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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.

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.

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.

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.

Rapid development of antisense therapies can enable on-demand responses to new viral pathogens and make personalized medicine for genetic diseases practical. Antisense phosphorodiamidate morpholino oligomers (PMOs) are promising candidates to fill such a role, but their challenging synthesis limits their widespread application. To rapidly prototype potential PMO drug candidates, we report a fully automated flow-based oligonucleotide synthesizer. Our optimized synthesis platform reduces coupling times by up to 22-fold compared to previously reported methods. We demonstrate the power of our automated technology with the synthesis of milligram quantities of three candidate therapeutic PMO sequences for an unserved class of Duchenne muscular dystrophy (DMD). To further test our platform, we synthesize a PMO that targets the genomic mRNA of SARS-CoV-2 and demonstrate its antiviral effects. This platform could find broad application not only in designing new SARS-CoV-2 and DMD antisense therapeutics, but also for rapid development of PMO candidates to treat new and emerging diseases., PMOs (phosphorodiamidate morpholino oligomers) have huge potential for antisense therapy but complex and slow synthesis limits application. Here, the authors report the development of automated flow synthesis methods which reduce nucleobase coupling times from hours to minutes removing human errors and allow for high-throughput production.

Automation mechanisms are increasingly established in the field of visual inspections. UAVs can be used for particularly large components, such as those used in ship production and for critical infrastructures. This paper concentrates on the problem of visual inspection in the field of perspective-dependent route planning. It is shown how the requirements for such a system can be implemented and elaborated. Furthermore we investigate how sensor positions can be calculated offline, based on optical and geometrical requirements and how a trajectory can be planned which contains the found sensor positions for each given area on the component. It is shown how the systems architecture can be designed in order to be able to adapt it to different requirements for the planning of sensor positions and trajectory. The implementation was tested in a simulation environment, evaluated using a benchmark data set and it was shown how above-average results can be achieved on this data set.

Purpose: To propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCiseTM 2 multileaf collimator. Methods: The AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil-beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in-house. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil-beams, segment dose depositions, and final dose calculations are performed with a stand-alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. Results: AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (−49%)), segments (59.5/98.9 (−40%)), and delivery times (17.1/22.3 min. (−23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (−21%)) and had on average shorter delivery times (18.6/22.3 min. (−17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. Conclusions: A new, vendor-independent optimization workflow for fully automated generation of deliverable high-quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (−23%) due to large reductions in beam and segment numbers.

Background The acute changes in brain function in newborn infants undergoing ET remain unclear. This study aimed to determine whether fully automated simultaneous peripheral arteriovenous ET would influence the brain function. Methods A retrospective analysis was conducted on the clinical data of 39 neonates with hyperbilirubinemia who received ET. Seventeen patients were in the encephalopathy group, and the other 22 patients were in the non-encephalopathy group. Changes in amplitude-integrated electroencephalogram (aEEG) during ETs were analyzed, including background activities, sleep-wake cycling (SWC), and seizures. Before and after the ET, routine blood test parameters, electrolytes, blood glucose, and blood gas parameters were measured. Results After ETs, there were no significant changes in the levels of pH, PaO2, PaCO2, lactate, and red blood cells, while the levels of total bilirubin, indirect bilirubin, blood potassium, blood sodium, serum calcium, while blood cells, and platelets were significantly lower and the level of blood glucose was significantly higher compared to those before therapy. There was no significant difference in the changes of electroencephalographic activities during ETs, including background activities, SWC, and seizures. However, there were significant differences in suppressions on background activities, while there were no significant statistical differences in SWC or seizures between the 2 groups. Conclusion Fully automated simultaneous peripheral arteriovenous ET is safe and efficient without significant influence on the disorder of the internal environment and electroencephalographic activities after ET in neonates. However, background activities are more significantly depressed in infants of bilirubin encephalopathy than infants of non-encephalopathy during ET.

Background: The detection and follow up of cytomegalovirus (CMV) and Epstein-Barr virus (EBV) viral loads (VL) are crucial in the management of immunocompromised patients. Recently, molecular CE-IVD assays for detection and quantification of CMV and EBV have been launched for use on the random-access and sample-to-result NeuMoDx 96 and 288 platforms (Qiagen). Objective: Evaluating the qualitative and quantitative performance of the NeuMoDx CMV and EBV assays in clinical specimens compared to a lab developed tests (LDT) and the CE-IVD assays on the Abbott m2000 system. Method: Both a prospective and a retrospective panel, compiled of non-detectable (ND), non-quantifiable (NQ) and quantifiable VLs in plasma samples have been evaluated for both CMV and EBV: NeuMoDx versus LDT and NeuMoDx versus Abbott m2000. Quantitative agreement was determined for samples with a quantifiable VL on both systems. Results: Qualitative and quantitative agreement between the NeuMoDx and LUMC's LDT CMV assays was 88%. Qualitative agreement between the NeuMoDx and Abbott m2000 CMV assays was 92% and quantitative agreement was 87%. Qualitative and quantitative agreement between the NeuMoDx and the LDT EBV assays was 87%. Qualitative agreement between the NeuMoDx and Abbott m2000 EBV assays was 91% and quantitative agreement was 0%. Conclusion: These data show that the NeuMoDx assays are suitable for both detection and quantification of CMV and EBV in a medium-to high throughput diagnostic setting, but that differences in sensitivity and quantification (for EBV, NeuMoDx versus Abbott m2000) warrant an extensive transition period when using the respective assays for following VL in patient samples.

Background Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated. Methods The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set. Results The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set. Conclusions EasyScan GO’s expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

Hand sanitization is the most useful way to prevent the spread of disease-causing microorganisms. Traditionally, to sanitize and clean our hands, we are using soaps and manual hand soap dispenser. Nowadays, due to advancement in the technology, we have optimized the health-care equipment. Currently used modern equipments are very complex and expensive. This research paper proposes the idea of fully automated sensor-based controlled sanitizer dispenser. This research paper advocates radically different approaches: we aim to fully automate the current technique using alternate components for system, thereby reducing the cost of the product. Our designed automated dispenser can be commercially used to develop a good sanitization approach toward a community. The main advantage of this design is that we could design a number of automated dispensers within a small period of time using basic components in a very fewer budget.

Introduction The ST Genesia is a benchtop, fully automated thrombin generation (TG) device. It is completely standardized and ensures a uniform heat distribution throughout the measurement. We aimed to determine reference values and to compare TG in men and women with and without the use of oral contraceptives (OCs). Materials and Methods Plasma from 117 healthy donors was measured on the ST Genesia with the available reagent kits: STG‐BleedScreen, STG‐DrugScreen, and STG‐ThromboScreen. All kits include at least two quality controls and a reference plasma to normalize data. STG‐ThromboScreen has a second trigger containing thrombomodulin (TM) to include the effect on the protein C pathway. Means were compared with one‐way analysis of variance and reference ranges were established with 2.5th to 97.5th percentiles on absolute TG parameters. Results Mean age of the donors was 35 years (SD ± 12); 49.6% were men, 37.6% women without OCs, and 12.8% women with OCs. Men and women without OCs had, respectively, a mean peak height of 167 nM and 164 nM with STG‐BleedScreen, 335 nM and 351 nM with STG‐DrugScreen, and 192 nM and 198 nM with STG‐ThromboScreen. Women taking OCs had a mean peak height of 263 nM, 473 nM, and 312nM, respectively (P

Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

Prostate‐specific membrane antigen (PSMA)‐targeted imaging and therapy of prostate cancer using theranostic pairs is rapidly changing clinical practice. To facilitate clinical trials, fully automated procedures for the radiosyntheses of [68Ga]Ga‐PSMA‐11 and [177Lu]Lu‐PSMA‐617 were developed from commercially available precursors using the cassette based iPHASE MultiSyn module. Formulated and sterile radiopharmaceuticals were obtained in 76 ± 3% (n = 20) and 91 ± 4% (n = 15) radiochemical yields after 17 and 20 min, respectively. Radiochemical purity was always >95% and molar activities exceeded 792 ± 100 and 88 ± 6 GBq/μmol, respectively. Quality control showed conformity with all relevant release criteria and radiopharmaceuticals were used in the clinic., Methods for the fully automated production of a relevant theranostic pair of prostate‐specific membrane antigen (PSMA)‐targeting molecules are described. Quality control parameters and test results for clinical application are presented and the performance of the procedures is compared other reported protocols.

The collection and presentation of accurate reproductive data from wild fish has historically been somewhat problematic, especially for serially spawning species. Therefore, the aim of the current study was to develop a novel method of assessing female spawning status that is robust to variation in oocyte dynamics between specimens. Atlantic cod (Barents Sea stock) were used to develop the new ‘ultrametric’ method, that is based on the progressive depletion of the vitellogenic oocyte pool relative to the rather constant previtellogenic oocyte (PVO) pool. Fish were subsequently partitioned into one of four categories that accurately reflected changes in their oocyte size frequency distribution characteristics and gonadosomatic index throughout spawning. The ultrametric method overcomes difficulties associated with presence of bimodal oocyte distributions, oocyte tails, lack of clear hiatus region, and presence of free ova, and can be implemented at a single sampling point. Much of the workflow is fully automated, and the technique may circumvent the need for histological analysis depending on the desired outcome. The ultrametric method differs from the traditional autodiametric method in that PVOs can be separated by ultrasonication and then enumerated, and ovarian homogeneity is not a mandatory requirement per se. The method is designed for determinate spawners but might be extended to include indeterminate spawners.

This review presents the state-of-the-art of optical sensors for determination of biogenic amines (BAs) in food by publications covering about the last 10 years. Interest in the development of rapid and preferably on-site methods for quantification of BAs is based on their important role in implementation and regulation of various physiological processes. At the same time, BAs can develop in different kinds of food by fermentation processes or microbial activity or arise due to contamination, which induces toxicological risks and food poisoning and causes serious health issues. Therefore, various optical chemosensor systems have been devised that are easy to assemble and fast responding and low-cost analytical tools. If amenable to on-site analysis, they are an attractive alternative to existing instrumental analytical methods used for BA determination in food. Hence, also portable sensor systems or dipstick sensors are described based on various probes that typically enable signal readouts such as photometry, reflectometry, luminescence, surface-enhanced Raman spectroscopy, or ellipsometry. The quantification of BAs in real food samples and the design of the sensors are highlighted and the analytical figures of merit are compared. Future instrumental trends for BA sensing point to the use of cell phone–based fully automated optical evaluation and devices that could even comprise microfluidic micro total analysis systems.

Purpose In automated treatment planning, configuration of the underlying algorithm to generate high-quality plans for all patients of a particular tumor type can be a major challenge. Often, a time-consuming trial-and-error tuning procedure is required. The purpose of this paper is to automatically configure an automated treatment planning algorithm for oropharyngeal cancer patients. Methods Recently, we proposed a new procedure to automatically configure the reference point method (RPM), a fast automatic multi-objective treatment planning algorithm. With a well-tuned configuration, the RPM generates a single Pareto optimal treatment plan with clinically favorable trade-offs for each patient. The automatic configuration of the RPM requires a set of computed tomography (CT) scans with corresponding dose distributions for training. Previously, we demonstrated for prostate cancer planning with 12 objectives that training with only 9 patients resulted in high-quality configurations. This paper further develops and explores the new automatic RPM configuration procedure for head and neck cancer planning with 22 objectives. Investigations were performed with planning CT scans of 105 previously treated unilateral or bilateral oropharyngeal cancer patients together with corresponding Pareto optimal treatment plans. These plans were generated with our clinically applied two-phase e-constraint method (Erasmus-iCycle) for automated multi-objective treatment planning, ensuring consistent high quality and Pareto optimality of all plans. Clinically relevant, nonconvex criteria, such as dose-volume parameters and NTCPs, were included to steer the RPM configuration. Results Training sets with 20-50 patients were investigated. Even with 20 training plans, high-quality configurations of the RPM were feasible. Automated plan generation with the automatically configured RPM resulted in Pareto optimal plans with overall similar or better quality than that of the Pareto optimal database plans. Conclusions Automatic configuration of the RPM for automated treatment planning is feasible and drastically reduces the time and workload required when compared to manual tuning of an automated treatment planning algorithm.

Objectives The genetic prediction of phenotypic antibiotic resistance based on analysis of WGS data is becoming increasingly feasible, but a major barrier to its introduction into routine use is the lack of fully automated interpretation tools. Here, we report the findings of a large evaluation of the Next Gen Diagnostics (NGD) automated bioinformatics analysis tool to predict the phenotypic resistance of MRSA. Methods MRSA-positive patients were identified in a clinical microbiology laboratory in England between January and November 2018. One MRSA isolate per patient together with all blood culture isolates (total n = 778) were sequenced on the Illumina MiniSeq instrument in batches of 21 clinical MRSA isolates and three controls. Results The NGD system activated post-sequencing and processed the sequences to determine susceptible/resistant predictions for 11 antibiotics, taking around 11 minutes to analyse 24 isolates sequenced on a single sequencing run. NGD results were compared with phenotypic susceptibility testing performed by the clinical laboratory using the disc diffusion method and EUCAST breakpoints. Following retesting of discrepant results, concordance between phenotypic results and NGD genetic predictions was 99.69%. Further investigation of 22 isolate genomes associated with persistent discrepancies revealed a range of reasons in 12 cases, but no cause could be found for the remainder. Genetic predictions generated by the NGD tool were compared with predictions generated by an independent research-based informatics approach, which demonstrated an overall concordance between the two methods of 99.97%. Conclusions We conclude that the NGD system provides rapid and accurate prediction of the antibiotic susceptibility of MRSA.

Introduction Autoantibodies against the M-type phospholipase A2 receptor (PLA2R) are important markers in the diagnosis and monitoring of primary membranous nephropathy (pMN). For the detection of anti-PLA2R autoantibodies, a standardized recombinant cell-based indirect immunofluorescence assay (RC-IFA) and enzyme-linked immunosorbent assay (ELISA) are widely used, the former providing higher sensitivity but lacking a finely graduated quantification of antibody titers. In this study, we evaluated the diagnostic performance characteristics of a novel standardized chemiluminescence immunoassay (ChLIA) by comparison with the established anti-PLA2R test systems. Methods Sera from 155 patients with biopsy-proven pMN and 154 disease controls were analyzed for autoantibodies against PLA2R by the novel ChLIA as well as by ELISA and RC-IFA. Results The clinical sensitivity of the ChLIA (83.9%) was higher compared with ELISA (73.5%) and equaled that of RC-IFA (83.2%), at similar specificities (≥99.4%). Among ELISA-negative pMN samples, ChLIA and RC-IFA yielded positive results in 39.0% and 36.6%, respectively. The qualitative agreement amounted to 94.5% (ChLIA vs. ELISA) and 99.4% (ChLIA vs. RC-IFA). Conclusion The novel anti-PLA2R ChLIA outperforms the ELISA in detecting patients with pMN and demonstrates almost perfect agreement with RC-IFA. It thus presents a promising alternative tool for accurate anti-PLA2R testing, with the advantage of rapid turnaround times and fully automated random-access processing., Graphical abstract

Reliable quantification of cellular treatment effects in many bioassays depends on the accuracy of cell colony counting. However, colony counting processes tend to be tedious, slow, and error-prone. Thus, pursuing an effective colony counting technique is ongoing, and varies from manual approaches to partly automated and fully automated techniques. Most fully automated techniques were developed using deep learning (DL). A significant problem in applying DL to this task is the lack of sizeable collections of annotated plate images. For this reason, here we propose an application of Transfer Learning to cell colony counting that can overcome this problem by exploiting models trained for other tasks. To demonstrate this idea's feasibility, we show how a small dataset can be used to transform a DL model designed for counting objects in congested scenes into a specialized cell colony counting model and achieve better performance than existing, more widely-used models.

This paper presents an automated system for extracting retinal vascular biomarkers for early detection of diabetes. The proposed retinal vessel enhancement, segmentation, optic disc (OD) and fovea detection algorithms provide fundamental tools for extracting the vascular network within the predefined region of interest. Based on that, the artery/vein classification, vessel width, tortuosity and fractal dimension measurement tools are used to assess a large number of quantitative vascular biomarkers. We evaluate our pipeline module by module against human annotations. The results indicate that our automated system is robust to the localisation of OD and fovea, segmentation of vessels and classification of arteries/veins. The proposed pipeline helps to increase the effectiveness of the biomarkers extraction and analysis for the early diabetes, and therefore, has the large potential of being further incorporated into a computer-aided diagnosis system.

Wireframe DNA origami has emerged as a powerful approach to fabricating nearly arbitrary 2D and 3D geometries at the nanometer-scale. Complex scaffold and staple routing needed to design wireframe DNA origami objects, however, render fully automated, geometry-based sequence design approaches essential for their synthesis. And wireframe DNA origami structural fidelity can be limited by wireframe edges that are composed only of one or two duplexes. Here we introduce a fully automated computational approach that programs 2D wireframe origami assemblies using honeycomb edges composed of six parallel duplexes. These wireframe assemblies show enhanced structural fidelity from electron microscopy-based measurement of programmed angles compared with identical geometries programmed using dual-duplex edges. Molecular dynamics provides additional theoretical support for the enhanced structural fidelity observed. Application of our top-down sequence design procedure to a variety of complex objects demonstrates its broad utility for programmable 2D nanoscale materials., Wireframe DNA origami is a powerful approach to creating 2D and 3D geometries. Here the authors introduce an automated computational design approach that programs structures with high structural fidelity.

Background Microsatellite instability (MSI) analysis is becoming increasingly important in many types of tumor including colorectal cancer (CRC). The commonly used MSI tests are either time-consuming or labor-intensive. A fully automated MSI test, the Idylla MSI assay, has recently been introduced. However, its diagnostic performance has not been extensively validated in clinical CRC samples. Methods We evaluated 133 samples whose MSI status had been rigorously validated by standard polymerase chain reaction (PCR), clinical nextgeneration sequencing (NGS) cancer panel test, or both. We evaluated the diagnostic performance of the Idylla MSI assay in terms of sensitivity, specificity, and positive and negative predictive values, as well as various sample requirements, such as minimum tumor purity and the quality of paraffin blocks. Results Compared with the gold standard results confirmed through both PCR MSI test and NGS, the Idylla MSI assay showed 99.05% accuracy (104/105), 100% sensitivity (11/11), 98.94% specificity (93/94), 91.67% positive predictive value (11/12), and 100% negative predictive value (93/93). In addition, the Idylla MSI assay did not require macro-dissection in most samples and reliably detected MSI-high in samples with approximately 10% tumor purity. The total turnaround time was about 150 minutes and the hands-on time was less than 2 minutes. Conclusions The Idylla MSI assay shows good diagnostic performance that is sufficient for its implementation in the clinic to determine the MSI status of at least the CRC samples. In addition, the fully automated procedure requires only a few slices of formalin-fixed paraffin-embedded tissue and might greatly save time and labor.

In the past few decades, aquatic animals have become popular model organisms in biology, spurring a growing need for establishing aquatic facilities. Zebrafish are widely studied and relatively easy to culture using commercial systems. However, a challenging aspect of maintaining aquatic facilities is animal feeding, which is both time- and resource-consuming. We have developed an open-source fully automatic daily feeding system, Zebrafish Automatic Feeder (ZAF). ZAF is reliable, provides a standardized amount of food to every tank, is cost-efficient and easy to build. The advanced version, ZAF+, allows for the precise control of food distribution as a function of fish density per tank, and has a user-friendly interface. Both ZAF and ZAF+ are adaptable to any laboratory environment and facilitate the implementation of aquatic colonies. Here, we provide all blueprints and instructions for building the mechanics, electronics, fluidics, as well as to setup the control software and its user-friendly graphical interface. Importantly, the design is modular and can be scaled to meet different user needs. Furthermore, our results show that ZAF and ZAF+ do not adversely affect zebrafish culture, enabling fully automatic feeding for any aquatic facility.

An efficient electrochemical flow process for the selective oxidation of sulfides to sulfoxides and\ud sulfones and of sulfoxides to N-cyanosulfoximines has been developed. In total, 69 examples of\ud sulfoxides, sulfones, and N-cyanosulfoximines have been synthesized in good to excellent \ud yields and with high current efficiencies. The synthesis was assisted and facilitated through a\ud supporting electrolyte-free, fully automated electrochemical protocol highlighting the advantages \ud of flow electrolysis.

Controller Area Network (CAN) is the most frequently used in-vehicle communication system in the automotive industry today. The communication inside the CAN bus is typically encoded using proprietary formats in order to prevent easy access to the information exchanged on the bus. However, it is still possible to decode this information through reverse engineering, performed either manually or via automated tools. Existing automated CAN bus reverse engineering methods are still time-consuming and require some manual effort, i.e., to inject diagnostic messages in order to trigger specific responses. In this paper, we propose CANMatch a fully automated CAN bus reverse engineering framework that does not require any manual effort and significantly decreases the execution time by exploiting the reuse of CAN frames across different vehicle models. We evaluate the proposed solution on a dataset of CAN logs, or traces, related to 479 vehicles from 29 different automotive manufacturers, demonstrating its improved performance with respect to the state of the art.

Aims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA).Methods and results: A total of 527 patients (33.0% female, mean age: 62.2 ± 10.2 years) with suspected coronary artery disease (CAD) who underwent CCTA and invasive coronary angiography (ICA) were enrolled from 27 hospitals from January 2016 to August 2019. Using ICA as a standard reference, the diagnostic accuracy of the DL algorithm in the detection of ≥50% stenosis was compared to that of expert readers. In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) and a significantly higher area under the curve (AUC) (0.83, p < 0.001). In the patient-based evaluation, the DL algorithm achieved a higher sensitivity (90.0%), NPV (52.2%) and AUC (0.81). Generalizability analysis of the DL algorithm was conducted by comparing its diagnostic performance in subgroups stratified by sex, age, geographic area and CT scanner type. The AUCs of the DL algorithm in the aforementioned subgroups ranged from 0.79 to 0.86 and from 0.75 to 0.93 in the vessel-based and patient-based evaluations, both without significant group differences (p > 0.05). The DL algorithm significantly reduced post-processing time (160 [IQR:139–192] seconds), in comparison to manual work (p < 0.001).Conclusions: The DL algorithm performed no inferior to expert readers in CAD diagnosis on CCTA and had good generalizability and time efficiency.

An essential operation in the mineral processing of copper ores into concentrates is blending, as it guarantees a constant feed for the flotation cells, increases metal recovery rate and reduces tailings. In this study, copper ores from Huelva province (Spain) were investigated by quantitative XRD (X-ray diffraction) methods to optimize blending and detect penalty minerals, which can affect flotation and concentrate quality. The Rietveld method in combination with cluster analysis, PLSR and more traditional chemical analysis provide a more complete and in-depth characterization of the ore and the whole process. The mineralogical monitoring can be fully automated to enable real-time decision making.

Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learning system for automated temporalis muscle quantification, with high accuracy (Dice coefficient 0.912), and showed muscle cross-sectional area is independently significantly associated with survival in GBM (HR 0.380). However, it required manual selection of the temporalis muscle-containing MRI slice. Thus, in this work we aimed to develop a fully automatic deep-learning system, using the eyeball as an anatomic landmark for automatic slice selection, to quantify temporalis and validate on independent datasets. Method 3D brain MRI scans were obtained from four datasets: our in-house glioblastoma patient dataset, TCGA-GBM, IVY-GAP and REMBRANDT. Manual eyeball and temporalis segmentations were performed on 2D MRI images by two experienced readers. Two neural networks (2D U-Nets) were trained, one to automatically segment the eyeball and the other to segment the temporalis muscle on 2D MRI images using Dice loss function. The cross sectional area of eyeball segmentations were quantified and thresholded, to select the superior orbital MRI slice from each scan. This slice underwent temporalis segmentation, whose cross sectional area was then quantified. Accuracy of automatically predicted eyeball and temporalis segmentations were compared to manual ground truth segmentations on metrics of Dice coefficient, precision, recall and Hausdorff distance. Accuracy of MRI slice selection (by the eyeball segmentation model) for temporalis segmentation was determined by comparing automatically selected slices to slices selected manually by a trained neuro-oncologist. Results 398 images from 185 patients and 366 images from 145 patients were used for the eyeball and temporalis segmentation models, respectively. 61 independent TCGA-GBM scans formed a validation cohort to assess the performance of the full pipeline. The model achieved high accuracy in eyeball segmentation, with test set Dice coefficient of 0.9029 ± 0.0894, precision of 0.8842 ± 0.0992, recall of 0.9297 ± 0.6020 and Hausdorff distance of 2.8847 ± 0.6020. High segmentation accuracy was also achieved by the temporalis segmentation model, with Dice coefficient of 0.8968 ± 0.0375, precision of 0.8877 ± 0.0679, recall of 0.9118 ± 0.0505 and Hausdorff distance of 1.8232 ± 0.3263 in the test set. 96.1% of automatically selected slices for temporalis segmentation were within 2 slices of the manually selected slice. Conclusion Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated pipeline. Combined with our and others’ previous results that demonstrate the prognostic significance of temporalis cross-sectional area and muscle width, our findings suggest a role for deep learning in muscle mass and sarcopenia screening in GBM, with the potential to add significant value to routine imaging. Possible clinical applications include risk profiling, treatment stratification and informing interventions for muscle preservation. Further work will be to validate the prognostic value of temporalis muscle cross sectional area measurements generated by our fully automatic deep learning system in the multiple in-house and external datasets.

In this paper, the results of an investigation on the application of air gauges in the measurement of out-of-roundness parameters are presented. The principle of the measuring system is explained, in particular the novel design of the floating gauge head. An algorithm for fully automated measurement and data processing is described. The results from a series of initial measurements provided the data for further simulations, which revealed possible sources of errors. The simulations helped to evaluate the influence of some of the parameters on the final measurement results. After various accuracy tests, the method accuracy (MA) parameter was calculated in relation to the dedicated reference form tester. The result MA = 9.29% was judged to be highly satisfactory considering the short measurement time and non-contact method.

Background: Laguna-ONhE is an application for the colorimetric analysis of optic nerve images, which topographically assesses the cup and the presence of haemoglobin. Its latest version has been fully automated with five deep learning models. In this paper, perimetry in combination with Laguna-ONhE or Cirrus-OCT was evaluated. Methods: The morphology and perfusion estimated by Laguna ONhE were compiled into a “Globin Distribution Function” (GDF). Visual field irregularity was measured with the usual pattern standard deviation (PSD) and the threshold coefficient of variation (TCV), which analyses its harmony without taking into account age-corrected values. In total, 477 normal eyes, 235 confirmed, and 98 suspected glaucoma cases were examined with Cirrus-OCT and different fundus cameras and perimeters. Results: The best Receiver Operating Characteristic (ROC) analysis results for confirmed and suspected glaucoma were obtained with the combination of GDF and TCV (AUC: 0.995 and 0.935, respectively. Sensitivities: 94.5% and 45.9%, respectively, for 99% specificity). The best combination of OCT and perimetry was obtained with the vertical cup/disc ratio and PSD (AUC: 0.988 and 0.847, respectively. Sensitivities: 84.7% and 18.4%, respectively, for 99% specificity). Conclusion: Using Laguna ONhE, morphology, perfusion, and function can be mutually enhanced with the methods described for the purpose of glaucoma assessment, providing early sensitivity.

Background An artificial intelligence vessel segmentation tool, Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE), was used to analyze a large databank of popliteal arteries imaged through the OAI (Osteoarthritis Initiative) to study the impact of atherosclerosis risk factors on vessel dimensions and characterize remodeling patterns. Methods and Results Magnetic resonance images from 4668 subjects contributing 9189 popliteal arteries were analyzed using FRAPPE. Age ranged from 45 to 79 years (median, 61), and 58% were women. Mean lumen diameter, mean outer wall diameter, and mean wall thickness (MWT) were measured per artery. Their median values were 5.8 mm (interquartile range, 5.2–6.5 mm), 7.3 mm (interquartile range, 6.7–8.1 mm), and 0.78 mm (interquartile range, 0.73–0.84 mm) respectively. MWT was associated with multiple cardiovascular risk factors, with age (4.2% increase in MWT per 10‐year increase in age; 95% CI, 3.9%–4.5%) and sex (8.6% higher MWT in men than women; 95% CI, 7.7%–9.3%) being predominant. On average, lumen and outer wall diameters increased with increasing MWT until the thickness was 0.92 mm for men and 0.84 mm for women. After this point, lumen diameter decreased steadily, more rapidly in men than women (−7.9% versus −6.1% per 25% increase in MWT; P Conclusions FRAPPE has enabled the analysis of the large OAI knee magnetic resonance imaging data set, successfully showing that popliteal atherosclerosis is predominantly associated with age and sex. The average vessel remodeling pattern consisted of an early phase of compensatory enlargement, followed by a negative remodeling, which is more pronounced in men.

In the struggle to understand and accurately diagnose Parkinson′s disease, radiopharmaceuticals and medical imaging techniques have played a major role. By being able to image and quantify the dopamine transporter density, noninvasive diagnostic imaging has become the gold standard. In the shift from the first generation of SPECT tracers, the fluorine-18-labeled tracer [18F]FE-PE2I has emerged as the agent of choice for many physicians. However, implementing suitable synthesis for the production of [18F]FE-PE2I has proved more challenging than expected. Through a thorough analysis of the relevant factors affecting the final radiochemical yield, we were able to implement high-yielding fully automated GMP-compliant synthesis of [18F]FE-PE2I on a Synthera®+ platform. By reaching RCYs up to 62%, it allowed us to isolate 25 GBq of the formulated product, and an optimized formulation resulted in the shelf life of 6 h, satisfying the increased demand for this radiopharmaceutical.

The use of animal models for substance use disorder (SUD) has made an important contribution in the investigation of the behavioral and molecular mechanisms underlying substance abuse and addiction. Here, we review a novel and comprehensive behavioral platform to characterize addiction-like traits in rodents using a fully automated learning system, the IntelliCage. This system simultaneously captures the basic behavioral navigation, reward preference, and aversion, as well as the multi-dimensional complex behaviors and cognitive functions of group-housed rodents. It can reliably capture and track locomotor and cognitive pattern alterations associated with the development of substance addiction. Thus, the IntelliCage learning system offers a potentially efficient, flexible, and sensitive tool for the high-throughput screening of the rodent SUD model.