Biofoundry-Assisted Golden Gate Cloning with AssemblyTron.

Golden Gate assembly is a requisite method in synthetic biology that facilitates critical conventions such as genetic part abstraction and rapid prototyping. However, compared to robotic implementation, manual Golden Gate implementation is cumbersome, error-prone, and inconsistent for complex assembly designs. AssemblyTron is an open-source python package that provides an affordable automation solution using open-source OpenTrons OT-2 lab robots. Automating Golden Gate assembly with AssemblyTron can reduce failure-rate, resource consumption, and training requirements for building complex DNA constructs, as well as indexed and combinatorial libraries. Here, we dissect a panel of upgrades to AssemblyTron's Golden Gate assembly capabilities, which include Golden Gate assembly into modular cloning part vectors, error-prone polymerase chain reaction (PCR) combinatorial mutant library assembly, and modular cloning indexed plasmid library assembly. These upgrades enable a broad pool of users with varying levels of experience to readily implement advanced Golden Gate applications using low-cost, open-source lab robotics.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

On the move: Influence of animal movements on count error during drone surveys.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost- and time-efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone-based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent-based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A  awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems.

Staphylococcus aureus SOS response: Activation, impact, and drug targets.

Staphylococcus aureus is a common cause of diverse infections, ranging from superficial to invasive, affecting both humans and animals. The widespread use of antibiotics in clinical treatments has led to the emergence of antibiotic-resistant strains and small colony variants. This surge presents a significant challenge in eliminating infections and undermines the efficacy of available treatments. The bacterial Save Our Souls (SOS) response, triggered by genotoxic stressors, encompasses host immune defenses and antibiotics, playing a crucial role in bacterial survival, invasiveness, virulence, and drug resistance. Accumulating evidence underscores the pivotal role of the SOS response system in the pathogenicity of S. aureus. Inhibiting this system offers a promising approach for effective bactericidal treatments and curbing the evolution of antimicrobial resistance. Here, we provide a comprehensive review of the activation, impact, and key proteins associated with the SOS response in S. aureus. Additionally, perspectives on therapeutic strategies targeting the SOS response for S. aureus, both individually and in combination with traditional antibiotics are proposed.

Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces.

Digital technology serves as a new industrial driving force, playing a crucial role in promoting the efficiency of regional tourism. To explore the inherent logic between digital technology and tourism development, the study employed the DEA-BCC model and the entropy-weighted TOPSIS method to measure the tourism efficiency and digital technology development level of 31 provinces (municipalities and districts) in China from 2011 to 2022. The evolutionary characteristics of the relationship between digital technology and tourism efficiency were analyzed from the spatio-temporal dimension, and empirical tests were carried out using the spatial error model (SEM) and the spatial lag model (SLM) to explore the mechanism of the impact of digital technology on tourism efficiency. The results show that: during the study period, the overall trend of digital technology development and tourism efficiency is upward, but there is a certain degree of spatial mismatch between the two; digital technology is not only conducive to the improvement of tourism efficiency in the province, but also acts on neighboring provinces through spatial spillover effects, mechanism of action tests show that digital technology can positively moderate the effects of tourism economic growth and tourism industry structure on tourism efficiency; in western and northeast China, the positive effect of digital technology on tourism efficiency is more obvious. The conclusions provide a new perspective for understanding and analyzing the development of provincial tourism in China, as well as a reference for the rational use of digital technology to promote tourism development.

Impact of Video-Based Error Correction Learning for Cardiopulmonary Resuscitation Training: Quasi-Experimental Study.

Background: Video-based error correction (VBEC) in medical education could offer immediate feedback, promote enhanced learning retention, and foster reflective practice. However, its application in cardiopulmonary resuscitation (CPR) training has not been investigated. Objective: The objective of this study is to assess whether the VBEC procedure could improve the training performance of CPR among anesthesiology residents. Methods: A quasi-experimental study was conducted among anesthesiology residents between December 2022 and April 2023. Primary outcomes included a posttraining knowledge test and practical assessment scores. Secondary outcomes included the number of residents who correctly conducted CPR at each step, the rate of common mistakes during the CPR process, and the self-assessment results. A total of 80 anesthesiology residents were divided into a VBEC group (n=40) and a control group (n=40). The VBEC group underwent a 15-minute VBEC CPR training, whereas the control group underwent a 15-minute video-prompting CPR training. Results: The posttraining knowledge test score of the VBEC group was significantly higher than that of the control group (73, SD 10.5 vs 65.1, SD 11.4; P=.002). The residents in the VBEC group had lower error rates in "failure to anticipate the next move" (n=3, 7.5% vs n=13, 32.5%; P=.01) and "failure to debrief or problem solve after the code" (n=2, 5% vs n=11, 27.5%; P=.01), as well as better performance in the "secure own safety" step (n=34, 85% vs n=18, 45%; P<.001) than those in the control group. The VBEC group showed significantly higher confidence in CPR than the control group (n=?, 62.5% vs n=?, 35%; P=.03). Conclusions: VBEC may be a promising strategy compared to video prompting for CPR training among anesthesiology residents.

Mice monitor their timing errors.

Animals often engage in representationally guided goal-directed behaviors. These behaviors are thus also subjected to representational uncertainty (e.g. timing uncertainty during waiting), which has been previously shown to adaptively guide behaviors normatively. These observations raise the question of whether non-human animals can track the direction and magnitude of their timing errors (i.e. temporal error monitoring). Only a few studies have investigated this question without addressing the key components of temporal error monitoring (e.g. due to differential reinforcement of metacognitive judgments and primary task representation). We conducted the critical test of temporal error monitoring in mice by developing a novel behavioral task that involved temporal production that exponentially favored temporal accuracy and minimized the contribution of sensorimotor noise. The response rate for an upcoming probabilistic reward following the timing performance was used as a proxy for confidence. We found that mice exhibited high reward expectancy after accurate and low reward expectancy after inaccurate timing performance. The reward expectancy decreased as a function of deviations from the target interval for the short and long reproductions; pointing to the symmetrical sensitivity of metacognition to shorter/longer than target responses. These findings suggest a complete temporal error monitoring ability for mice with human-like metacognitive features.

An innovative subdivision collocation algorithm for heat conduction equation with non-uniform thermal diffusivity.

This paper presents a subdivision collocation algorithm for numerically solving the heat conduction equation with non-uniform thermal diffusivity, considering both initial and boundary conditions. The algorithm involves transforming the differential form of the heat conduction equation into a system of equations and discretizing the time variable using the finite difference formula. The numerical solution of the system of heat conduction equations is then obtained. The feasibility of the algorithm is verified through theoretical and numerical analyses. Additionally, numerical and graphical representations of the obtained numerical solutions are provided, along with a comparison to existing methods. The results demonstrate that our proposed method outperforms the existing methods in terms of accuracy.

Rapid identification of primary atopic disorders (PAD) by a clinical landmark-guided, upfront use of genomic sequencing.

Primary atopic disorders (PAD) are monogenic disorders caused by pathogenic gene variants encoding proteins that are key for the maintenance of a healthy skin barrier and a well-functioning immune system. Physicians face the challenge to find single, extremely rare PAD patients/families among the millions of individuals with common allergic diseases. We describe case scenarios with signature PAD. We review the literature and deduct specific clinical red flags for PAD detection. They include a positive family history and/or signs of pathological susceptibility to infections, immunodysregulation, or syndromic disease. Results of conventional laboratory and most immunological lab studies are not sufficient to make a definitive diagnosis of PAD. In the past, multistep narrowing of differential diagnoses by various immunological and other laboratory tests led to testing of single genes or gene panel analyses, which was a time-consuming and often unsuccessful approach. The implementation of whole-genomic analyses in the routine diagnostics has led to a paradigm shift. Upfront genome-wide analysis by whole genome sequencing (WGS) will shorten the time to diagnosis, save patients from unnecessary investigations, and reduce morbidity and mortality. We propose a rational, clinical landmark-based approach for deciding which cases pass the filter for carrying out early WGS. WGS result interpretation requires a great deal of caution regarding the causal relationship of variants in PAD phenotypes and absence of proof by adequate functional tests. In case of negative WGS results, a re-iteration attitude with re-analyses of the data (using the latest data base annotation)) may eventually lead to PAD diagnosis. PAD, like many other rare genetic diseases, will only be successfully managed, if physicians from different clinical specialties and geneticists interact regularly in multidisciplinary conferences.

Multitask learning to predict successful weaning in critically ill ventilated patients: A retrospective analysis of the MIMIC-IV database.

Objective: Weaning is an essential issue in critical care. This study explores the efficacy of multitask learning models in predicting successful weaning in critically ill ventilated patients using the Medical Information Mart for Intensive Care (MIMIC) IV database. Methods: We employed a multitask learning framework with a shared bottom network to facilitate common knowledge extraction across all tasks. We used the Shapley additive explanations (SHAP) plot and partial dependence plot (PDP) for model explainability. Furthermore, we conducted an error analysis to assess the strength and limitation of the model. Area under receiver operating characteristic curve (AUROC), calibration plot and decision curve analysis were used to determine the performance of the model. Results: A total of 7758 critically ill patients were included in the analyses, and 78.5% of them were successfully weaned. Multitask learning combined with spontaneous breath trial achieved a higher performance to predict successful weaning compared with multitask learning combined with shock and mortality (area under receiver operating characteristic curve, AUROC, 0.820 ± 0.002 vs 0.817 ± 0.001, p < 0.001). We assessed the performance of the model using calibration and decision curve analyses and further interpreted the model through SHAP and PDP plots. The error analysis identified a relatively high error rate among those with low disease severities, including low mean airway pressure and high enteral feeding. Conclusion: We demonstrated that multitask machine learning increased predictive accuracy for successful weaning through combining tasks with a high inter-task relationship. The model explainability and error analysis should enhance trust in the model.

Label refinement network from synthetic error augmentation for medical image segmentation.

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: (1) a model that generates synthetic structural errors, and (2) a label appearance simulation network that produces segmentations with synthetic errors that are similar in appearance to the real initial segmentations. Using these segmentations with synthetic errors and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net, four previous label refinement methods, and a U-Net trained with a loss tailored for tubular structures. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method.

Dealing with imperfect randomization: Inference for the highscope perry preschool program.

This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of characteristics that may be observed or unobserved by the analyst. We develop a partial identification approach to this problem that makes use of information limiting the extent to which randomization is imperfect to show that it is still possible to make nontrivial inferences about the effects of the program in such settings. We consider a family of null hypotheses in which each null hypothesis specifies that the program has no effect on one of several outcomes of interest. Under weak assumptions, we construct a procedure for testing this family of null hypotheses in a way that controls the familywise error rate - the probability of even one false rejection - in finite samples. We develop our methodology in the context of a reanalysis of the HighScope Perry Preschool program. We find statistically significant effects of the program on a number of different outcomes of interest, including outcomes related to criminal activity for males and females, even after accounting for the imperfectness of the randomization and the multiplicity of null hypotheses.

One-Year Results of a Posterior Chamber Toric Phakic Intraocular Lens Implantation in Patients with Keratoconus.

Purpose: To determine clinical and refractive results after the implantation of EyeCryl Phakic Toric intraocular lens in patients with stable keratoconus. Methods: The study included all patients diagnosed with keratoconus who underwent implantation of an EyeCryl Phakic Toric intraocular lens (Biotech Healthcare Holding; Ahmedabad, India) in at least one eye and had a follow-up of at least 12 months. Visual and refractive data were collected for all patients, along with corneal tomography measurements using Pentacam, and vault measurement using optical coherence tomography. This retrospective study was conducted at a high-volume private refractive surgery center in Medellín, Colombia. Results: A total of 83 eyes from 47 patients were included in the study. The majority (71.1%) were female, with a mean age of 31.2 ± 5.1 years. After 12 months of follow-up post-surgery, the spherical equivalent improved significantly from -8.19 ± 4.04 D to -0.06 ± 0.48 D (p < 0.001). Furthermore, 77% of eyes had a post-surgical spherical equivalent within ±0.50 D, while 92% had residual astigmatism ≤0.50 D. Twelve months after surgery, mean manifest astigmatism was -0.28 ± 0.27 D. Uncorrected visual acuity also showed improvement, from 1.11 ± 0.35 LogMAR to 0.14 ± 0.11 LogMAR. Moreover, 52.4% of eyes demonstrated an improvement of at least one line in best-corrected visual acuity. Notably, no intraoperative or postoperative complications were observed in the study population. Conclusion: The implantation of EyeCryl Phakic Toric intraocular lenses represents a highly effective and safe option for correcting refractive errors in patients with a history of keratoconus. Refractive accuracy is excellent, and a significant proportion of patients experienced an improvement in their best-corrected vision by at least one line.

Refractive errors, amblyopia risk factors and vision screening in children aged 7-10 years in Aotearoa New Zealand.

CLINICAL RELEVANCE: Vision screening is important for detecting undiagnosed vision conditions that are common in school-aged children. However, current vision screening protocols are poor at detecting vision conditions that are most common in the Aotearoa New Zealand paediatric population. BACKGROUND: Uncorrected refractive error and amblyopia are the most common causes of visual impairment in children. The most appropriate vision screening method depends on the refractive error profile of the population. This study aimed to: estimate the prevalence of refractive errors and amblyopia risk factors among children living in Aotearoa New Zealand; describe previous participation in preschool vision screening and determine the diagnostic accuracy of potential screening methods. METHODS: Children aged 7-10 years received comprehensive eye examinations, including cycloplegic refraction, in their school. Eye examination results were assessed for refractive error and amblyopia risk factors. The sensitivity and specificity of individual vision tests for detecting any vision conditions was calculated to assess the most effective tests for vision screening. RESULTS: Eye examinations were completed for 237 children and cycloplegic refraction data was available for 220 of these children. Significant refractive error (need for glasses) was detected in 23.6% of children (7.7% myopia, 7.7% hyperopia, 15.0% astigmatism). Amblyopia risk factors were detected in 9.1% of children. Preschool vision screening had been completed by 78.5% of children. Distance visual acuity screening alone had a sensitivity of 39% for detecting vision conditions, with addition of the Spot Vision Screener improving sensitivity to 65%. CONCLUSION: Astigmatism is the most frequent refractive error among children aged 7-10 years living in Aotearoa New Zealand. Distance visual acuity screening alone is ineffective in detecting refractive error in children in Aotearoa New Zealand. Further research investigating refractive errors across the paediatric population in Aotearoa New Zealand is required to determine the optimal timing and appropriate protocols for school-aged vision screening.

Assessment of patient safety culture among healthcare providers in tertiary hospitals in Malaysia-A cross-sectional study.

Background and Aim: Patient safety culture is crucial for every health care institution, as a lack of it may harm patients seeking treatment. The current study aimed to identify the level of safety culture and assess the knowledge, attitude, and perception of patient safety culture among healthcare providers (HCPs') in tertiary hospital settings. Methods: A cross-sectional study was conducted among HCPs from two private tertiary hospitals in Johor and Selangor. A structured validated questionnaire, including the Hospital Survey on Patient Safety Culture, was used to assess the level of patient safety culture in these hospitals. Results: In this study, the calculated sample size was 320, and all 550 eligible participants from both hospitals were approached to participate. However, only 158 responded, resulting in a response rate of 49.38%. The majority of the HCPs (n = 110; 69%) rated their hospital as very good or excellent in maintaining an overall patient safety culture. The study revealed that communication about the errors (PRR = 80) and organizational learning and continuous improvement (PRR = 74) were good in their hospital settings. However, staffing and work pace (PRR = 28), response to errors (PRR = 40), reporting patient safety events (PRR = 48), and handoffs and information exchange (PRR = 39) were inadequate. These findings indicate the negative attitudes among HCPs and the need for further improvement to maintain a culture of patient safety. Conclusion: HCPs in the study settings had optimal knowledge but negative attitudes towards the culture of patient safety in their organization. Inadequate staffing, work pace, and a lack of response to mistakes were commonly observed, which may increase the chances of errors and pose health threats to patients that need to be addressed immediately. Every healthcare organization is urged to address the issue of patient safety culture as a matter of urgency.

Eliminating quantization errors in classification-based sound source localization.

Sound Source Localization (SSL) involves estimating the Direction of Arrival (DOA) of sound sources. Since the DOA estimation output space is continuous, regression might be more suitable for DOA, offering higher precision. However, in practice, classification often outperforms regression, exhibiting greater robustness. Conversely, classification's drawback is inherent quantization error. Within the classification paradigm, the DOA output space is discretized into several intervals, each treated as a class. These classes show strong inter-class correlations, being inherently ordered, with higher similarity as intervals grow closer. Nevertheless, this characteristic has not been fully exploited. To address this, we propose Unbiased Label Distribution (ULD) to eliminate quantization error in training targets. Furthermore, we introduce Weighted Adjacent Decoding (WAD) to overcome quantization error during the decoding stage. Finally, we tailor two loss functions for the soft labels: Negative Log Absolute Error (NLAE) and Mean Squared Error without activation (MSE(wo)). Experimental results show our approach surpasses classification quantization limits, achieving state-of-the-art performance. Our code and supplementary material are available at https://github.com/linfeng-feng/ULD.

Dynamic modeling of human error in industrial maintenance through structural analysis and system dynamics.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.