Bartonella clarridgeiae infection in a patient with aortic root abscess and endocarditis

Introduction. Bartonella species are increasingly recognized as agents of culture-negative endocarditis. However, to date, almost all human cases have been associated with two members of the genus, Bartonella henselae and Bartonella quintana. B. henselae infections are zoonotic, with domestic cats serving as reservoir hosts for the pathogen. Bartonella clarridgeiae also exploits cats as reservoir hosts, but its zoonotic potential is far less established. Case presentation. A 34-year-old male presented with palpitations after a history of aortic incompetence. During surgery for an aortic valve replacement, two vegetations were found on the aortic valve. PCR analysis of the vegetation demonstrated the presence of Bartonella species and so the patient was treated post-operatively with ceftriaxone and doxycycline, making a good recovery. Further PCR-based analysis of the patient’s aortic vegetation confirmed the presence of B. clarridgeiae . Conclusion. This report expands the number of Bartonella species associated with endocarditis and provides clear evidence that B. clarridgeiae should be considered a zoonotic pathogen

Academic buoyancy, student's achievement, and the linking role of control: A cross-lagged analysis of high school students

Background Previous research has indicated that although academic buoyancy and student's achievement are associated, the relationship is relatively modest. Aims We sought to determine whether another construct might link academic buoyancy and student's achievement. Based on prior theoretical and empirical work, we examined a sense of control as one possible linking mechanism. Sample The study analysed data from 2,971 students attending 21 Australian high schools. Methods We conducted a cross-lagged panel design as a first means of disentangling the relative salience of academic buoyancy, control, and achievement (Phase 1). Based upon these results, we proceeded with follow-up analyses of an ordered process model linking the constructs over time (Phase 2). Results Findings showed that buoyancy and achievement were associated with control over time, but not with one another (Phase 1). In addition, control appeared to play a role in how buoyancy influenced achievement and that a cyclical process may operate among the three factors over time (Phase 2). Conclusion The findings suggest that control may play an important role in linking past experiences of academic buoyancy and achievement to subsequent academic buoyancy and achievement.The authors would like to thank the Australian Research Council for funding this research

Predicting hip-knee-ankle and femorotibial angles from knee radiographs with deep learning

BACKGROUND: Knee alignment affects the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ankle angle (HKA) measurement from radiographs could improve reliability and save time. Further, if HKA could be predicted from knee-only radiographs then radiation exposure could be reduced and the need for specialist equipment and personnel avoided. The aim of this research was to assess if deep learning methods could predict FTA and HKA angle from posteroanterior (PA) knee radiographs. METHODS: Convolutional neural networks with densely connected final layers were trained to analyse PA knee radiographs from the Osteoarthritis Initiative (OAI) database. The FTA dataset with 6149 radiographs and HKA dataset with 2351 radiographs were split into training, validation, and test datasets in a 70:15:15 ratio. Separate models were developed for the prediction of FTA and HKA and their accuracy was quantified using mean squared error as loss function. Heat maps were used to identify the anatomical features within each image that most contributed to the predicted angles. RESULTS: High accuracy was achieved for both FTA (mean absolute error 0.8°) and HKA (mean absolute error 1.7°). Heat maps for both models were concentrated on the knee anatomy and could prove a valuable tool for assessing prediction reliability in clinical application. CONCLUSION: Deep learning techniques enable fast, reliable and accurate predictions of both FTA and HKA from plain knee radiographs and could lead to cost savings for healthcare providers and reduced radiation exposure for patients

Predicting the operability of damaged compressors using machine learning

Abstract The application of machine learning to aerospace problems faces a particular challenge. For successful learning a large amount of good quality training data is required, typically tens of thousands of cases. However, due to the time and cost of experimental aerospace testing, this data is scarce. This paper shows that successful learning is possible with two novel techniques: The first technique is rapid testing. Over the last five years the Whittle Laboratory has developed a capability where rebuild and test times of a compressor stage now take 15 minutes instead of weeks. The second technique is to base machine learning on physical parameters, derived from engineering wisdom developed in industry over many decades. The method is applied to the important industry problem of predicting the effect of blade damage on compressor operability. The current approach has high uncertainty, it is based on human judgement and correlation of a handful of experimental test cases. It is shown using 100 training cases and 25 test cases that the new method is able to predict the operability of damaged compressor stages with an accuracy of 2% in a 95% confidence interval; far better than is possible by even the most experienced compressor designers. Use of the method is also shown to generate new physical understanding, previously unknown by any of the experts involved in this work. Using this method in the future offers an exciting opportunity to generate understanding of previously intractable problems in aerospace.Aerospace Technology Institute Rolls-Royce plc

HOSTED—England's Household Transmission Evaluation Dataset: preliminary findings from a novel passive surveillance system of COVID-19

BACKGROUND: Household transmission of SARS-CoV-2 is an important component of the community spread of the pandemic. Little is known about the factors associated with household transmission, at the level of the case, contact or household, or how these have varied over the course of the pandemic. METHODS: The Household Transmission Evaluation Dataset (HOSTED) is a passive surveillance system linking laboratory-confirmed COVID-19 cases to individuals living in the same household in England. We explored the risk of household transmission according to: age of case and contact, sex, region, deprivation, month and household composition between April and September 2020, building a multivariate model. RESULTS: In the period studied, on average, 5.5% of household contacts in England were diagnosed as cases. Household transmission was most common between adult cases and contacts of a similar age. There was some evidence of lower transmission rates to under-16s [adjusted odds ratios (aOR) 0.70, 95% confidence interval (CI) 0.66-0.74). There were clear regional differences, with higher rates of household transmission in the north of England and the Midlands. Less deprived areas had a lower risk of household transmission. After controlling for region, there was no effect of deprivation, but houses of multiple occupancy had lower rates of household transmission [aOR 0.74 (0.66-0.83)]. CONCLUSIONS: Children are less likely to acquire SARS-CoV-2 via household transmission, and consequently there was no difference in the risk of transmission in households with children. Households in which cases could isolate effectively, such as houses of multiple occupancy, had lower rates of household transmission. Policies to support the effective isolation of cases from their household contacts could lower the level of household transmission

Linking water quality to living resources in a mid-Atlantic lagoon system, USA

The mid-Atlantic coastal bays are shallow coastal lagoons, separated from the Atlantic Ocean by barrier sand islands with oceanic exchanges restricted to narrow inlets. The relatively poor flushing of these lagoon systems makes them susceptible to eutrophication resulting from anthropogenic nutrient loadings. An intensive water quality and seagrass monitoring program was initiated to track ecological changes in the Maryland and Virginia coastal bays. The purpose of this study was to analyze existing monitoring data to determine status and trends in eutrophication and to determine any associations between water quality and living resources. Analysis of monitoring program data revealed several trends: (1) decadal decreases in nutrient and chlorophyll concentrations, followed by recently increasing trends; (2) decadal increases in seagrass coverage, followed by a recent period of no change; (3) blooms of macroalgae and brown tide microalgae; and (4) exceedance of water quality thresholds: chlorophyll a (15 mu g/L), total nitrogen (0.65 mg/L or 46 mu mol/L), total phosphorus (0.037 mg/L or 1.2 mu mol/L), and dissolved oxygen (5 mg/L) in many areas within the Maryland coastal bays. The water quality thresholds were based on habitat requirements for living resources (seagrass and fish) and used to calculate a water quality index, which was used to compare the bay segments. Strong gradients in water quality were correlated to changes in seagrass coverage between segments. These factors indicate that these coastal bays are in a state of transition, with a suite of metrics indicating degrading conditions. Continued monitoring and intensified management will be required to avert exacerbation of the observed eutrophication trends. Coastal lagoons worldwide are experiencing similar degrading trends due to increasing human pressures, and assessing status and trends relative to biologically relevant thresholds can assist in determining monitoring and management priorities and goals

Survivorship Patterns of Larval Amphibians Exposed to Low Concentrations of Atrazine

Amphibians can be exposed to contaminants in nature by many routes, but perhaps the most likely route is agricultural runoff in amphibian breeding sites. This runoff results in high-level pulses of pesticides. For example, atrazine, the most widely used pesticide in the United States, can be present at several parts per million in agricultural runoff. However, pesticide levels are likely to remain in the environment at low levels for longer periods. Nevertheless, most studies designed to examine the impacts of contaminants are limited to short-term (~ 4 days) tests conducted at relatively high concentrations. To investigate longer-term (~ 30 days) exposure of amphibians to low pesticide levels, we exposed tadpoles of four species of frogs—spring peepers (Pseudacris crucifer), American toads (Bufo americanus), green frogs (Rana clamitans), and wood frogs (Rana sylvatica)—at early and late developmental stages to low concentrations of a commercial preparation of atrazine (3, 30, or 100 ppb; the U.S. Environmental Protection Agency drinking water standard is 3 ppb). We found counterintuitive patterns in rate of survivorship. Survival was significantly lower for all animals exposed to 3 ppb compared with either 30 or 100 ppb, except the late stages of B. americanus and R. sylvatica. These survival patterns highlight the importance of investigating the impacts of contaminants with realistic exposures and at various developmental stages. This may be particularly important for compounds that produce greater mortality at lower doses than higher doses, a pattern characteristic of many endocrine disruptors