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Touch-response experimentation in zebrafish helps researchers better understand the link between genetics, drug effects, and behaviors. However, commonly manually conducted experimentation cannot fulfill a high-throughput screening and often delivers low accuracy and lacks reproducibility. Thus, the main aim of this work is to establish a fully automated robot-assisted experimentation system with minimal human participation to conduct the touch-response experimentation with freely swimming zebrafish larvae. Our designed system is able to undertake the role of repeated touch-response experiments at predefined specific location of the larvae in different ages and under different conditions, with high accuracy, robustness, and repeatability, and can also get comparable experimental results. The errors of the detection methods are less than 3 pixels and the offset errors of the touching points are less than 5%. Designed for high-efficiency experimentation, this system will promisingly release a great amount of the burden for the biological operators from touch-response experiments and may also have potential applications in other organisms for touch-evoked response analysis.

High-speed highway on-ramp merging is a significant challenge toward realizing fully automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine communication and autonomous driving technologies, may improve greatly the safety performances when performing highway on-ramp merging. However, even with the emergence of CAVs, some keys constraints should be considered to achieve a safe on-ramp merging. First, human-driven vehicles will still be present on the road, and it may take decades before all the commercialized vehicles will be fully autonomous and connected. Also, onboard vehicle sensors may provide inaccurate or incomplete data due to sensors limitations and blind spots, especially in such critical situations. To resolve these issues, the present work introduces a novel solution that uses an off-board Road-Side Unit (RSU) to realize fully automated highway on-ramp merging for connected and automated vehicles. Our proposed approach is based on an Artificial Neural Network (ANN) to predict drivers' intentions. This prediction is used as an input state to a Deep Reinforcement Learning (DRL) agent that outputs the longitudinal acceleration for the merging vehicle. To achieve this, we first propose a data-driven model that can predict the behavior of the human-driven vehicles in the main highway lane, with 99% accuracy. We use the output of this model as input state to train a Twin Delayed Deep Deterministic Policy Gradients (TD3) agent that learns ``safe'' and ``cooperative'' driving policy to perform highway on-ramp merging. We show that our proposed decision-making strategy improves performance compared to the solutions proposed previously.

Frequency-selective surfaces (FSSs) refer to planar structures that behave with specific electromagnetic (EM) responses within a frequency range and are widely applied in wireless propagation systems. Given the fact that different EM responses correspond to distinguished topologies, conventional inverse design methods of FSSs are usually labor-intensive, as they rely on experienced human engineers to determine the topology and then rationally tune its structures. There have been great attempts using optimization algorithms (e.g., genetic algorithms) or machine learning to automate the second tuning stage after the initial EM topologies are determined by human engineers. However, the first topology selection stage still requires engagements with experienced engineers. This article proposes a fully automated framework for the inverse design of FSSs. We achieved a fully automated inverse design by establishing a machine-friendly mapping flow. The mapping flow derives its continuity and compactness from representation learning, which enables both autoselection of the topology and autoevolution of the unit cell based on the topology. The autoselection stage automatically determines the appropriate topology by compressing the EM constraints through the principal component analysis (PCA) and classifying the topology using the support vector machine (SVM). Afterward, the autoevolution system can efficiently evolve until it yields an optimal unit cell. We developed a self-monitor strategy to control the evolution and maximize the evolution efficiency by adaptively tuning the three modules within the autoevolution system. We validated the presented framework with four FSS designs. The results proved its potential as a highly efficient fully automated tool for the inverse design of FSSs.

Highly and fully automated driving has been under development for the past two decades in order to increase comfort, efficiency, and traffic safety. Particularly in the latter domain, experts agree on automated driving, especially in case of automated vehicles (AV) with SAE level 4 or higher, having the most promising effects. Automated driving is expected to decrease the number of seriously injured or even killed road users to zero (Vision Zero). However, automated driving is still in an early stage of development and many AV tend to drive very carefully to avoid crashes. So, the goal is to make driving more efficient while maintaining the highest level of safety. In the project “Digitaler Knoten 4.0” cooperative automated driving was assessed regarding efficiency and safety aspects. One of the use cases investigated was turning left with oncoming traffic at an urban intersection as this situation represents one of the most complex situations in urban areas yielding to crashes with—in many cases—serious consequences for the involved road users. At the Application Platform Intelligent Mobility (AIM) Research Intersection in Braunschweig, Germany, an SAE level 3 AV was turning left interacting with oncoming manually driven vehicles (MV). The performance of the AV was compared to MV executing the same manoeuvre. The recorded video-based trajectories of the respective AV as well as MV were analysed regarding the influence of situational factors (e.g. position of the vehicle in the queue and gap acceptance) and kinematic factors (e.g. speed and acceleration) on traffic safety. The similarities and differences between this specific AV and MV were identified yielding insight for further developing algorithms for more efficient driving while maintaining the same traffic safety level. For instance, it appears that the AV shows a very conservative left turning behaviour leading to very safe PET distributions in comparison to left turning MV.

To form monodisperse water-in-oil droplets in a fully automated fashion, we fabricated a pumpless microfluidic device consisting of top and bottom polydimethylsiloxane slabs. All microstructures re...

Objectives We conducted a nationwide external quality assessment (EQA) study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification testing in Japan. Methods A total of 563 public health and private sector laboratories participated. The EQA samples comprised 6 RNA and full-process controls. Results The overall agreements were 99.3% and 97.9% for the RNA and full-process controls, respectively. A total of 530/563 (94.1%) laboratories reported correct results; public health laboratories had the highest accuracy. Thirty-three laboratories reported at least one incorrect result (26 laboratories of medical facilities, 5 commercial laboratories, 1 public health laboratory, and 1 other). Sixteen laboratories of medical facilities that used a fully automated assay system failed to detect the presence of the full-process control, due to inherent insufficiency in the limit of detection (LOD). Other causes of incorrect results included failure to ensure the LOD (n = 13), error in result judging or reporting (n = 3), and error in sample handling (n = 1). Conclusions Performance was mostly dependent on the laboratory category and assay evaluation, particularly the LOD. Guidance should be developed based on these results, particularly in the phase of new entry into laboratory services for SARS-CoV-2.

Energy losses of photovoltaic (PV) plants because of soiling are a problem in all regions, including Germany. Soft soiling, caused by a uniform dust film, is shading a PV module and is reducing yield depending on the thickness of the debris layer. Hard soiling, caused by agglomerations of dust or dirt, only covers parts of a PV module, and it causes local shading. A spot check of modules from different PV power plants in Germany revealed a power reduction because of soft- and hard soiling of up to 6%. Determination of soiling type and amount is a prerequisite for decisions about cleaning and for optimizing yield. In this article, we show that luminescence imaging can be used to detect and characterize soiling and quantify losses. For this purpose, we compare luminescence images before and after cleaning and we show that soiling becomes detectable and power losses quantifiable from difference images. We also show that the impact of hard soiling can be quantified from a single photoluminescence (PL) image. This technique may enable a fully automated quantification of power losses because of hard soiling in PV- modules and strings in the future.

BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. OBJECTIVE. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. METHODS. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. RESULTS. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1–11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture (p = .18–.98) or death (p = .19–.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). CONCLUSION. Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. CLINICAL IMPACT. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.

In this study, a fully automated two-dimensional liquid chromatography (2D-LC) system was used for the investigation of the clean-up effect and was compared with a traditional Quick Easy Cheap Effective Rugged and Safe (QuEChERS) liquid chromatography (LC) method. The focus of those investigations was on negative electrospray ionization (ESI) mode. For that purpose, matrix fingerprinting profiles were created. The results allowed a comparison of both methods regarding the estimation of the number and the polarity of detected compounds. Moreover, the results of the present study were compared with the results generated in positive ESI mode (presented in a previous study). Furthermore, the two methods were compared with regard to matrix effects (ME) of 321 analytes in positive ESI mode and 96 analytes in negative ESI mode. In general, fewer compounds could be detected when 2D-LC and/or the negative ESI mode was used. Especially, very polar compounds with m/z values >1000 could be separated and could not be detected anymore when 2D-LC was applied. Furthermore, the best results were obtained for most analytes when 2D-LC was used, although the extent of ME seemed to be higher with 2D-LC.

BACKGROUND Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images. AIM To test the feasibility of DL-based classifiers for the frequently occurring mutations from the H and E-stained GC tissue whole slide images (WSIs). METHODS From the GC dataset of The Cancer Genome Atlas (TCGA-STAD), wild-type/mutation classifiers for CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes were trained on 360 × 360-pixel patches of tissue images. RESULTS The area under the curve (AUC) for the receiver operating characteristic (ROC) curves ranged from 0.727 to 0.862 for the TCGA frozen WSIs and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded (FFPE) WSIs. The performance of the classifier can be improved by adding new FFPE WSI training dataset from our institute. The classifiers trained for mutation prediction in colorectal cancer completely failed to predict the mutational status in GC, indicating that DL-based mutation classifiers are incompatible between different cancers. CONCLUSION This study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.

Background Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients. Results A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose. Conclusion The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.

Contribution: Research has shown that computer science (CS) students who start programming assignments (PAs) early generally receive higher grades. This article presents and evaluates a gamification approach that utilizes software engineering tools to motivate CS students to start and finish PAs earlier. Background: CS can be difficult to learn because students often struggle with errors and how to properly test their code. For these reasons, it is essential that students start their PAs early. Furthermore, software engineering tools, such as version control and unit testing, are increasingly important for students to learn early in their career. Intended Outcomes: This gamification approach aims to motivate CS students to start and complete PAs earlier, as well as instill software engineering best practices. Application Design: To motivate students to start and finish assignments early, an open-source gamification system called the Leaderboard was developed. Using gamified points, the Leaderboard rewards students who pass PA unit tests well before the assignment is due. The system is fully automated using Github Classroom, a build server, and the Moodle learning management system. Findings: Results indicate students who used the Leaderboard did not start assignments significantly earlier; however, they finished assignments earlier, committed code more frequently, and passed more unit tests. The students found the Leaderboard to be motivating and passing unit tests was exciting for them.

Lumbar disc degeneration (LDD) may be related to aging, biomechanical and genetic factors. Despite the extensive work on understanding its etiology, there is currently no automated tool for accurate prediction of its progression. We aim to establish a novel deep learning-based pipeline to predict the progression of LDD-related findings using lumbar MRIs. We utilized our dataset with MRIs acquired from 1,343 individual participants (taken at the baseline and the 5-year follow-up timepoint), and progression assessments (the Schneiderman score, disc bulging, and Pfirrmann grading) that were labelled by spine specialists with over ten years clinical experience. Our new pipeline was realized by integrating the MRI-SegFlow and the Visual Geometry Group-Medium (VGG-M) for automated disc region detection and LDD progression prediction correspondingly. The LDD progression was quantified by comparing the Schneiderman score, disc bulging and Pfirrmann grading at the baseline and at follow-up. A fivefold cross-validation was conducted to assess the predictive performance of the new pipeline. Our pipeline achieved very good performances on the LDD progression prediction, with high progression prediction accuracy of the Schneiderman score (Accuracy: 90.2 ± 0.9%), disc bulging (Accuracy: 90.4% ± 1.1%), and Pfirrmann grading (Accuracy: 89.9% ± 2.1%). This is the first attempt of using deep learning to predict LDD progression on a large dataset with 5-year follow-up. Requiring no human interference, our pipeline can potentially achieve similar predictive performances in new settings with minimal efforts.

Rapid DNA platforms are fully automated systems capable of processing DNA from biological samples and interpreting the results in approximately 90 minutes with minimal human intervention. With a greater reliance on the system than on the analyst, validation data are especially needed to define the performance and limitations of commercially available Rapid DNA systems. Thus, validation studies of a Rapid DNA workflow consisting of the Applied Biosystems RapidHIT ID Instrument and RapidLINK software with a focus on the ACE GlobalFiler Express Sample Cartridge and reference buccal swabs were performed in accordance with Scientific Working Group on DNA Analysis Methods Validation Guidelines. These validation studies included assessments of sensitivity, contamination, concordance, reproducibility and repeatability, stability, inhibition, mixtures, sample reprocessing, precision, and first-pass success rate. Overall, the current Applied Biosystems RapidHIT ID Instrument with the ACE GlobalFiler Express sample cartridge was found to be a reliable tool for generation of STR profiles from reference-type buccal swabs. Supplementary Information The online version contains supplementary material available at 10.1007/s00414-021-02722-9.

Automatic brain abnormality detection is a major challenge in medical image processing. Manual lesion delineation techniques are susceptible to subjective errors, and therefore, computer aided preliminary screening of a lesion is necessary. This study introduces an efficient and automated algorithm based on the symmetry of the brain structures in the two hemispheres for brain tumour segmentation using multimodal brain magnetic resonoce imaging. Symmetry is a vital clue for determining intensity‐based lesion difference in the two hemispheres of brain. A reliable method is proposed for extracting the cancerous region in order to improve the speed and accuracy of brain tumour segmentation. First, a symmetry plane is detected and then through features extracted from both sides of the brain, a similarity measure for comparing the hemisphere is defined. The cancerous region is extracted using similarity measurement, and the accuracy is improved using postprocessing operation. This algorithm is evaluated against the BRATS datasets including high‐ and low‐grade glioma brain tumours. The performance indices are calculated and comparative analysis is implemented as well. Experimental results demonstrate accuracy close to manual lesion demarcation with performance indices.

Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks (CNNs) have shown impressive results and potential towards fully automated segmentation in medical imaging, and particularly PET imaging. To cope with the limited access to annotated data needed in supervised AI methods, given tedious and prone-to-error manual delineations, semi-supervised and unsupervised AI techniques have also been explored for segmentation of tumors or normal organs in single and bi-modality scans. This work provides a review of existing AI techniques for segmentation tasks and the evaluation criteria for translational AI-based segmentation efforts towards routine adoption in clinical workflows., Comment: This manuscript has been accepted for publication in PET Clinics, Volume 16, Issue 4, 2021

Objective: To evaluate the performance of Nucleated RBC (NRBC) Count using a fully automated haematology analyzer versus manual counting. Study Design: Cross-Sectional Study. Place and Duration of Study: Department of Hematology, Armed Forces Institute of Pathology, from Sep 2019-Jun 2020. Methodology: Routine fresh whole blood samples were run on Sysmex XN-3000 automated haematology analyzer and 384 samples with results of ≥0.1% Nucleated red blood cells were included in this study. Manual NRBC counting was carried out twice on Leishman-stained peripheral blood smears from all 384 samples. Comparison between manual and automated nucleated red blood cell counting methods was statistically analyzed through linear regression analysis & coefficient correlation. The degree of agreement between two methods was analyzed through Bland-Altman plot. Finally, concordance between the two methods was also analyzed at 5 different ranges of nucleated red blood cells. Results: Linear regression analysis revealed a (r2) value of 0.97. Regression equation was calculated as XN = 0.76MC ± 1.28, with 95% limits of agreement between ± 40.42% and -24.47%. A mean bias of 7.97% was demonstrated through Bland-Altman plot. Concordance analysis revealed a concordance rate of 93.74% (360/384). Nucleated red blood cell counting between two methods were more concordant when nucleated red blood cell counts were

Fully automated immunoassays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies that are strongly correlated with neutralization antibodies (nAbs) are clinically important because they enable assessment of humoral immunity after infection and vaccination. Access SARS-CoV-2 IgM and IgG II antibody tests are semi-quantitative, fully automated immunoassays that detect anti-receptor-binding domain (RBD) antibodies and might reflect nAb levels in coronavirus disease 2019 (COVID-19). However, no studies have investigated the clinical utility of these tests in association with nAbs to date. To evaluate the clinical utility of Access SARS-CoV-2 IgM and IgG II antibody tests and their correlation with the SARS-CoV-2 surrogate virus neutralization test (sVNT) that measures nAbs in patients with COVID-19. We analyzed 54 convalescent serum samples from COVID-19 patients and 89 serum samples from non-COVID-19 patients. The presence of anti-RBD antibodies was detected by Access SARS-CoV-2 IgM and IgG II antibody tests, while nAbs were measured by sVNT. The sensitivity and specificity of sVNT were 94.4% and 98.9%, respectively. There were strong positive correlations between the inhibition values of sVNT and the results of the Access SARS-CoV-2 IgM (R = 0.95, R2 = 0.90, p < 0.001) and IgG II antibody tests (R = 0.96, R2 = 0.92, p < 0.001). In terms of the presence of nAbs, the sensitivity and specificity were 98.1% and 98.9% in the IgM assay, and 100.0% and 100.0% in the IgG II assay, respectively. The Access SARS-CoV-2 IgM and IgG II antibody tests showed high sensitivity and specificity for the detection of nAbs in COVID-19 patients and might be alternatives for measuring nAbs. This article is protected by copyright. All rights reserved.

Noncoplanar radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planner's active treatment planning time and remove inter-planner variability. To address the limitations of traditional treatment planning, we have been developing a suite of algorithms called station parameter optimized radiation therapy (SPORT). Within the SPORT suite of algorithms, we propose a method called NC-POPS to produce noncoplanar (NC) plans using the fully automated Pareto Optimal Projection Search (POPS) algorithm. Our NC-POPS algorithm extends the original POPS algorithm to the noncoplanar setting with potential applications to both IMRT and VMAT. The proposed algorithm consists of two main parts: 1) noncoplanar beam angle optimization (BAO) and 2) fully automated inverse planning using the POPS algorithm. We evaluate the performance of NC-POPS by comparing between various noncoplanar and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram (DVH) statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity. Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of noncoplanar IMRT cases with the potential to substantially improve treatment planning workflow and plan quality.

Background Recently, second-generation thyroglobulin (Tg) sandwich immunoassays have been used in clinical laboratories to measure the serum Tg levels, which is a tumor marker used to monitor postoperative patients with differentiated thyroid cancers. However, these immunoassays are often subject to Tg autoantibody (TgAb) interference. TgAb interference is inevitable for almost all Tg immunoassays, resulting in unreliable Tg measurement values of TgAb-positive samples. Methods To address TgAb interference, we have developed a novel immunoassay based on a fully automated chemiluminescent enzyme immunoassay system using the effective specimen-pretreatment process to inactivate TgAb in blood and evaluated its assay performance. Results The developed assay was traceable to BCR457 IRMM reference material with a limit of quantification of 0.03 ng/mL. The pretreatment process inactivated almost all TgAb in specimens and allowed accurate Tg measurements in TgAb-positive samples in which TgAb interference was observed using the immunoassays. Size-exclusion chromatography analysis of immunoreactive Tg molecule in a TgAb-positive serum verified disruption of the Tg–TgAb immune complex by the pretreatment process. Good correlation of Tg values in TgAb-negative specimens was observed between the new Tg immunoassay and the second-generation sandwich immunoassays. However, there were numerous discrepant samples on bias plots between the new Tg immunoassay and the second-generation sandwich immunoassays for TgAb-positive specimens. Conclusions This study indicates the new Tg immunoassay with the specimen-pretreatment process is both robust and free from interference by TgAb. Thus, this novel assay is superior to second-generation sandwich immunoassays and gives accurate Tg concentrations even for TgAb-positive cases.

OBJECTIVES: Early placental volume (PV) has been associated with small-for-gestational-age infants born under the 10(th)/5(th) centiles (SGA10/SGA5). Manual or semi-automated PV quantification from 3DUS is time-intensive, limiting its incorporation into clinical care. We devised a novel convolutional neural network (CNN) pipeline for fully-automated placenta segmentation from 3DUS images, exploring the association between the calculated PV and SGA. METHODS: 3DUS volumes obtained from singleton pregnancies at 11–14 weeks’ gestation were automatically segmented by our CNN pipeline trained and tested on 99/25 images, combining two 2D and one 3D models with downsampling/upsampling architecture. The PVs derived from the automated segmentations (PV(CNN)) were used to train multi-variable logistic-regression classifiers for SGA10/SGA5. The test performance for predicting SGA was compared to PVs obtained via the semi-automated VOCAL (GE-Healthcare) method (PV(VOCAL)). RESULTS: We included 442 subjects with 37 (8.4%) and 18 (4.1%) SGA10/SGA5 infants, respectively. Our segmentation pipeline achieved a mean Dice score of 0.88 on an independent test-set. Adjusted models including PV(CNN) or PV(VOCAL) were similarly predictive of SGA10 (AUCs: PV(CNN)=0.780, PV(VOCAL)=0.768). The addition of PV(CNN) to a clinical model without any PV included (AUC=0.725) yielded statistically significant improvement in AUC (P

An automated continuous flow system capable of producing protected deoxy-sugar donors from commercial material is described. Four 2,6-dideoxy and two 3-amino-2,3,6-trideoxy sugars with orthogonal protecting groups were synthesized in 11-32 % overall yields in 74-131.5 minutes of total reaction time. Several of the reactions were able to be concatenated into a continuous process, avoiding the need for chromatographic purification of intermediates. The modular nature of the experimental setup allowed for reaction streams to be split into different lines for the parallel synthesis of multiple donors. Further, the continuous flow processes were fully automated and described through the design of an open-source Python-controlled automation platform.

Automated vehicles (AVs) have garnered increasing attention since they have the potential to dramatically reshape our lives in the near future. At the same time, people are concerned about various risks associated with the new technologies. Thus, people’s attitudes toward AVs pose a major challenge to the wider adoption of them. Previous studies examined the effect of benefit/risk perception on people’s acceptance of AVs, but they did not address the multidimensionality of benefit/risk perception. We conducted a survey (n = 840) to reveal the underlying dimensions of how people construe the benefits and risks of conditionally/fully automated vehicles. Our results showed that there were two dimensions underlying benefit perception (i.e., the perception that AVs would increase convenience and reduce harm) and three dimensions underlying risk perception (i.e., the perception of risk to physical safety and comfort, cybersecurity, and ease of use). The perception that AVs would reduce harm positively impacted people’s intention to use both fully automated vehicles and conditionally automated vehicles. The perception that AVs would increase convenience and the perception that AVs would pose a risk to ease of use had a positive and negative effect, respectively, on intention to use fully automated vehicles. This study makes theoretical contributions by questioning the assumption that benefit/risk perception is a one-dimensional factor that impacts people’s acceptance of AVs. This study also has practical implications as it suggests an effective method for automobile manufacturers and policymakers to communicate with the public regarding the new technologies and diffuse them safely.

BACKGROUND: The transport of the inpatients to and from locations inside the hospital can vary in complexity depending on the patient location, status, and logistical needs. Most transport systems have not developed at the same speed as other medically related technologies. We conducted a pilot study of a new automated transport system for patients within the hospital. METHODS: Our innovative system was introduced in January 2020. We present a retrospective case review of all in-patient transport request during April 15, 2020 through May 30, 2020 at the Shaare Zedek Medical Center, Jerusalem, Israel. The system is fully automated and works via smartphone and electronic medical record integration. Transfer requests are processed on the basis of priority, proximity, and availably. RESULTS: During the study period there were 15, 581 transfer requests. Mean times to hospital destinations ranged from 9:25 to 28:02 minutes. Overall, mean times were quicker for emergency and surgical services. Trip times by priority code were likely influence by unmeasured confounders. There were no reported patient identification adverse events. Peak requests occurred during 0900-1500, and at the beginning of the week. CONCLUSION: Our automated in-patient transfer system appears to be efficient, safe, well received, and capable of servicing our large tertiary care medical center. Future controlled studies are needed to assess efficacy, adverse events, and clinical outcomes.

A non-invasive, no radiation, out-of-hospital automated system is proposed to identify low arch integrated in the design and manufacturing of personalized orthoses using parametric modelling. The aim of the design process is to integrate assistive technology with assessment and prevent low arch progressing to a more serious case - flatfoot. In the automated procedure, we developed an assessment method including reliable thresholds of foot type classification and test protocol to reduce interferences due to preceding activities, an automation to translate scanned data into parametric design for orthotic customization, finite element model evaluating effectiveness of the personalized design, and a personalized comparative test to evaluate the long-term improvement of foot arch shape. Our low arch threshold established by subject-specific 3D models reduced the misclassification rate from 55%, as previously reported to 6.9%. Individuals who engaged in sedentary activity (i.e. sitting) had the greater change in arch height compared to active activity (i.e. standing and walking), which is more likely to affect the obtained measure. Therefore, a test protocol now states that participants are not allowed to sit over 100 min prior the measurement to reduce such interference. We have proposed and tested an automated algorithm to translate scanned data including seven foot's parameters into customised parametric design of the insert. The method decreases the required time of orthotic computer-aided design from over 3 h to less than 2 min. A finite element analysis procedure was additionally developed to assess the performance of geometries and material of designed orthotic based on the distribution of plantar pressure and internal stress. The personalized comparative assessment based on midfoot contact area was carried out periodically for follow-up and the orthotic could be re-designed if necessary. The proposed automated procedure develops a pre-screening system to distinguish low arch and provide preventatives before it becomes symptomatic. Furthermore, non-symptom flatfoot can be detected at early stages and referred to medics for further diagnosis or treatment.

This paper proposes a fully automated approach to map and assess roadside clearance parameters using mobile Light Detection and Ranging (lidar) data on rural highways. Compared with traditional manual surveying methods, lidar data could provide a more efficient and cost-effective source to extract roadside information. This study proposes a novel voxel-based raycasting approach focused primarily on automating roadside mapping and assessment. First, the scanning vehicle trajectory is extracted. Pavement surface points are then detected, and a method is proposed to extract pavement edge trajectories. Once pavement edges are extracted, guardrails were identified using a conical frustum emitted from the edge trajectory points. Target points and flexion points are then generated and located on the roadside, and a voxel-based raycasting approach is used to search for roadside obstacles and query their locations. Finally, roadside slopes and embankment heights were mapped at specific intervals, and roadside design guidelines and requirements were automatically checked against the mapping results. Noncompliant locations with substandard conditions were automatically queried. The method was tested on four highway segments in Alberta, Canada. The accuracy of the edge detection reached up to 98.5%. Furthermore, the method proved to be accurate in object detection, being able to detect all obstructions on the roadside in each tested segment. The proposed method can help transportation authorities automatically map and inventory roadside clearance parameters. Moreover, the safety performance of existing road infrastructure can be studied using collected information and crash data to support decision making on road maintenance and upgrades.

The longest ozone column measurement series are based on the Dobson sun spectrophotometers developed in the 1920s by Prof. G. B. W. Dobson​​​​​​​. These ingenious and robustly designed instruments still constitute an important part of the global network presently. However, the Dobson sun spectrophotometer requires manual operation, which has led to the discontinuation of its use at many stations, thus disrupting long-term records of observation. To overcome this problem, MeteoSwiss developed a fully automated version of the Dobson spectrophotometer. The description of the data acquisition and automated control of the instrument is presented here with some technical details. The results of different tests performed regularly to assess the instrument's good working conditions are illustrated and discussed. Compared to manual operation, automation results in a higher number of daily measurements with lower random error and additional housekeeping information to characterize the measuring conditions. The automated Dobson instrument allows for continuous observation of the ozone column with a resolution of ∼ 1 DU under clear-sky conditions.