Validation of Golden Gate Assemblies Using Highly Multiplexed Nanopore Amplicon Sequencing.

Golden Gate cloning has revolutionized synthetic biology. Its concept of modular, highly characterized libraries of parts that can be combined into higher order assemblies allows engineering principles to be applied to biological systems. The basic parts, typically stored in Level 0 plasmids, are sequence validated by the method of choice and can be combined into higher order assemblies on demand. Higher order assemblies are typically transcriptional units, and multiple transcriptional units can be assembled into multi-gene constructs. Higher order Golden Gate assembly based on defined and validated parts usually does not introduce sequence changes. Therefore, simple validation of the assemblies, e.g., by colony polymerase chain reaction (PCR) or restriction digest pattern analysis is sufficient. However, in many experimental setups, researchers do not use defined parts, but rather part libraries, resulting in assemblies of high combinatorial complexity where sequencing again becomes mandatory. Here, we present a detailed protocol for the use of a highly multiplexed dual barcode amplicon sequencing using the Nanopore sequencing platform for in-house sequence validation. The workflow, called DuBA.flow, is a start-to-finish procedure that provides all necessary steps from a single colony to the final easy-to-interpret sequencing report.

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.

Mitigating calcar fracture risk with automated impaction during total hip arthroplasty.

Background: Automated broaching has recently been introduced for total hip arthroplasty (THA), with the goal of improving surgical efficiency and reducing surgeon workload. While studies have suggested that this technique may improve femoral sizing and alignment, little has been published regarding its safety, particularly with regard to calcar fractures. The purpose of our study was to evaluate the risk of calcar fracture during automated broaching, and to determine if this risk can be mitigated. Methods: We queried our prospective institutional database and identified 1596 unilateral THAs performed by the senior author using automated impaction between 2019 and 2023. We identified the incidence of calcar fracture with automated impaction, and whether the fracture occurred during broaching or stem insertion. We additionally determined calcar fracture incidence within two consecutive subgroups of patients using different stem insertion techniques; subgroup (1): automated broaching with automated stem insertion for all patients; versus subgroup (2): automated broaching with automated stem insertion ONLY if a cushion of cancellous bone separated the broach from the calcar, otherwise the stem was placed manually. Continuous and categorical variables were analyzed with Student's t-test and Fisher's exact test, respectively. Results: Seventeen calcar fractures occurred intraoperatively (1.1 %). Only two fractures occurred during automated broaching (0.1 %), while fifteen occurred during final stem impaction (0.9 %) (p = 0.007). Four calcar fractures (1.4 %) occurred in subgroup 1, compared to two in subgroup 2 (0.6 %) (p = 0.28). Conclusions: Our study found a calcar fracture incidence of 1.1 % using automated impaction, consistent with historically reported rates of 0.4-3.7 %. We found that calcar fractures are more likely to occur during stem insertion than during femoral broaching. We recommend that if any part of the final broach is in direct contact with the calcar, the final stem should be impacted manually to minimize fracture risk.

Robots in the kitchen: The automation of food preparation in restaurants and the compounding effects of perceived love and disgust on consumer evaluations.

Restaurants are swiftly embracing automation to prepare food, experimenting with innovations from robotic arms for frying foods to pizza-making robots. While these advances promise to enhance efficiency and productivity, their impact on consumer psychology remains largely unexplored. We present four experiments that demonstrate how food service automation leads to negative downstream effects (i.e., diminished taste perceptions, decreased willingness to pay, less favorable attitudes towards food items) across multiple food categories. This stems in part from two distinct contagion effects, whereby automation appears to undermine the food's ability to contain symbolic love (positive contagion from human contact) while simultaneously increasing feelings of disgust (negative contagion from machine contact). Moreover, we highlight how communicating the consumer-oriented benefits of automation can suppress the disgust associated with automation and subsequently mitigate the deleterious effects on consumer evaluations. Our findings suggest that service retailers should consider the psychological impact on consumers when shifting away from human involvement in a category as intimate and consequential as the production of our food.

Chartered Accountants' perception of the Fourth Industrial Revolution.

The Fourth Industrial Revolution (4IR) is an era of enormous technical progress that has impacted professionals across industries, including Chartered Accountants (CAs). This study explored how CAs view the impact of the 4IR on their profession, focusing on the shifting roles, competencies, and challenges they face during this transitional period. The research adopted a qualitative approach to data collecting, including 14 semi-structured interviews with participants from various CA backgrounds. This research provides a thorough knowledge of the 4IR's consequences for the profession and the perceptions of CA's of the 4IR. Rapid task automation via technologies such as AI is posing a challenge to traditional CA roles, forcing a change towards more analytical thinking and strategic insight. CAs need to develop critical thinking abilities and data analysis ability. Older generations might need support to adapt to the technological changes. Despite fears about job loss due to technology, members were largely optimistic about the 4IR's professional development potential. Conclusions are drawn and recommendations are given.

Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue.

OBJECTIVE: Our objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. BACKGROUND: Control transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. METHOD: We used data from a driving simulation to develop an active inference model of takeover performance. After validating the model's predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. RESULTS: The model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. CONCLUSION: The results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. APPLICATION: The active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

Management of experimental workflows in robotic cultivation platforms.

In the last decades, robotic cultivation facilities combined with automated execution of workflows have drastically increased the speed of research in biotechnology. In this work, we present the design and deployment of a digital infrastructure for robotic cultivation platforms. We implement a workflow management system, using Directed Acyclic Graphs, based on the open-source platform Apache Airflow to increase traceability and the automated execution of experiments. We demonstrate the integration and automation of experimental workflows in a laboratory environment with a heterogeneous device landscape including liquid handling stations, parallel cultivation systems, and mobile robots. The feasibility of our approach is assessed in parallel E. coli fed-batch cultivations with glucose oscillations in which different elastin-like proteins are produced. We show that the use of workflow management systems in robotic cultivation platforms increases automation, robustness and traceability of experimental data.

Knowledge-based planning for fully automated radiation therapy treatment planning of 10 different cancer sites.

PURPOSE: Radiation treatment planning is highly complex and can have significant inter- and intra-planner inconsistency, as well as variability in planning time and plan quality. Knowledge-based planning (KBP) is a tool that can be used to efficiently produce high-quality, consistent, clinically acceptable plans, independent of planner skills and experience. In this study, we created and validated multiple clinically acceptable and fully automatable KBP models, with the goal of creating VMAT plans without user intervention. METHODS: Ten KBP models were configured using high quality clinical plans from a single institution. They were then honed to be part of a fully automatable system by incorporating scriptable planning structures, plan creation, and plan optimization. These models were verified and validated using quantitative (model statistics) and qualitative (dose-volume histogram estimation review) analysis. The resulting KBP-generated plans were reviewed by physicians and rated for clinical acceptability. RESULTS: Autoplanning models were created for anorectal, bladder, breast/chest wall, cervix, esophagus, head and neck, liver, lung/mediastinum, prostate, and prostate with nodes treatment sites. All models were successfully created to be part of a fully automated system without the need for human intervention to create a fully optimized plan. The physician review indicated that, on average, 88% of all KBP-generated plans were "acceptable as is" and 98% were "acceptable after minor edits." CONCLUSION: KBP models for multiple treatment sites were used as a basis to generate fully automatable, efficient, consistent, high-quality, and clinically acceptable plans. These plans do not require human intervention, demonstrating the potential this work has to significantly impact treatment planning workflows.

Assessment of pencil beam scanning proton therapy beam delivery accuracy through machine learning and log file analysis.

PURPOSE: Comprehensive Quality Assurance (QA) protocols are necessary for complex beam delivery systems like Pencil Beam Scanning (PBS) proton therapy. This study focuses on automating the evaluation of beam delivery accuracy using irradiation log files and machine learning (ML) models. METHODS: Irradiation log files of 935 clinical treatment fields and routine QA beams were analysed to evaluate spot parameters and Monitor Unit (MU) accuracy. ML models predicted spot size along the X, Y, major, and minor axes. In-house scripts automated log file analysis and spot size predictions. Predicted spot sizes were compared with expected baselines, and the accuracy of spot position, symmetry, and MU for each spot in the beam was evaluated. RESULTS: More than 99.5 % of spot positions were accurate within a 1 mm. The mean and Standard Deviation (SD) of X positional error were -0.021 mm (SD: 0.181 mm), and for Y positional error, they were -0.002 mm (SD: 0.132 mm). ML models accurately predicted spot sizes, with over 95 % of spots demonstrating size variations within 10 % of the baseline. The Root Mean Squared Error (RMSE) of X and Y spot size differences were 0.15 mm and 0.16 mm, respectively. Spot symmetry was within 10 %, and MU accuracy showed 95 % of spots with MU per spot variation less than 2 %. CONCLUSION: This method can validate the vendor's beam delivery safety interlock system and serve as a quick alternative to patient-specific QA in adaptive treatment, where time is limited, as well as for routine QA spot parameter evaluations.

Unveiling the relationship between food unit operations and food industry 4.0: A short review.

The fourth industrial revolution (Industry 4.0) is driving significant changes across multiple sectors, including the food industry. This review examines how Industry 4.0 technologies, such as smart sensors, artificial intelligence, robotics, and blockchain, among others, are transforming unit operations within the food sector. These operations, which include preparation, processing/transformation, preservation/stabilization, and packaging and transportation, are crucial for converting raw materials into high-quality food products. By incorporating advanced digital, physical, and biological innovations, Industry 4.0 technologies are enhancing precision, productivity, and environmental responsibility in food production. The review highlights innovative applications and key findings that showcase how these technologies can streamline processes, minimize waste, and improve food product quality. The adoption of Industry 4.0 innovations is increasingly reshaping the way food is prepared, transformed, preserved, packaged, and transported to the final consumer. The work provides a valuable roadmap for various sectors within agriculture and food industries, promoting the adoption of Industry 4.0 solutions to enhance efficiency, quality, and sustainability throughout the entire food supply chain.

Software-driven chronic disease management: Algorithm design and implementation in a community-based blood pressure control pilot.

Background: Optimal guideline-directed medical therapy is rarely attained in practice, resulting in inadequate control of diseases such as hypertension, with poorer results in under-resourced communities. Technology, including artificial intelligence-driven decision support and software-driven workflow transformation, can potentially improve disease outcomes at a reduced cost, although it must be integrated with a holistic approach. Methods: We describe the design of a software platform that enables rapid iterative remote management of >20 conditions across cardiac-kidney-metabolic disease. The platform distributes work across a care team of providers and care navigators, automates decision-making, ordering, and documentation, supports rapid incorporation of new evidence, and launches pragmatic trials. We describe software used in a 500-person community-based blood pressure control implemented as a single-arm quality improvement program. The primary endpoint was the proportion of patients meeting the Healthcare Effectiveness Data and Information Set quality measure blood pressure goal (<140/90) at 12 weeks. Results: A total of 1609 patients were screened, 945 (59%) were found to have uncontrolled hypertension, and 512 patients consented to join the program. The average age was 61 ± 11 years; 59% were female, and 99% self-identified as Black. Blood pressure distribution was: 10% Stage 1 (SBP 130-139 mmHg or DBP 80-89 mmHg), 69% Stage 2 (SBP 140-179 mmHg or DBP 90-119 mmHg), and 21% Stage 3 (SBP >180 mmHg or DBP >120 mmHg). Two hundred four patients (39%) proceeded to a provider encounter, and 160 of these (78%) completed the program. The Healthcare Effectiveness Data and Information Set blood pressure goal was achieved in <12 weeks of enrollment for 141 participants (69% of those enrolled, 88% of those who completed the program). Conclusion: Software-driven remote blood pressure is feasible, although strategies to improve patient enrollment will be needed to achieve maximum impact. Future work will be required to compare outcomes to usual care and evaluate concurrent management of multiple cardiac-kidney-metabolic conditions.

Biological Reference Ranges for Healthy Indian Blood Donors: Experience of a Tertiary Care Center Vis-à-Vis International Literature.

INTRODUCTION: Complete blood count is the most common, basic test requisitioned in hematology. The normal reference ranges of hematological parameters are required owing to variable socioeconomic, environmental, and genetic factors in populations. The current study determines the reference ranges of the healthy Indian donor population of a high socioeconomic group. METHODS: The study was conducted in the Department of Transfusion Medicine at a tertiary care hospital in India and included 4098 individuals, aged 18-65 years coming for voluntary blood donation from July 2021 to October 2022. Blood samples were collected in K2EDTA, analyzed on the Sysmex XN-31 hematology analyzer, and using statistical tools, the normal reference ranges were calculated. RESULTS: The reference ranges for hemoglobin (HB) (137-185 g/L), WBC (5.1-1.7 × 109/L), platelet count (115.6-370.0 × 109/L) were noted. No statistically significant changes were observed in different age groups. There were gender-wise differences noted in nearly all parameters. The HB and hematocrit (HCT) range was slightly higher in other Indian and other Asian populations with comparable values with the Chinese, Korean populations, and Western populations; RBC parameters were overall comparable with minor differences; the WBC count was higher than the other Indian and Asian populations particularly the upper limit of lymphocyte and monocyte; and the range of platelet counts had a comparable upper limit with all populations and had the lowest lower value in males in our study, which was comparable to only the Chinese population. CONCLUSIONS: It is concluded that reference ranges of common parameters were calculated with minor changes noted in all hematological parameters on comparing with other Indian, Asian population, and Western data.

A Simulation-Based Approach for Evaluating the Effectiveness of Robotic Automation Systems in HMR Product Loading.

The food industry has tried to enhance production processes in response to the increasing demand for safe, high-quality Home Meal Replacement (HMR) products. While robotic automation systems are recognized for their potential to improve efficiency, their high costs and risks make them less accessible to small and medium-sized enterprises (SMEs). This study presents a simulation-based approach to evaluating the feasibility and impact of robotic automation on HMR production, focusing on two distinct production cases. By modeling large-scale and order-based production cases using simulation software, the study identified key bottlenecks, worker utilization, and throughput improvements. It demonstrated that robotic automation increased throughput by 31.2% in large-scale production (Case A) and 12.0% in order-based production (Case B). The actual implementation showed results that closely matched the simulation, validating the approach. Moreover, the study confirmed that a single worker could operate the robotic system effectively, highlighting the practicality of robotics for SMEs. This research provides critical insights into integrating robotics to enhance productivity, reduce labor dependency, and facilitate digital transformation in food manufacturing.

repDilPCR: a tool for automated analysis of qPCR assays by the dilution-replicate method.

BACKGROUND: The dilution-replicate experimental design for qPCR assays is especially efficient. It is based on multiple linear regression of multiple 3-point standard curves that are derived from the experimental samples themselves and thus obviates the need for a separate standard curve produced by serial dilution of a standard. The method minimizes the total number of reactions and guarantees that Cq values are within the linear dynamic range of the dilution-replicate standard curves. However, the lack of specialized software has so far precluded the widespread use of the dilution-replicate approach. RESULTS: Here we present repDilPCR, the first tool that utilizes the dilution-replicate method and extends it by adding the possibility to use multiple reference genes. repDilPCR offers extensive statistical and graphical functions that can also be used with preprocessed data (relative expression values) obtained by usual assay designs and evaluation methods. repDilPCR has been designed with the philosophy to automate and speed up data analysis (typically less than a minute from Cq values to publication-ready plots), and features automatic selection and performance of appropriate statistical tests, at least in the case of one-factor experimental designs. Nevertheless, the program also allows users to export intermediate data and perform more sophisticated analyses with external statistical software, e.g. if two-way ANOVA is necessary. CONCLUSIONS: repDilPCR is a user-friendly tool that can contribute to more efficient planning of qPCR experiments and their robust analysis. A public web server is freely accessible at https://repdilpcr.eu without registration. The program can also be used as an R script or as a locally installed Shiny app, which can be downloaded from https://github.com/deyanyosifov/repDilPCR where also the source code is available.

Identification and Counting of Pirapitinga Piaractus brachypomus Fingerlings Fish Using Machine Learning.

Identifying and counting fish are crucial for managing stocking, harvesting, and marketing of farmed fish. Researchers have used convolutional networks for these tasks and explored various approaches to enhance network learning. Batch normalization is one technique that improves network stability and accuracy. This study aimed to evaluate machine learning for identifying and counting pirapitinga Piaractus brachypomus fry with different batch sizes. The researchers used one thousand photographic images of Pirapitinga fingerlings, labeled with bounding boxes. They trained the adapted convolutional network model with batch normalization layers added at the end of each convolution block. They set the training to one hundred and fifty epochs and tested batch sizes of 5, 10, and 20. Furthermore, they measured network performance using precision, recall, and mAP@0.5. Models with smaller batch sizes performed less effectively. The training with a batch size of 20 achieved the best performance, with a precision of 96.74%, recall of 95.48%, mAP@0.5 of 97.08%, and accuracy of 98%. This indicates that larger batch sizes improve accuracy in detecting and counting pirapitinga fry across different fish densities.

Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.

PURPOSE: This scoping review aims to explore the current applications of ChatGPT in the retina field, highlighting its potential, challenges, and limitations. METHODS: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, MEDLINE, and Embase, to identify relevant articles published from 2022 onwards. The inclusion criteria focused on studies evaluating the use of ChatGPT in retinal healthcare. Data were extracted and synthesized to map the scope of ChatGPT's applications in retinal care, categorizing articles into various practical application areas such as academic research, charting, coding, diagnosis, disease management, and patient counseling. RESULTS: A total of 68 articles were included in the review, distributed across several categories: 8 related to academics and research, 5 to charting, 1 to coding and billing, 44 to diagnosis, 49 to disease management, 2 to literature consulting, 23 to medical education, and 33 to patient counseling. Many articles were classified into multiple categories due to overlapping topics. The findings indicate that while ChatGPT shows significant promise in areas such as medical education and diagnostic support, concerns regarding accuracy, reliability, and the potential for misinformation remain prevalent. CONCLUSION: ChatGPT offers substantial potential in advancing retinal healthcare by supporting clinical decision-making, enhancing patient education, and automating administrative tasks. However, its current limitations, particularly in clinical accuracy and the risk of generating misinformation, necessitate cautious integration into practice, with continuous oversight from healthcare professionals. Future developments should focus on improving accuracy, incorporating up-to-date medical guidelines, and minimizing the risks associated with AI-driven healthcare tools.

Contemporary Applications and Future Perspectives of Robots in Endodontics: A Scoping Review.

BACKGROUND: In the era of modern advanced endodontics, the reduction of reliance on human hands and shifting towards robotics could benefit the precision and accuracy of endodontic treatment. This scoping review aims to describe current and emerging trends in the applications of robots in endodontics and highlight their limitations and future perspectives. METHODS: This review followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) standards. A comprehensive search of internet databases was conducted until February 2024. Studies that specifically examined robots in the field of endodontics were included. RESULTS: The studies focused on root canal cleaning, shaping, surgical procedures, and other applications. Robotic systems demonstrated improved accuracy, precision, reduced errors, and time savings compared with manual techniques. CONCLUSION: Robotics in endodontics show promise, especially in surgical procedures, with AI integration enhancing accuracy and efficiency. Challenges such as cost, physical limitations, and absence of tactile feedback require further research and investment.

Manual flagging failed to identify pseudohyperkalemia in acute myeloid leukemia: case report.

BACKGROUND: Pseudohyperkalemia is well known in acute or chronic lymphocytic leukemia, but it is very rare in acute myeloid leukemia (AML). The lab flagging system for leukocytosis to prevent pseudohyperkalemia may not work. CASE PRESENTATION: A 55 year-old white man with AML was sent to emergency department for transfusion due to severe anemia. Blood test showed severe leukocytosis and elevated potassium. Repeated blood test showed his potassium was even higher. Anti-hyperkalemic medical treatment was given. He was then diagnosed with pseudohyperkalema. INVESTIGATION: I was repeatedly reassured that the lab's manual flagging system for leukocytosis was the key in reaching the correct diagnosis. My persistent inquiries, however, revealed that the flagging system was not functioning in the care of this patient. It was clinicians' suspicion of pseudohyperkalema that led to the correct diagnosis, although the clinicians' recommendation of obtaining a heparinized plasma for test did not play a role because all blood samples were already heparinized. The cause of pseudohyperkalemia was pneumatic tube transport. After this incident, our laboratory is investigating the options of using the Laboratory Information System to automatically flag the results and Clinical Laboratory Scientists to make the chemistry team more aware of potentially erroneous potassium results due to pseudohyperkalemia. CONCLUSIONS: Pseudohyperkalemia associated with leukocytosis still occurs. This is the first case of pneumatic tube transport causing pseudohyperkalemia associated with AML. When significant leukocytosis, thrombocytosis, hyperproteinemia, or hyperlipidemia is present, whole blood should be utilized for potassium measurements and walked to the lab instead of sent by pneumatic tube transport. Even in a lab with a manual flagging system, there is still room to improve by implementing an automatic flagging system.

Assessment of a LPG hybrid solar dryer assisted with smart air circulation system for drying basil leaves.

The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135-210 min and winter drying took 145-225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10- 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945-15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly.

External validation of a fully automated evaluation tool: a retrospective analysis on 68,471 scored embryos.

OBJECTIVE: To externally validate a fully automated embryo classification in in vitro fertilization (IVF) treatments. DESIGN: Retrospective cohort study SUBJECTS: A total of 6,434 patients undergoing 7,352 IVF treatments contributed 70,456 embryos. EXPOSURE: Embryos were evaluated by conventional morphology and retrospectively scored using a fully automated deep learning-based algorithm across conventional IVF, oocyte donation, and PGT-A cycles. MAIN OUTCOME MEASURES: The primary outcomes were implantation and live birth including odds ratios (ORs) from generalized estimating equation (GEE) models. Secondary outcomes were embryo morphology, euploidy and miscarriage. Exploratory outcomes included comparison between conventional methodology and artificial intelligence (AI) algorithm with areas under the ROC curves (AUCs), agreement degree between AI and embryologists, Cohen's Kappa coefficient and relative risk (RR). RESULTS: Implantation and live birth rates increased as the automatic embryo score rose. The GEE model, controlling for confounders, showed the automatic score was associated with an OR of 1.31 (95%CI[1.25-1.36]) for implantation in treatments using oocytes from patients, and an OR of 1.17 (95%CI[1.14-1.20]) in the oocyte donation program, with no significant association in PGT-A treatments. For live birth, the ORs were 1.27 (95%CI[1.21-1.33]) for patients, 1.16 (95%CI[1.13-1.19]) for donors, and 1.05 (95%CI[1-1.10]) for PGT-A cycles. The average score was higher in embryos with better morphology, in euploid embryos compared to aneuploid embryos, and in embryos that resulted in a full-term pregnancy compared to those that miscarried. Concordance between the highest-scoring embryo and the embryo with the best conventional morphology was 71.4%(95%CI[67.7%-75.0%]) in treatments with patient oocytes and 61.0%(95%CI[58.6%-63.4%]) in the oocyte donation program. Overall, the Cohen's Kappa coefficient was 0.63. The automatic embryo score showed similar AUCs to conventional morphology, although implantation was higher when the transferred embryo matched the highest-scoring embryo from each cohort (57.36% vs. 49.98%). RR indicated a 1.14-fold increase in implantation likelihood when the top-ranked embryo was transferred. CONCLUSION: Fully automated embryo scoring effectively ranked embryos based on their potential for implantation and live birth. The performance of the conventional methodology was comparable to that of the artificial intelligence-based technology; however, better clinical outcomes were observed when the highest-scoring embryo in the cohort was transferred.