Characterization of maize genotypes using microsatellite markers associated with QTLs for kernel iron and zinc

224-234Crop genetic resources rich in Fe and Zn provide sustainable and cost-effective solution to alleviate micronutrient malnutrition. Maize being the leading staple crop assumes great significance as a target crop for biofortification. We report here wide genetic variation for kernel Fe and Zn among 20 diverse maize inbreds lines, majority of which were bred for quality protein maize (QPM) and provitamin-A. Kernel Fe ranged from 30.0 - 46.13 mg/kg, while kernel Zn ranged from 8.68-39.56 mg/kg. Moderate but positive correlation was observed between the micronutrients. Characterization using 25 Single sequence repeats (SSRs) linked to QTLs for kernel Fe produced 58 alleles. Similarly, 86 alleles were identified from 35 SSRs linked to QTLs for kernel Zn. One unique allele for kernel Fe and three unique alleles for kernel Zn were identified. The mean polymorphic information content (PIC) was 0.40 for both kernel Fe and  Zn. Jaccard’s dissimilarity coefficients varied from 0.25 - 0.91 with a mean of 0.58 for kernel-Fe while 0.27- 0.88 with a mean of 0.57 for kernel Zn. Principal coordinate analysis depicted diversity of inbreds. Cluster analysis grouped the inbreds into three major clusters for both kernel Fe and Zn. Potential cross combinations have been proposed to develop micronutrient rich hybrids and novel inbreds with higher Fe and Zn. The information generated here would help the maize biofortification programme to develop nutritionally enriched hybrids

Scalable noninvasive amplicon-based precision sequencing (SNAPseq) for genetic diagnosis and screening of β-thalassemia and sickle cell disease using a next-generation sequencing platform

β-hemoglobinopathies such as β-thalassemia (BT) and Sickle cell disease (SCD) are inherited monogenic blood disorders with significant global burden. Hence, early and affordable diagnosis can alleviate morbidity and reduce mortality given the lack of effective cure. Currently, Sanger sequencing is considered to be the gold standard genetic test for BT and SCD, but it has a very low throughput requiring multiple amplicons and more sequencing reactions to cover the entire HBB gene. To address this, we have demonstrated an extraction-free single amplicon-based approach for screening the entire β-globin gene with clinical samples using Scalable noninvasive amplicon-based precision sequencing (SNAPseq) assay catalyzing with next-generation sequencing (NGS). We optimized the assay using noninvasive buccal swab samples and simple finger prick blood for direct amplification with crude lysates. SNAPseq demonstrates high sensitivity and specificity, having a 100% agreement with Sanger sequencing. Furthermore, to facilitate seamless reporting, we have created a much simpler automated pipeline with comprehensive resources for pathogenic mutations in BT and SCD through data integration after systematic classification of variants according to ACMG and AMP guidelines. To the best of our knowledge, this is the first report of the NGS-based high throughput SNAPseq approach for the detection of both BT and SCD in a single assay with high sensitivity in an automated pipeline

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Fluid flow conditioning apparatus

A fluid flow conditioning apparatus having a plurality of flexible microstructures that reduce flow losses within a conduit. The plurality of microstructures is affixed to an insertion plate-type flow conditioner. One or more ends of the microstructures are secured to internal walls of the flow conditioner. The microstructures are configured to move and flex in response to static and dynamic pressure exerted onto the microstructures by the fluid flow. The microstructures may be made of a hyperelastic material configured to undergo an elastic deformation due to the dynamic pressure of the fluid flow

Fluid flow conditioning apparatus

A fluid flow conditioning apparatus having self-adjusting tab members that reduce flow losses within a conduit. A plurality of tabular members is affixed to an insertion plate-type flow conditioner. Tabular members are cojoined in pairs at their leading edges. When the cojoined pair of the first tabular member and the second tabular member are placed into a fluid flow, an angle between the first tabular member and the second tabular member is configured to decrease in response to static and dynamic pressure exerted onto the outer surfaces of the tabular members by the fluid flow. The tabular members may be made of a hyperplastic material configured to undergo an elastic deformation and exhibit flapping due to the dynamic pressure of the fluid flow. Tabular members maybe cojoined by a hinge configured to partially close in response to pressure exerted by the fluid flow, decreasing the angle between the tabular members