Production of 3D printed scale models from microscope volume datasets for use in STEM education

Understanding the three-dimensional morphology of a biological sample at the microscopic level is a prerequisite to a functional understanding of cell biology, tissue development and growth. Images of microscopic samples obtained by compound light microscopy are customarily recorded and represented in two dimensions from a single orientation making it difficult to extrapolate 3D context from the 2D information. The commercialisation of fast, laser-based microscope systems (e.g. confocal, multi-photon or lightsheet microscopy) capable of generating volume datasets of microscopic samples through optical sectioning, coupled with advances in computer technology allowing accurate volume rendering of these datasets, have facilitated significant improvement in our 3D understanding of the microscopic world in virtual space. The advent of affordable 3D printing technology now offers the prospect of generating morphologically accurate, physical models from these microscope volume datasets for use in science education, outreach and engagement. 3D printed scale replicas will provide improved sensory perception, offering tactile as well as visual interaction, leading to improved understanding of structure function relationships. Here we present a technique to reliably generate detailed, physical 3D models from Z-stacks of optical sections from confocal and lightsheet microscopes using affordable, entry-level 3D printing technology. We use the technique to generate 3D printed models of a variety of different biological samples at a range of scales including pollen grains from two species of plant; blood cells from both human and earthworm species, a section of plant root; the compound eye of an ant; and a developing Zebrafish larva; all of which have been used in our teaching, engagement and outreach activities. Our methods can, in principle, be used to generate 3D printed models from microscope volume datasets of any small fluorescent or reflective samples

Production of 3D printed scale models from microscope volume datasets for use in STEM education

Understanding the three-dimensional morphology of a biological sample at the microscopic level is a prerequisite to a functional understanding of cell biology, tissue development and growth. Images of microscopic samples obtained by compound light microscopy are customarily recorded and represented in two dimensions from a single orientation making it difficult to extrapolate 3D context from the 2D information. The commercialisation of fast, laser-based microscope systems (e.g. confocal, multi-photon or lightsheet microscopy) capable of generating volume datasets of microscopic samples through optical sectioning, coupled with advances in computer technology allowing accurate volume rendering of these datasets, have facilitated significant improvement in our 3D understanding of the microscopic world in virtual space. The advent of affordable 3D printing technology now offers the prospect of generating morphologically accurate, physical models from these microscope volume datasets for use in science education, outreach and engagement. 3D printed scale replicas will provide improved sensory perception, offering tactile as well as visual interaction, leading to improved understanding of structure function relationships. Here we present a technique to reliably generate detailed, physical 3D models from Z-stacks of optical sections from confocal and lightsheet microscopes using affordable, entry-level 3D printing technology. We use the technique to generate 3D printed models of a variety of different biological samples at a range of scales including pollen grains from two species of plant; blood cells from both human and earthworm species, a section of plant root; the compound eye of an ant; and a developing Zebrafish larva; all of which have been used in our teaching, engagement and outreach activities. Our methods can, in principle, be used to generate 3D printed models from microscope volume datasets of any small fluorescent or reflective samples

Genome wide SNP comparative analysis between EGFR and KRAS mutated NSCLC and characterization of two models of oncogenic cooperation in non-small cell lung carcinoma

<p>Abstract</p> <p>Background</p> <p>Lung cancer with EGFR mutation was shown to be a specific clinical entity. In order to better understand the biology behind this disease we used a genome wide characterization of loss of heterozygosity and amplification by Single Nucleotide Polymorphism (SNP) Array analysis to point out chromosome segments linked to <it>EGFR </it>mutations. To do so, we compared genetic profiles between <it>EGFR </it>mutated adenocarcinomas (ADC) and <it>KRAS </it>mutated ADC from 24 women with localized lung cancer.</p> <p>Results</p> <p>Patterns of alterations were different between <it>EGFR </it>and <it>KRAS </it>mutated tumors and specific chromosomes alterations were linked to the <it>EGFR </it>mutated group. Indeed chromosome regions 14q21.3 (p = 0.027), 7p21.3-p21.2 (p = 0.032), 7p21.3 (p = 0.042) and 7p21.2-7p15.3 (p = 0.043) were found significantly amplified in EGFR mutated tumors. Within those regions 3 genes are of special interest <it>ITGB8</it>, <it>HDAC9 </it>and <it>TWIST1</it>. Moreover, homozygous deletions at <it>CDKN2A </it>and LOH at <it>RB1 </it>were identified in <it>EGFR </it>mutated tumors. We therefore tested the existence of a link between EGFR mutation, CDKN2A homozygous deletion and cyclin amplification in a larger series of tumors. Indeed, in a series of non-small-cell lung carcinoma (n = 98) we showed that homozygous deletions at <it>CDKN2A </it>were linked to <it>EGFR </it>mutations and absence of smoking whereas cyclin amplifications (<it>CCNE1 </it>and <it>CCND1</it>) were associated to <it>TP53 </it>mutations and smoking habit.</p> <p>Conclusion</p> <p>All together, our results show that genome wide patterns of alteration differ between <it>EGFR </it>and <it>KRAS </it>mutated lung ADC, describe two models of oncogenic cooperation involving either <it>EGFR </it>mutation and <it>CDKN2A </it>deletion or cyclin amplification and <it>TP53 </it>inactivating mutations and identified new chromosome regions at 7p and 14q associated to EGFR mutations in lung cancer.</p

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

Differential expression of the keratan sulphate proteoglycan, keratocan, during chick corneal embryogenesis

Keratan sulphate (KS) proteoglycans (PGs) are key molecules in the connective tissue matrix of the cornea of the eye, where they are believed to have functional roles in tissue organisation and transparency. Keratocan, is one of the three KS PGs expressed in cornea, and is the only one that is primarily cornea-specific. Work with the developing chick has shown that mRNA for keratocan is present in early corneal embryogenesis, but there is no evidence of protein synthesis and matrix deposition. Here, we investigate the tissue distribution of keratocan in the developing chick cornea as it becomes compacted and transparent in the later stages of development. Indirect immunofluorescence using a new monoclonal antibody (KER-1) which recognises a protein epitope on the keratocan core protein demonstrated that keratocan was present at all stages investigated (E10–E18), with distinct differences in localisation and organisation observed between early and later stages. Until E13, keratocan appeared both cell-associated and in the stromal extracellular matrix, and was particularly concentrated in superficial tissue regions. By E14 when the cornea begins to become transparent, keratocan was located in elongate arrays, presumably associated along collagen fibrils in the stroma. This fibrillar label was still concentrated in the anterior stroma, and persisted through E15–E18. Presumptive Bowman’s layer was evident as an unlabelled subepithelial zone at all stages. Thus, in embryonic chick cornea, keratocan, in common with sulphated KS chains in the E12–E14 developmental period, exhibits a preferential distribution in the anterior stroma. It undergoes a striking reorganisation of structure and distribution consistent with a role in relation to stromal compaction and corneal transparency

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