Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Tooth loss and associated risk factors among rural population of Wardha District: A cross-sectional study

Introduction: Regular dental care and daily cleansing habits are one of the key aspects of keeping healthy teeth for a lifetime. Common Indian concept is, with age people become more prone to oral health problems. If they follow their oral hygiene practices meticulously, then age may not act as a risk factor for tooth loss. Aim: The aim of this study is to evaluate the risk factors associated with tooth loss among adults and the elderly among the rural population of Wardha District. Materials and Methods: In this cross-sectional study, among the rural population, two World Health Organization index age groups (35–44 and 65–74 years) were selected. A self-administered questionnaire was distributed, and complete clinical oral examination was done. The data were statistically analyzed using descriptive statistics and Chi-square test. The value of P < 0.05 was considered statistically significant. Results: Nearly 75.3% of laborers were partially edentulous. Habits, including smoking, tobacco chewing, and alcohol consumption, had an impact on tooth loss. Patients suffering from diabetes and hypertension had 97.5% and 100% had tooth loss, respectively. Regarding the first visit to the dentist, 65.6% population underwent dental treatment from the dental college in the vicinity. “No dental problems” were reported by 68.4% of patients of the total population and among them 81.3% were edentulous. Regarding “Self-perceived treatment” the result revealed that 72% of them had felt the need for dental treatment. Conclusion: The study showed that risk factors such as habits, systemic diseases, and self-perceived oral health played a significant role in tooth loss. Brushing type, method, and material used for cleaning were some other factors that influenced tooth loss

Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation.

Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in "batches." Batches are quantized groups of individual sections that, in LASSO, are cut with a diamond knife, picked up from an attached waterboat, and placed onto microfabricated TEM substrates using rapid, accurate, and repeatable robotic tools. Additionally, we introduce mathematical models for ssTEM with respect to yield, throughput, and cost to access ssTEM scalability. To validate the method experimentally, we processed 729 serial sections of human brain tissue (~40 nm x 1 mm x 1 mm). Section yield was 727/729 (99.7%). Sections were placed accurately and repeatably (x-direction: -20 ± 110 μm (1 s.d.), y-direction: 60 ± 150 μm (1 s.d.)) with a mean cycle time of 43 s ± 12 s (1 s.d.). High-magnification (2.5 nm/px) TEM imaging was conducted to measure the image quality. We report no significant distortion, information loss, or substrate-derived artifacts in the TEM images. Quantitatively, the edge spread function across vesicle edges and image contrast were comparable, suggesting that LASSO does not negatively affect image quality. In total, LASSO compares favorably with traditional serial sectioning methods with respect to throughput, yield, and cost for large-scale experiments, and represents a flexible, scalable, and accessible technology platform to enable the next generation of neuroanatomical studies