRNAs in viruses

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism, which play an important role in a variety of biological activities. Viruses can encode circRNA, and viral circRNAs have been found in multiple single-stranded and double-stranded viruses. However, the characteristics and functions of viral circRNAs remain unknown. Sequence alignment showed that viral circRNAs are less conserved than circRNAs in animal, indicating that the viral circRNAs may evolve rapidly. Through the analysis of the sequence characteristics of viral circRNAs and circRNAs in animal, it was found that viral circRNAs and animals circRNAs are similar in nucleic acid composition, but have obvious differences in secondary structure and autocorrelation characteristics. Based on these characteristics of viral circRNAs, machine learning algorithms were employed to construct a prediction model to identify viral circRNA. Additionally, analysis of the interaction between viral circRNA and miRNAs showed that viral circRNA is expected to interact with 518 human miRNAs, and preliminary analysis of the role of viral circRNA. And it has been also found that viral circRNAs may be involved in many KEGG pathways related to nervous system and cancer. We curated an online server, and the data and code are available: http://server.malab.cn/viral-CircRNA/.

rBPDL:Predicting RNA-Binding Proteins Using Deep Learning

RNA-binding protein (RBP) is a powerful and wide-ranging regulator that plays an important role in cell development, differentiation, metabolism, health and disease. The prediction of RBPs provides valuable guidance for biologists. Although experimental methods have made great progress in predicting RBP, they are time-consuming and not flexible. Therefore, we developed a network model, rBPDL, by combining a convolutional neural network and long short-term memory for multilabel classification of RBPs. Moreover, to achieve better prediction results, we used a voting algorithm for ensemble learning of the model. We compared rBPDL with state-of-the-art methods and found that rBPDL significantly improved identification performance for the RBP68 dataset, with a macro-Area Under Curve (AUC), micro-AUC, and weighted AUC of 0.936, 0.962, and 0.946, respectively. Furthermore, through AUC statistical analysis of the RBP domain, we analyzed the performance of rBPDL and found that the RBP identification performance in the same domain was similar. In addition, we analyzed the performance preferences and physicochemical properties of the binding protein amino acids and explored the characteristics that affect the binding by using the RBP86 dataset.

Removing leakage-induced correlated errors in superconducting quantum error correction

Quantum computing can become scalable through error correction, but logical error rates only decrease with system size when physical errors are sufficiently uncorrelated. During computation, unused high energy levels of the qubits can become excited, creating leakage states that are long-lived and mobile. Particularly for superconducting transmon qubits, this leakage opens a path to errors that are correlated in space and time. Here, we report a reset protocol that returns a qubit to the ground state from all relevant higher level states. We test its performance with the bit-flip stabilizer code, a simplified version of the surface code for quantum error correction. We investigate the accumulation and dynamics of leakage during error correction. Using this protocol, we find lower rates of logical errors and an improved scaling and stability of error suppression with increasing qubit number. This demonstration provides a key step on the path towards scalable quantum computing.

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks

KEY MESSAGE

We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there is a lack of effective bioinformatics tools to identify the activity of sgRNA in agronomic species. Therefore, it is necessary to develop a method based on machine learning to identify sgRNA high on-target activity. In this work, we proposed a simple convolutional neural network method to identify sgRNA high on-target activity. Our study used one-hot encoding and k-mers for sequence data conversion and a voting algorithm for constructing the convolutional neural network ensemble model sgRNACNN for the prediction of sgRNA activity. The ensemble model sgRNACNN was used for predictions in four crops: Glycine max, Zea mays, Sorghum bicolor and Triticum aestivum. The accuracy rates of the four crops in the sgRNACNN model were 82.43%, 80.33%, 78.25% and 87.49%, respectively. The experimental results showed that sgRNACNN realizes the identification of high on-target activity sgRNA of agronomic data and can meet the demands of sgRNA activity prediction in agronomy to a certain extent. These results have certain significance for guiding crop gene editing and academic research. The source code and relevant dataset can be found in the following link: https://github.com/nmt315320/sgRNACNN.git .

Diffusion-tensor imaging for differentiating uterine sarcoma from degenerative uterine fibroids

AIM

To explore the applicability of diffusion-tensor imaging (DTI) sequence quantitative parameters in differentiating uterine sarcoma (USr) from degenerative uterine fibroids (DUF).

MATERIALS AND METHODS

Fourteen cases of USr and 30 cases of DUF were analysed retrospectively. The diffusion-weighted imaging (DWI) and DTI images were analysed by two observers using Functool software on a ADW4.6 workstation. The images were post-processed to generate an apparent diffusion coefficient (ADC) map of DWI, ADC map of DTI (ADC^T map), and fractional anisotropy (FA) map. Three regions of interest (ROI) were selected from the ADC, ADC^T, and FA maps to obtain the ADC, ADC^T, and FA values. The receiver operating characteristic (ROC) curves of all parameters were used to analyse and compare the diagnostic value of USr and DUF.

RESULTS

The ADC value, ADC^T value, and FA value of USr (1.190 ± 0.262 × 10^-3mm²/s, 1.165 ± 0.270 × 10^-9mm²/s, 0.168 ± 0.063) were significantly lower compared to the values for DUF (1.525 ± 0.314 × 10^-3mm²/s, 1.650 ± 0.332 × 10^-9mm²/s, 0.254 ± 0.111; all p<0.001). The diagnostic threshold values for USr were: ADC ≤1.290 × 10^-3mm²/s, ADC^T ≤1.322 × 10^-9mm²/s and FA ≤0.192. The corresponding sensitivities and specificities were 78.6%/90%, 96.7%/92.9%, and 86.7%/85.7%, respectively. The areas under the curve (AUC) were 0.875, 0.974, and 0.831, respectively.

CONCLUSIONS

DTI quantitative parameters can be used to differentiate USr from DUF. The ADC^T value had the highest diagnostic efficacy.

Demonstrating a Continuous Set of Two-Qubit Gates for Near-Term Quantum Algorithms

Quantum algorithms offer a dramatic speedup for computational problems in material science and chemistry. However, any near-term realizations of these algorithms will need to be optimized to fit within the finite resources offered by existing noisy hardware. Here, taking advantage of the adjustable coupling of gmon qubits, we demonstrate a continuous two-qubit gate set that can provide a threefold reduction in circuit depth as compared to a standard decomposition. We implement two gate families: an imaginary swap-like (iSWAP-like) gate to attain an arbitrary swap angle, θ, and a controlled-phase gate that generates an arbitrary conditional phase, ϕ. Using one of each of these gates, we can perform an arbitrary two-qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic simulation (fSim) gate set. We benchmark the fidelity of the iSWAP-like and controlled-phase gate families as well as 525 other fSim gates spread evenly across the entire fSim(θ,ϕ) parameter space, achieving a purity-limited average two-qubit Pauli error of 3.8×10^{-3} per fSim gate.

A longitudinal study on α-synuclein in plasma neuronal exosomes as a biomarker for Parkinson's disease development and progression

BACKGROUND AND PURPOSE

The identification of reliable diagnostic and prognostic biomarkers for Parkinson's disease (PD) is urgently needed. Here, we explored the potential use of α-synuclein (α-syn) in plasma neuronal exosomes as a biomarker for early PD diagnosis and disease progression.

METHODS

This study included both cross-sectional and longitudinal designs. The subjects included 36 patients with early-stage PD, 17 patients with advanced PD, 20 patients with idiopathic rapid eye movement sleep behavior disorder and 21 healthy controls (HCs). α-syn levels were measured by electrochemiluminescence immunoassay. A subgroup of patients with early-stage PD (n = 18) participated in a follow-up examination with repeated blood collection and clinical assessments after an average of 22 months.

RESULTS

The α-syn levels in plasma neuronal exosomes were significantly higher in patients with early-stage PD compared with HCs (P = 0.007). Differences in α-syn levels between patients with idiopathic rapid eye movement sleep behavior disorder and HCs did not reach statistical significance (P = 0.08). In addition, Spearman correlation analysis revealed that neuronal exosomal α-syn concentrations were correlated with Movement Disorders Society Unified Parkinson's Disease Rating Scale III/(I + II + III) scores, Non-Motor Symptom Questionnaire scores and Sniffin' Sticks 16-item test scores of patients with PD (P < 0.05). After a mean follow-up of 22 months in patients with early-stage PD, a Cox regression analysis adjusted for age and gender showed that longitudinally increased α-syn rather than baseline α-syn levels were associated with higher risk for motor symptom progression in PD (P = 0.039).

CONCLUSIONS

Our results suggested that α-syn in plasma neuronal exosomes may serve as a biomarker to aid early diagnosis of PD and also as a prognostic marker for PD progression.

An Inductive Logic Programming Algorithm Based on Artificial Bee Colony

Inductive logic programming (ILP) is a hot research field in machine learning. Although ILP has obtained great success in many domains, in most ILP system, deterministic search are used to search the hypotheses space, and they are easy to trap in local optima. To overcome the shortcomings, an ILP system based on artificial bee colony (ABCILP) is proposed in this article. ABCILP adopts an ABC stochastic search to examine the hypotheses space, the shortcoming of deterministic search is conquered by stochastic search. ABCILP regard each first-order rule as a food source and propose some discrete operations to generate the neighborhood food sources. A new fitness is proposed and an adaptive strategy is adopted to determine the parameter of the new fitness. Experimental results show that: 1) the proposed new fitness function can more precisely measure the quality of hypothesis and can avoid generating an over-specific rule; 2) the performance of ABCILP is better than other systems compared with it.

[Prevalence and risk factors of diabetic retinopathy in hospital patients]

Objective: To analyze the prevalence and risk factors of diabetic retinopathy (DR) in ophthalmic patients. Methods: A cross-sectional study was performed. Diabetic patients who were admitted to Department of Ophthalmology, Traditional Chinese Medicine Hospital of Muping between October 2012 and June 2013 were included. General information and medical history were obtained from each subject by questionaires. Laboratory and detailed ophthalmic examinations were performed during the study. DR was diagnosed and graded by mydriatic fundus photography. Prevalence of DR was calculated and logistic regression was used to analyze the relationship between DR and various factors. Results: A total of 676 diabetic patients were included, and 455 of them presented with DR at a morbidity rate of 67.31%. Among DR patients, the number of mild, moderate, severe non-proliferative diabetic retinopathy (NPDR) patients and proliferative diabetic retinopathy (PDR) patients were 211 (46.37%), 167 (36.70%), 57 (12.53%) and 20 (4.40%), respectively. There was no significant difference in the prevalence of DR among different age groups (χ(2)=6.527, P=0.089). However, there was a significant difference between different disease duration groups (χ(2)=39.401, P<0.001), as well as between insulin therapy group and non-insulin therapy group (χ(2)=7.378, P=0.007). The multivariate logistic regression analysis demonstrated the independent risk factors for DR occurrence were hemoglobin A1c (HbA1c) (OR=1.131, 95%CI: 1.022-1.252, P=0.011) and duration of diabetes (OR=1.077, 95%CI: 1.046-1.108, P<0.001). Conclusions: The prevalence of DR in ophthalmic patients was associated with duration of diabetes, HbA1c, obesity, smoke, nephropaty and insulin therapy. Increased HbA1c level and longer duration of diabetes were independent risk factors for DR in diabetic patients.

[Analysis of clinicopathological characteristics of papillary thyroid carcinoma and BRAF V600E gene mutation]

Objective: To study the correlation between the BRAF V600E mutation and the clinicopathological features of papillary thyroid carcinoma(PTC), and to explore the application value of BRAF V600E mutation in PTC. Method: The BRAF V600E gene mutation was detected in 108 PTC cases and 30 control cases by qPCR. The results were statistically analyzed by using SPSS 21.0 software. Result: The BRAF V600E mutation was absent in the control group. The BRAF V600E mutation rate was 72.20% in 108 PTC cases. Compared with patients with wild type,the mean age of patients with the mutant BRAF V600E was significantly older(P<0.05). BRAF V600E mutations were associated with PTC tissue subtypes and central area lymph node metastasis(P<0.05). BRAF V600E mutations were not correlated with tumor diameter, patient gender, multifocality, tumor site, concomitant disease, lymph node metastasis in side region of neck, capsule invasion, and TNM staging(P>0.05). Conclusion: The value of BRAF V600E mutation in the prognosis of PTC remains to be studied. BRAF V600E mutation is of value for pathological diagnosis of PTC.

RFAmyloid: A Web Server for Predicting Amyloid Proteins

Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.

Dissociation Energy of the Hydrogen Molecule at 10^{-9} Accuracy

The ionization energy of ortho-H_{2} has been determined to be E_{I}^{o}(H_{2})/(hc)=124 357.238 062(25)  cm^{-1} from measurements of the GK(1,1)-X(0,1) interval by Doppler-free, two-photon spectroscopy using a narrow band 179-nm laser source and the ionization energy of the GK(1,1) state by continuous-wave, near-infrared laser spectroscopy. E_{I}^{o}(H_{2}) was used to derive the dissociation energy of H_{2}, D_{0}^{N=1}(H_{2}), at 35 999.582 894(25)  cm^{-1} with a precision that is more than one order of magnitude better than all previous results. The new result challenges calculations of this quantity and represents a benchmark value for future relativistic and QED calculations of molecular energies.

Effect of Dairy Manure Storage Conditions on the Survival of E. coli O157:H7 and Listeria

Dairy manure is regularly applied to crop fields as a solid or liquid to improve the soil nutrient status. However, pathogens may survive during manure storage and enter the environment during application. In this study, three storage practices were evaluated to understand the survival patterns of O157:H7 and spp. in dairy manure using a culture-based approach. To replicate common farm manure storage techniques, solid manure was stacked as piles with periodic turning or as static piles without turning, whereas liquid manure (feces, urine, and water) was stored as a slurry in small tanks to simulate lagoon conditions. The and levels in the manure samples were determined for 29 wk. Results showed that there was an initial reduction in bacteria levels in the first month; however, both and managed to survive in the solid manure piles for the full study period. In slurry samples, was not detected after 14 wk, but survived until the end of the experiment at relatively lower levels than in the solid manure piles. Ambient weather and pile size were identified as the main reasons for bacteria survival during the course of the experiment. The outcome of this study is important in terms of understanding pathogen survival in manure piles and slurries prior to their application to crop fields.

Short communication: The effects of morning compared with evening feed delivery in lactating dairy cows during the summer

Delivering fresh feed in the evening is a management strategy sometimes used during periods of heat stress, but previous experiments have observed that night feeding increased feed intake during the 2 h after feeding and did not change intake during the overnight period. The objective of this study was to determine the effect of night feeding on daily rhythms of the dairy cow during the summer season. Twelve Holstein cows were used in a crossover design with 14-d periods. An automated system recorded the timing of feed intake over the last 7 d of each period. Treatments were ad libitum feeding with fresh feed delivery 1×/d at 0830 h or 2030 h. Milk yield and composition were not changed by treatment, but night feeding decreased the concentration of preformed fatty acids in milk fat. Night feeding decreased feed intake 1.7 kg/d, and decreased total-tract dry matter and neutral detergent fiber (NDF) digestibility by 0.7 and 0.8 percentage units, respectively. The amount of feed consumed in the first 2 h after feeding was 64% greater with night feeding, but intake did not differ between treatments during the night or early afternoon. A treatment by time of day interaction was observed for fecal NDF and indigestible NDF concentration and plasma glucose, insulin, and urea concentrations. Night feeding resulted in an increase in plasma insulin after feeding, which decreased plasma glucose. The daily rhythm of core body temperature was entrained by treatment, with the phase shifted and the amplitude decreased by night feeding indicating alteration of the central clock. In conclusion, feeding cows once per day in the evening during the summer caused a similar increase in feed intake and plasma insulin after feeding as previously observed during non-heat stress seasons. Night feeding also decreased intake and total-tract digestibility. The daily pattern of feed intake and other behaviors should be considered before initiation of night feeding.

Exogenous β-mannanase improves feed conversion efficiency and reduces somatic cell count in dairy cattle

Exogenous fibrolytic enzymes have been shown to be a promising way to improve feed conversion efficiency (FCE). β-Mannanase is an important enzyme digesting the polysaccharide β-mannan in hemicellulose. Supplementation of diets with β-mannanase to improve FCE has been more extensively studied in nonruminants than in ruminants. The objective of this study was to investigate the effects of β-mannanase supplementation on nutrient digestibility, FCE, and nitrogen utilization in lactating Holstein dairy cows. Twelve post-peak-lactation multiparous Holstein cows producing 45.5±6.6kg/d of milk at 116±19.0d in milk were randomly allotted to 1 of 3 treatments in a 3×3 Latin square design with 3 periods of 18d (15d for adaptation plus 3d for sample collection). All cows were fed the same basal diet and the 3 treatments differed only by the β-mannanase dose: 0% dry matter (DM; control), 0.1% of DM (low supplement, LS), and 0.2% of DM (high supplement, HS) supplemented to the basal diet. Supplementation of β-mannanase enzyme at the LS dose reduced dry matter intake (DMI) but did not affect milk yield or milk composition. Cows receiving LS produced 90g more milk per kg of DMI compared with control cows. Somatic cell count (SCC) in milk was lower for cows fed the LS diet compared with cows fed control diets. Cows fed LS diet had lower DM, organic matter and crude protein digestibility compared with cows fed control diets. Starch, neutral detergent fiber, and acid detergent fiber digestibility were not affected by LS. Milk yield, DMI, SCC, and nutrient digestibility did not change for HS. Despite the reduced crude protein digestibility, reduced N intake led to similar fecal N excretions in LS cows and control cows (234 vs. 235g/cow per day). Urinary N excretions remained similar between enzyme-fed and control cows (~190g/cow per day), although the percentage of N intake partitioned to urinary N tended to be greater in LS than in control cows (31 vs. 27%). Cows fed LS significantly improved the percentage of apparently absorbed N partitioned to milk protein N (42 vs. 38%). When supplemented at 0.1% of dietary DM, β-mannanase can improve FCE and lower the SCC of dairy cows without affecting milk yield, milk composition, or total manure N excretions of dairy cows.

MiR-133b ameliorates axon degeneration induced by MPP(+) via targeting RhoA

Increasing evidence suggests that microRNAs (miRs) play a significant role in the pathogenesis of Parkinson's disease (PD). MiR-133b, which is significantly decreased in the PD midbrain, has recently been shown to promote neurite outgrowth and enhance neural functional recovery. However, the role of miR-133b in PD has not been clearly established. Here, using a well-established PD model culture based on the neurotoxin 1-methyl-4-phenyl-pyridinium (MPP(+)), we demonstrated that miR-133b could promote axon outgrowth in dopaminergic neurons (DNs) and ameliorated MPP(+)-induced axon degeneration. Additional experiments suggested that the mechanisms of this miR-133b-mediated effect might rely on RhoA inhibition. We demonstrated that RhoA, an inhibitor of axonal growth, was increased in DNs under MPP(+) treatment, and this increase could be attenuated by miR-133b overexpression. Moreover, we demonstrated that the induced expression of miR-133b could inhibit α-synuclein, which is critically involved in the pathological process of PD. Furthermore, we found that overexpression of miR-133b abrogated the MPP(+)-induced decrease in the Bcl-2/Bax ratio and upregulated phosphorylated Akt (p-Akt), which is a pro-survival kinase. Together these findings reveal novel roles for miR-133b in the pathogenesis of PD and provide new therapeutic avenues for the treatment of the disease.

Effect of dietary crude protein and forage contents on enteric methane emissions and nitrogen excretion from dairy cows simultaneously

The study aimed to examine, simultaneously, the effects of changing dietary forage and crude protein (CP) contents on enteric methane (CH4) emissions and nitrogen (N) excretion from lactating dairy cows. Twelve post-peak lactating Holstein cows (157 ± 31 days postpartum; mean ± s.d.) were randomly assigned to four treatments from a 2 × 2 factorial arrangement of two dietary forage levels [37.4% (LF) vs 53.3% (HF) of DM] and two dietary CP levels [15.2% (LP) vs 18.5% (HP) of DM] in a 4 × 4 Latin square design with four 18-day periods. Alfalfa hay was the sole source of dietary forage. Cows were fed ad libitum and milked twice daily. During the first 14 days, cows were housed in a free-stall barn, where enteric CH4 emissions were measured using the GreenFeed system from Days 8 to 14 in each period. Cows were then moved to metabolic cages, where faeces and urine output (kg/cow.day) were measured by total collection from Days 16 to 18 of each period. No dietary forage by CP interactions were detected for DM intake, milk production, enteric CH4 emissions, or N excretions. There was a tendency for DM intake to increase 0.6 kg/day in cows fed LF (P = 0.06). Milk production increased 2.1 kg/day in LF compared with HF (P < 0.01). Milk fat content decreased in cows fed LF compared with HF (1.07 vs 1.17 kg/day; P < 0.01). Milk contents of true protein, lactose and solid non-fat were greater in cows fed LF (P < 0.01). No difference in DM intake, milk yield and milk contents of true protein, lactose and solid non-fat was found between cows fed HP or LP. However, milk fat content increased 0.16 kg/day in cows fed HP (P < 0.05). Enteric CH4 emissions, and CH4 per unit of DM intake, energy-corrected milk, total digested organic matter and neutral detergent fibre were not affected by dietary CP, but decreased by LF compared with HF (P < 0.01). Milk true protein N was not affected by dietary CP content but was higher for LF compared with HF. Dietary N partitioned to milk true protein was greater in cows fed LF compared with HF (29.4% vs 26.7%; P < 0.01), also greater in cows fed LP compared with HP (30.8% vs 25.2%; P < 0.01). Dietary N partitioned to urinary N excretion was greater in cows fed HP compared with LP (39.5% vs 29.6%; P < 0.01) but was not affected by dietary CP content. Dietary N partitioned to faeces was not affected by dietary CP but increased in cows fed LP compared with HP (34.2% vs 27.8%; P < 0.01). Total N excretion (urinary plus faecal) as proportion to N intake did not differ between HP and LP, but tended to be lower in cows fed LF compared with the HF diet (64.2% vs 67.9%; P = 0.09). Both milk urea N (P < 0.01) and blood urea N (P < 0.01) declined with decreasing dietary CP or forage contents. Based on purine derivative analysis, there was a tendency for interaction between dietary CP and forage content on microbial protein synthesis (P < 0.09). Rumen microbial protein synthesis tended to be lower for high forage and low protein treatments. Increasing dietary forage contents resulted in greater CH4 emission (g/kg of energy-corrected milk) and manure N excretion (g/kg of energy-corrected milk) intensities of lactating dairy cows. Cows receiving reduced CP diets had low manure N outputs and improved milk true protein production efficiencies, regardless of dietary forage content.

Serum S100A4 levels as a novel biomarker for detection of acute myocardial infarction

OBJECTIVE

Myocardial infarction causes significant mortality and morbidity. Timely diagnosis allows clinicians to risk stratify their patients and select appropriate treatment. Biomarkers have been used to assist with timely diagnosis, while an increasing number of novel markers have been identified to predict outcome following an acute myocardial infarction or acute coronary syndrome. The objective of this study was to determine S100A4 expression in AMI and determine whether S100A4 could be a biomarker for detection of acute myocardial infarction (AMI).

PATIENTS AND METHODS

We measured circulating S100A4 levels in 173 patients (median age 58.3 years) who presented with first-time AMI 8 hours after the incident. The circulating S100A4 levels in 173 healthy volunteers (median age 57.3 years) was also measured. S100A4 was detected using enzyme immunoassay in both groups.

RESULTS

Serum S100A4 levels were significantly higher in patients with AMI [89.6 (4.3-214.6) pg/ml] compared to controls [11.8 (0-41.5) pg/ml] (p < 0.001). We found that a S100A4 level > 41.5 pg/ml had a Sensitivity 76.3% and specificity 87.5% for predicting AMI. S100A4 revealed the higher sensitivity for diagnosing AMI.

CONCLUSIONS

Elevated S100A4 in plasma may be a novel biomarker for early detection of AMI.

VEGF Trapon inhibits tumor growth in papillary thyroid carcinoma

OBJECTIVE

Vascular endothelial growth factor (VEGF) is the most potent inducer of neovasculature, and its increased expression has been related to a worse clinical outcome in many disease. Angiogenesis from thyroid cancer cell plays the important roles in post-surgical persistent, recurrent, and metastatic papillary thyroid cancer (PTC). Vascular endothelial growth factor (VEGF) Trapon is a newly developed VEGF-blocking agent with stronger affinity and broader activity than the anti-VEGF antibody bevacizumab. In this study, we tested the activity of VEGF Trapon on a PTC model in vivo.

MATERIALS AND METHODS

BC-PAP (derived from papillary carcinomas) transfected with a luciferase-expressing vector were injected into the back to mice. I.p. treatment with VEGF Trapon or control protein (25 mg/kg twice weekly) was started shortly after tumor injection to prevent tumor development (prevention model) or after established tumors were formed to inhibit tumor growth and metastasis formation (intervention model).

RESULTS

In the prevention model, VEGF Trapon inhibited tumor growth by 73 ± 12% compared with control (p = 0.014) and significantly prolonged survival. In the intervention model, VEGF Trapon inhibited tumor growth by 68 ± 7% (p < 0.01). Microvascular density was reduced by 56% due to VEGF Trapon treatment (p < 0.01).

CONCLUSIONS

VEGF Trapon is a potent inhibitor of BC-PAP tumor growth, angiogenesis and blocks the biological function of VEGF in vivo. These results support further clinical development of VEGF Trapon for PTC and other cancer types.

The effects of feeding time on milk production, total-tract digestibility, and daily rhythms of feeding behavior and plasma metabolites and hormones in dairy cows

The timing of feed intake entrains circadian rhythms regulated by internal clocks in many mammals. The objective of this study was to determine if the timing of feeding entrains daily rhythms in dairy cows. Nine Holstein cows were used in a replicated 3 × 3 Latin square design with 14-d periods. An automated system recorded the timing of feed intake over the last 7 d of each period. Treatments were feeding 1×/d at 0830 h (AM) or 2030 h (PM) and feeding 2×/d in equal amounts at 0830 and 2030 h. All treatments were fed at 110% of daily intake. Cows were milked 2×/d at 0500 and 1700 h. Milk yield and composition were not changed by treatment. Daily intake did not differ, but twice-daily feeding tended to decrease total-tract digestibility of organic matter and neutral detergent fiber (NDF). A treatment by time of day interaction was observed for feeding behavior. The amount of feed consumed in the first 2h after feeding was 70% greater for PM compared with AM feeding. A low rate of intake overnight (2400 to 0500 h; 2.2 ± 0.74% daily intake/h, mean ± SD) and a moderate rate of intake in the afternoon (1200 to 1700 h; 4.8 ± 1.1% daily intake/h) was noted for all treatments, although PM slightly reduced the rate during the afternoon period compared with AM. A treatment by time of day interaction was seen for fecal NDF and indigestible NDF (iNDF) concentration, blood urea nitrogen, plasma glucose and insulin concentrations, body temperature, and lying behavior. Specifically, insulin increased and glucose decreased more after evening feeding than after morning feeding. A cosine function within a 24-h period was used to characterize daily rhythms using a random regression. Rate of feed intake during spontaneous feeding, fecal NDF and iNDF concentration, plasma glucose, insulin, NEFA, body temperature, and lying behavior fit a cosine function within a 24-h period that was modified by treatment. In conclusion, feeding time can reset the daily rhythms of feeding and lying behavior, core body temperature, fecal NDF and iNDF concentration, and plasma blood urea nitrogen, glucose, and insulin concentration of dairy cows, but has no effect on daily DMI and milk production.

Downregulation of CD24 inhibits invasive growth, facilitates apoptosis and enhances chemosensitivity in gastric cancer AGS cells

BACKGROUND AND AIM

The human CD24 antigen is a small heavily glycosylated cell surface protein, which is expressed in a large variety of solid tumors,including gastric cancer. Enriched on the surface of many tumor cells,CD24 promotes tumor growth,invasion and metastasis and confers resistance to some chemotherapeutic drugs.In this study, we investigated the possible effect of CD24 suppression on proliferation, apoptosis, migration, invasion and chemosensitivity of gastric cancer (GC) cells.

MATERIALS AND METHODS

We down-regulated CD24 expression by RNA interference and investigated the effects on proliferation, apoptosis, chemosensitivity to doxorubicin, malignant and metastatic potential of a human gastric cancer cell line, AGS, CD24-suppressed clones, AGS-CD24-siRNA-C2, AGS-CD24-siRNA-C4 and AGS-CD24-siRNA-C5 in vitro. We evaluated the effects of CD24 suppression in vivo on xenograft tumor growth and metastatic properties following tail iv AGS-CD24-siRNA-C2, AGS-CD24-siRNA-C4 and AGS-CD24-siRNA-C5 clones. We also investigated the effect of CD24-siRNA followed by doxorubicin administration treatment on the xenograft tumor growth.

RESULTS

CD24 suppressed showed significantly decreased proliferation, invasion and increased apoptosis as well as increased chemosensitivity to doxorubicin in vitro and in vivo.

CONCLUSIONS

CD24 involves in proliferation, invasion and chemosensitivity of human gastric cancer cell line AGS, and that down-regulation of CD24 protein expression decreases the metastatic potential and increases chemosensitivity of gastric cancer (GC) cells. Thus, CD24 may be a promising therapeutic target for gastric cancer.

Knockdown of PLA2G2A sensitizes gastric cancer cells to 5-FU in vitro

BACKGROUND-AIM

Elevated expression of the PLA2G2A phospholipase in gastric cancer (GC) is associated with improved patient survival. PLA2G2A is also an important regulator of proliferation, invasion and metastasis in GC. However, no relation about PLA2G2A and chemosensitivity in GC cells was reported. 5-Fluorouracil (5-FU) is widely used for treatment of advanced gastric cancer. However, it is common for such patients to develop resistance to 5-FU, and this drug resistance becomes a critical problem for chemotherapy. The mechanisms underlying this resistance are largely unknown. In the present study, we investigated whether PLA2G2A could confer 5-FU resistance or sensitise in GC cells in vitro.

MATERIALS AND METHODS

The 5-FU sensitivity of GC cell lines SGC-7901, MKN-45, RF-48, N87, AGS, MKN-28, RF-1, MGC-803 were determined by MTT assays. PLA2G2A levels were determined by western blot assays. The effects of 5-FU on PLA2G2A expression were determined in vitro. PLA2G2A was inhibited by silencing of the PLA2G2A using small interfering RNA in vitro. PLA2G2A was overexpressed by transfection of full-long PLA2G2A cDNA in vitro, and the effects were evaluated on 5-FU sensitivity.

RESULTS

The cell lines SGC-7901, MKN-45, RF-48 and N87 were sensitive, whereas AGS, MKN-28, RF-1 and MGC-803 were resistant to 5-FU. Significant correlation was observed between basal PLA2G2A and 5-FU sensitivity. Silencing of PLA2G2A increased 5-FU killing in 5-FU-treated cells, and overexpression of PLA2G2A decreased 5-FU killing in 5-FU-treated cells.

CONCLUSIONS

PLA2G2A was correlated with sensitivity to 5-FU. Silencing of PLA2G2A was sensitive to 5-FU treatment. Thus, PLA2G2A may be a useful therapeutic target for a subset of gastric cancers.

Differential roles for the p101 and p84 regulatory subunits of PI3Kγ in tumor growth and metastasis

Phosphoinositide 3-kinase γ (PI3Kγ) consists of a catalytic subunit p110γ, which forms mutually exclusive dimers with one of the regulatory subunits called p101 and p84/p87(PIKAP). Recently, PI3Kγ emerged as being a potential oncogene because overexpression of the catalytic subunit p110γ or the regulatory subunit p101 leads to oncogenic cellular transformation and malignancy. However, the contribution of the individual subunits to tumor growth and metastasis and the mechanisms involved are not understood. We therefore individually knocked down the PI3Kγ subunits (p84, p101 and p110γ) in MDA-MB-231 cells, which reduced in vitro migration of the cell lines. Knockdown of p110γ or p101 inhibited apoptosis, Akt phosphorylation and lung colonization in SCID mice. Similarly, the knockdown of p110γ and p101 in murine epithelial carcinoma 4T1.2 cells inhibited primary tumor growth and spontaneous metastasis, as well as lung colonization. In contrast, knockdown of p84 in MDA-MB-231 cells enhanced Akt phosphorylation and lung colonization. These findings are the first to implicate differential functions of the two PI3Kγ regulatory subunits in the process of oncogenesis, and indicate that loss of p101 is sufficient to reduce in vivo tumor growth and metastasis to the same extent as that of p110γ.