BACKGROUND: Artificial intelligence (AI)-based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially those that have been subjected to biopsy.
OBJECTIVE: Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy.
METHODS: This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling.
RESULTS: Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P<.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01).
CONCLUSIONS: Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist\'s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account.

BACKGROUND: Ricin is a potential biowarfare agent. It is a phytotoxin isolated from castor seeds. At present there is no antidote available for ricin poisoning, patients only get supportive treatment based on their symptoms. This highlights the importance of early detection to avoid severity of accidents and reduce the risk factor. Considering this, our study aimed to develop a highly sensitive and specific sandwich ELISA for the detection of ricin.
METHODS: Ricin was purified from castor seeds. Anti-ricin polyclonal and monoclonal antibodies were generated from rabbit antisera and hybridoma cell (1H6F1) supernatant using a protein A/G column. Antibody titer estimation was done using Indirect ELISA. A streptavidin-biotin-based sandwich ELISA was developed and the limit of detection (LOD), linear range, intra and inter-assay coefficient of variation (CV), and cross-reactivity with other similar toxins were determined. Interference of human plasma samples spiked with ricin was also checked.
RESULTS: The LOD of the ELISA was found to be 0.45 ng/ml, with a linear range of 0.90-62 ng/ml, intra and inter-assay CV ranged from 3.34 % to 5 % and 5.17 % to 10.80 % respectively. The assay was not cross-reactive with other similar ribosome-inactivating protein (RIP) toxins. Ricin was detected in spiked plasma samples.
CONCLUSIONS: The developed assay is highly sensitive and specific for detecting ricin and is not cross-reactive with other similar types of toxins. The assay can detect ricin in spiked plasma samples, so it has the potential to be used for the analysis of clinical samples after ricin poisoning.

This study involved the preparation of an all-solid-state ion-selective electrode (ASS-ISE) with copper and a poly(3,4-ethylenedioxythiophene) and polystyrene sulfonate (PEDOT/PSS) conversion layer through electrode deposition. The morphology of the PEDOT/PSS film was characterized, and the performance of the copper ion-selective film was optimized. Additionally, a microfluidic chip for the ASS-ISE with copper was designed and prepared. An integrated microfluidic chip test system with an ASS-ISE was developed using a self-constructed potential detection device. The accuracy of the system was validated through comparison testing with atomic absorption spectrophotometry (AAS). The experimental findings indicate that the relative standard deviation (RSD) of the integrated ASS-ISE with the copper microfluidic chip test system is 4.54%, as compared to the industry standard method. This value complies with the stipulated requirement of an RSD ≤ 5% in DL/T 955-2016.

The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone\'s 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of sensor usage and the system\'s working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation.

Introduction Road traffic accidents (RTAs) are considered a major cause of death in Saudi Arabia. As seat belt compliance provides significant safety among drivers, a camera detection system has been implemented in March 2018 to enforce seat belt utilization, which can decrease the severity of road traffic injuries. There are no previous studies in the country that have assessed the effectiveness of a seat belt camera detection system on the severity of RTA-related injuries. Methods A retrospective cohort study was conducted at King Abdulaziz Medical Trauma Center in Riyadh, Saudi Arabia. The study included 688 adult patients who were involved in RTAs from the period of March 2016 to March 2020. A data extraction form included sociodemographics, clinical variables, and outcome measures. The data were analyzed using Statistical Analysis Software (SAS) to evaluate the primary outcome measures: mortality, ejection from the vehicle, ICU admissions, and severity measures (injury severity score (ISS) and Glasgow Coma Scale (GCS)) before and after the implementation of seat belt detection system. Associations of the outcome measures in the pre-implementation and the post-implementation periods\' seat belt detection were assessed using regression tests. Results There was no significant difference in the mean age between the pre-implementation and post-implementation periods of the seat belt detection system (31.39 years and 32.57 years, respectively). All of the outcome measures have improved following the implementation of the seat belt detection system. Mortality and ejection rates decreased significantly with 58% lower risk of death (OR= 0.42; 95% CI= 0.2,0.8) and 37% lower risk of ejection (OR= 0.63; 95% CI= 0.42,0.94). ICU admissions showed a slight decline in the post-implementation period compared to the pre-implementation period (30.37% vs. 31.37, p<0.7764). Severity measures (ISS and GCS) were slightly improved in the post-implementation period. Head and neck injuries were dominant in the pre-implementation period, and chest injuries were the most common body injuries after the implementation. Conclusion This study highlights the direct association between compliance with seat belt use and the primary outcome measures among patients who survived a road traffic accident. All of the outcome measures showed improvement in the post-implementation period, which indicates the effectiveness of the newly implemented seat belt detection system. These findings raise awareness to the public in regard to seat belt compliance.

Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products-ethylene glycol, crude oil, gasoil, and gasoline-were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics-variance, fourth order moment, skewness, and kurtosis-were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

Umami is one of five basic tastes, the elucidation of its mechanism by the study of the interaction between umami polypeptides and hT1R1 umami receptors is of great significance. However, research on umami peptides targeting human T1R1 receptors is lacking, and the molecular mechanism remains elusive. Here, we successfully established a system to detect umami peptides targeting human T1R1 receptors by fluorescence spectroscopy, Surface Plasmon Resonance (SPR) and computational simulation. The sensory evaluation, calculated Kd value, and experimental affinity results between the four selected umami peptides (GRVSNCAA, KGDEESLA, KGGGGP, and TGDPEK) and glutamate were tested using this system, and all matched well. The maximum Ka value of GRVSNCAA was 479.55 M-1, and the minimum affinity of TGDPEK was 2.67 M-1. Computational simulations showed that the different peptide binding sites in the hT1R1 binding pocket occupied due to conformational changes are important factors for different taste thresholds, and that peptide hydrophobicity plays an important role in regulating affinity. Thus, our study enables rapid screening of high-intensity umami peptides and the development of T1R1 receptor-based umami detection sensors.

With the decrease in the cost and size of drones in recent years, their number has also increased exponentially. As such, the concerns regarding security aspects that are raised by their presence are also becoming more serious. The necessity of designing and implementing systems that are able to detect and provide defense actions against such threats has become apparent. In this paper, we perform a survey regarding the different drone detection and defense systems that were proposed in the literature, based on different types of methods (i.e., radio frequency (RF), acoustical, optical, radar, etc.), with an emphasis on RF-based systems implemented using software-defined radio (SDR) platforms. We have followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines in order to provide a concise and thorough presentation of the current status of the subject. In the final part, we also describe our own solution that was designed and implemented in the framework of the DronEnd research project. The DronEnd system is based on RF methods and uses SDR platforms as the main hardware elements.