Automated vehicles for older adults with cognitive impairment: a survey study.

As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.

A Bayesian Regression Analysis of the Effects of Alert Presence and Scenario Criticality on Automated Vehicle Takeover Performance.

OBJECTIVE: This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. BACKGROUND: Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers' responses, but silent failures and their interactions with scenario criticality are understudied. METHOD: A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher's exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. RESULTS: The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality. CONCLUSION: Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role-by leading to an unsafe maneuver-in critical automated vehicle takeovers. APPLICATION: Understanding the implications of silent failure on driver's takeover performance can benefit the assessment of automated vehicles' safety and provide guidance for fail-safe system designs.

Predicting Procedure Step Performance From Operator and Text Features: A Critical First Step Toward Machine Learning-Driven Procedure Design.

OBJECTIVE: The goal of this study is to assess machine learning for predicting procedure performance from operator and procedure characteristics. BACKGROUND: Procedures are vital for the performance and safety of high-risk industries. Current procedure design guidelines are insufficient because they rely on subjective assessments and qualitative analyses that struggle to integrate and quantify the diversity of factors that influence procedure performance. METHOD: We used data from a 25-participant study with four procedures, conducted on a high-fidelity oil extraction simulation to develop logistic regression (LR), random forest (RF), and decision tree (DT) algorithms that predict procedure step performance from operator, step, readability, and natural language processing-based features. Features were filtered using the Boruta approach. The algorithms were trained and optimized with a repeated 10-fold cross-validation. After training, inference was performed using variable importance and partial dependence plots. RESULTS: The RF, DT, and LR algorithms with all features had an area under the receiver operating characteristic curve (AUC) of 0.78, 0.77, and 0.75, respectively, and significantly outperformed the LR with only operator features (LROP), with an AUC of 0.61. The most important features were experience, familiarity, total words, and character-based metrics. The partial dependence plots showed that steps with fewer words, abbreviations, and characters were correlated with correct step performance. CONCLUSION: Machine learning algorithms are a promising approach for predicting step-level procedure performance, with acknowledged limitations on interpolating to nonobserved data, and may help guide procedure design after validation with additional data on further tasks. APPLICATION: After validation, the inferences from these models can be used to generate procedure design alternatives.

Understanding Heart Rate Reactions to Post-Traumatic Stress Disorder (PTSD) Among Veterans: A Naturalistic Study.

OBJECTIVE: We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate. BACKGROUND: PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown. METHOD: Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case. RESULTS: Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified. CONCLUSION: Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events. APPLICATION: These findings show promise for future work to detect the onset of PTSD symptoms.

Paediatric Buruli ulcer in Australia.

AIM: This study describes an Australian cohort of paediatric Buruli ulcer (BU) patients and compares them with adult BU patients. METHODS: Analysis of a prospective cohort of all BU cases managed at Barwon Health, Victoria, from 1 January 1998 to 31 May 2018 was performed. Children were defined as ≤15 years of age. RESULTS: A total of 565 patients were included: 52 (9.2%) children, 289 (51.2%) adults aged 16-64 years and 224 (39.6%) adults aged ≥65 years. Among children, half were female and the median age was 8.0 years (interquartile range 4.8-12.3 years). Six (11.5%) cases were diagnosed from 2001 to 2006, 14 (26.9%) from 2007 to 2012 and 32 (61.5%) from 2013 to 2018. Compared to adults, children had a significantly higher proportion of non-ulcerative lesions (32.7%, P < 0.001) and a higher proportion of severe lesions (26.9%, P < 0.01). The median duration of symptoms prior to diagnosis was shorter for children compared with adults aged 16-64 years (42 vs. 56 days, P = 0.04). Children were significantly less likely to experience antibiotic complications (6.1%) compared with adults (20.6%, P < 0.001), but had a significantly higher rate of paradoxical reactions (38.8%) compared with adults aged 16-64 (19.2%) (P < 0.001). Paradoxical reactions in children occurred significantly earlier than in adults (median 17 vs. 56 days, P < 0.01). Cure rates were similarly high for children compared to adults treated with antibiotics alone or with antibiotics and surgery. CONCLUSIONS: Paediatric BU cases in Australia are increasing and represent an important but stable proportion of Australian BU cohorts. Compared with adults, there are significant differences in clinical presentation and treatment outcomes.

Classification of Driver Distraction: A Comprehensive Analysis of Feature Generation, Machine Learning, and Input Measures.

OBJECTIVE: The objective of this study was to analyze a set of driver performance and physiological data using advanced machine learning approaches, including feature generation, to determine the best-performing algorithms for detecting driver distraction and predicting the source of distraction. BACKGROUND: Distracted driving is a causal factor in many vehicle crashes, often resulting in injuries and deaths. As mobile devices and in-vehicle information systems become more prevalent, the ability to detect and mitigate driver distraction becomes more important. METHOD: This study trained 21 algorithms to identify when drivers were distracted by secondary cognitive and texting tasks. The algorithms included physiological and driving behavioral input processed with a comprehensive feature generation package, Time Series Feature Extraction based on Scalable Hypothesis tests. RESULTS: Results showed that a Random Forest algorithm, trained using only driving behavior measures and excluding driver physiological data, was the highest-performing algorithm for accurately classifying driver distraction. The most important input measures identified were lane offset, speed, and steering, whereas the most important feature types were standard deviation, quantiles, and nonlinear transforms. CONCLUSION: This work suggests that distraction detection algorithms may be improved by considering ensemble machine learning algorithms that are trained with driving behavior measures and nonstandard features. In addition, the study presents several new indicators of distraction derived from speed and steering measures. APPLICATION: Future development of distraction mitigation systems should focus on driver behavior-based algorithms that use complex feature generation techniques.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures.

OBJECTIVE: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. BACKGROUND: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. METHOD: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. RESULTS: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. CONCLUSION: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. APPLICATION: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

Using Topic Modeling to Develop Multi-level Descriptions of Naturalistic Driving Data from Drivers with and without Sleep Apnea.

One challenge in using naturalistic driving data is producing a holistic analysis of these highly variable datasets. Typical analyses focus on isolated events, such as large g-force accelerations indicating a possible near-crash. Examining isolated events is ill-suited for identifying patterns in continuous activities such as maintaining vehicle control. We present an alternative approach that converts driving data into a text representation and uses topic modeling to identify patterns across the dataset. This approach enables the discovery of non-linear patterns, reduces the dimensionality of the data, and captures subtle variations in driver behavior. In this study topic models are used to concisely described patterns in trips from drivers with and without untreated obstructive sleep apnea (OSA). The analysis included 5000 trips (50 trips from 100 drivers; 66 drivers with OSA; 34 comparison drivers). Trips were treated as documents, and speed and acceleration data from the trips were converted to "driving words." The identified patterns, called topics, were determined based on regularities in the co-occurrence of the driving words within the trips. This representation was used in random forest models to predict the driver condition (i.e., OSA or comparison) for each trip. Models with 10, 15 and 20 topics had better accuracy in predicting the driver condition, with a maximum AUC of 0.73 for a model with 20 topics. Trips from drivers with OSA were more likely to be defined by topics for smaller lateral accelerations at low speeds. The results demonstrate topic modeling as a useful tool for extracting meaningful information from naturalistic driving datasets.

Visibility of dielectrically passivated graphene films.

The visibility of monolayer graphene is dependent on its surrounding dielectric environment and the presence of any contamination associated with 2D layer transfer. Here, the optical contrast of residually contaminated monolayer graphene encased within a range of dielectric stacks characteristic of realistic devices is examined, highlighting the utility of optical microscopy for a graphene assessment, both during and after lithographic processing. Practically, chemical vapor deposited graphene is encapsulated in dielectric stacks of varying thicknesses of SiO2. Optical contrast is then measured and compared to predictions of a multilayer model. Experimentally measured contrast is in close agreement with simulation only when contamination is included.

Treatment and prevention of Mycobacterium ulcerans infection (Buruli ulcer) in Australia: guideline update.

• Guidelines reflecting contemporary clinical practice in the management of Buruli ulcer (Mycobacterium ulcerans infection) in Australia were published in 2007. • Management has continued to evolve, as new evidence has become available from randomised trials, case series and increasing clinical experience with oral antibiotic therapy. • Therefore, guidelines on the diagnosis, treatment and prevention of Buruli ulcer in Australia have been updated. They include guidance on the new role of antibiotics as first-line therapy; the shortened duration of antibiotic treatment and the use of all-oral antibiotic regimens; the continued importance, timing and role of surgery; the recognition and management of paradoxical reactions during antibiotic treatment; and updates on the prevention of disease.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Evaluation of a Proportional Response Addition Approach to Mixture Risk Assessment and Predictive Toxicology Using Data on Four Trihalomethanes from the U.S. EPA's Multiple-Purpose Design Study.

In this study, proportional response addition (Prop-RA), a model for predicting response from chemical mixture exposure, is demonstrated and evaluated by statistically analyzing data on all possible binary combinations of the four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate Prop-RA. The experimental design used a set of doses common to all components and mixtures, providing hepatotoxicity data on the four single THMs and the binary combinations. In Prop-RA, the contribution of each component to mixture toxicity is proportional to its fraction in the mixture based on its response at the total mixture dose. The primary analysis consisted of 160 evaluations. Statistically significant departures from the Prop-RA prediction were found for seven evaluations, with three predications that were greater than and four that were less than the predicted response; interaction magnitudes (n-fold difference in response vs. prediction) ranged from 1.3 to 1.4 for the former and 2.6 to 3.8 for the latter. These predictions support the idea that Prop-RA works best with chemicals where the effective dose ranges overlap. Prop-RA does not assume the similarity of toxic action or independence, but it can be applied to a mixture of components that affect the same organ/system, with perhaps unknown toxic modes of action.

Evaluation of the Interaction-Based Hazard Index Formula Using Data on Four Trihalomethanes from U.S. EPA's Multiple-Purpose Design Study.

The interaction-based hazard index (HIINT), a mixtures approach to characterizing toxicologic interactions, is demonstrated and evaluated by statistically analyzing data on four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate the HIINT formula. This HIINT evaluation uses single, binary and quaternary mixture THM data. While this research is considered preliminary, the results provide insights on the application of HIINT when toxicology mixture data are available and on improvements to the method. The results for relative liver weight show the HIINT was generally not conservative but did adjust the additive hazard index (HI) in the correct direction, predicting greater than dose-additivity, as seen in the mixture data. For the liver serum enzyme endpoint alanine aminotransferase, the results were mixed, with some indices giving an estimated effective dose lower than the observed mixture effective dose and others higher; in general, the HIINT adjusted the HI in the correct direction, predicting less than dose-additivity. In addition, a methodological improvement was made in the calculation of maximum interaction magnitude. Suggested refinements to the HIINT included mixture-specific replacements for default parameter values and approaches for supplementing the usual qualitative discussions of uncertainty with numerical descriptions.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy.

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the ß-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Thermal Transport and Mechanical Stress Mapping of a Compression Bonded GaN/Diamond Interface for Vertical Power Devices.

Bonding diamond to the back side of gallium nitride (GaN) electronics has been shown to improve thermal management in lateral devices; however, engineering challenges remain with the bonding process and characterizing the bond quality for vertical device architectures. Here, integration of these two materials is achieved by room-temperature compression bonding centimeter-scale GaN and a diamond die via an intermetallic bonding layer of Ti/Au. Recent attempts at GaN/diamond bonding have utilized a modified surface activation bonding (SAB) method, which requires Ar fast atom bombardment immediately followed by bonding within the same tool under ultrahigh vacuum (UHV) conditions. The method presented here does not require a dedicated SAB tool yet still achieves bonding via a room-temperature metal-metal compression process. Imaging of the buried interface and the total bonding area is achieved via transmission electron microscopy (TEM) and confocal acoustic scanning microscopy (C-SAM), respectively. The thermal transport quality of the bond is extracted from spatially resolved frequency-domain thermoreflectance (FDTR) with the bonded areas boasting a thermal boundary conductance of >100 MW/m2·K. Additionally, Raman maps of GaN near the GaN-diamond interface reveal a low level of compressive stress, <80 MPa, in well-bonded regions. FDTR and Raman were coutilized to map these buried interfaces and revealed some poor thermally bonded areas bordered by high-stress regions, highlighting the importance of spatial sampling for a complete picture of bond quality. Overall, this work demonstrates a novel method for thermal management in vertical GaN devices that maintains low intrinsic stresses while boasting high thermal boundary conductances.

Inversion for Thermal Properties with Frequency Domain Thermoreflectance.

3D integration of multiple microelectronic devices improves size, weight, and power while increasing the number of interconnections between components. One integration method involves the use of metal bump bonds to connect devices and components on a common interposer platform. Significant variations in the coefficient of thermal expansion in such systems lead to stresses that can cause thermomechanical and electrical failures. More advanced characterization and failure analysis techniques are necessary to assess the bond quality between components. Frequency domain thermoreflectance (FDTR) is a nondestructive, noncontact testing method used to determine thermal properties in a sample by fitting the phase lag between an applied heat flux and the surface temperature response. The typical use of FDTR data involves fitting for thermal properties in geometries with a high degree of symmetry. In this work, finite element method simulations are performed using high performance computing codes to facilitate the modeling of samples with arbitrary geometric complexity. A gradient-based optimization technique is also presented to determine unknown thermal properties in a discretized domain. Using experimental FDTR data from a GaN-diamond sample, thermal conductivity is then determined in an unknown layer to provide a spatial map of bond quality at various points in the sample.

Driving among individuals with chronic conditions: A systematic review of applied research using kinematic driving sensors.

BACKGROUND: Kinematic driving data studies are a novel methodology relevant to health care, but prior studies have considerable variance in their methods, populations, and findings suggesting a need for critical analysis and appraisal for feasibility and methodological guidelines. METHODS: We assessed kinematic driving studies of adults with chronic conditions for study feasibility, characteristics, and key findings, to generate recommendations for future study designs, and to identify promising directions for applications of kinematic driving data. PRISMA was used to guide the review and searches included PubMed, CINAHL, and Compendex. Of 379 abstract/titles screened, 49 full-text articles were reviewed, and 29 articles met inclusion criteria of analyzing trip-level kinematic driving data from adult drivers with chronic conditions. RESULTS: The predominant chronic conditions studied were Alzheimer's disease and related Dementias, obstructive sleep apnea, and diabetes mellitus. Study objectives included feasibility testing of kinematic driving data collection in the context of chronic conditions, comparisons of simulation with real-world kinematic driving behavior, assessments of driving behavior effects associated with chronic conditions, and prognostication or disease classification drawn from kinematic driving data. Across the studies, there was no consensus on devices, measures, or sampling parameters; however, studies showed evidence that driving behavior could reliably differentiate between adults with chronic conditions and healthy controls. CONCLUSIONS: Vehicle sensors can provide driver-specific measures relevant to clinical assessment and interventions. Using kinematic driving data to assess and address driving measures of individuals with multiple chronic conditions is positioned to amplify a functional outcome measure that matters to patients.

Incidence, clinical spectrum, diagnostic features, treatment and predictors of paradoxical reactions during antibiotic treatment of Mycobacterium ulcerans infections.

BACKGROUND: Paradoxical reactions from antibiotic treatment of Mycobacterium ulcerans have recently been recognized. Data is lacking regarding their incidence, clinical and diagnostic features, treatment, outcomes and risk factors in an Australian population. METHODS: Data was collected prospectively on all confirmed cases of M. ulcerans infection managed at Barwon Health Services, Australia, from 1/1/1998-31/12/2011. Paradoxical reactions were defined on clinical and histological criteria and cases were determined by retrospectively reviewing the clinical history and histology of excised lesions. A Poisson regression model was used to examine associations with paradoxical reactions. RESULTS: Thirty-two of 156 (21%) patients developed paradoxical reactions a median 39 days (IQR 20-73 days) from antibiotic initiation. Forty-two paradoxical episodes occurred with 26 (81%) patients experiencing one and 6 (19%) multiple episodes. Thirty-two (76%) episodes occurred during antibiotic treatment and 10 (24%) episodes occurred a median 37 days after antibiotic treatment. The reaction site involved the original lesion (wound) in 23 (55%), was separate to but within 3 cm of the original lesion (local) in 11 (26%) and was more than 3 cm from the original lesion (distant) in 8 (19%) episodes. Mycobacterial cultures were negative in 33/33 (100%) paradoxical episodes. Post-February 2009 treatment involved more cases with no antibiotic modifications (12/15 compared with 11/27, OR 5.82, 95% CI 1.12-34.07, p = 0.02) and no further surgery (9/15 compared with 2/27, OR 18.75, 95% CI 2.62-172.73, p < 0.001). Six severe cases received prednisone with marked clinical improvement. On multivariable analysis, age ≥ 60 years (RR 2.84, 95% CI 1.12-7.17, p = 0.03), an oedematous lesion (RR 3.44, 95% CI 1.11-10.70, p=0.03) and use of amikacin in the initial antibiotic regimen (RR 6.33, 95% CI 2.09-19.18, p < 0.01) were associated with an increased incidence of paradoxical reactions. CONCLUSIONS: Paradoxical reactions occur frequently during or after antibiotic treatment of M. ulcerans infections in an Australian population and may be increased in older adults, oedematous disease forms, and in those treated with amikacin. Recognition of paradoxical reactions led to changes in management with less surgery, fewer antibiotic modifications and use of prednisolone for severe reactions.

Comprehensive assessment of a chlorinated drinking water concentrate in a rat multigenerational reproductive toxicity study.

Some epidemiological studies report associations between drinking water disinfection byproducts (DBPs) and adverse reproductive/developmental effects, e.g., low birth weight, spontaneous abortion, stillbirth, and birth defects. Using a multigenerational rat bioassay, we evaluated an environmentally relevant "whole" mixture of DBPs representative of chlorinated drinking water, including unidentified DBPs as well as realistic proportions of known DBPs at low-toxicity concentrations. Source water from a water utility was concentrated 136-fold, chlorinated, and provided as drinking water to Sprague-Dawley rats. Timed-pregnant females (P0 generation) were exposed during gestation and lactation. Weanlings (F1 generation) continued exposures and were bred to produce an F2 generation. Large sample sizes enhanced statistical power, particularly for pup weight and prenatal loss. No adverse effects were observed for pup weight, prenatal loss, pregnancy rate, gestation length, puberty onset in males, growth, estrous cycles, hormone levels, immunological end points, and most neurobehavioral end points. Significant, albeit slight, effects included delayed puberty for F1 females, reduced caput epidydimal sperm counts in F1 adult males, and increased incidences of thyroid follicular cell hypertrophy in adult females. These results highlight areas for future research, while the largely negative findings, particularly for pup weight and prenatal loss, are notable.

Risk factors for recurrent Mycobacterium ulcerans disease after exclusive surgical treatment in an Australian cohort.

OBJECTIVE: To describe risk factors for recurrence after exclusive surgical treatment of Mycobacterium ulcerans infection. DESIGN, SETTING AND PARTICIPANTS: Prospective observational cohort study of all M. ulcerans cases managed with surgery alone at Barwon Health, a tertiary referral hospital, from 1 January 1998 to 31 December 2011. A random-effects Poisson regression model was used to assess rates and associations of treatment failure. MAIN OUTCOME MEASURES: Rates of treatment failure and rate ratios (RRs) for factors associated with treatment failure. RESULTS: Of 192 patients with M. ulcerans infection, 50 (26%) had exclusive surgical treatment. Median age was 65.0 2013s (interquartile range [IQR], 45.5-77.7 2013s), and median duration of symptoms was 46 days (IQR, 26-90 days). There were 20 recurrences in 16 patients. For first lesions, the recurrence incidence rate was 41.8 (95% CI, 25.6-68.2) per 100 person-2013s, and median time to recurrence was 50 days (IQR, 30-171 days). Recurrence occurred ≤ 3 cm from the original lesion in 13 cases, and > 3 cm in nine. On univariable analysis, age ≥ 60 2013s (RR 13.84; 95% CI, 2.21-86.68; P < 0.01), distal lesions (RR, 20.43; 95% CI, 1.97-212.22; P < 0.01), positive histological margins (RR, 21.02; 95% CI, 5.51-80.26; P < 0.001), immunosuppression (RR, 17.97; 95% CI, 4.17-77.47; P < 0.01) and duration of symptoms > 75 days (RR, 10.13; 95% CI, 1.76-58.23; P = 0.02) were associated with treatment failure. On multivariable analysis, positive margins (RR, 7.72; 95% CI, 2.71-22.01; P < 0.001) and immunosuppression (RR, 6.45; 95% CI, 2.42-17.20; P = 0.01) remained associated with treatment failure. CONCLUSIONS: Recurrence rates after exclusive surgical treatment of M. ulcerans disease in an Australian cohort are high, with increased rates associated with immunosuppression or positive histological margins.