Genome-wide analysis to uncover how Pocillopora acuta survives the challenging intertidal environment.

Characterisation of genomic variation among corals can help uncover variants underlying trait differences and contribute towards genotype prioritisation in coastal restoration projects. For example, there is growing interest in identifying resilient genotypes for transplantation, and to better understand the genetic processes that allow some individuals to survive in specific conditions better than others. The coral species Pocillopora acuta is known to survive in a wide range of habitats, from reefs artificial coastal defences, suggesting its potential use as a starter species for ecological engineering efforts involving coral transplantation onto intertidal seawalls. However, the intertidal section of coastal armour is a challenging environment for corals, with conditions during periods of emersion being particularly stressful. Here, we scanned the entire genome of P. acuta corals to identify the regions harbouring single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) that separate intertidal colonies (n = 18) from those found in subtidal areas (n = 21). Findings revealed 74,391 high quality SNPs distributed across 386 regions of the P. acuta genome. While the majority of the detected SNPs were in non-coding regions, 12% were identified in exons (i.e. coding regions). Functional SNPs that were significantly associated with intertidal colonies were found in overrepresented genomic regions linked to cellular homeostasis, metabolism, and signalling processes, which may represent local environmental adaptation in the intertidal. Interestingly, regions that exhibited CNVs were also associated with metabolic and signalling processes, suggesting P. acuta corals living in the intertidal have a high capacity to perform biological functions critical for survival in extreme environments.

On the crashworthiness analysis of bio-inspired DNA tubes.

This study presents a thorough numerical evaluation of the crashworthiness properties of a new bio-inspired DNA tubes (BIDNATs) with circular, elliptical, and rectangular cross-sections. Deformation and crashworthiness behaviors are evaluated using axial quasi-static crushing simulations by ABAQUS/Explicit (Abaqus 6.14, https://www.3ds.com/products-services/simulia/products/abaqus/ ). The study compares the performance of conventional tubes with rectangular and elliptical cross-sections to DNA-inspired tubes. Increasing the rotation angle leads to more helices and a pronounced helix angle, resulting in lower initial peak force (IPF). However, lower cross-section aspect ratios generally have higher IPF and specific energy absorption (SEA) values. BIDNATs with rectangular cross-sections and a 540° rotation angle have the lowest SEA and IPF values across all aspect ratios. Notably, for the 110/100 aspect ratio, the SEA of E110/100 is 71% higher than the conventional tube. Overall, BIDNATs with elliptical cross-sections and a 360° rotation angle exhibit higher SEA values and lower IPF values, particularly for a width (W) of 100 mm. Conventional circular and elliptical tubes generally have SEA values exceeding 6 J/g, with only E110/100 surpassing this among DNA-inspired tubes. The NE110/100 tube has the highest SEA, surpassing E110/100 by 54%, while its IPF is 10% greater than DNA-inspired E110/100. It's worth noting that conventional circular and elliptical tubes have higher IPF values compared to their DNA-inspired counterparts. These findings offer valuable insights for engineers and researchers in the design of crash tubes to improve overall vehicle safety for both occupants and pedestrians.

Microbial chassis design and engineering for production of gamma-aminobutyric acid.

Gamma-aminobutyric acid (GABA) is a non-protein amino acid which is widely applied in agriculture and pharmaceutical additive industries. GABA is synthesized from glutamate through irreversible α-decarboxylation by glutamate decarboxylase. Recently, microbial synthesis has become an inevitable trend to produce GABA due to its sustainable characteristics. Therefore, reasonable microbial platform design and metabolic engineering strategies for improving production of GABA are arousing a considerable attraction. The strategies concentrate on microbial platform optimization, fermentation process optimization, rational metabolic engineering as key metabolic pathway modification, promoter optimization, site-directed mutagenesis, modular transporter engineering, and dynamic switch systems application. In this review, the microbial producers for GABA were summarized, including lactic acid bacteria, Corynebacterium glutamicum, and Escherichia coli, as well as the efficient strategies for optimizing them to improve the production of GABA.

Problem choice and decision trees in science and engineering.

Scientists and engineers often spend days choosing a problem and years solving it. This imbalance limits impact. Here, we offer a framework for problem choice: prompts for ideation, guidelines for evaluating impact and likelihood of success, the importance of fixing one parameter at a time, and opportunities afforded by failure.

A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors.

Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario.

Innovative Metaheuristic Optimization Approach with a Bi-Triad for Rehabilitation Exoskeletons.

The present work proposes a comprehensive metaheuristic methodology for the development of a medical robot for the upper limb rehabilitation, which includes the topological optimization of the device, kinematic models (5 DOF), human-robot interface, control and experimental tests. This methodology applies two cutting-edge triads: (1) the three points of view in engineering design (client, designer and community) and (2) the triad formed by three pillars of Industry 4.0 (autonomous machines and systems, additive manufacturing and simulation of virtual environments). By applying the proposed procedure, a robotic mechanism was obtained with a reduction of more than 40% of its initial weight and a human-robot interface with three modes of operation and a biomechanically viable kinematic model for humans. The digital twin instance and its evaluation through therapeutic routines with and without disturbances was assessed; the average RMSEs obtained were 0.08 rad and 0.11 rad, respectively. The proposed methodology is applicable to any medical robot, providing a versatile and effective solution for optimizing the design and development of healthcare devices. It adopts an innovative and scalable approach to enhance their processes.

Optimising HIV drug resistance testing laboratory networks in Kenya: insights from systems engineering modelling.

BACKGROUND: HIV drug resistance (DR) is a growing threat to the durability of current and future HIV treatment success. DR testing (DRT) technologies are very expensive and specialised, relying on centralised laboratories in most low and middle-income countries. Modelling for laboratory network with point-of-care (POC) DRT assays to minimise turnaround time (TAT), is urgently needed to meet the growing demand. METHODS: We developed a model with user-friendly interface using integer programming and queueing theory to improve the DRT system in Kisumu County, Kenya. We estimated DRT demand based on both current and idealised scenarios and evaluated a centralised laboratory-only network and an optimised POC DRT network. A one-way sensitivity analysis of key user inputs was conducted. RESULTS: In a centralised laboratory-only network, the mean TAT ranged from 8.52 to 8.55 working days, and the system could not handle a demand proportion exceeding 1.6%. In contrast, the mean TAT for POC DRT network ranged from 1.13 to 2.11 working days, with demand proportion up to 4.8%. Sensitivity analyses showed that expanding DRT hubs reduces mean TAT substantially while increasing the processing rate at national labs had minimal effect. For instance, doubling the current service rate at national labs reduced the mean TAT by only 0.0%-1.9% in various tested scenarios, whereas doubling the current service rate at DRT hubs reduced the mean TAT by 37.5%-49.8%. In addition, faster batching modes and transportation were important factors influencing the mean TAT. CONCLUSIONS: Our model offers decision-makers an informed framework for improving the DRT system using POC in Kenya. POC DRT networks substantially reduce mean TAT and can handle a higher demand proportion than a centralised laboratory-only network, especially for children and pregnant women living with HIV, where there is an immediate push to use DRT results for patient case management.

Program Evaluation's Path to Greater Policy Relevance: Learning From Rossi's Iron Laws.

In a 1987 article, Peter R. Rossi promulgated "The Iron Law of Evaluation and Other Metallic Rules." The Metallic Laws were meant as an informal (and humorous) overstatement of the weakness of contemporary evaluations of social programs. Rossi' s underlying worry was not so much about the state of evaluation technology in the abstract, but, rather, in its inability to advance our broad understanding of social problems and what to do about them---in other words, to make evaluation policy relevant. Rossi attributed the continuing failure to develop successful "large-scale social programs" to the failure to build a strong knowledge base for this kind of "social engineering." The qualities of studies that enable such accumulated learning are variously labeled "external validity," "generalizability," "applicability," or "transferability." This Special Issue includes five papers that seek to explore and apply this understanding.

The relations between growth mindset, motivational beliefs, and career interest in math intensive fields in informal STEM youth programs.

Past research has shown that growth mindset and motivational beliefs have an important role in math and science career interest in adolescence. Drawing on situated expectancy-value theory (SEVT), this study extends these findings by investigating the role of parental motivational beliefs (e.g., expectancy beliefs, utility values) and parent growth mindset in math on adolescent career interest in math-intensive fields (e.g., mathematics, computer science, statistics, and engineering; MCSE) through adolescent motivational beliefs in math. Structural equation modeling was used to test the hypothesized model using data from 290 adolescents (201 girls, 69.3%; Mage = 15.20), who participate in informal STEM (science, technology, engineering, mathematics) youth programs, and their parents (162 parents, 87.7% female) in the United Kingdom and the United States. As hypothesized, adolescent expectancy beliefs, utility values, and growth mindset in math had a significant direct effect on MCSE career interest. Further, there was a significant indirect effect of parental expectancy beliefs in math on MCSE career interest through adolescents' expectancy beliefs. Similarly, there was a significant indirect effect from parental utility values in math to MCSE career interest through adolescents' utility values. The findings suggest that parents' math motivational beliefs play a critical role in adolescent math motivational beliefs and their career interest in math-intensive fields.

Engineering surface state density of monolayer CVD grown 2D MoS2 for enhanced photodetector performance.

This study demonstrates the effect of nitrogen doping on the surface state densities (Nss) of monolayer MoS2 and its effect on the responsivity and the response time of the photodetector. Our experimental results shows that by doping monolayer MoS2 by nitrogen, the surface state (Nss) increases thereby increasing responsivity. The mathematical model included in the paper supports the relation of photocurrent gain and its dependency on trap level which states that the increasing the trap density increases the photocurrent gain and the same is observed experimentally. The experimental results at room temperature revealed that nitrogen doped MoS2 have a high NSS of 1.63 X 1013 states/m2/eV compared to undoped MoS2 of 4.2 x 1012 states/m2/eV. The increase in Nss in turn is the cause for rise in trap states which eventually increases the value of photo responsivity from 65.12 A/W (undoped MoS2) to 606.3 A/W (nitrogen doped MoS2). The response time calculated for undoped MoS2 was 0.85 sec and for doped MoS2 was 0.35 sec. Finally, to verify the dependence of surface states on the responsivity, the surface states were varied by varying temperature and it was observed that upon increment in temperature, the surface states decreases which causes the responsivity values also to decrease.

How syllabi relate to outcomes in higher education: A study of syllabi learner-centeredness and grade inequities in STEM.

Fostering equity in undergraduate science, technology, engineering, and mathematics (STEM) programs can be accomplished by incorporating learner-centered pedagogies, resulting in the closing of opportunity gaps (defined here as the difference in grades earned by minoritized and non-minoritized students). We assessed STEM courses that exhibit small and large opportunity gaps at a minority-serving, research-intensive university, and evaluated the degree to which their syllabi are learner-centered, according to a previously validated rubric. We specifically chose syllabi as they are often the first interaction students have with a course, establish expectations for course policies and practices, and serve as a proxy for the course environment. We found STEM courses with more learner-centered syllabi had smaller opportunity gaps. The syllabus rubric factor that most correlated with smaller gaps was Power and Control, which reflects Student's Role, Outside Resources, and Syllabus Focus. This work highlights the importance of course syllabi as a tool for instructors to create more inclusive classroom environments.

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered. OBJECTIVE: We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test-related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches. METHODS: We collected laboratory test result-related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects. RESULTS: Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4-generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one's medical context, incorrect statements, and lack of references. CONCLUSIONS: By evaluating LLMs in generating responses to patients' laboratory test result-related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4's responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

[18F]-Radiolabelled Nanoplatforms: A Critical Review of Their Intrinsic Characteristics, Radiolabelling Methods, and Purification Techniques.

A wide range of nano-objects is found in many applications of our everyday life. Recognition of their peculiar properties and ease of functionalization has prompted their engineering into multifunctional platforms that are supposed to afford efficient tools for the development of biomedical applications. However, bridging the gap between bench to bedside cannot be expected without a good knowledge of their behaviour in vivo, which can be obtained through non-invasive imaging techniques, such as positron emission tomography (PET). Their radiolabelling with [18F]-fluorine, a technique already well established and widely used routinely for PET imaging, with [18F]-FDG for example, and in preclinical investigation using [18F]-radiolabelled biological macromolecules, has, therefore, been developed. In this context, this review highlights the various nano-objects studied so far, the reasons behind their radiolabelling, and main in vitro and/or in vivo results obtained thereof. Then, the methods developed to introduce the radioelement are presented. Detailed indications on the chemical steps involved are provided, and the stability of the radiolabelling is discussed. Emphasis is then made on the techniques used to purify and analyse the radiolabelled nano-objects, a point that is rarely discussed despite its technical relevance and importance for accurate imaging. The pros and cons of the different methods developed are finally discussed from which future work can develop.

Calculation of additional load and deformation of the receiving well enclosure structure caused by shield tunneling.

The bulkhead additional thrust during shield tunneling, the force of friction between shield and soil, and the additional grouting pressure can cause additional stress in the surrounding soil, thereby disturbing existing buildings and structures. However, few studies focused on the disturbance situation when the shield tunneling machine approaches the receiving well. If the additional stress and deformation of the receiving well are too excessive, it could result in the collapse of the receiving well. Based on the two-stage method, this study derived the calculation formula of the additional stress and deformation of the receiving well enclosure structure caused by shield tunneling. Taking a shield machine receiving engineering as the context, this study established a numerical simulation model and compared theoretical calculation, the results of numerical simulation model and on-site monitoring data. Finally, the additional stress of the receiving well is analyzed. The research findings demonstrate that the theoretical prediction results, numerical simulation calculation results, and on-site monitoring data exhibit relatively small calculation errors, which validated the applicability of the theoretical prediction formula and numerical simulation model. As the distance between the shield machine and the receiving well decreases, the disturbance to the receiving well increases sharply. When the distance between the cutter head and the receiving well is less than three times the shield length, it is crucial to enhance the deformation monitoring of the receiving well. The primary factors affecting the additional load and deformation of the receiving well enclosure structure are the force of friction between shield and soil and the additional thrust of the cutterhead. The disturbance caused by the additional grouting pressure on the enclosure structure can be ignored.

CTF-former: A novel simplified multi-task learning strategy for simultaneous multivariate chaotic time series prediction.

Multivariate chaotic time series prediction is a challenging task, especially when multiple variables are predicted simultaneously. For multiple related prediction tasks typically require multiple models, however, multiple models are difficult to keep synchronization, making immediate communication between predicted values challenging. Although multi-task learning can be applied to this problem, the principles of allocation and layout options between shared and specific representations are ambiguous. To address this issue, a novel simplified multi-task learning method was proposed for the precise implementation of simultaneous multiple chaotic time series prediction tasks. The scheme proposed consists of a cross-convolution operator designed to capture variable correlations and sequence correlations, and an attention module proposed to capture the information embedded in the sequence structure. In the attention module, a non-linear transformation was implemented with convolution, and its local receptive field and the global dependency of the attention mechanism achieve complementarity. In addition, an attention weight calculation was devised that takes into account not only the synergy of time and frequency domain features, but also the fusion of series and channel information. Notably the scheme proposed a purely simplified design principle of multi-task learning by reducing the specific network to single neuron. The precision of the proposed solution and its potential for engineering applications were verified with the Lorenz system and power consumption. The mean absolute error of the proposed method was reduced by an average of 82.9% in the Lorenz system and 19.83% in power consumption compared to the Gated Recurrent Unit.

PHEIGES: all-cell-free phage synthesis and selection from engineered genomes.

Bacteriophages constitute an invaluable biological reservoir for biotechnology and medicine. The ability to exploit such vast resources is hampered by the lack of methods to rapidly engineer, assemble, package genomes, and select phages. Cell-free transcription-translation (TXTL) offers experimental settings to address such a limitation. Here, we describe PHage Engineering by In vitro Gene Expression and Selection (PHEIGES) using T7 phage genome and Escherichia coli TXTL. Phage genomes are assembled in vitro from PCR-amplified fragments and directly expressed in batch TXTL reactions to produce up to 1011 PFU/ml engineered phages within one day. We further demonstrate a significant genotype-phenotype linkage of phage assembly in bulk TXTL. This enables rapid selection of phages with altered rough lipopolysaccharides specificity from phage genomes incorporating tail fiber mutant libraries. We establish the scalability of PHEIGES by one pot assembly of such mutants with fluorescent gene integration and 10% length-reduced genome.

What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors.

The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.

Spatial engineering of single-atom Fe adjacent to Cu-assisted nanozymes for biomimetic O2 activation.

The precise design of single-atom nanozymes (SAzymes) and understanding of their biocatalytic mechanisms hold great promise for developing ideal bio-enzyme substitutes. While considerable efforts have been directed towards mimicking partial bio-inspired structures, the integration of heterogeneous SAzymes configurations and homogeneous enzyme-like mechanism remains an enormous challenge. Here, we show a spatial engineering strategy to fabricate dual-sites SAzymes with atomic Fe active center and adjacent Cu sites. Compared to planar Fe-Cu dual-atomic sites, vertically stacked Fe-Cu geometry in FePc@2D-Cu-N-C possesses highly optimized scaffolds, favorable substrate affinity, and fast electron transfer. These characteristics of FePc@2D-Cu-N-C SAzyme induces biomimetic O2 activation through homogenous enzymatic pathway, resembling functional and mechanistic similarity to natural cytochrome c oxidase. Furthermore, it presents an appealing alternative of cytochrome P450 3A4 for drug metabolism and drug-drug interaction. These findings are expected to deepen the fundamental understanding of atomic-level design in next-generation bio-inspired nanozymes.

Comparative analysis of gender disparity in academic positions based on U.S. region and STEM discipline.

Despite a move toward gender parity in the United States (U.S.) workforce, a large gender gap persists in the fields of science, technology, engineering, and mathematics (STEM); this is particularly true for academic (i.e., instructor and tenure track) STEM positions. This gap increases as women advance through the traditional steps of academia, with the highest degree of gender disparity in tenured positions. As policies, politics, and culture, which all contribute to gender equity across the world, vary across regions in the United States, we expect that the gender gap in STEM might also vary across geographic regions. Here, we evaluated over 20,000 instructor and tenure track positions in university STEM departments across the U.S. to evaluate whether and how the geographic region of a university might determine its proportion of women in STEM academic positions. Similar to previous research, regardless of geographic region, more men were employed in both tenure track and instructor positions across STEM fields. However, variation existed regionally within the U.S., with the Mountain region employing the lowest proportion of women in tenure track positions and the East North Central and Pacific regions employing the greatest proportion. We expect this regional variation could be caused by differences in state and local policies, regional representation, and mentorship, resulting in inconsistent support for women, leading to differences in work environments, hiring, and job retention rates across the country. A better understanding of which geographic areas within the U.S. have more equal distributions of women in the STEM field will help us to identify the specific mechanisms that facilitate more equal and inclusive opportunities for women and other underrepresented groups across all levels of STEM academia.

Precursory elements of safety culture: Exploratory analyses of engineering students' safety attitudes.

INTRODUCTION: Safety culture as a concept has been well-researched in the literature. There is less work, however, on how individuals entering the workforce acquire and partake in safety culture over time and how they might be primed to partake in the positive elements of safety culture. METHOD: We begin this exploration by surveying engineering students' attitudes toward safety and experiences with safety education at the Georgia Institute of Technology (n = 316). RESULTS: We find disparities among engineering disciplines, where some majors have more negative views toward safety than others. This may point to the need for more [effective] safety education to prevent negative attitudes toward safety in the workplace. In addition to describing trends among engineering students' attitudes, this study also uses factor analysis to characterize the latent constructs of precursory safety culture: the safety-related attitudes that may direct how people engage with safety culture as early-career engineers. PRACTICAL APPLICATIONS: The analysis provides a conceptual construction of precursor safety culture attitudes, which can serve as a guide to future measurement efforts.