Dry Matter Yield and Nutritive Value of Coast-Cross N1 Preserved as Hay, Silage, and Haylage

Dry matter yield and nutritive value of coast-cross n° 1 (Cynodon dactylon (L.) Pers preserved as hay, silage and haylage were studied. The forage was harvested after 28, 35, 42 and 49 days of growth. Fertilization was completed with 400 kg/ha single superphosphate and 500 kg/ha 20-0-20 at the beginning of the experiment. The experimental design for dry matter yield (2 x 2 m plots) was a randomized block with four replications. Nutritive value was also analyzed in a randomized block design being the treatments arranged in a 3 x 4 factorial (three preservation methods and four ages). Dry matter yield increased linearly (P \u3c 0,05) with age, whilst crude protein content had an opposite effect. Dry matter content was not affected by age (P \u3e 0.05). Nutritive value of haylage was greater than those observed for hay and silage

Sensitivity to millicharged particles in future proton-proton collisions at the LHC with the milliQan detector

We report on the expected sensitivity of dedicated scintillator-based detectors at the LHC for elementary particles with charges much smaller than the electron charge. The dataset provided by a prototype scintillator-based detector is used to characterise the performance of the detector and provide an accurate background projection. Detector designs, including a novel slab detector configuration, are considered for the data taking period of the LHC to start in 2022 (Run 3) and for the high luminosity LHC. With the Run 3 dataset, the existence of new particles with masses between 10 MeV and 45 GeV could be excluded at 95% confidence level for charges between 0.003e and 0.3e, depending on their mass. With the high luminosity LHC dataset, the expected limits would reach between 10 MeV and 80 GeV for charges between 0.0018e and 0.3e, depending on their mas

Sensitivity to millicharged particles in future proton-proton collisions at the LHC with the milliQan detector

We report on the expected sensitivity of dedicated scintillator-based detectors at the LHC for elementary particles with charges much smaller than the electron charge. The dataset provided by a prototype scintillator-based detector is used to characterize the performance of the detector and provide an accurate background projection. Detector designs, including a novel slab detector configuration, are considered for the data taking period of the LHC to start in 2022 (Run 3) and for the high luminosity LHC. With the Run 3 dataset, the existence of new particles with masses between 10 MeV and 45 GeV could be excluded at 95% confidence level for charges between 0.003 e and 0.3 e, depending on their mass. With the high luminosity LHC dataset, the expected limits would reach between 10 MeV and 80 GeV for charges between 0.0018 e and 0.3 e, depending on their mass

Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms

Background: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods.Results: Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes.Conclusions: The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2Journal Articleinfo:eu-repo/semantics/publishe

Avaliação do capim-elefante (Pennisetum purpureum Schum.) cultivar taiwan A-148, ensilado com diferentes técnicas de redução de umidade

O objetivo deste trabalho foi avaliar o efeito de diferentes técnicas de redução de umidade sobre a composição química, digestibilidade in vitro da matéria seca (DIVMS) e degradação ruminal da matéria seca da silagem de capim-elefante (Pennisetum purpureum Schum.) cv. Taiwan A-148. Os tratamentos (A, Controle; B, 20,0; C, 30,0; D, 40,0% de sabugo de milho; E, emurchecimento por 12 horas; F, emurchecimento por 24 horas; e G, esmagamento + emurchecimento por 24 horas) foram distribuídos em delineamento inteiramente casualizado com três repetições. Os tratamentos da silagem B e E não foram eficientes em reduzir a umidade excessiva da forragem. O emurchecimento por 24 horas aumentou o teor de MS, sem influir no teor de PB e na DIVMS. Os tratamentos C e D favoreceram o desenvolvimento da população de clostrídeos, aumentaram a concentração de N-NH3 e reduziram a concentração de ácido lático e a DIVMS das silagens. O tratamento G aumentou o teor de MS e reduziu a concentração de N-NH3 e DIVMS. A adiçao de sabugo de milho (tratamentos B, C e D) reduziu a degradação ruminal da MS das silagens. O sabugo de milho reduziu a umidade, mas apresentou efeitos negativos na qualidade da silagem, enquanto o esmagamento e/ou emurchecimento foram procedimentos que mostraram bons resultados em relação à conservação do material ensilado.The objective of this work was to evaluate the effect of different techniques of moisture reduction on the chemical composition, in vitro deseapperance of dry matter (IVDMD) and ruminal dry matter disappearance of the elephant grass (Pennisetum purpureum Schum), cv. Taiwan A-148 silage. The treatments (A, control, B, 20, C, 30, D, 40% of corn cobs; E, wilting by 12 h, F, wilting by 24 h; G, crushing + wilting by 24 h) were allotted to a completely randomized design with three replicates. The silage treatments B and E were not efficient to reduce the excessive moisture of the forage. The wilting by 24 h increased the DM content, without affect in the CP and IVDMD. The silage treatments C and D favor the development of clostridium population, increase the concentration of NH3-N, decreased the lactic acid concentration and IVDMD of the silage. The treatment G increased the DM content and decreased the NH3-N and IVDMD. The addition of corn cobs (treatments B, C and D) decreased the ruminal DM degradation of the silage. The corn cobs decreased the moisture, but presented negative effects on the silage quality while the wilting and/or crushing are procedures that showed good results on the preservation of the ensiled material

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole genome mutation screening in Candida albicans and aeruginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors