Identification Of Alarms Research Articles

Evaluation of Alectinib Metabolic Stability in HLMs Using Fast LC-MS/MS Method: In Silico ADME Profile, P450 Metabolic Lability, and Toxic Alerts Screening.

Alectinib, also known as Alecensa®, is prescribed for the therapeutic treatment of individuals diagnosed with metastatic non-small cell lung cancer (NSCLC) who have a specific genetic mutation referred to as anaplastic lymphoma kinase (ALK) positivity. The Food and Drug Administration granted regular approval to alectinib, a drug developed by Hoffmann-La Roche, Inc. (Basel, Switzerland)/Genentech, Inc. (South San Francisco, CA, USA), on 6 November 2017. The screening of the metabolic stability and identification of hazardous alarms within the chemical structure of ALC was conducted using the StarDrop software package (version 6.6), which incorporates the P450 metabolic module and DEREK software (KB 2018 1.1). The primary aim of this investigation was to develop a high-throughput and accurate LC-MS/MS technique for the quantification of ALC in the metabolic matrix (human liver microsomes; HLMs). The aforementioned methodology was subsequently employed to assess the metabolic stability of ALC in HLMs through in vitro tests, with the obtained results further validated using in silico software. The calibration curve of the ALC showed a linear correlation that exists within the concentration range from 1 to 3000 ng/mL. The LC-MS/MS approach that was recommended exhibited accuracy and precision levels for both inter-day and intra-day measurements. Specifically, the accuracy values ranged from -2.56% to 3.45%, while the precision values ranged from -3.78% to 4.33%. The sensitivity of the established approach was proved by its ability to adhere to an LLOQ of 0.82 ng/mL. The half-life (t1/2) and intrinsic clearance (Clint) of ALC were estimated to be 22.28 min and 36.37 mL/min/kg, correspondingly, using in vitro experiments. The ALC exhibited a moderate extraction ratio. The metabolic stability and safety properties of newly created derivatives can be enhanced by making modest adjustments to the morpholine and piperidine rings or by substituting the substituent, as per computational software. In in silico ADME prediction, ALC was shown to have poor water solubility and high gastrointestinal absorption along with inhibition of some cytochrome P450s (CYP2C19 and CYP2C9) without inhibition of others (CYP1A2, CYP3A4, and CYP2D6) and P-glycoprotein substrate. The study design that involves using both laboratory experiments and different in silico software demonstrates a novel and groundbreaking approach in the establishment and uniformization of LC-MS/MS techniques for the estimation of ALC concentrations, identifying structural alerts and the assessment of its metabolic stability. The utilization of this study strategy has the potential to be employed in the screening and optimization of prospective compounds during the drug creation process. This strategy may also facilitate the development of novel derivatives of the medicine that maintain the same biological action by targeted structural modifications, based on an understanding of the structural alerts included within the chemical structure of ALC.

Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection

Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic aperture radar (SAR) renders the identification of false alarms using detectors challenging. Polarimetric SAR has the potential to improve ship detection performance owing to its distinctive polarization characteristics. The present study proposes a dualistic cascade convolutional neural network (DCCNN) algorithm driven by polarization characteristics for ship detection with fully PolSAR data. First, the new characterizations of fully PolSAR data—Optimized SPAN (OSPAN) and 6-D polarization vector (P6), were mined and defined based on the polarization coherence matrix to introduce more intact information of targets. Then, a backbone feature extraction network with parallel dualistic cascade architecture, basic geometric feature extraction network (BGFENet), and polarization feature enhancement network (PFENet) was specifically constructed, which provided the comprehensive feature representation of targets via feature fusion. Finally, the classification and regression tasks were accomplished in the fully convolutional detection subnetwork relying on extracted multi-scale fusion feature maps, while focusing on target location regression, leading to a decrease in the cost of detection efficiency caused by redundant PFENet. In addition, the corresponding training strategy according to the special architecture of DCCNN was designed to overcome the problem of labeled fully PolSAR data insufficiency and migrate refined geometric knowledge of targets in the single-polarization SAR amplitude image. Experimental results on the established fully polarized SAR dataset show that DCCNN outperforms the competitive CNN-based target detection methods by at least 6.79% in terms of average precision. Moreover, experimental results on the typical large scenes show that DCCNN is at least 0.88% higher than the well-known conventional methods in terms of F1 score.

A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.

Identification of protein-coding and non-coding transcripts is paramount for understanding their biological roles. Computational approaches have been addressing this task for over a decade; however, generalized and high-performance models are still unreliable. This benchmark study assessed the performance of 24 tools producing >55 models on the datasets covering a wide range of species. We have collected 135 small and large transcriptomic datasets from existing studies for comparison and identified the potential bottlenecks hampering the performance of current tools. The key insights of this study include lack of standardized training sets, reliance on homogeneous training data, gradual changes in annotated data, lack of augmentation with homology searches, the presence of false positives and negatives in datasets and the lower performance of end-to-end deep learning models. We also derived a new dataset, RNAChallenge, from the benchmark considering hard instances that may include potential false alarms. The best and least well performing models under- and overfit the dataset, respectively, thereby serving a dual purpose. For computational approaches, it will be valuable to develop accurate and unbiased models. The identification of false alarms will be of interest for genome annotators, and experimental study of hard RNAs will help to untangle the complexity of the RNA world.

Attention Limitations in the Detection and Identification of Alarms in Close Temporal Proximity.

The aim of this study was to establish the effects of simultaneous and asynchronous masking on the detection and identification of visual and auditory alarms in close temporal proximity. In complex and highly coupled systems, malfunctions can trigger numerous alarms within a short period of time. During such alarm floods, operators may fail to detect and identify alarms due to asynchronous and simultaneous masking. To date, the effects of masking on detection and identification have been studied almost exclusively for two alarms during single-task performance. This research examines 1) how masking affects alarm detection and identification in multitask environments and 2) whether those effects increase as a function of the number of alarms. Two experiments were conducted using a simulation of a drone-based package delivery service. Participants were required to ensure package delivery and respond to visual and auditory alarms associated with eight drones. The alarms were presented at various stimulus onset asynchronies (SOAs). The dependent measures included alarm detection rate, identification accuracy, and response time. Masking was observed intramodally and cross-modally for visual and auditory alarms. The SOAs at which asynchronous masking occurred were longer than reported in basic research on masking. The effects of asynchronous and, even more so, simultaneous masking became stronger as the number of alarms increased. Masking can lead to breakdowns in the detection and identification of alarms in close temporal proximity in complex data-rich domains. The findings from this research provide guidance for the design of alarm systems.

Comparison of machine learning classifiers: A case study of temperature alarms in a pharmaceutical supply chain

Temperature deviations are critical in a pharmaceutical supply chain (SC) due to quality deterioration concerns and resulting health risks. The current solutions ensuring temperature maintenance are either labor-intensive or prone to triggering alarms that require no corrective measures, which, in turn, increase the alarm investigation costs. Machine learning (ML) methods have fared well both in the areas characterized by the execution of repetitive tasks and in the identification of false alarms; however, they have not been applied in the context of temperature monitoring in a pharmaceutical SC. In this paper, we used the real-world data of a large international logistics service provider for the period of 2013–2018 and compared the optimized performance of 10 ML classification methods in the task of false temperature alarm identification. Such additional features as temperature in the location of possible physical handling and average temperature deviation were either externally collected or estimated to enrich the models. In general, gradient boosting achieved the best performance in our evaluations, with an accuracy of 95.9% in comparison with the value of 16.6% demonstrated by the current legacy rule-based system. The feature ranking and sensitivity tests pointed to the strength of the features indicating an absolute temperature deviation and the location of cargo along the SC. The tests simulating model applications on new dissimilar observations showed various performance losses across classifiers, with the best stability retained for a new customer scenario and largest performance decreases for a new temperature range scenario.

SAVIOR ICU: sonification and vibrotactile interface for the operating room and intensive care unit.

Alarm fatigue is an issue for healthcare providers in the intensive care unit, and may result from desensitization of overbearing and under-informing alarms. To directly increase the overall identification of medical alarms and potentially contribute to a downstream decrease in the prevalence of alarm fatigue, we propose advancing alarm sonification by combining auditory and tactile stimuli to create a multisensory alarm. Participants completed four trials-two multisensory (auditory and tactile) and two unisensory (auditory). Analysis compared the unisensory trials to the multisensory trials based on the percentage of correctly identified point of change, direction of change and identity of three physiological parameters (indicated by different instruments): heart rate (drums), blood pressure (piano), blood oxygenation (guitar). A repeated-measures of ANOVA yielded a significant improvement in performance for the multisensory group compared to the unisensory group (p < 0.05). Specifically, the multisensory group had better performance in correctly identifying parameter (p < 0.05) and point of change (p < 0.05) compared to the unisensory group. Participants demonstrated a higher accuracy of identification with the use of multisensory alarms. Therefore, multisensory alarms may relieve the auditory burden of the medical environment and increase the overall quality of care and patient safety.

The Effects of Masking on the Detection of Alarms in Close Temporal Proximity

The Effects of Masking on the Detection of Alarms in Close Temporal Proximity

Optimized Energy Efficient Trust Aware System in Wireless Sensor Networks

Energy and security are very important issues in Wireless Sensor Networks (WSN) which need to be handled. These issues are interrelated because of limited energy there are some restrictions on implementation of security. Insider packet drop attack is one of the dangerous attacks for wireless sensor network that causes a heavy damage to WSN functionalities by dropping packets. It becomes necessary to identify such attack for secure routing of data in WSN. To detect this attack, trust mechanism has been proven as a successful technique. In this mechanism, each node verifies the trustworthiness of its neighbor node before packet transmission so that packets can only be transmitted to trustworthy nodes. But there is a problem of False Alarm with such trust-aware scheme. False alarm occurs when a good node’s trust value goes down due to natural packet dropping and being eliminated from the routing paths. This wastes network’s resources that further shortens network lifetime. In this paper, we have proposed a system for identification and recovery of false alarms (IRFA) which is the optimization of existing trust based system. But security solution needs to be energy efficient due to scarcity of energy resources in WSN. To provide energy efficiency, we have implemented proposed IRFA system in cluster based environment which detects insider packet drop attackers in an energy efficient manner. We have conducted OMNET++ simulation and results demonstrate that the proposed system performance is better than existing trust-based system in terms of packet delivery rate and energy efficiency which improves network lifetime.

Comparative study for identification of multiple alarms in telecommunication networks

Telecommunication networks consist of communication units interconnected physically or by means of protocols in order to provide basic services like data, voice, or image transfers. In this study, a modeling frame for network units and their links in a topological frame is presented based on a real mobile communication network named TASMUS (TAktik Saha MUharebe Sistemi - Tactical Field Combat System). Alarm handling is one of the most critical features required in communication networks. Based on simulated single alarm and multiple (double) alarm scenarios, known powerful alarm estimation approaches, namely the coding method, neural networks, and knowledge-based systems, have been studied to assess their capabilities for identifying multiple faults that might occur simultaneously in real time. They have also been compared in order to evaluate the performance of alarms under different noise levels for specific TASMUS networks.

Development and experimental evaluation of an alarm concept for an integrated surgical workstation.

Alarm conditions of the technical equipment in operating rooms represent a prevalent cause for interruptions of surgeons and scrub nurses, resulting in an increase of workload and potential reduction of patient safety. In this work, an alarm concept for an integrated operating room system based on open communication standards is developed and tested. In a laboratory experiment, the reactions of surgeons were analysed, comparing the displaying of alarms on an integrated workstation and on single devices: disruptive effects of alarm handling on primary task (ratings of perceived distraction, resumption lag, deterioration of speed, accuracy, and prospective memory), efficiency and effectiveness of identification of alarms, as well as perceived workload were included. The identification of the alarm cause is significantly more efficient and effective with the integrated alarm concept. Moreover, a slightly lower deterioration of performance of the primary task due to the interruption of alarm handling was observed. Displaying alarms on an integrated workstation supports alarm handling and consequently reduces disruptive effects on the primary task. The findings show that even small changes can reduce workload in a complex work environment like the operating room, resulting in improved patient safety.

Detection of onset of failure in prestressed strands by cluster analysis of acoustic emissions

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

ドットマトリックス解析によるプラント運転データからの連鎖アラーム抽出

ドットマトリックス解析によるプラント運転データからの連鎖アラーム抽出

“If I feel disgusted, I must be getting ill”: Emotional reasoning in the context of contamination fear

Patients suffering from anxiety disorders have been shown to infer danger on the basis of their anxiety responses “if I feel anxious, there must be danger.” This tendency logically hampers the identification of false alarms and may thus act in a way to confirm the a priori threat value of the feared stimuli/situations. Since disgust is assumed to play a critical role in the persistence of contamination fears in OCD, the question rises whether individuals suffering from fear of contamination perhaps similarly infer danger on the basis of their disgust response: “If I feel disgusted, it must be contagious.” Therefore, this study tested whether indeed disgust-based reasoning might be involved in fear of contamination. On the basis of the contamination fear subscale of the Padua Inventory (PI), we selected a group of participants scoring higher than the established clinical range (n = 31, PI > 13) and a group of participants low (n = 27, PI < 5) in contamination fear. Each participant was presented with a series of 2 times 4 types of scripts that systematically varied in the absence/presence of objective threat of contamination and the absence/presence of the actor's disgust response. Following each script, participants rated their perceived threat of contamination/illness. In line with the hypothesis that disgust-based reasoning might be involved in fear of contamination, specifically high contamination fearful individuals inferred risk of becoming ill on the basis of experienced disgust (in addition to objective threat), as was evidenced by a significant Group (high vs. low) × Threat (yes vs. no) × Disgust response (yes vs. no) interaction. This finding might not only help to explain the persistence of contamination fears, but also provides some fresh clues to improve currently available treatment options.

Response characteristics of an aquatic biomonitor used for rapid toxicity detection.

The response characteristics of an aquatic biomonitor that detects toxicity by monitoring changes in bluegill (Lepomis macrochirus Rafinesque) ventilatory and movement patterns were evaluated in single chemical laboratory studies at concentrations near the 96-h LC(50) concentration and at the EILATox-Oregon Workshop in sequential tests of multiple unknown samples. Baseline data collected prior to exposure allows each fish to serve as its own control. When at least 70% of exposed fish exhibit ventilatory or movement parameters significantly different from baseline observations, a group alarm is declared. In the laboratory studies, the aquatic biomonitor responded to the majority of chemicals at the 96-h lc(50) within an hour or less, although substantially higher response times were found for malathion and pentachlorophenol. Workshop tests of single chemical concentrations presented as blind samples were consistent with the laboratory test results. There were no alarms under control conditions in any test. Although data are limited, the aquatic biomonitor appears to respond more rapidly to chemicals causing membrane irritation, narcosis or polar narcosis than to acetylcholinesterase inhibitors or oxidative phosphorylation uncouplers. All four monitored parameters (ventilatory rate, cough rate, ventilatory depth and movement) contributed to identification of first alarms at acutely toxic levels. Understanding these response patterns can be useful in data interpretation for biomonitor applications such as surface water monitoring for watershed protection, wastewater treatment plant effluent monitoring or source water monitoring for drinking water protection.

Audibility and identification of auditory alarms in the operating room and intensive care unit

The audibility and the identification of 23 auditory alarms in the intensive care unit (ICU) and 26 auditory alarms in the operating rooms (ORs) of a 214-bed Canadian teaching hospital were investigated. Digital tape recordings of the alarms were made and analysed using masked-threshold software developed at the Université de Montréal. The digital recordings were also presented to the hospital personnel responsible for monitoring these alarms on an individual basis in order to determine how many of the alarms they would be able to identify when they heard them. Several of the alarms in both areas of the hospital could mask other alarms in the same area, and many of the alarms in the operating rooms could be masked by the sound of a surgical saw or a surgical drill. The staff in the OR (anaesthetists, anaesthesia residents, and OR technologists) were able to identify a mean of between 10 and 15 of the 26 alarms found in their operating theatres. The ICU nurses were able to identify a mean of between 9 and 14 of the 23 alarms found in their ICU. Alarm importance was positively correlated with the frequency of alarm identification in the case of the OR, rho = 0.411, but was not significantly correlated in the case of the ICU, rho = 0.155. This study demonstrates the poor design of auditory warning signals in hospitals and the need for standardization of alarms on medical equipment.