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To evaluate the clinical immunogenicity of eight antibody–drug conjugates (ADCs), multi-domain biotherapeutics that could theoretically pose a greater immunogenicity risk than monoclonal antibodies (mAbs) because they contain non-natural structural motifs. Immunogenicity strategies and assays for these ADCs included those commonly used for conventional biotherapeutics with additional characterization. A tiered approach was adopted for testing Phase I and II clinical study samples with screening, confirmatory assays and additional domain characterization. Antidrug antibody incidences with these ADCs were within those reported for mAb biotherapeutics with no apparent impact on clinical outcomes. These data suggest that the ADC hapten-like structure across these eight ADCs does not appear to increase patient immune responses beyond those generally observed for mAb biotherapeutics.

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

This short review highlights the main sensor structures that are used in the ADAS (Advanced Driver Assisted System) field, in order to outline the progress at this moment (2023). Starting from this achieved level, we have proposed another approach to the ADAS problematics, regarding the prospects for development after the current moment, a different perspective than the one that has AV (Autonomus Vehicle) as its endpoint. Computer Assisted Driving represents a necessary and sufficient solution for increasing traffic safety in the near future. The integrated driver concept in the assisted environment implies a symbiotic human-machine collaboration. This collaboration requires a psychological acceptance of the ADAS system on behalf of humans, as an essential part of the current and future automobile. The progress highlighted by the documentation undertaken allows us to imagine a concept of a symbiotic human-machine system, based on the communication between ADAS and an external computing resource that assists the driver, based on the long analysis of recorded car events. Future automobiles must include an ADAS system that provides a subtle human-machine synapse, based on a series of computerized warnings to the driver generated in a timely manner, resulting from the high predictive capacity of AI-DLM algorithms. The computing speed of the local ADAS algorithms will be supplemented by the external server processing resource, accessed at the right time, to resolve an unexpected deadlock in the car roll.

The ADA testing strategy for protein therapeutics was established almost two decades ago when assay methodologies were rudimentary, and serious immunogenicity-related safety issues had recently been observed with some biotherapeutics. The current testing paradigm employs multiple tiers and stringent cut points to minimize false negatives, reflecting a conservative stance towards ADA analysis. The development of highly sensitive ADA assay platforms and technologies such as humanized or fully human monoclonal antibody (mAb) drugs has put the traditional, resource-intensive 3-tiered testing approach under scrutiny. ADA data from clinical studies for three different mAb programs were re-assessed to explore the feasibility of a simplified 1-tiered ADA testing strategy with a 1% false positive cut point versus the traditional 3-tiered approach. The analysis demonstrated moderate to strong correlations between screening results (signal-to-noise, S/N) and those of confirmation and titer results, with the vast majority of samples (~ 97%) across all studies having the same ADA positive/negative classification with either testing approach. Furthermore, at the subject level, over 92% had the same ADA category (pre-existing, treatment-emergent, treatment-boosted) under both testing approaches. The re-categorized subjects had low titer ADA responses with no observed clinical implications on pharmacokinetics, efficacy, or safety. Finally, the treatment-emergent ADA incidences were comparable between the 1-tiered and 3-tiered approaches. The results demonstrate that the 1-tiered testing strategy is suitable for ADA assessment in these programs and is likely more widely applicable. Additionally, the 1-tiered approach could expedite data delivery and reduce resource needs in clinical development without compromising data quality or clinical interpretation. Graphical Abstract

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

In recent years, researchers of deep neural networks (DNNs)-based facial expression recognition (FER) have reported results showing that these approaches overcome the limitations of conventional machine learning-based FER approaches. However, as DNN-based FER approaches require an excessive amount of memory and incur high processing costs, their application in various fields is very limited and depends on the hardware specifications. In this paper, we propose a fast FER algorithm for monitoring a driver’s emotions that is capable of operating in low specification devices installed in vehicles. For this purpose, a hierarchical weighted random forest (WRF) classifier that is trained based on the similarity of sample data, in order to improve its accuracy, is employed. In the first step, facial landmarks are detected from input images and geometric features are extracted, considering the spatial position between landmarks. These feature vectors are then implemented in the proposed hierarchical WRF classifier to classify facial expressions. Our method was evaluated experimentally using three databases, extended Cohn-Kanade database (CK+), MMI and the Keimyung University Facial Expression of Drivers (KMU-FED) database, and its performance was compared with that of state-of-the-art methods. The results show that our proposed method yields a performance similar to that of deep learning FER methods as 92.6% for CK+ and 76.7% for MMI, with a significantly reduced processing cost approximately 3731 times less than that of the DNN method. These results confirm that the proposed method is optimized for real-time embedded applications having limited computing resources.

The increasing demand in the development of autonomous driving systems makes the employment of automotive radars unavoidable. Such a motivation for the demonstration of fully-autonomous vehicles brings the challenge of secure driving under high traffic jam conditions. In this paper, we present the investigation of Advanced Driver Assistance Systems (ADAS) radars from the perspective of electronic warfare (EW). Close to real life, four ADAS jamming scenarios have been defined. Considering these scenarios, the necessary jamming power to jam ADAS radars is calculated. The required jamming Effective Radiated Power (ERP) is −2 dBm to 40 dBm depending on the jamming scenario. These ERP values are very low and easily realizable. Moreover, the effect of the jamming has been investigated on the radar detection at radar Range Doppler Map (RDM) and 2-Dimensional Constant False Alarm Rate (2D-CFAR). Furthermore, the possible jamming system requirements have been investigated. It is noted that the required jamming system will not require high-end technology. It is concluded that for the security of automotive driving, the ADAS radar manufacturer should consider the intentional jamming and related Electronic Counter Countermeasures (ECCM) features in the design of ADAS radars.

Despite the advancement of advanced driver assistance systems (ADAS) and autonomous driving systems, surpassing the threshold of level 3 of driving automation remains a challenging task. Level 3 of driving automation requires assuming full responsibility for the vehicle’s actions, necessitating the acquisition of safer and more interpretable cues. To approach level 3, we propose a novel method for detecting driving vehicles and their brake light status, which is a crucial visual cue relied upon by human drivers. Our proposal consists of two main components. First, we introduce a fast and accurate one-stage brake light status detection network based on YOLOv8. Through transfer learning using a custom dataset, we enable YOLOv8 not only to detect the driving vehicle, but also to determine its brake light status. Furthermore, we present the publicly available custom dataset, which includes over 11,000 forward images along with manual annotations. We evaluate the performance of our proposed method in terms of detection accuracy and inference time on an edge device. The experimental results demonstrate high detection performance with an mAP50 (mean average precision at IoU threshold of 0.50) ranging from 0.766 to 0.793 on the test dataset, along with a short inference time of 133.30 ms on the Jetson Nano device. In conclusion, our proposed method achieves high accuracy and fast inference time in detecting brake light status. This contribution effectively improves safety, interpretability, and comfortability by providing valuable input information for ADAS and autonomous driving technologies.

INTRODUCTION This proof‐of‐concept, dose‐finding phase IIb trial evaluated treatment with ABBV‐552 compared with placebo in participants with clinically diagnosed mild Alzheimer's disease (AD). METHODS Participants aged 50 to 90 years with a Mini‐Mental State Examination score of 20 to 26 and a global Clinical Dementia Rating score of 0.5 to 1.0 were randomized 1:1:1:1 to placebo or ABBV‐552 (1, 5, or 15 mg) daily. The primary endpoint was the change from baseline in the 14‐item Alzheimer's Disease Assessment Scale‐Cognitive Subscale (ADAS‐Cog 14) at week 12. RESULTS Two hundred sixty‐three participants were randomized. The least‐squares mean difference (vs placebo) in change from baseline at week 12 in ADAS‐Cog 14 total score (95% confidence interval) for ABBV‐552 1 mg was −0.02 (−1.87, 1.83), nominal p = 0.9819; 5 mg, −0.42 (−2.25, 1.42), nominal p = 0.6545; 15 mg, 0.25 (−1.58, 2.08), nominal p = 0.7860. Treatment‐emergent adverse events occurred in 48.5% of ABBV‐552 recipients versus 42.2% in the placebo group; no safety concerns were identified. DISCUSSION ABBV‐552 did not demonstrate a meaningful difference versus placebo on the primary endpoint. Highlights ABBV‐552 is a small molecule that modulates the SV2A receptor in neurons ABBV‐552 may enhance synaptic efficiency leading to improved cognition in patients with Alzheimer's disease (AD) Participants with mild AD were treated with either placebo, 1 mg, 5 mg, or 15 mg of ABBV‐552 covering an estimated 35% to 80% SV2A receptor occupancy in a phase II randomized clinical trial Results failed to show efficacy over placebo as measured by ADAS‐Cog 14 at week 12 ABBV‐552 was generally safe and well tolerated

There is an urgency to achieve equitable representation of Latinx‐Hispanic participants in Alzheimer's disease (AD) clinical trials. Valid instruments for these communities contribute to the generalizability of clinical trial outcomes. The aim of this study was to review articles reporting studies that conducted a thorough assessment of the validity and reliability of three gold‐standard cognitive measures with U.S. Latinx‐Hispanic communities. We reviewed validity/reliability studies of the Clinical Dementia Rating (CDR), Alzheimer's Disease Assessment Scale—Cognition (ADAS‐Cog), and Repeatable Battery of the Assessment of Neuropsychological Scales (RBANS) among U.S. Latinx‐Hispanic communities. Database searches included PubMed and PsycINFO. For the CDR, one study assessed validity and the other reliability. For the RBANS, one study evaluated the diagnostic accuracy, and another conducted an equating analysis. No studies addressed the validation/reliability of ADAS‐Cog. Our literature review revealed limited studies examining the validity and reliability of the CDR, ADAS‐Cog, and RBANS in U.S. Latinx‐Hispanic communities. Highlights Various government entities are mandating increased enrollment of the Latinx‐Hispanic communities in AD clinical trials without considering the potential negative impact of study results when invalid COAs are used in underrepresented communities. Our investigation demonstrates three gold‐standard COAs routinely used in AD clinical trials have mostly not been validated on the Latinx‐Hispanic communities residing in the United States. We recommend specific actions to support the development of valid clinical outcome measures for the Latinx‐Hispanic communities.