Dense reinforcement learning for safety validation of autonomous vehicles.

One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critical events1. Here we report the development of an intelligent testing environment, where artificial-intelligence-based background agents are trained to validate the safety performances of autonomous vehicles in an accelerated mode, without loss of unbiasedness. From naturalistic driving data, the background agents learn what adversarial manoeuvre to execute through a dense deep-reinforcement-learning (D2RL) approach, in which Markov decision processes are edited by removing non-safety-critical states and reconnecting critical ones so that the information in the training data is densified. D2RL enables neural networks to learn from densified information with safety-critical events and achieves tasks that are intractable for traditional deep-reinforcement-learning approaches. We demonstrate the effectiveness of our approach by testing a highly automated vehicle in both highway and urban test tracks with an augmented-reality environment, combining simulated background vehicles with physical road infrastructure and a real autonomous test vehicle. Our results show that the D2RL-trained agents can accelerate the evaluation process by multiple orders of magnitude (103 to 105 times faster). In addition, D2RL will enable accelerated testing and training with other safety-critical autonomous systems.

Ferrobotic swarms enable accessible and adaptable automated viral testing.

Expanding our global testing capacity is critical to preventing and containing pandemics1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies21-29. Here we use a swarm of millimetre-sized magnets as mobile robotic agents ('ferrobots') for precise and robust handling of magnetized sample droplets and high-fidelity delivery of flexible workflows based on nucleic acid amplification tests to overcome these limitations. Within a palm-sized printed circuit board-based programmable platform, we demonstrated the myriad of laboratory-equivalent operations involved in pooled testing. These operations were guided by an introduced square matrix pooled testing algorithm to identify the samples from infected patients, while maximizing the testing efficiency. We applied this automated technology for the loop-mediated isothermal amplification and detection of the SARS-CoV-2 virus in clinical samples, in which the test results completely matched those obtained off-chip. This technology is easily manufacturable and distributable, and its adoption for viral testing could lead to a 10-300-fold reduction in reagent costs (depending on the viral prevalence) and three orders of magnitude reduction in instrumentation cost. Therefore, it is a promising solution to expand our testing capacity for pandemic preparedness and to reimagine the automated clinical laboratory of the future.

Automated radial synthesis of organic molecules.

Automated synthesis platforms accelerate and simplify the preparation of molecules by removing the physical barriers to organic synthesis. This provides unrestricted access to biopolymers and small molecules via reproducible and directly comparable chemical processes. Current automated multistep syntheses rely on either iterative1-4 or linear processes5-9, and require compromises in terms of versatility and the use of equipment. Here we report an approach towards the automated synthesis of small molecules, based on a series of continuous flow modules that are radially arranged around a central switching station. Using this approach, concise volumes can be exposed to any reaction conditions required for a desired transformation. Sequential, non-simultaneous reactions can be combined to perform multistep processes, enabling the use of variable flow rates, reuse of reactors under different conditions, and the storage of intermediates. This fully automated instrument is capable of both linear and convergent syntheses and does not require manual reconfiguration between different processes. The capabilities of this approach are demonstrated by performing optimizations and multistep syntheses of targets, varying concentrations via inline dilutions, exploring several strategies for the multistep synthesis of the anticonvulsant drug rufinamide10, synthesizing eighteen compounds of two derivative libraries that are prepared using different reaction pathways and chemistries, and using the same reagents to perform metallaphotoredox carbon-nitrogen cross-couplings11 in a photochemical module-all without instrument reconfiguration.

An open-source drug discovery platform enables ultra-large virtual screens.

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

Computational planning of the synthesis of complex natural products.

Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years1-7. However, the field has progressed greatly since the development of early programs such as LHASA1,7, for which reaction choices at each step were made by human operators. Multiple software platforms6,8-14 are now capable of completely autonomous planning. But these programs 'think' only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary15,16 and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program's knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships17,18, allowing it to 'strategize' over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization.

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy.

Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.

Computational Analysis of Behavior.

In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with.

Automated detection of methicillin-resistant Staphylococcus aureus with the MRSA CHROM imaging application on BD Kiestra Total Lab Automation System.

The virulence of methicillin-resistant Staphylococcus aureus (MRSA) and its potentially fatal outcome necessitate rapid and accurate detection of patients colonized with MRSA in healthcare settings. Using the BD Kiestra Total Lab Automation (TLA) System in conjunction with the MRSA Application (MRSA App), an imaging application that uses artificial intelligence to interpret colorimetric information (mauve-colored colonies) indicative of MRSA pathogen presence on CHROMagar chromogenic media, anterior nares specimens from three sites were evaluated for the presence of mauve-colored colonies. Results obtained with the MRSA App were compared to manual reading of agar plate images by proficient laboratory technologists. Of 1,593 specimens evaluated, 1,545 (96.98%) were concordant between MRSA App and laboratory technologist reading for the detection of MRSA growth [sensitivity 98.15% (95% CI, 96.03, 99.32) and specificity 96.69% (95% CI, 95.55, 97.60)]. This multi-site study is the first evaluation of the MRSA App in conjunction with the BD Kiestra TLA System. Using the MRSA App, our results showed 98.15% sensitivity and 96.69% specificity for the detection of MRSA from anterior nares specimens. The MRSA App, used in conjunction with laboratory automation, provides an opportunity to improve laboratory efficiency by reducing laboratory technologists' labor associated with the review and interpretation of cultures.

Automated Tomographic Assessment of Structural Defects of Freeze-Dried Pharmaceuticals.

The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not only time-consuming and labor-intensive but also expensive and prone to errors. In this paper, we present an approach for fully automated, non-destructive inspection of freeze-dried pharmaceuticals, leveraging robotics, computed tomography, and machine learning.

A parallelized, automated platform enabling individual or sequential ChIP of histone marks and transcription factors.

Despite its popularity, chromatin immunoprecipitation followed by sequencing (ChIP-seq) remains a tedious (>2 d), manually intensive, low-sensitivity and low-throughput approach. Here, we combine principles of microengineering, surface chemistry, and molecular biology to address the major limitations of standard ChIP-seq. The resulting technology, FloChIP, automates and miniaturizes ChIP in a beadless fashion while facilitating the downstream library preparation process through on-chip chromatin tagmentation. FloChIP is fast (<2 h), has a wide dynamic range (from 106 to 500 cells), is scalable and parallelized, and supports antibody- or sample-multiplexed ChIP on both histone marks and transcription factors. In addition, FloChIP's interconnected design allows for straightforward chromatin reimmunoprecipitation, which allows this technology to also act as a microfluidic sequential ChIP-seq system. Finally, we ran FloChIP for the transcription factor MEF2A in 32 distinct human lymphoblastoid cell lines, providing insights into the main factors driving collaborative DNA binding of MEF2A and into its role in B cell-specific gene regulation. Together, our results validate FloChIP as a flexible and reproducible automated solution for individual or sequential ChIP-seq.

Exploration into the Needs and Requirements of the Remote Driver When Teleoperating the 5G-Enabled Level 4 Automated Vehicle in the Real World-A Case Study of 5G Connected and Automated Logistics.

Connected and automated vehicles have the potential to deliver significant environmental, safety, economic and social benefits. The key advancement for automated vehicles with higher levels of automation (SAE Level 4 and over) is fail-operational. One possible solution for the failsafe mode of automated vehicles is a 5G-enabled teleoperation system controlled by remote drivers. However, knowledge is missing regarding understanding of the human-machine interaction in teleoperation from the perspective of remote drivers. To address this research gap, this study qualitatively investigated the acceptance, attitudes, needs and requirements of remote drivers when teleoperating a 5G-enabled Level 4 automated vehicle (5G L4 AV) in the real world. The results showed that remote drivers are positive towards the 5G L4 AV. They would like to constantly monitor the driving when they are not controlling the vehicle remotely. Improving their field of vision for driving and enhancing the perception of physical motion feedback are the two key supports required by remote drivers in 5G L4 AVs. The knowledge gained in this study provides new insights into facilitating the design and development of safe, effective and user-friendly teleoperation systems in vehicle automation.

Quantitative and qualitative workload assessment in steep terrain forest operations: fostering a safer work environment through yarder automation.

Many forestry roles have changed from being manual tasks with a high physical workload to being a machine operator task with a high mental workload. Automation can support a decrease in mental fatigue by removing tasks that are repetitive and monotonous for the operators. Cable yarding presents an ideal opportunity for early adoption of automation technology; specifically the carriage movement along a defined corridor. A Valentini V-850 cable yarder was used in an Italian harvesting setting, in order to gauge the ergonomic benefit of carriage control automation. The study showed that automating yarder carriage movements improved the ergonomic situation of the workers directly involved in the related primary tasks. However, the caveat is that improving one work task may negatively affect the other work tasks, and therefore introducing automation to a worksite must be done after considering all impacts on the whole system. Practitioner summary: Automation decreased the winch operator's mental workload while improving overall productivity. At the same time, the mental and physiological workload of the operator tasked with bucking were slightly increased. Ideally, winch automation should be coupled with bucking mechanisation to balance the intervention and boost both operator well-being and productivity.

grasviq: an image analysis framework for automatically quantifying vein number and morphology in grass leaves.

Beyond facilitating transport and providing mechanical support to the leaf, veins have important roles in the performance and productivity of plants and the ecosystem. In recent decades, computational image analysis has accelerated the extraction and quantification of vein traits, benefiting fields of research from agriculture to climatology. However, most of the existing leaf vein image analysis programs have been developed for the reticulate venation found in dicots. Despite the agroeconomic importance of cereal grass crops, like Oryza sativa (rice) and Zea mays (maize), a dedicated image analysis program for the parallel venation found in monocots has yet to be developed. To address the need for an image-based vein phenotyping tool for model and agronomic grass species, we developed the grass vein image quantification (grasviq) framework. Designed specifically for parallel venation, this framework automatically segments and quantifies vein patterns from images of cleared leaf pieces using classical computer vision techniques. Using image data sets from maize inbred lines and auxin biosynthesis and transport mutants in maize, we demonstrate the utility of grasviq for quantifying important vein traits, including vein density, vein width and interveinal distance. Furthermore, we show that the framework can resolve quantitative differences and identify vein patterning defects, which is advantageous for genetic experiments and mutant screens. We report that grasviq can perform high-throughput vein quantification, with precision on a par with that of manual quantification. Therefore, we envision that grasviq will be adopted for vein phenomics in maize and other grass species.

Automated multi-attribute method sample preparation using high-throughput buffer exchange tips.

RATIONALE: The multi-attribute method (MAM) has become a valuable mass spectrometry (MS)-based tool that can identify and quantify the site-specific product attributes and purity information for biotherapeutics such as monoclonal antibodies (mAbs) and fusion molecules in recent years. As we expand the use of the MAM at various stages of drug development, it is critical to enhance the sample preparation throughput without additional chemical modifications and variability. METHODS: In this study, a fully automated MAM sample preparation protocol is presented, where rapid desalting in less than 15 minutes is achieved using miniaturized size-exclusion chromatography columns in pipette tips on an automated liquid handler. The peptide samples were analyzed using an electrospray ionization (ESI) orbitrap mass spectrometer coupled to an ultra-high-performance liquid chromatography (UHPLC) system with a dual column switching system. RESULTS: No significant change was observed in product attributes and their quantities compared with manual, low-artifact sample preparation. The sample recovery using the buffer exchange tips was greatly enhanced over the manual spin cartridges while still demonstrating excellent reproducibility for a wide variety of starting sample concentrations. Unlike a plate desalting system, the individual columns provide flexibility in the number of samples prepared at a time and sample locations within plates. CONCLUSIONS: This automated protocol enables the preparation of up to 96 samples with less "at-bench" time than the manual preparation of a smaller batch of samples, thereby greatly facilitating support of process development and the use of the MAM in quality control.

A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach.

Currently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities.

Adaptable (Not Adaptive) Automation: Forefront of Human-Automation Teaming.

OBJECTIVE: Identify a critical research gap for the human factors community that has implications for successful human-automation teaming. BACKGROUND: There are a variety of approaches for applying automation in systems. Flexible application of automation such that its level and/or type changes during system operations has been shown to enhance human-automation system performance. METHOD: This mini-review describes flexible automation in which the level of automated support varies across tasks during system operation, rather than remaining fixed. Two types distinguish the locus of authority to change automation level: adaptable automation (the human operator assigns how automation is applied) has been found to aid human's situation awareness and provide more perceived control versus adaptive automation (the system assigns automation level) that may impose less workload and attentional demands by automatically adjusting levels in response to changes in one or more states of the human, task, environment, and so on. RESULTS: In contrast to vast investments in adaptive automation approaches, limited research has been devoted to adaptable automation. Experiments directly comparing adaptable and adaptive automation are particularly scant. These few studies show that adaptable automation was not only preferred over adaptive automation, but it also resulted in improved task performance and, notably, less perceived workload. CONCLUSION: Systematic research examining adaptable automation is overdue, including hybrid approaches with adaptive automation. Specific recommendations for further research are provided. APPLICATION: Adaptable automation together with effective human-factored interface designs to establish working agreements are key to enabling human-automation teaming in future complex systems.

Comparison of four commercial, automated antigen tests to detect SARS-CoV-2 variants of concern.

A versatile portfolio of diagnostic tests is essential for the containment of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic. Besides nucleic acid-based test systems and point-of-care (POCT) antigen (Ag) tests, quantitative, laboratory-based nucleocapsid Ag tests for SARS-CoV-2 have recently been launched. Here, we evaluated four commercial Ag tests on automated platforms and one POCT to detect SARS-CoV-2. We evaluated PCR-positive (n = 107) and PCR-negative (n = 303) respiratory swabs from asymptomatic and symptomatic patients at the end of the second pandemic wave in Germany (February-March 2021) as well as clinical isolates EU1 (B.1.117), variant of concern (VOC) Alpha (B.1.1.7) or Beta (B.1.351), which had been expanded in a biosafety level 3 laboratory. The specificities of automated SARS-CoV-2 Ag tests ranged between 97.0 and 99.7% (Lumipulse G SARS-CoV-2 Ag (Fujirebio): 97.03%, Elecsys SARS-CoV-2 Ag (Roche Diagnostics): 97.69%; LIAISON® SARS-CoV-2 Ag (Diasorin) and SARS-CoV-2 Ag ELISA (Euroimmun): 99.67%). In this study cohort of hospitalized patients, the clinical sensitivities of tests were low, ranging from 17.76 to 52.34%, and analytical sensitivities ranged from 420,000 to 25,000,000 Geq/ml. In comparison, the detection limit of the Roche Rapid Ag Test (RAT) was 9,300,000 Geq/ml, detecting 23.58% of respiratory samples. Receiver-operating-characteristics (ROCs) and Youden's index analyses were performed to further characterize the assays' overall performance and determine optimal assay cutoffs for sensitivity and specificity. VOCs carrying up to four amino acid mutations in nucleocapsid were detected by all five assays with characteristics comparable to non-VOCs. In summary, automated, quantitative SARS-CoV-2 Ag tests show variable performance and are not necessarily superior to a standard POCT. The efficacy of any alternative testing strategies to complement nucleic acid-based assays must be carefully evaluated by independent laboratories prior to widespread implementation.

Development of an automated platform for the optimal production of glycoconjugate vaccines expressed in Escherichia coli.

Protein Glycan Coupling Technology (PGCT) uses purposely modified bacterial cells to produce recombinant glycoconjugate vaccines. This vaccine platform holds great potential in this context, namely due to its modular nature, the simplified production process in comparison to traditional chemical conjugation methods, and its amenability to scaled-up operations. As a result, a considerable reduction in production time and cost is expected, making PGCT-made vaccines a suitable vaccine technology for low-middle income countries, where vaccine coverage remains predominantly low and inconsistent. This work aims to develop an integrated whole-process automated platform for the screening of PGCT-made glycoconjugate vaccine candidates. The successful translation of a bench scale process for glycoconjugate production to a microscale automated setting was achieved. This was integrated with a numerical computational software that allowed hands-free operation and a platform adaptable to biological variation over the course of a production process. Platform robustness was proven with both technical and biological replicates and subsequently the platform was used to screen for the most favourable conditions for production of a pneumococcal serotype 4 vaccine candidate. This work establishes an effective automated platform that enabled the identification of the most suitable E. coli strain and genetic constructs to be used in ongoing early phase research and be further brought into preclinical trials.

Impact of 24/7 loading of blood culture bottles in a new automated incubator on the diagnosis of bloodstream infections.

Blood culturing (BC) remains the gold standard for bloodstream diagnosis but its workflow is slow. We aimed reducing this time by implementing a new automated incubator with a 24/7 BC workflow. With this new strategy, time to incubation was shorter (1.52 h vs 6.82 h), positivity rates were higher (10.6% vs 8.9%, p<0.05), and the number of BSI diagnostics increased (16.1% vs 13.8% patients and 2.3 vs 1.9 density episode per 1000 hospital days). Our results show that implementing automatic loading of BC bottles with a 24/7 strategy not only shortened time to diagnosis but significantly increased the BSI diagnosis rate.