Standardization of seed ball media for fodder sorghum to increase green cover and fodder availability in degraded lands

Fodder sorghum (Sorghum bicolor L.) is a tall, erect annual grass. It is a drought resistant crop due to its effective root system. A seed ball is one of the low-cost technologies which was prepared with locally available materials on the farm. In seed balls, the seeds are protected from external factors. At the same time, vigorous seedling was established through seed ball. In order to improve the degraded lands with green cover, the following experiment was framed and carried out. Seed balls were prepared with a combination of red soil and vermicompost at different ratios with 230-250ml of water per kg of medium to get an optimum size and quality. After the preparation, seed balls were shade dried for 24-36 hrs. Among the different ratio of media combinations, 2:1 and 4:2 ratio was found to be the best media for seed balls preparation with good physical and physiological qualities. The maximum seedling quality parameters speed of germination (7.6), germination (98%), root length (10.6 cm), shoot length (19.4 cm) and vigour index (2900) obtained in the present study were due to vermicompost, which contained an optimum concentration of nutrients that helped improve the seedling vigour. This experiment confirmed that using seed balls with best media combinations for the regeneration of degraded lands was very effective

Identification and characterization of popular rice (Oryza sativa L.) varieties through chemical tests

Identification and characterization of crop varieties are crucial for ensuring the genetic purity of seeds. The present investigation was carried out to identify suitable chemical methods that are fast, reliable and easy for seed analysts, breeders and seed producers for identification of a variety. Twenty-five popular rice varieties in the seed supply chain of Tamil Nadu were subjected to phenol, modified phenol, NaOH, aroma, gelatinization temperature (alkali spreading value), GA3 and 2,4-D tests. The results of the experiment revealed that phenol and modified phenol tests changed the colour of TKM 9 and TRY 1 variety to brown but no colour change was observed in the variety I.W. Ponni variety. The NaOH test is useful for the identification of TKM 9 variety as it changed the colourless solution to red. GA3 and 2,4-D tests characterized the varieties based on the shoot growth into two and three groups respectively. However, all the variety lacked aroma and exhibited a high gelatinization temperature

Reducing False-Positive Prediction of Minimotifs with a Genetic Interaction Filter

Background: Minimotifs are short contiguous peptide sequences in proteins that have known functions. At its simplest level, the minimotif sequence is present in a source protein and has an activity relationship with a target, most of which are proteins. While many scientists routinely investigate new minimotif functions in proteins, the major web-based discovery tools have a high rate of false-positive prediction. Any new approach that reduces false-positives will be of great help to biologists. Methods and Findings: We have built three filters that use genetic interactions to reduce false-positive minimotif predictions. The basic filter identifies those minimotifs where the source/target protein pairs have a known genetic interaction. The HomoloGene genetic interaction filter extends these predictions to predicted genetic interactions of orthologous proteins and the node-based filter identifies those minimotifs where proteins that have a genetic interaction with the source or target have a genetic interaction. Each filter was evaluated with a test data set containing thousands of true and false-positives. Based on sensitivity and selectivity performance metrics, the basic filter had the best discrimination for true positives, whereas the node-based filter had the highest sensitivity. We have implemented these genetic interaction filters on the Minimotif Miner 2.3 website. The genetic interaction filter is particularly useful for improving predictions of posttranslational modifications such as phosphorylation and proteolytic cleavage sites

Seed priming with endophytes on physiological, biochemical and antioxidant activity of hybrid maize (Zea mays l.) COH (M) 8 seeds

Endophytes are important microorganisms that enhance the plant's stability through a symbiotic relationship, without any harmful effects and symptoms in the host plant. To study the effect of endophytes on overall performance of COH(M)8 hybrid maize seeds, the present study was conducted with different endophytic seed priming for  12 hrs duration with Beauveria bassiana @ 5% (T2), Metarhizium anisopliae @ 5% (T3) and Bacillus subtilis @ 8% (T4) along with hydro priming (T1) and untreated control (T0).  The seed priming treatments with all the above three endophytes enhanced the seed quality parameters, among which M. anisopliae @ 5% (T3) registered maximum increase of germination (4.34%), shoot length (20.73%), root length (15.04%), dry matter production (15.22%) and vigour index (22.68%) over control. Similarly, the seeds primed with M. anisopliae @ 5% (T3) recorded the highest value of dehydrogenase activity (0.441 OD value), α- amylase activity (2.06 mg maltose min-1) and antioxidant activity viz., catalase (1.55 μmol H2O2 min-1 g-1 protein) and peroxidase  (0.87 U mg-1protein min-1). Results of this study revealed that the endophytes can enhance overall the seed quality in maize

Achieving High Accuracy Prediction of Minimotifs

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease

Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy

Background: Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions. Methodology/Principal Findings: Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components. Conclusions/Significance: Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is,4.6 times that of random minimotifs. For the molecular function filter this ratio is,2.9. These results, together with the comparison with the published frequency score filter, strongly suggest tha

Sex differences evident in elevated anxiety symptoms in multiple sclerosis, inflammatory bowel disease, and rheumatoid arthritis

IntroductionImmune-mediated inflammatory diseases (IMID), such as multiple sclerosis (MS), inflammatory bowel disease (IBD) or rheumatoid arthritis (RA) have high rates of elevated anxiety symptoms. This can may worsen functioning and increase IMID disease burden. The rate of and factors associated with elevated anxiety symptoms may differ between males and females, which, in turn can affect diagnosis and disease management. We evaluated whether the frequency and factors associated with comorbid elevated anxiety symptoms in those with an IMID differed by sex.MethodsParticipants with an IMID (MS, IBD or RA) completed two anxiety measures (HADS, GAD-7). We used logistic regression to investigate whether sex differences exist in the presence of comorbid elevated anxiety symptoms or in the endorsement of individual anxiety items in those with an IMID.ResultsOf 656 participants, females with an IMID were more likely to have elevated anxiety symptoms compared to males (adjusted odds ratio [aOR] 2.05; 95%CI: 1.2, 3.6). Younger age, higher depressive symptoms and income were also associated with elevated anxiety symptoms in IMID. Lower income in males with an IMID, but not females, was associated with elevated anxiety symptoms (aOR: 4.8; 95%CI: 1.5, 15.6). No other factors demonstrated a sex difference. Males had nearly twice the odds of endorsing restlessness on the GAD-7 (OR = 1.8, 95%CI: 1.07, 3.15) compared to females.DiscussionWe found evidence for sex differences in the factors associated with experiencing elevated anxiety symptoms in those with an IMID. These findings could be helpful to sensitize clinicians to monitor for comorbid anxiety symptoms in males with an IMID

Artificial Intelligent Network (AIN) and Machine Learning (ML) using Python

<p><i>Welcome to a thrilling journey through the world of Artificial Intelligence (AI) and Machine Learning (ML), two of the most transformative technologies of our time, and the versatile programming language that powers them – Python. This book is your entry point to understanding how Python has become the language of choice for AI and ML, and how these technologies are reshaping industries, enhancing decision-making, and fueling innovation. Whether you are a seasoned programmer, a data enthusiast, or someone just starting to explore the vast realm of AI and ML, this text promises to be an enlightening adventure. In the age of data, AI and ML have emerged as the driving forces behind some of the most remarkable advancements in science, business, healthcare, and beyond. Python, with its simplicity, versatility, and a wealth of libraries and tools, has become the preferred choice for those looking to harness the potential of AI and ML. The primary aim of this book is to provide you with a comprehensive understanding of AI and ML using Python. As you embark on this journey, you will learn about the core principles, algorithms, and real-world applications of these technologies. The authors have thoughtfully crafted this content to make it accessible to both experienced professionals and those new to the world of data science. Our journey takes us through the fundamentals of AI and ML, delving into the essential mathematics, the Python programming required to build and deploy models, and the best practices for handling data. Whether it's predictive analytics, natural language processing, computer vision, or recommendation systems, we will explore how Python can be applied to a diverse range of AI and ML applications. Each chapter offers a balanced blend of technical insights and practical applications, ensuring that you can grasp the intricacies of AI and ML while appreciating their real-world impact. We will also explore the ethical considerations, best practices, and the potential challenges that come with using AI and ML in various domains. As you dive into the world of AI and ML with Python, we invite you to envision the possibilities that these technologies present. The advancements discussed in these pages are not just about building models but about how AI and ML are enhancing decision-making, automating processes, and driving innovation across industries.</i></p&gt

ROC curves for the GI filter with different types of minimotifs.

<p>ROC curves are generated using R software package with activity and sub-activity as the underlying variables. The binomial curve fit is shown. The areas under the ROC curves are 0.93 for all minimotifs (red lines), 0.95 for binding motifs (blue lines) and 0.87 for phosphorylation minimotifs (orange lines).</p

Screen Shot of Minimotif Miner 2.4 filter menu.

<p>GI filters were added as part of MnM website 2.4 located under the motif filter pull down section. The options for filtering with ‘GIs’ are outlined with a red box. This filter can be used on its own or in combination with filters. There are also options to check boxes to include or exclude minimotifs with GIs.</p