Positive Alerts Research Articles

Evaluation and characterization of estrus alerts and behavioral parameters generated by an ear-attached accelerometer-based system for automated detection of estrus

Our objectives were to evaluate the performance of an ear-attached automated estrus detection (AED) system (Smartbow; Zoetis) that monitored physical activity and rumination time, and to characterize AED system estrus alert features (i.e., timing and duration). Lactating Holstein cows (n = 216) commenced a protocol for the synchronization of estrus at 50 ± 3 DIM or 18 ± 3 d after artificial insemination. For 7 d after induction of luteolysis with PGF2α (d 0), we used visual observation of estrous behavior (30 min, 2 times per day) and data from an automated mounting behavior monitoring system based on a pressure-activated tail-head sensor (HeatWatch; Cowchips LLC) as a reference test (RTE) to detect behavioral estrus. Concomitantly, estrus alerts and their features were collected from the AED system. Progesterone levels confirmed luteal regression, and transrectal ultrasonography confirmed the occurrence and timing of ovulation. Performance metrics for the AED system were estimated with PROC FREQ in SAS, using the RTE or ovulation only as a reference. Performance was also estimated after the removal of cows with a discrepancy between the RTE and ovulation. Continuous outcomes with or without repeated measurements were evaluated by ANOVA using PROC MIXED in SAS. Based on the RTE, 86.6% (n = 187) of the cows presented estrus and ovulated; 1.4% (n = 3) presented estrus and did not ovulate; 6.4% (n = 14) did not present estrus but ovulated; and 5.6% (n = 12) did not present estrus or ovulation. We found no difference in the proportion of cows detected in estrus and with ovulation for the AED system (83.4%) and the RTE (86.6%). Compared with estrus events as detected by the RTE, sensitivity for the AED was 91.6% (95% CI: 87.6-95.5) and specificity was 69.2% (95% CI: 51.5-87.0). Using ovulation as reference, sensitivity was 89.6% (95% CI: 85.3-93.8) and specificity was 86.7% (95% CI 69.5-100). For all cows with agreement between the RTE and ovulation, sensitivity was 92.5% (95% CI: 88.7-96.3) and specificity was 91.7% (95% CI: 76.0-100). The mean (±SD) interval from induction of luteolysis to estrus alerts, estrus alert duration, and the onset of estrus alerts to ovulation interval were 72.2 ± 18.1, 13.5 ± 3.8, and 23.8 ± 7.1 h, respectively. We concluded that an ear-attached AED system that monitored physical activity and rumination time was effective at detecting cows in estrus and generated few false positive alerts when accounting for ovulation, cow physiological limitations, and the limitations of the RTE.

One size does not fit all: Factors associated with increased frequency of radiation overexposure alerts based on fixed-alert thresholds.

Quantify the expected rate of CT radiation dose alerts for three body regions using accepted radiation dose benchmarks and assess key determinants of alert frequency. This IRB-approved retrospective cohort study evaluated consecutive CT examinations performed between July and December 2013 within an academic medical system. CTDIvol x-ray tube output metrics were compared to the body-region-specific benchmark levels, Achievable Doses (AD), Diagnostic Reference Levels (DRL), and Dose Notification Values (DNV). A logistic regression model for the simulated alerts was fit as a function of the independent predictors: scanner, body region, gender, weight, and age. For 17,000 exams, the proportion of events triggering alerts increased with patient weight. Significant covariates were scanner, body region, patient weight and patient age (all p<0.0001). Odds of alert generation for the AD, DRL, and DNV benchmarks increased by 7.6%, 6.6% and 2.9% per kilogram, respectively, and by 0.8%, 1.1% and -2.7% per year of age (all p<0.0001). Compared to the most highly optimized scanner, odds of alert generation varied by a factor of 595 for AD, 1126 for DRL, and 13 for DNV. Alert frequency was significantly correlated with weight, age, body region and scanner. Controllable factors include scanner functionality and associated protocol optimization. Patient factors driving alert frequency are predominantly weight, and to a lesser degree, age. Size-agnostic fixed dose thresholds can frequently produce false positive alerts in appropriately performed exams of large patients, while missing opportunities to identify outlier scans of higher-than-expected dose in small patients.

A Combination Techniques of Intrusion Prevention and Detection for Cloud Computing

Cloud computing provides scalable, on-demand, and highly available computing services over the Internet to both the public and organizations on a pay-per-use basis. It provides a variety of services such as networking, storage space, and applications. The key issue for cloud computing is ensuring the confidentiality and privacy of cloud resources and data. Enticing the user to purchase cloud services requires their trust which cannot be guaranteed unless the infrastructure is effectively protected because attacks at this level will threaten the whole system. To this end, we propose the Integrated Intrusion Prevention and Detection System (IIPDS) to prevent and detect different types of attacks to the infrastructure level of the cloud system. The proposed design uses Trusted Third Party (TTP) services and SSL/TLS protocols as intrusion prevention methods to secure the communication between the cloud provider and the user. It also uses multiple Intrusion Detection Systems (IDS) distributed over multiple cloud regions. The IDS system is capable of detecting known and unknown attacks using anomaly and rule-based (signature) intrusion detection techniques. The simulation results proved the efficiency of the system in detecting a wide range of attacks with low false positive alerts and low computational overhead.

POTENTIAL DISGUISING ATTACK VECTORS ON SECURITY OPERATION CENTERS AND SIEM SYSTEMS

In this article we highlight several potential vectors of attacks that can be carried out on a monitoring capacities powered by SOC SIEM using its common features and misconfigurations. Widely spread problems like excessive amounts of false positive alerts or not absolutely accurate configuration of the correlation rules may lead to situation where an attacker is able to trigger an undesired state of the monitoring system. We’ve find three potential vectors for evasion the SIEM powered SOCs monitoring. The first vector grounds on mechanisms used to collect event data – log collectors: the malfunctioning SIEM state can be achieved with generating and submitting the bogus event data to the processing party like SIEM. Fake data flow may cause generation of mistaken alerts which can confuse the analytics stuff. The second vector employs some of the attacker’s knowledge about actual SIEM configuration – exploitation of correlation rule flaws. Taking into account the fact that correlation rules are mostly hand-written, they are prone to some logic flaws – certain detection rules may not be triggered by all of the malicious attack indicators. An attacker with knowledge about that feature may fulfill the unrecorded conditions and trick the SIEM to treat the attack flow as benign activity. The last researched vector is based on redundantly sensitive detection rules which produce a lot of false positive alarms but are not removed. An attacker may trigger the malfunctioning alarm continuously to distract the analytics stuff and perform its actions under the cover of noise. Those discussed vectors are derived from analysis of the actual SIEM installations and SOC processes used as best practices. We have no actual indicators that those attacks are carried out “in wild” at the moment of issuing of this article, but it is highly probable that those tactics may be used in the future. The purpose of this research is to highlight the possible risks for the security operation centers connected with actual processes and practices used in industry and to develop the remediation strategy in perspective.

Methods to Accurately Exclude Renal Dialysis Patients from The Welsh Acute Kidney Injury E-Alert System

IntroductionAcute Kidney Injury (AKI) effects up to 20% of emergency hospital admissions. It can be identified from serum creatinine (SCr) levels and as such an e-alert system has been introduced to hospitals in England and Wales to identify AKI. This system relies on manual review by pathology laboratory staff to prevent false alerts in dialysis patients.&#x0D; Objectives and ApproachOur aim is to use routinely collected dialysis timeline and dialysis treatment data in order to create dialysis treatment spells using Structured Query Language (SQL), which can then be compared with dialysis prevalence results in the UK renal registry. Furthermore, we will link this data with biochemical results in Secure Anonymised Information Linkage (SAIL) Databank to create an AKI cohort.&#x0D; Through identifying when patients are on renal replacement therapy (RRT) at a specific time we are able to exclude false positive AKI Alerts. This is done in two ways – one from a timeline within the data which is used in the UK renal registry. The other using individual dialysis sessions to get accurate coverage, as short periods of acute dialysis are often not been recorded in UK renal registry data.&#x0D; ResultsUsing the renal dataset in SAIL we were able identify more renal dialysis patients than the UK renal registry between (2009-17). By applying this to Swansea and Bridgend pathology data in SAIL we were able to identify 60,072 SCr tests in dialysis patients between 2011 and 2013 out of 1,776,101 SCr tests. In this cohort, without ruling out dialysis patients there would be 91,140 AKI alerts, however 19.6% (17,855) of the alerts are during dialysis treatments spells.&#x0D; Conclusion / ImplicationsWe were able to recreate the ‘e-Alert’ algorithm using SQL with SAIL and get an accurate picture of RRT in Wales, enabling more accurate AKI analysis.

Implementing a Statewide Prehospital Sepsis Protocol: Perspectives of Emergency Medical Services Medical Directors

BackgroundSepsis is a medical emergency that requires prompt recognition and treatment. Multiple Emergency Medical Services (EMS) agencies across the United States have implemented sepsis protocols. In 2016, Maryland instituted its own state-wide EMS sepsis protocol which includes fluid resuscitation, vasopressor administration, and requires alerting the hospital of an incoming sepsis patient.ObjectiveThe purpose of this study was to quantify the perspectives of EMS medical directors in Maryland regarding prehospital sepsis care and to identify challenges encountered during the implementation of the statewide sepsis protocol.MethodsA 17-question survey was sent to all 24 jurisdictional medical directors in the state of Maryland.ResultsA total of 14 (58%) medical directors responded, representing four of the five EMS regions in the state. More than half (57%) stated sepsis alerting was a priority in their jurisdiction; however, in a listing of eight initiatives, sepsis was the least prioritized. Most (64%) respondents believed their clinicians had adequate training on sepsis. A majority (79%) of medical directors surveyed felt that core measures of sepsis management would be beneficial. The potentially most helpful core measures were the volume of IV fluid administration (92%), true positive sepsis alerts (83%), and cases of failure to activate a sepsis alert (75%). Engagement of field personnel was rated as the biggest challenge for the implementation of a sepsis protocol in general, and lack of a thermometer on EMS units (50%) was the largest hurdle specifically in the 2016 statewide sepsis protocol. Surveyed medical directors (86%) believe the most difficult obstacle to overcome for EMS clinicians in the treatment of sepsis are nonspecific signs and symptoms.ConclusionsPrehospital sepsis care was viewed to be important amongst the medical directors surveyed. However, significant challenges to implementation of a sepsis protocol and delivery of prehospital sepsis care are perceived by jurisdictional medical directors. Additional investment and dedication to sepsis care will advance prehospital sepsis treatment in Maryland.

An Individualized, Alert-based Heartlogic Management Strategy Can Identify Patients At Risk For Impending Heart Failure Events

Background HeartLogic is a diagnostic tool that uses multiple sensors (heart sounds, thoracic impedance, respiration, heart rate, patient activity) to track physiologic trends and combine them into one composite index designed to alert clinicians of patients at risk for impending heart failure events (HFE). While prior studies have shown an alert threshold of 16 to be predictive of HFE, there is currently no standard treatment protocol to manage these alerts. Objective To design a management algorithm and assess the utility of HeartLogic alerts in predicting HFE among patients with chronic systolic heart failure. Methods All patients with activated HeartLogic devices at a large academic center were followed prospectively for three months and monitored for elevated HeartLogic index defined as 16 or greater. All alerts were managed by the heart failure clinical team who assessed each patient via telephone call. Diuretic changes were made at the discretion of the clinical team. HFE, defined as unscheduled visits or heart failure hospitalizations occurring within 6 weeks after the initial alert were recorded. Results We identified a total of 80 patients with activated HeartLogic devices. A total of 38 patients (48%) had at least one HeartLogic alert during the study period. Of these, 26 patients (68%) appeared to have false positive alerts and did not require diuretic change nor had HFE, while 12 (32%) had heart failure symptoms requiring diuretic adjustment or urgent follow-up. Three of these 12 patients failed outpatient diuretic augmentation and were admitted for decompensated heart failure. Among the 42 patients (52%) who did not have an alert, only one patient had a HFE. Overall, HeartLogic demonstrated a sensitivity of 92%, specificity of 61%, positive predictive value of 32% and negative predictive value of 98%. Increasing HeartLogic alert threshold to >20 in this cohort would improve specificity to 90% and positive predictive value to 56%, thereby reducing staff burden, with an acceptable sensitivity (69%) and negative predictive value (94%). Conclusion This is the first study to evaluate the effects of an individualized HeartLogic threshold in an alert-based follow-up management strategy. This strategy identified actionable alerts leading to change in diuretic management. We do not recommend a standardized diuretic change algorithm for alerts given the heterogeneity of the index value and high false positive rate of alerts. Future studies are needed to determine if this management strategy leads to decrease in heart failure admissions. HeartLogic is a diagnostic tool that uses multiple sensors (heart sounds, thoracic impedance, respiration, heart rate, patient activity) to track physiologic trends and combine them into one composite index designed to alert clinicians of patients at risk for impending heart failure events (HFE). While prior studies have shown an alert threshold of 16 to be predictive of HFE, there is currently no standard treatment protocol to manage these alerts. To design a management algorithm and assess the utility of HeartLogic alerts in predicting HFE among patients with chronic systolic heart failure. All patients with activated HeartLogic devices at a large academic center were followed prospectively for three months and monitored for elevated HeartLogic index defined as 16 or greater. All alerts were managed by the heart failure clinical team who assessed each patient via telephone call. Diuretic changes were made at the discretion of the clinical team. HFE, defined as unscheduled visits or heart failure hospitalizations occurring within 6 weeks after the initial alert were recorded. We identified a total of 80 patients with activated HeartLogic devices. A total of 38 patients (48%) had at least one HeartLogic alert during the study period. Of these, 26 patients (68%) appeared to have false positive alerts and did not require diuretic change nor had HFE, while 12 (32%) had heart failure symptoms requiring diuretic adjustment or urgent follow-up. Three of these 12 patients failed outpatient diuretic augmentation and were admitted for decompensated heart failure. Among the 42 patients (52%) who did not have an alert, only one patient had a HFE. Overall, HeartLogic demonstrated a sensitivity of 92%, specificity of 61%, positive predictive value of 32% and negative predictive value of 98%. Increasing HeartLogic alert threshold to >20 in this cohort would improve specificity to 90% and positive predictive value to 56%, thereby reducing staff burden, with an acceptable sensitivity (69%) and negative predictive value (94%). This is the first study to evaluate the effects of an individualized HeartLogic threshold in an alert-based follow-up management strategy. This strategy identified actionable alerts leading to change in diuretic management. We do not recommend a standardized diuretic change algorithm for alerts given the heterogeneity of the index value and high false positive rate of alerts. Future studies are needed to determine if this management strategy leads to decrease in heart failure admissions.

Solar photo-Fenton treatment of the anti-cancer drug anastrozole in different aqueous matrices at near-neutral pH: Transformation products identification, pathways proposal, and in silico (Q)SAR risk assessment

Anastrozole (ANZ) is a breast cancer drug that was introduced onto the pharmaceutical market in the 1990s and is still one of the most widely consumed cytotoxic compounds. Due to the persistence of the drug, its continued presence after passing through wastewater treatment plants can lead to harm to aquatic environments. The present study investigates use of the solar photo-Fenton (SPF) process applied for ANZ degradation, considering the fate of ANZ and its transformation products (TPs). The SPF process was performed using different concentrations of ferrous iron (Fe2+) and H2O2 in solutions produced with deionized water (DW) and hospital wastewater (HWW), at pH close to neutrality. When solar irradiation in the SPF process was carried out the best ANZ removal rates were found under the following conditions: (i) for the DW matrix, [ANZ]0 = 50 μg L−1, [Fe2+] = 5 mg L−1, and [H2O2]0 = 25 mg L−1, achieving 95% primary ANZ elimination; (ii) for the HWW matrix, [ANZ]0 = 50 μg L−1, [Fe2+] = 10 mg L−1(multiple additions), and [H2O2]0 = 25 mg L−1, achieving 51% primary ANZ elimination. LC-QTOF MS analysis allowed to identify tentatively five transformation products (TPs) formed during the ANZ degradation process in DW, and two TPs when HWW was used. The main proposed degradation pathways were demethylation and hydroxylation. Different in silico models free available (quantitative) structure-activity relationship ((Q)SAR) software were used to predict the ecotoxicities and environmental fates of ANZ and the TPs. The in silico (Q)SAR predictions indicated that ANZ and the TPs were non-biodegradable compounds. In silico (Q)SAR predictions for mutagenicity and carcinogenicity end-points identified some TPs that require further study. Attention is drawn to the formation of several TPs for which statistical and rule-based positive alerts for mutagenic activities were found, requiring further confirmatory in vitro validation tests.

Analysis of Missed Sepsis Patients in a Pediatric Emergency Department With a Vital Sign-Based Electronic Sepsis Alert.

To characterize the cohort of missed sepsis patients since implementation of an electronic sepsis alert in a pediatric emergency department (ED). Retrospective cohort study in a tertiary care children's hospital ED from July 1, 2014, to June 30, 2017. Missed patients met international consensus criteria for severe sepsis requiring intensive care unit admission within 24 hours of ED stay but were not treated with the sepsis pathway/order set in the ED. We evaluated characteristics of missed patients compared with sepsis pathway patients including alert positivity, prior intensive care unit admission, and laboratory testing via medical record review. Outcomes included timeliness of antibiotic therapy and need for vasoactive medications. There were 919 sepsis pathway patients and 53 (5%) missed patients during the study period. Of the missed patients, 41 (77%) had vital signs that flagged the sepsis alert. Of these 41 patients, 13 (32%) had a documented sepsis huddle where the team determined that the sepsis pathway was not indicated and 28 (68%) had no sepsis alert-related documentation. Missed patients were less likely to receive timely antibiotics (relative risk, 0.4; 95% confidence interval, 0.3-0.7) and more likely to require vasoactive medications (relative risk, 4.3; 95% confidence interval, 2.9-6.5) compared with sepsis patients. In an ED with an electronic sepsis alert, missed patients often had positive sepsis alerts but were not treated for sepsis. Missed patients were more likely than sepsis pathway patients to require escalation of care after admission and less likely to receive timely antibiotics.

MP41: Crowdsourcing to save lives: A scoping review of bystander alert technologies for out-of-hospital cardiac arrest

Introduction: Out-of-hospital cardiac arrest (OHCA) constitutes a significant global health burden, with a survival rate of only 10-12%. Early intervention is vital but limited by ambulance response times, low rates of bystander assistance, and access to AEDs. Smartphone technologies have been developed that crowdsource willing volunteers to nearby OHCAs in order to initiate resuscitation prior to ambulance arrival. We performed a scoping review to map the available literature on these crowdsourcing technologies and compared their key operational features. Methods: A search strategy was developed for five online databases: Medline, Cochrane, Embase, and Web of Science, as well as Google Scholar. We searched for primary studies and grey literature describing mobile phone technologies that alerted users of nearby cardiac arrests in the community. Two reviewers independently screened all articles and extracted relevant study information. Subsequently, we performed a search of the Google and Apple app stores, a general internet search, and consulted experts to identify all available technologies that might not be described in literature. We contacted developers for information on technology use and specifications to create a detailed features table. Results: We included 72 articles examining bystander alerting technologies from 15 countries worldwide, owing to the increasing importance of this topic. We identified 25 unique technologies, of which 18 were described in the included literature. Technologies were either text message-based systems (n = 4) or mobile phone applications (n = 21). Most (23/25) used global positioning systems to direct bystanders to victims and nearby AEDs. Response radii for alerts varied widely from 200m to 10km. Some technologies had advanced features such as video-conferencing with ambulance dispatch and detailed alert settings. Not all systems required volunteers to have first aid training. There were 18 studies examining effects on bystander intervention, all of which showed significant improvements using the technologies. However, only six studies assessed impact on survival outcomes. Key barriers discussed included false positive alerts, legal liability, and potential psychological impact on volunteers. Conclusion: Our review provides a comprehensive overview of crowdsourcing technologies for bystander intervention in out-of-hospital cardiac arrest. Future work in this growing field should focus on survival outcomes and methods of addressing barriers to implementation.

Improving SIEM alert metadata aggregation with a novel kill-chain based classification model

Today’s information networks face increasingly sophisticated and persistent threats, where new threat tools and vulnerability exploits often outpace advancements in intrusion detection systems. Current detection systems often create too many alerts, which contain insufficient data for analysts. As a result, the vast majority of alerts are ignored, contributing to security breaches that might otherwise have been prevented. Security Information and Event Management (SIEM) software is a recent development designed to improve alert volume and content by correlating data from multiple sensors. However, insufficient SIEM configuration has thus far limited the promise of SIEM software for improving intrusion detection. The focus of our research is the implementation of a hybrid kill-chain framework as a novel configuration of SIEM software. Our research resulted in a new log ontology capable of normalizing security sensor data in accordance with modern threat research. New SIEM correlation rules were developed using the new log ontology, and the effectiveness of the new configuration was tested against a baseline configuration. The novel configuration was shown to improve detection rates, give more descriptive alerts, and lower the number of false positive alerts.

DeepAlerts: Deep Learning Based Multi-Horizon Alerts for Clinical Deterioration on Oncology Hospital Wards

Machine learning and data mining techniques are increasingly being applied to electronic health record (EHR) data to discover underlying patterns and make predictions for clinical use. For instance, these data may be evaluated to predict clinical deterioration events such as cardiopulmonary arrest or escalation of care to the intensive care unit (ICU). In clinical practice, early warning systems with multiple time horizons could indicate different levels of urgency, allowing clinicians to make decisions regarding triage, testing, and interventions for patients at risk of poor outcomes. These different horizon alerts are related and have intrinsic dependencies, which elicit multi-task learning. In this paper, we investigate approaches to properly train deep multi-task models for predicting clinical deterioration events via generating multi-horizon alerts for hospitalized patients outside the ICU, with particular application to oncology patients. Prior knowledge is used as a regularization to exploit the positive effects from the task relatedness. Simultaneously, we propose task-specific loss balancing to reduce the negative effects when optimizing the joint loss function of deep multi-task models. In addition, we demonstrate the effectiveness of the feature-generating techniques from prediction outcome interpretation. To evaluate the model performance of predicting multi-horizon deterioration alerts in a real world scenario, we apply our approaches to the EHR data from 20,700 hospitalizations of adult oncology patients. These patients' baseline high-risk status provides a unique opportunity: the application of an accurate model to an enriched population could produce improved positive predictive value and reduce false positive alerts. With our dataset, the model applying all proposed learning techniques achieves the best performance compared with common models previously developed for clinical deterioration warning.

Differences in the Search Behavior of Cancer Detection Dogs Trained to Have Either a Sit or Stand-Stare Final Response.

Recent literature has demonstrated that dogs have the potential to detect, and communicate the presence of, various human diseases. However, there is a lack of investigation into whether commonplace training differences within the field could influence a dog's behavior during a biomedical detection task. Here we report on the behavior of four dogs trained to alert to blood plasma samples taken from individuals with ovarian cancer. One hundred trials per dog were selected from routine video recordings collected over a period of 13 months. Videos were coded frame by frame to quantify sample checking, alerting behavior, and durations of alert. Dogs had previously been trained to elicit a final response behavior once they had located the target odor. Two dogs had a “sit” response while the other two had a “stand-stare” response. Alert behavior was categorized as true positive (a correct alert to a cancer sample) or false positive (an incorrect alert to biological and non-biological controls and distractors). Hesitations were also recorded, where the dog either checks the sample twice or, spends a longer duration of time sniffing the sample than a true pass without carrying out their final response. Results show individual variation in the total frequency of false alerts elicited. However, the rate of hesitations appears to be influenced by alert style, with stand-stare dogs carrying out 40 and 32, respectively (total = 72) and sit dogs carrying out 7 and 8, respectively (total = 15). The stand-stare dogs had a non-significant difference in the duration of their true and false positive alerts. In contrast, the sit dogs showed a significant difference (p < 0.001), maintaining their false alerts for, on average, two times the duration of their true alerts. Stand-stare dogs increased the duration of time spent in contact with the port when plasma samples were present, whereas sit dogs spent on average 0.3 s in contact with the port regardless of what sample type it contained. These findings suggest that the type of operant response a biomedical detection dog has been trained may influence their sample checking and response behavior.

Degradation of the anticancer drug flutamide by solar photo-Fenton treatment at near-neutral pH: Identification of transformation products and in silico (Q)SAR risk assessment

Flutamide (FLUT) is a non-steroidal drug mainly used in the treatment of prostate cancer and has been detected in the aquatic environment at ng L−1 levels. The environmental fate and effects of FLUT have not yet been studied. Conventional treatment technologies fail to completely remove pharmaceuticals, so the solar photo-Fenton process (SPF) has been proposed as an alternative. In this study, the degradation of FLUT, at two different initial concentrations in ultra-pure water, was carried out by SPF. The initial SPF conditions were pH0 5, [Fe2+]0 = 5 mg L−1, and [H2O2]0 = 50 mg L−1. Preliminary elimination rates of 53.4% and 73.4%. The kinetics of FLUT degradation could be fitted by a pseudo-first order model and the kobs were 6.57 × 10−3 and 9.13 × 10−3 min−1 t30W and the half-life times were 95.62 and 73.10 min t30W were achieved for [FLUT]0 of 5 mg L−1 and 500 μg L−1, respectively. Analysis using LC-QTOF MS identified thirteen transformation products (TPs) during the FLUT degradation process. The main degradation pathways proposed were hydroxylation, hydrogen abstraction, demethylation, NO2 elimination, cleavage, and aromatic ring opening. Different in silico (quantitative) structure-activity relationship ((Q)SAR) freeware models were used to predict the toxicities and environmental fates of FLUT and the TPs. The in silico predictions indicated that these substances were not biodegradable, while some TPs were classified near the threshold point to be considered as PBT compounds. The in silico (Q)SAR predictions gave positive alerts concerning the mutagenicity and carcinogenicity endpoints. Additionally, the (Q)SAR toolbox software provided structural alerts corresponding to the positive alerts obtained with the different mutagenicity and carcinogenicity models, supporting the positive alerts with more proactive information.

Contextual‐based approach to reduce false positives

The high rate of false positive alerts generated by the intrusion detection system (IDS), raises a crucial problem in the face of the security operator to differentiate between true attacks and failed ones. In order to solve this problem, several approaches have been developed relying on contextual information such as applications, services, network location, and vulnerabilities. The change of the context can be an effective factor to reduce false positive rate. However, most approaches in the literature have not dealt with this factor. Therefore, the authors propose non-monotonic ontology contextual-based approach (NOC-IDS), which represents a set of helpful contextual information in flexible format and dynamic reasoning. NOC-IDS aims in general to filter false positive alerts and to figure out relevant alerts, and helping the security operator to analyse relevant ones. NOC-IDS is defined by the description logic J C l a s s i c δ ϵ that provides non-monotonic reasoning. They illustrate the effectiveness and the powerfulness of the authors’ approach using the well-known DARPA 2000 data set.

A Collaborative Security Framework for Software-Defined Wireless Sensor Networks

With the advent of 5G, technologies such as Software-Defined Networks (SDNs) and Network Function Virtualization (NFV) have been developed to facilitate simple programmable control of Wireless Sensor Networks (WSNs). However, WSNs are typically deployed in potentially untrusted environments. Therefore, it is imperative to address the security challenges before they can be implemented. In this paper, we propose a software-defined security framework that combines intrusion prevention in conjunction with a collaborative anomaly detection systems. Initially, an IPS-based authentication process is designed to provide a lightweight intrusion prevention scheme in the data plane. Subsequently, a collaborative anomaly detection system is leveraged with the aim of supplying a cost-effective intrusion detection solution near the data plane. Moreover, to correlate the true positive alerts raised by the sensor nodes in the network edge, a Smart Monitoring System (SMS) is exploited in the control plane. The performance of the proposed model is evaluated under different security scenarios as well as compared with other methods, where the model’s high security and reduction of false alarms are demonstrated.

A novel approach to integrated care using mobile technology within home services. The ADMR pilot study

BackgroundThe care model for supporting elderly people living independently at home relies on the informal and formal assistance of caregivers. Information and communication technology (ICT) offers new approaches for informal care services for this group. MethodsA longitudinal observational pilot study was carried out in home services in France. Employees of the ADMR home services followed 130 elderly people living at home and who were no more than moderately impaired. A single visual analogue scale (VAS) was used on a smartphone to assess global health every time a person was visited. An alert system was devised to inform the elderly person and/or a responsible person of any deterioration in health status. All medical and social events were recorded throughout the 9-month study. Results138 people were enrolled and 106 were evaluated. 37 alerts were observed. 21 were confirmed and 16 were false positives. Only employees untrained in the use of the system generated false positive alerts. Six severe medical alerts were observed, including one cancer undetected by the physician, one hospitalization for diabetes, one hospitalization which led to death 6 months later and one hospitalization which resulted in follow-up care. ConclusionsSocial workers can participate in the health system with all the ethical criteria of medicine. To our knowledge, this is the first ICT-based alert system that has been found to produce severe medical alerts by employees of home services.

SAFE@HOME – Feasibility study of a telemonitoring platform combining blood pressure and preeclampsia symptoms in pregnancy care

ObjectiveTo study the feasibility of a telemonitoring platform for hypertensive disease in pregnancy, consisting of a wireless blood pressure monitor and an app in combination with an integrated preeclampsia symptom checklist. Study designProspective observational study with 14 pregnant women during a 15 weekday study period. For feasibility purposes, compliance was measured by evaluating the number of entered BP and symptom checklists. Comparing all the entered values with the threshold values checked the accuracy of the automatic alerts. Usability and patient satisfaction were measured using questionnaires. ResultsCompliance rates for blood pressure and symptom checklist were 93% and 85% respectively. No false positive or missing alerts were found in the alarm system. The telemonitoring system alarmed 7 times for BP thresholds (3.8% of all received values), Of 167 returned symptom checklists, 93% of symptom alarms could be handled with expectant management because of concurrent normal blood pressure. The majority of participants were satisfied with the system. ConclusionsThis is the first feasibility study of a telemonitoring platform, combining remote monitoring of BP with preeclampsia symptoms in pregnancy care. Action from health care providers during telemonitoring is only needed in case of alarming combinations of results. This system is potentially very useful in care for women at risk for hypertensive disorders during pregnancy.

Influenza laboratory testing and its application in timely Department of Defense biosurveillance

ObjectiveTo describe influenza laboratory testing and results in the Military Health System and how influenza laboratory results may be used in DoD Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE)IntroductionTimely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. DoD ESSENCE has the unique advantage of ingesting multiple data sources from the Military Health System (MHS), including outpatient, inpatient, and emergency department (ED) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. The Influenza-like Illness (ILI) syndrome definition includes ICD-9 or ICD-10 codes that may increase the number of false positive alerts. Laboratory-confirmed influenza data provides an increased positive predictive value (PPV). The gold standard for influenza testing is molecular assays or viral culture. However, the tests may take 3-10 days to result. Rapid influenza diagnostic tests (RIDTs) have a lower sensitivity, but the timeliness of receiving a result improves to within &lt;15 minutes. We evaluate the utility of RIDTs for routine ILI surveillance.MethodsAdministrative medical encounters for ILI and influenza laboratory-confirmed data were analyzed from the MHS from June 2013 – September 2017 (Figure 1). The medical encounters and laboratory data include outpatient, inpatient, and ED data. The ILI syndrome case definition is a medical encounter during the study period with an ICD-9 or ICD-10 codes in any diagnostic position (ICD-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; ICD-10 codes = B97.89, H66.9, J00, J01.9, J06.9, J09, J09.X, J10, J10.0, J10.1, J10.2, J10.8, J11, J11.0, J11.1, J11.2, J11.8, J12.89, J12.9, J18, J20.9, J40, R05, R50.9). The ILI dataset was limited to care provided in the MHS as laboratory data is only available for direct care. We describe influenza laboratory testing practices in the MHS. We aggregated the ILI encounters and RIDT positive results into daily counts and generated a weekly Pearson’s correlation.ResultsInfluenza tests are ordered throughout the year; the mean weekly percentage of ILI encounters in which an influenza laboratory test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. The mean weekly percentage of positive influenza laboratory results among all ILI encounters is 0.82%, with a range from 0.01% to 5.73% (Figure 2). The percent of ILI encounters in which a test is ordered increases as the influenza season progresses. Influenza laboratory tests conducted in the MHS include RIDTs, PCR, culture, and DFA. Among all influenza tests ordered in the MHS, 66.0% were RIDTs, 22.7% were PCR, and 11.3% were viral culture. Often, a confirmatory test is ordered following a RIDT; 20% of RIDTs have follow-up tests. The mean timeliness of influenza test result data in the MHS was 11.26 days for viral culture, 2.94 days for PCR, and 0.11 days for RIDTs. The RIDT results were moderately correlated with ILI encounters for the entire year (mean weekly Pearson correlation coefficient rho=0.60, 95% CI: 0.55, 0.66, Figure 3). During the influenza season, the mean weekly Pearson correlation coefficient increases to rho=0.75, 95% CI: 0.70, 0.79.ConclusionsThe DoD has the unique advantage of access to the electronic health record and laboratory tests and results of all MHS beneficiaries. This analysis provides evidence for increased utilization of positive RIDTs in ESSENCE. The moderate correlation between the ILI syndrome and positive RIDTs may be associated with ICD-10 codes included in the ILI syndrome definition that contribute to false positive influenza cases. Ongoing research is focused on improving this ILI syndrome definition using ICD-10 codes. Rapid influenza diagnostic tests provide more timely results than other influenza test types. In conjunction with ILI medical encounter data, positive RIDT data provides a more complete and timely picture of the true burden of influenza on the MHS population for early warning of influenza outbreaks.

Quantifying the Value of Real‐Time Geodetic Constraints for Earthquake Early Warning Using a Global Seismic and Geodetic Data Set

AbstractGeodetic earthquake early warning (EEW) algorithms complement point‐source seismic systems by estimating fault‐finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because such earthquakes are rare, it has been difficult to demonstrate that geodetic warnings improve ground motion estimation significantly. Here, we quantify and compare timeliness and accuracy of magnitude and ground motion estimates in simulated real time from seismic and geodetic observations for a suite of globally distributed, large earthquakes. Magnitude solutions saturate for the seismic EEW algorithm (we use ElarmS) while the ElarmS‐triggered Geodetic Alarm System (G‐larmS) reduces the error even for its first solutions. Shaking intensity (Modified Mercalli Intensity, MMI) time series calculated for each station and each event are assessed based on MMI threshold crossings, allowing us to accurately characterize warning times per station. We classify alerts and find that MMI 4 thresholds result in true positive alerts for 11.2% of sites exceeding MMI 4 with a median warning time of 20.1 s for ElarmS, while G‐larmS issues true positive alerts for 20.9% of all sites exceeding MMI 4 with a significantly longer median warning time of 34.2 s. The geodetic EEW system reduces the number of missed alerts for a threshold of MMI 4 from 20.5% to 10.8% for all sites, but also increases the number of false positive alerts from 1.6% to 19.9% of all sites. By quantifying increased accuracy in magnitude, ground motion estimation, and alert timeliness, we demonstrate that finite‐fault geodetic algorithms add significant value, including better cost savings performance, to point‐source seismic EEW systems for large earthquakes.