Development Time Research Articles

Decomposition of phenotypic heterogeneity in autism reveals distinct and coherent genetic programs.

Unraveling the phenotypic and genetic complexity of autism is extremely challenging yet critical for understanding the biology, inheritance, trajectory, and clinical manifestations of the many forms of the condition. Here, we leveraged broad phenotypic data from a large cohort with matched genetics to characterize classes of autism and their patterns of core, associated, and co-occurring traits, ultimately demonstrating that phenotypic patterns are associated with distinct genetic and molecular programs. We used a generative mixture modeling approach to identify robust, clinically-relevant classes of autism which we validate and replicate in a large independent cohort. We link the phenotypic findings to distinct patterns of de novo and inherited variation which emerge from the deconvolution of these genetic signals, and demonstrate that class-specific common variant scores strongly align with clinical outcomes. We further provide insights into the distinct biological pathways and processes disrupted by the sets of mutations in each class. Remarkably, we discover class-specific differences in the developmental timing of genes that are dysregulated, and these temporal patterns correspond to clinical milestone and outcome differences between the classes. These analyses embrace the phenotypic complexity of children with autism, unraveling genetic and molecular programs underlying their heterogeneity and suggesting specific biological dysregulation patterns and mechanistic hypotheses.

XOL-1 regulates developmental timing by modulating the H3K9 landscape in C. elegans early embryos.

Sex determination in the nematode C. elegans is controlled by the master regulator XOL-1 during embryogenesis. Expression of xol-1 is dependent on the ratio of X chromosomes and autosomes, which differs between XX hermaphrodites and XO males. In males, xol-1 is highly expressed and in hermaphrodites, xol-1 is expressed at very low levels. XOL-1 activity is known to be critical for the proper development of C. elegans males, but its low expression was considered to be of minimal importance in the development of hermaphrodite embryos. Our study reveals that XOL-1 plays an important role as a regulator of developmental timing during hermaphrodite embryogenesis. Using a combination of imaging and bioinformatics techniques, we found that hermaphrodite embryos have an accelerated rate of cell division, as well as a more developmentally advanced transcriptional program when xol-1 is lost. Further analyses reveal that XOL-1 is responsible for regulating the timing of initiation of dosage compensation on the X chromosomes, and the appropriate expression of sex-biased transcriptional programs in hermaphrodites. We found that xol-1 mutant embryos overexpress the H3K9 methyltransferase MET-2 and have an altered H3K9me landscape. Some of these effects of the loss of xol-1 gene were reversed by the loss of met-2. These findings demonstrate that XOL-1 plays an important role as a developmental regulator in embryos of both sexes, and that MET-2 acts as a downstream effector of XOL-1 activity in hermaphrodites.

Local differences in robustness to ocean acidification.

Ocean acidification (OA) caused by increased atmospheric carbon dioxide is affecting marine systems globally and is more extreme in coastal waters. A wealth of research to determine how species will be affected by OA, now and in the future, is emerging. Most studies are discrete and generally do not include the full life cycle of animals. Studies that include the potential for adaptation responses of animals from areas with different environmental conditions and the most vulnerable life stages are needed. Therefore, we conducted experiments with the widely distributed blue mussel, Mytilus edulis, from populations regularly exposed to different OA conditions. Mussels experienced experimental conditions prior to spawning, through embryonic and larval development, both highly vulnerable stages. Survivorship to metamorphosis of larvae from all populations was negatively affected by extreme OA conditions (pH 7.3, Ωar, 0.39, pCO2 2479.74), but, surprisingly, responses to mid OA (pH 7.6, Ωar 0.77, pCO21167.13) and low OA (pH 7.9, Ωar 1.53, pCO2 514.50) varied among populations. Two populations were robust and showed no effect of OA on survivorship in this range. One population displayed the expected negative effect on survivorship with increased OA. Unexpectedly, survivorship in the fourth population was highest under mid OA conditions. There were also significant differences in development time among populations that were unaffected by OA. These results suggest that adaptation to OA may already be present in some populations and emphasizes the importance of testing animals from different populations to see the potential for adaptation to OA.

Prediction of technological indicators of selective water isolation of water-watered wells by nanostructured gel

In this work the composition forming nanostructured gel, which does not change phase permeability of oil, easily penetrating into water-saturated depth of the formation in watered wells, effectively closing the drainage channels, is established, the possibilities of its rapid heating up to formation temperature, gel formation within a certain time in contact with water depending on the concentration of the selected composition and with its use of involvement in the development of residual oil reserves, which are in watered wells, are investigated at injection of this composition into the well. As an optimal variant of well isolation in terrigenous reservoirs of oil fields it is suggested to use nanocomposite on the basis of silicate gel (with the content of liquid glass 4 % (modulus 3.1), 0.6 % of hydrochloric acid and 0.063 % of metal nanoparticles). The mechanism of selective isolation of water flow in production wells is based on the fact that when the composition is injected into the production well, part of its volume gets into the oil-saturated layer, and the rest – into the water-saturated layer. The composition is distributed in proportion to the permeability of the layers. In the process of well development the composition in the oil-saturated layer is removed, and in the water-saturated layer it retains its isolating properties. The greater the volume of injected fluid, the greater the isolation factor and pressure gradient. The isolation factor is directly proportional to the volume injected. Gel formed in the water-saturated layer corresponding to the volume of the injected composition reduces the phase conductivity on water at least 30 times. Practically does not change the phase conductivity by oil. On the basis of the calculation model including physical and chemical characteristics of selective isolation process taking into account real field conditions, the influence of possibility to isolate bottomhole zone of wells by gel on technological indicators of its operation was estimated. Thus, when putting into operation a well after isolation by water, the production of which was flooded with water, in the initial moments there was a sharp increase in oil flow rate (2–3 times), and at the subsequent stage – a slight decrease in the rate, compared to the flow rate before isolation, the effective time of development or flushing was about 150 days.

Artificial Intelligence: A Catalyst for Breakthroughs in Nanotechnology and Pharmaceutical Research

Artificial intelligence (AI) is revolutionizing nanotechnology and pharmaceutical research by streamlining drug discovery, optimizing formulations, and personalizing treatments through predictive modelling and data analysis. Without AI, the pharmaceutical industry requires more time due to less effective drug discovery, inefficient clinical trials, and prolonged regulatory processes, resulting in higher costs and delayed treatments1. The integration of AI with nanotechnology and pharmaceutical science is revolutionizing medicine, opening up new possibilities for diagnosis, treatment, and personalized healthcare. It also enhances clinical treatments and identifies new uses for existing drugs, reducing development time and costs2. By leveraging machine learning algorithms, researchers can predict the properties and behaviour of nanomaterials, facilitating the development of nanoparticles that can deliver drugs more efficiently to specific cells or tissues3. AI accelerates nano product development by optimizing nanomaterial design, predicting nanoparticle toxicity, and enhancing nanomedicine formulation. For example, AI has been used to design nanoparticles for targeted drug delivery, improving their efficiency and safety4. AI-enabled nanotechnology can enhance molecular profiling and early diagnosis, refine the design of nanomedicines, and improve their efficacy. By optimizing nanomedicine properties, achieving effective drug synergy, and reducing nanotoxicity, AI facilitates better targetability and accelerates the development of personalized treatments.

MicroRNAomic Analysis of Spent Media from Slow- and Fast-Growing Bovine Embryos Reveal Distinct Differences.

In bovine embryos, the microRNA (miRNA) expression has been profiled at each stage of early development in vitro. The miRNAomic analysis of spent media has the potential to reveal characteristics of embryo health; however, applications are limited without categorizing miRNA profiles by embryo quality. Time-lapse imaging has shown the timing of embryo development in vitro may be indicative of their developmental potential. The study aimed to characterize miRNAs in the spent media of bovine embryos with different growth rates during the pre-implantation phase. Bovine cumulus-oocyte complexes were aspirated from ovaries, fertilized, and cultured to blastocyst stage of development. At the 2-cell, 8-cell, and blastocyst stage, each microdrop of 30 presumptive zygotes were classified as slow- or fast-growing based on the percentage of embryos that had reached the desired morphological stage. A comparative analysis was performed on the spent media of slow- and fast-growing embryos using the results of a GeneChip miRNA 4.0 array hybridization. In total, 34 differentially expressed miRNAs were identified between the comparison groups: 14 miRNAs were found in the 2-cell samples, 7 in the 8-cell samples, and 12 in the blastocyst samples. The results demonstrate distinct miRNAs populations can be identified between slow- and fast-growing embryos, highlighting the novel biomarkers of developmental potential at each stage of pre-implantation development.

Zero-trust design and assurance patterns for cyber–physical systems

Security is paramount in all mission-critical domains, including the aerospace industry. Cyber-attacks are increasing both in number and sophistication. Zero-trust is an emerging initiative that has proven very effective for enterprise systems in the Information Technology domain; however, research is lacking on applicable zero-trust mechanisms and their assurance for cyber–physical systems (CPS). We have already identified various zero-trust mechanisms in our previous work. In this paper, we present our zero-trust architecture design patterns and provide a methodology for the assurance of these mechanisms. Towards this objective, we have identified an initial set of assurance patterns covering individual zero-trust components in a system design. Our design and assurance patterns are made available to system engineers in pattern libraries. Engineers can model system architectures and utilize one or more of these patterns to provide design assurance based on individual zero-trust security requirements to improve the overall system cyber-security. To demonstrate our approach, we apply our assurance patterns to an unmanned aerial vehicle surveillance application. We discuss how our framework leverages the use of these patterns to develop zero-trust-enabled systems with different security requirements. Furthermore, our assurance patterns enable engineers to identify any design flaws and correct them during the initial system design phase, thus saving development time, effort, and cost. As a result, the overall approach can be utilized to design system models with specific zero-trust security requirements to improve the security posture of a CPS.

Wide-ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster.

The progress made in aging research using laboratory organisms is undeniable. Yet, with few exceptions, these studies are conducted in a limited number of isogenic strains. The path from laboratory discoveries to treatment in human populations is complicated by the reality of genetic variation in nature. To model the effect of genetic variation on the action of the drug rapamycin, here we use the growth of Drosophila melanogaster larvae. We screened 140 lines from the Drosophila Genetic References Panel for the extent of developmental delay and found wide-ranging variation in their response, from lines whose development time is nearly doubled by rapamycin, to those that appear to be completely resistant. Sensitivity did not associate with any single genetic marker, nor with any gene. However, variation at the level of genetic pathways was associated with rapamycin sensitivity and might provide insight into sensitivity. In contrast to the genetic analysis, metabolomic analysis showed a strong response of the metabolome to rapamycin, but only among the sensitive larvae. In particular, we found that rapamycin altered levels of amino acids in sensitive larvae, and in a direction strikingly similar to the metabolome response to nutrient deprivation. This work demonstrates the need to evaluate interventions across genetic backgrounds and highlights the potential of omic approaches to reveal biomarkers of drug efficacy and to shed light on mechanisms underlying sensitivity to interventions aimed at increasing lifespan.

Phase change material properties identification for the design of efficient thermal management system for cylindrical Lithium-ion battery module

Performance and life of Lithium-ion battery packs in EVs and energy storage applications are limited by the thermal profile of cells during its life. Passive cooling technique based on Phase Change Material (PCM) is employed for mitigating thermal issues associated with Lithium-ion cells. The challenge with PCM materials is the trade-off between its latent heat of fusion, thermal conductivity and specific heat. Detailed CFD analysis is carried out to study the impact of PCM thermal properties on battery pack thermal profile and provide guidelines for PCM properties selection and usage of existing PCM materials. Impact of critical PCM properties such as latent heat (100–250 kJkg−1), thermal conductivity (0.2–23 Wm−1K−1), melting temperature and thickness of PCM material on thermal performance of a battery module during rapid discharge rate of 25 A (3.2C) is investigated. Battery module maximum temperature (Tmax) reaches 54°C and temperature difference (∆T) to 5.5°C while operating at 35°C with 124 kJkg−1and 0.2 Wm−1K−1. It is found that, PCM with higher latent heat (≥ 175 kJkg−1), thermal conductivity (≥ 3 Wm−1K−1), PCM thickness (≥ 3 mm) and appropriate phase change temperature 41-44°C exhibit improved heat dissipation for high discharge rates by effectively reducing the Tmax to 44.4°C and ∆T to 2.4°C (25 A and 35°C ambient). Further, ways to mitigate low latent heat capacity and thermal conductivity of PCM through increasing PCM material usage (i.e. inter cell distance) is emphasized. Results of the present study can be considered as a design tool for PCM development engineers which could be cost effective both in-terms of development time and effort.

A new observer-based controller for continuous-time nonlinear systems with applications on electrical energy systems

In this paper a new observer-based controller is developed for continuous-time input constrained nonlinear systems. Firstly, a new developed continuous time Regularized Least Square (CRLS) estimator is presented. The estimated states are then used to generate the desired constrained state feedback control strategies to regulate the performance of a perturbed system to the desired steady state. Unlike the well-known continuous-time nonlinear state estimators, such as the high gain observer and others, the developed observer does not show large overshoots at the start of the estimation process, the estimation errors reach zero steady state very shortly, has no pre-specified limitations on the class of nonlinear systems to be estimated, …etc. As a result, when used as an element in observer-based controlled systems, then the desired behaviors of the state trajectories of the controlled system are reached very shortly. The asymptotic stability of the proposed constrained observer-based controlled nonlinear system is rigorously analyzed. Simulation results are presented to justify the applicability and the efficiency of the proposed approach. Firstly, to show the superiority of the developed CRLS estimator, the states of illustrative practical problems are firstly estimated using the proposed approach. The achieved results are then compared with those achieved from applying the most famous and widely used observers in the literature. Then, two nonlinear practical control problems are used to demonstrate the effectiveness of the developed observer- based control approach.

Rompi Pintar Pengendara Menggunakan Sensor Kemiringan Untuk Kebutuhan Keselamatan Berbasis IOT

Traffic accidents often occur in everyday life. Many factors cause traffic accidents to occur. Contributors to the high number of traffic accidents are two-wheeled drivers. So an air vest is needed that can reduce the effects of impact if an accident occurs. In this research, an air vest for two-wheeled riders will be designed. Equipped with an IoT-based tilt sensor. This research designs an IoT-based air vest for two-wheeled riders to reduce the impact effects during an accident. This vest is equipped with a tilt sensor which is similar to the function of airbags in four-wheeled vehicles. The way the vest works is that when the sensor detects a certain tilt programmed into the Arduino, the vest will inflate. The main components of this vest are the Arduino Uno ATMega 328 and the MPU-6050 gyroscope sensor. Prototype testing showed a 60% success rate with a development time of 7-10 seconds. However, hands-on testing provided satisfactory results with a 100% success rate and consistent development time of 7-10 seconds. This vest is expected to improve the safety of everyday two-wheeled riders.

Integrating Audiovisual Immersion Into Pediatric Radiation Therapy Across Multiple Centers: Methodology, Timeliness, and Cost of the Audiovisual-Assisted Therapeutic Ambience in Radiation Therapy Prospective Multi-Institutional Trial

PurposeThe Audiovisual Assisted Therapeutic Ambience in Radiotherapy (AVATAR) trial was a prospective multicenter study (NCTXXXXXXXX) examining the combination of video immersion with radiotherapy and was successfully conducted through the collaboration of pediatric radiation oncology teams at 10 institutions independent of any preexisting consortium. We sought to analyze and report the methodology of trial conception and development, process map, and cost. Methods and MaterialsThe study enrolled patients aged 3-10 years preparing to undergo radiation therapy, integrated the combination of AVATAR-based video immersion during radiation therapy at each institution, and offered AVATAR use as an alternative to anesthesia, with rates of anesthesia use and outcomes of serial standardized anxiety and quality of life assessments assessed among the 81 children enrolled. A process map was created based on the trial timeline with the following components: study development time (time from conception of the trial to accrual of first patient, including design phase, agreement and approval phase and site preparation phase), and accrual duration time (time from the first to last accrual). Costs and institutional success rates were calculated. ResultsTime from inception of study to last accrual was 3.6 years (1313 days). The study development time was 417 days (31.7%) and accrual duration time was 896 days (68.3%), with the final 50% of accrual occurring in less than 6 months. Equipment cost was approximately $550 per institution and was covered by funding from the lead study institution. All 10 centers were successful with AVATAR implementation, defined as ≥50% of patients able to avoid anesthesia with use of AVATAR, including centers with both photon and proton therapy. ConclusionsThis report elaborates on the methodology and timeline of trial conception and development using data from a previously published supportive care study combining video immersion with radiotherapy among 10 cooperating pediatric oncology institutions. It highlights the potential for multicenter collaborations on prospective trials integrating supportive care therapies with radiotherapy.

(Invited) Parameter Prediction in Colloidal Quantum Dot Solar Cells Using Residual Neural Networks Trained on Experimental Data

Colloidal quantum dots (CQDs) are an attractive third-generation material for photovoltaics, especially as infrared materials for multi-junction solar cells. Characterization and materials parameter prediction both play a critical role in the rising efficiencies in this field. However, there are numerous materials properties that need to be measured in order to fully characterize a device, leading to high research costs and slow development times. Here, we leverage recent advancements in machine learning (ML), along with a recently-demonstrated multimodal spectral characterization method [1], to predict complex materials parameters in CQD solar cells from simple current density-voltage (JV) curves, using an algorithm trained on experimental data.[2]Past work ML prediction of materials properties has focused on using simulated data due to the difficulty in collecting enough experimental data to fully train an algorithm. While it is not feasible to fabricate thousands of devices in a typical research lab, it is possible to measure several thousand points on a single device. We used a micrometer-resolution 2D characterization method with millimeter-scale field of view to acquire enough training data using only a handful of devices, while learning the spatial relationships between materials parameters. Photoluminescence, trap state density, transient photovoltage, transient photocurrent, and carrier mobility were predicted (Figure 1) by five different residual neural networks, each using a ResNet block and containing over 300k learnable parameters. The design of the networks were based on the ResNet50 architecture [3]. We also implemented a novel neighborhood approach to reduce errors and account for spatial correlations in device behavior, leading to insights into transport mechanisms and correlation lengths in CQD thin films. This method could be used to study a wide range of material systems and devices, ultimately leading to a universal prediction model that greatly simplifies the characterization process for optoelectronic devices and accelerates development times.

Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

This paper presents advanced workflows that combine 3D X-ray microscopy (XRM), nanoscale tomography, and deep learning (DL) to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques, such as the integration of DL-based algorithms for 3D image reconstruction to improve scan quality through increased contrast and denoising, are also discussed in this article. In addition, a DL-based tool called DeepScout is presented. DeepScout uses 3D XRM scans in targeted regions of interest as training data for upscaling high-resolution in a low-resolution dataset, of a wider field of view, using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and they will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and machine learning—perhaps complemented with some additional correlative microscopy investigations—can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies.

Effects of injudicious use of spirotetramat on Encarsia formosa's ability to control Bemisia tabaci

The excessive and frequent use of insecticides has led to serious problems with insecticide residues, impacting nontarget organisms such as the parasitoid Encarsia formosa. This study examined the growth, development, and enzyme activity of E. formosa exposed to spirotetramat at LC10, LC30, and LC50. The regression equation for the toxicity of spirotetramat toward E. formosa was Y = 5.25X–11.07. After exposure to spirotetramat, the survival rates of E. formosa sharply decreased, which occurred earlier than those in the control batch. Although the maximum daily parasitism quantity of E. formosa increased and the average parasitism number, enumerated from the 1st to the 5th day, was 53.97 after being exposed to spirotetramat at LC10, the life span of its F1 generation adults was only 8.47 days, which was significantly shorter than that in the control batch. After being exposed to spirotetramat at LC50, the average parasitism number of E. formosa was 63.30, and the developmental time of its F1 generation, enumerated from the 1st to the 5th day after exposure to spirotetramat, was significantly longer than that of the control batch. The activities of mixed function oxidase, acetylcholinesterase, carboxylesterase, and catalase increased significantly, and the rate of increase in enzyme activity was directly proportional to the increase in the concentration of spirotetramat. These results revealed that the parasitic ability of E. formosa decreased after exposure to spirotetramat at LC10, LC30, and LC50. This leads to a change in parasitoid control of pests, revealing the potential environmental threat of insecticide residues to nontarget organisms.

Exploration of Materials for Three-Dimensional NMR Microcoil Production via CNC Micromilling and Laser Etching.

The excellent versatility of 5-axis computer numerical control (CNC) micromilling has led to its application for prototyping NMR microcoils tailored to mass-limited samples (reducing development time and cost). However, vibrations during 5-axis milling can hinder the creation of complex 3D volume microcoils (i.e., solenoids and saddle coils). To address these limitations, a high-resolution NSCNC ELARA 4-axis milling machine was developed with the extra precision required for making complex 3D volume microcoils. Upon investigating the performance of resonators made with various copper-coated dielectrics, resonators with poly(methyl methacrylate) (PMMA) provided the best SNR/line shape. Thus, complex 1.7 mm microcoil designs were machined from Cu-coated PMMA. A milled 6.4 mm solenoid also provided 6.6× the total carbon signal for a 13C-labeled broccoli seed compared to a commercial inverse 5 mm NMR probe (demonstrating potential for larger coil designs). However, the manufacture of coils <1.7 mm with copper-coated PMMA rods was challenging as ∼0.5 mm of remaining PMMA was needed to retain their structural integrity. To manufacture smaller microcoils, both a solenoid and saddle coil (both with 1 mm O.D., 0.1 mm thick walls) were etched from Cu-coated glass capillaries using a UV picosecond laser that was mounted onto an NSCNC 5-axis MiRA7L. Both resonators showed excellent signal and identified a wide range of metabolites in a 13C-labeled algae extract, while the solenoid was further tested on two copepod egg sacs (∼4 μg of total sample). In summary, the flexibility to prototype complex microcoils in-house allows laboratories to tailor microcoils to specific mass-limited samples while avoiding the costs of cleanrooms.

Widespread changes in gene expression accompany body size evolution in nematodes.

Body size is a fundamental trait that drives multiple evolutionary and ecological patterns. Caenorhabditis inopinata is a fig-associated nematode that is exceptionally large relative to other members of the genus, including Caenorhabditis elegans. We previously showed that C. inopinata is large primarily due to postembryonic cell size expansion that occurs during the larval-to-adult transition. Here, we describe gene expression patterns in C. elegans and C. inopinata throughout this developmental period to understand the transcriptional basis of body size change. We performed RNA-seq in both species across the L3, L4, and adult stages. Most genes are differentially expressed across all developmental stages, consistent with C. inopinata's divergent ecology and morphology. We also used a model comparison approach to identify orthologues with divergent dynamics across this developmental period between the 2 species. This included genes connected to neurons, behavior, stress response, developmental timing, and small RNA/chromatin regulation. Multiple hypodermal collagens were also observed to harbor divergent developmental dynamics across this period, and genes important for molting and body morphology were also detected. Genes associated with transforming growth factor β signaling revealed idiosyncratic and unexpected transcriptional patterns given their role in body size regulation in C. elegans. This widespread transcriptional divergence between these species is unexpected and maybe a signature of the ecological and morphological divergence of C. inopinata. Alternatively, transcriptional turnover may be the rule in the Caenorhabditis genus, indicative of widespread developmental system drift among species. This work lays the foundation for future functional genetic studies interrogating the bases of body size evolution in this group.

Evaluation and comparison of nine growth and development-based measures of pubertal timing

BackgroundPubertal timing is heritable, varies between individuals, and has implications for life-course health. There are many different indicators of pubertal timing, and how they relate to each other is unclear. Our aim was to quantitatively compare nine indicators of pubertal timing.MethodsWe used data from questionnaires and height, weight, and bone measurements from ages 7–17 y in a population-based cohort of 4267 females and 4251 males to compare nine growth and development-based indicators of pubertal timing. We summarise age of each indicator, their phenotypic and genetic correlations, and how they relate to established genetic risk score (GRS) for puberty timing, and phenotypic childhood body composition measures.ResultsWe show that pubic hair in males (mean: 12.6 y) and breasts in females (11.5 y) are early indicators of puberty, and voice breaking (14.2 y) and menarche (12.7 y) are late indicators however, there is substantial variation between individuals in pubertal age. All indicators show evidence of positive phenotypic intercorrelations (e.g., r = 0.49: male genitalia and pubic hair ages), and positive genetic intercorrelations. An age at menarche GRS positively associates with all other pubertal age indicators (e.g., difference in female age at peak height velocity per SD higher GRS: 0.24 y, 95%CI: 0.21 to 0.26), as does an age at voice breaking GRS (e.g., difference in age at male axillary hair: 0.11 y, 0.07 to 0.15). Higher childhood fat mass and lean mass associated with earlier puberty timing.ConclusionsOur findings provide insights into the measurements of the timing of pubertal growth and development and illustrate value of various pubertal timing indicators in life-course research.

Integrating E-learning into Medical Education – Phase-I of MBBS Programme in Bangladesh: Prospects and Challenges for the Students, Instructors and Policy Makers

Over the past few decades, there has been a shift in medical education from the traditional didactic model of instruction to other modalities like online or electronic learning (e-learning). E-learning is being increasingly utilized in medical education across the globe through adaptive tutorials, audiovisual clips, simulation, and virtual models/atlas. This review aims to identify the prospects and challenges of integration of e-learning into Phase-I of MBBS Programme in Bangladesh and discuss solutions from medical educators’ perspectives. Elearning has been demonstrated to be as effective as conventional teaching and learning. It allows more personalized, self-directed learning experience for the medical students. Instructors can evaluate competencies objectively through online assessments, provide personalized feedback for self-improvement to the students. For policy makers, e-learning could hold the key to success in delivering quality medical education even in those medical colleges established in the remote semi-urban areas addressing the nation’s most entrenched medical teacher vacancies especially in basic subject areas like anatomy, physiology, and biochemistry. Challenges that affect the development and implementation of e-learning in medical education include lack of time and efforts, low technical skills, resource-poor infrastructure, lack of institutional strategies and support and stakeholders' negative attitude towards e-learning. Solutions to those problems may include training for professional development of the faculties with improvement of technical skills, incentives for the time and efforts in development and delivery of online content, favorable digital and tech-friendly institutional strategies and growing positive attitude towards online medical education, and above all, political commitment to ensure quality medical education. CBMJ 2024 July: vol. 13 no. 02 P: 290-297

Genome-Wide Association Study and transcriptome analysis reveals a complex gene network that regulates opsin gene expression and cell fate determination in Drosophila R7 photoreceptor cells.

An animal's ability to discriminate between differing wavelengths of light (i.e., color vision) is mediated, in part, by a subset of photoreceptor cells that express opsins with distinct absorption spectra. In Drosophila R7 photoreceptors, expression of the rhodopsin molecules, Rh3 or Rh4, is determined by a stochastic process mediated by the transcription factor spineless. The goal of this study was to identify additional factors that regulate R7 cell fate and opsin choice using a Genome Wide Association Study (GWAS) paired with transcriptome analysis via RNA-Seq. We examined Rh3 and Rh4 expression in a subset of fully-sequenced inbred strains from the Drosophila Genetic Reference Panel and performed a GWAS to identify 42 naturally-occurring polymorphisms-in proximity to 28 candidate genes-that significantly influence R7 opsin expression. Network analysis revealed multiple potential interactions between the associated candidate genes, spineless and its partners. GWAS candidates were further validated in a secondary RNAi screen which identified 12 lines that significantly reduce the proportion of Rh3 expressing R7 photoreceptors. Finally, using RNA-Seq, we demonstrated that all but four of the GWAS candidates are expressed in the pupal retina at a critical developmental time point and that five are among the 917 differentially expressed genes in sevenless mutants, which lack R7 cells. Collectively, these results suggest that the relatively simple, binary cell fate decision underlying R7 opsin expression is modulated by a larger, more complex network of regulatory factors. Of particular interest are a subset of candidate genes with previously characterized neuronal functions including neurogenesis, neurodegeneration, photoreceptor development, axon growth and guidance, synaptogenesis, and synaptic function.