Understanding the efficacy of wastewater surveillance for SARS-CoV-2 in two diverse communities.

During the COVID-19 pandemic, wastewater-based surveillance has been shown to be a useful tool for monitoring the spread of disease in communities and the emergence of new viral variants of concern. As the pandemic enters its fourth year and clinical testing has declined, wastewater offers a consistent non-intrusive way to monitor community health in the long term. This study sought to understand how accurately wastewater monitoring represented the actual burden of disease between communities. Two communities varying in size and demographics in Michigan were monitored for SARS-CoV-2 in wastewater between March of 2020 and February of 2022. Additionally, each community was monitored for SARS-CoV-2 variants of concern from December 2020 to February 2022. Wastewater results were compared with zipcode and county level COVID-19 case data to determine which scope of clinical surveillance was most correlated with wastewater loading. Pearson r correlations were highest in the smaller of the two communities (population of 25,000) for N1 GC/person/day with zipcode level case data, and date of the onset of symptoms (r = 0.81). A clear difference was seen with more cases and virus signals in the wastewater of the larger community (population 110,000) when examined based on vaccine status, which reached only 50%. While wastewater levels of SARS-CoV-2 had a lower correlation to cases in the larger community, the information was still seen as valuable in supporting public health actions and further data including vaccination status should be examined in the future.

Waist-What? Can a single sensor positioned at the waist detect parameters of gait at a speed and distance reflective of older adults' activity?

One of the problems facing an ageing population is functional decline associated with reduced levels of physical activity (PA). Traditionally researcher or clinician input is necessary to capture parameters of gait or PA. Enabling older adults to monitor their activity independently could raise their awareness of their activitiy levels, promote self-care and potentially mitigate the risks associated with ageing. The ankle is accepted as the optimum position for sensor placement to capture parameters of gait however, the waist is proposed as a more accessible body-location for older adults. This study aimed to compare step-count measurements obtained from a single inertial sensor positioned at the ankle and at the waist to that of a criterion measure of step-count, and to compare gait parameters obtained from the sensors positioned at the two different body-locations. Step-count from the waist-mounted inertial sensor was compared with that from the ankle-mounted sensor, and with a criterion measure of direct observation in healthy young and healthy older adults during a three-minute treadmill walk test. Parameters of gait obtained from the sensors at both body-locations were also compared. Results indicated there was a strong positive correlation between step-count measured by both the ankle and waist sensors and the criterion measure, and between ankle and waist sensor step-count, mean step time and mean stride time (r = .802-1.0). There was a moderate correlation between the step time variability measures at the waist and ankle (r = .405). This study demonstrates that a single sensor positioned at the waist is an appropriate method for the capture of important measures of gait and physical activity among older adults.

Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge.

BACKGROUND: Monitoring systems have been developed during the COVID-19 pandemic enabling clinicians to remotely monitor physiological measures including pulse oxygen saturation (SpO2), heart rate (HR), and breathlessness in patients after discharge from hospital. These data may be leveraged to understand how symptoms vary over time in COVID-19 patients. There is also potential to use remote monitoring systems to predict clinical deterioration allowing early identification of patients in need of intervention. METHODS: A remote monitoring system was used to monitor 209 patients diagnosed with COVID-19 in the period following hospital discharge. This system consisted of a patient-facing app paired with a Bluetooth-enabled pulse oximeter (measuring SpO2 and HR) linked to a secure portal where data were available for clinical review. Breathlessness score was entered manually to the app. Clinical teams were alerted automatically when SpO2 < 94 %. In this study, data recorded during the initial ten days of monitoring were retrospectively examined, and a random forest model was developed to predict SpO2 < 94 % on a given day using SpO2 and HR data from the two previous days and day of discharge. RESULTS: Over the 10-day monitoring period, mean SpO2 and HR increased significantly, while breathlessness decreased. The coefficient of variation in SpO2, HR and breathlessness also decreased over the monitoring period. The model predicted SpO2 alerts (SpO2 < 94 %) with a mean cross-validated. sensitivity of 66 ± 18.57 %, specificity of 88.31 ± 10.97 % and area under the receiver operating characteristic of 0.80 ± 0.11. Patient age and sex were not significantly associated with the occurrence of asymptomatic SpO2 alerts. CONCLUSION: Results indicate that SpO2 alerts (SpO2 < 94 %) on a given day can be predicted using SpO2 and heart rate data captured on the two preceding days via remote monitoring. The methods presented may help early identification of patients with COVID-19 at risk of clinical deterioration using remote monitoring.

Generalizability of deep learning models for predicting outdoor irregular walking surfaces.

Observations from laboratory-based gait analysis are difficult to extrapolate to real-world environments where gait behavior is modulated in response to complex environmental conditions and surface profiles. Inertial measurement units (IMUs) permit real-world gait analysis; however, automatic detection of surfaces encountered remains largely unexplored. The aims of this study are to quantify for machine learning models the effect of (1) random and subject-wise data splitting and (2) sensor location and count on surface classification performance. Thirty participants walked on nine surface conditions (flat-even, slope-up, slope-down, stairs-up, stairs-down, cobblestone, grass, banked-left, banked-right) wearing IMUs (wrist, trunk, bilateral thighs, bilateral shanks). Data were separated into gait cycles, normalized to 101 samples, and spilt into train and test sets (85 and 15%, respectively). For random splitting, trials were randomly assigned to the train or test set. In subject-wise splitting, all trials from 4 random participants were selected for testing. Linear discriminant analysis extracted features from the IMUs. Features were delivered to a neural network. F1-score evaluated model performance. Models achieved F1 scores of 0.96 and 0.78 using random and subject-wise splitting, respectively. Random splitting performance was mainly invariant to sensor location/count; however, subject-wise splitting showed best performance using lower-limb sensors. In general, stairs and sloped surfaces were easily predicted (F1 > 0.85) while banked surfaces were challenging, especially for subject-wise models (F1 ≈ 0.6). Neural networks can detect surfaces based on subtle changes in walking behavior captured by IMUs. Data splitting approaches and sensor location/count (subject-wise) have a non-negligible effect on model performance.

Connecting microbial, nutrient, physiochemical, and land use variables for the evaluation of water quality within mixed use watersheds.

As non-point sources of pollution begin to overtake point sources in watersheds, source identification and complicating variables such as rainfall are growing in importance. Microbial source tracking (MST) allows for identification of fecal contamination sources in watersheds; when combined with data on land use and co-occuring variables (e.g., nutrients, sediment runoff) MST can provide a basis for understanding how to effectively remediate water quality. To determine spatial and temporal trends in microbial contamination and correlations between MST and nutrients, water samples (n = 136) were collected between April 2017 and May of 2018 during eight sampling events from 17 sites in 5 mixed-use watersheds. These samples were analyzed for three MST markers (human - B. theta; bovine - CowM2; porcine - Pig2Bac) along with E. coli, nutrients (nitrogen and phosphorus species), and physiochemical paramaters. These water quality variables were then paired with data on land use, streamflow, precipitation and management practices (e.g., tile drainage, septic tank density, tillage practices) to determine if any significant relationships existed between the observed microbial contamination and these variables. The porcine marker was the only marker that was highly correlated (p value <0.05) with nitrogen and phosphorus species in multiple clustering schemes. Significant relationships were also identified between MST markers and variables that demonstrated temporal trends driven by precipitation and spatial trends driven by septic tanks and management practices (tillage and drainage) when spatial clustering was employed.

EntropyHub: An open-source toolkit for entropic time series analysis.

An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.

Methods Evaluation for Rapid Concentration and Quantification of SARS-CoV-2 in Raw Wastewater Using Droplet Digital and Quantitative RT-PCR.

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging public health tool to understand the spread of Coronavirus Disease 2019 (COVID-19) in communities. The performance of different virus concentration methods and PCR methods needs to be evaluated to ascertain their suitability for use in the detection of SARS-CoV-2 in wastewater. We evaluated ultrafiltration and polyethylene glycol (PEG) precipitation methods to concentrate SARS-CoV-2 from sewage in wastewater treatment plants and upstream in the wastewater network (e.g., manholes, lift stations). Recovery of viruses by different concentration methods was determined using Phi6 bacteriophage as a surrogate for enveloped viruses. Additionally, the presence of SARS-CoV-2 in all wastewater samples was determined using reverse transcription quantitative PCR (RT-qPCR) and reverse transcription droplet digital PCR (RT-ddPCR), targeting three genetic markers (N1, N2 and E). Using spiked samples, the Phi6 recoveries were estimated at 2.6-11.6% using ultrafiltration-based methods and 22.2-51.5% using PEG precipitation. There was no significant difference in recovery efficiencies (p < 0.05) between the PEG procedure with and without a 16 h overnight incubation, demonstrating the feasibility of obtaining same day results. The SARS-CoV-2 genetic markers were more often detected by RT-ddPCR than RT-qPCR with higher sensitivity and precision. While all three SARS-CoV-2 genetic markers were detected using RT-ddPCR, the levels of E gene were almost below the limit of detection using RT-qPCR. Collectively, our study suggested PEG precipitation is an effective low-cost procedure which allows a large number of samples to be processed simultaneously in a routine wastewater monitoring for SARS-CoV-2. RT-ddPCR can be implemented for the absolute quantification of SARS-CoV-2 genetic markers in different wastewater matrices.

A comparison of E. coli concentration estimates quantified by the EPA and a Michigan laboratory network using EPA Draft Method C.

We evaluated data from 10 laboratories that analyzed water samples from 82 recreational water sites across the state of Michigan between 2016 and 2018. Water sample replicates were analyzed by experienced U.S. Environmental Protection Agency (EPA) analysts and Michigan laboratories personnel, many of whom were newly trained, using EPA Draft Method C-a rapid quantitative polymerase chain reaction (qPCR) technique that provides same day Escherichia coli (E. coli) concentration results. Beach management decisions (i.e. remain open or issue an advisory or closure) based on E. coli concentration estimates obtained by Michigan labs and by the EPA were compared; the beach management decision agreed in 94% of the samples analyzed. We used the Wilcoxon one-sample signed rank test and nonparametric quantile regression to assess (1) the degree of agreement between E. coli concentrations quantified by Michigan labs versus the EPA and (2) Michigan lab E. coli measurement precision, relative to EPA results, in different years and water body types. The median quantile regression curve for Michigan labs versus EPA approximated the 1:1 line of perfect agreement more closely as years progressed. Similarly, Michigan lab E. coli estimates precision also demonstrated yearly improvements. No meaningful difference was observed in the degree of association between Michigan lab and EPA E. coli concentration estimates for inland lake and Great Lakes samples (median regression curve average slopes 0.93 and 0.95, respectively). Overall, our study shows that properly trained laboratory personnel can perform Draft Method C to a degree comparable with experienced EPA analysts. This allows health departments that oversee recreational water quality monitoring to be confident in qPCR results generated by the local laboratories responsible for analyzing the water samples.

Quantitative clinical assessment of motor function during and following LSVT-BIG® therapy.

BACKGROUND: LSVT-BIG® is an intensively delivered, amplitude-oriented exercise therapy reported to improve mobility in individuals with Parkinson's disease (PD). However, questions remain surrounding the efficacy of LSVT-BIG® when compared with similar exercise therapies. Instrumented clinical tests using body-worn sensors can provide a means to objectively monitor patient progression with therapy by quantifying features of motor function, yet research exploring the feasibility of this approach has been limited to date. The aim of this study was to use accelerometer-instrumented clinical tests to quantify features of gait, balance and fine motor control in individuals with PD, in order to examine motor function during and following LSVT-BIG® therapy. METHODS: Twelve individuals with PD undergoing LSVT-BIG® therapy, eight non-exercising PD controls and 14 healthy controls were recruited to participate in the study. Functional mobility was examined using features derived from accelerometry recorded during five instrumented clinical tests: 10 m walk, Timed-Up-and-Go, Sit-to-Stand, quiet stance, and finger tapping. PD subjects undergoing therapy were assessed before, each week during, and up to 13 weeks following LSVT-BIG®. RESULTS: Accelerometry data captured significant improvements in 10 m walk and Timed-Up-and-Go times with LSVT-BIG® (p <  0.001), accompanied by increased stride length. Temporal features of the gait cycle were significantly lower following therapy, though no change was observed with measures of asymmetry or stride variance. The total number of Sit-to-Stand transitions significantly increased with LSVT-BIG® (p <  0.001), corresponding to a significant reduction of time spent in each phase of the Sit-to-Stand cycle. No change in measures related to postural or fine motor control was observed with LSVT-BIG®. PD subjects undergoing LSVT-BIG® showed significant improvements in 10 m walk (p <  0.001) and Timed-Up-and-Go times (p = 0.004) over a four-week period when compared to non-exercising PD controls, who showed no week-to-week improvement in any task examined. CONCLUSIONS: This study demonstrates the potential for wearable sensors to objectively quantify changes in motor function in response to therapeutic exercise interventions in PD. The observed improvements in accelerometer-derived features provide support for instrumenting gait and sit-to-stand tasks, and demonstrate a rescaling of the speed-amplitude relationship during gait in PD following LSVT-BIG®.

Gait Event Detection From Accelerometry Using the Teager-Kaiser Energy Operator.

OBJECTIVE: A novel method based on the application of the Teager-Kaiser Energy Operator is presented to estimate instances of initial contact (IC) and final contact (FC) from accelerometry during gait. The performance of the proposed method was evaluated against four existing gait event detection (GED) methods under three walking conditions designed to capture the variance of gait in real-world environments. METHODS: A symmetric discrete approximation of the Teager-Kaiser energy operator was used to capture simultaneous amplitude and frequency modulations of the shank acceleration signal at IC and FC during flat treadmill walking, inclined treadmill walking, and flat indoor walking. Accuracy of estimated gait events were determined relative to gait events detected using force-sensitive resistors. The performance of the proposed algorithm was assessed against four established methods by comparing mean-absolute error, sensitivity, precision, and F1-score values. RESULTS: The proposed method demonstrated high accuracy for GED in all walking conditions, yielding higher F1-scores (IC: >0.98, FC: >0.9) and lower mean-absolute errors (IC: <0.018s, FC: <0.039s) than other methods examined. Estimated ICs from shank-based methods tended to exhibit unimodal distributions preceding the force-sensitive resistor estimated ICs, whereas estimated gait events for waist-based methods had quasiuniform random distributions and lower accuracy. CONCLUSION: Compared with the established gait event detection methods, the proposed method yielded comparably high accuracy for IC detection, and was more accurate than all other methods examined for FC detection. SIGNIFICANCE: The results support the use of the Teager-Kaiser Energy Operator for accurate automated GED across a range of walking conditions.

Optical Coherence Tomography Angiography Findings in a Case of Cosmetic Laser Induced Retinal Injury.

We report the first known case of iris and retinal injury using optical coherence tomography angiography (OCTA) images following cosmetic laser injury. A 23-year-old female developed left iris and retinal injury post inadvertent firing of a 755 nm Alexandrite cosmetic laser. The patient had no significant past medical history and the injury resulted from inappropriate eye protection during laser use. Injury from the laser caused damage to the retinal pigment epithelium (RPE), iris pigment epithelium (IPE), and ciliary body epithelium (CPE). The OCTA imaging modality detected the vascular injury caused by the Alexandrite 755 nm laser to the choroid and RPE with subsequent images visualizing the healing response in the months postinjury.

Beta-band motor unit coherence and nonlinear surface EMG features of the first dorsal interosseous muscle vary with force.

Motor unit firing times are weakly coupled across a range of frequencies during voluntary contractions. Coherent activity within the beta-band (15-35 Hz) has been linked to oscillatory cortical processes, providing evidence of functional connectivity between the motoneuron pool and motor cortex. The aim of this study was to investigate whether beta-band motor unit coherence is altered with increasing abduction force in the first dorsal interosseous muscle. Coherence between motor unit firing times, extracted from decomposed surface electromyography (EMG) signals, was investigated in 17 subjects at 10, 20, 30, and 40% of maximum voluntary contraction. Corresponding changes in nonlinear surface EMG features (specifically sample entropy and determinism, which are sensitive to motor unit synchronization) were also examined. A reduction in beta-band and alpha-band coherence was observed as force increased [F(3, 151) = 32, P < 0.001 and F(3, 151) = 27, P < 0.001, respectively], accompanied by corresponding changes in nonlinear surface EMG features. A significant relationship between the nonlinear features and motor unit coherence was also detected (r = -0.43 ± 0.1 and r = 0.45 ± 0.1 for sample entropy and determinism, respectively; both P < 0.001). The reduction in beta-band coherence suggests a change in the relative contribution of correlated and uncorrelated presynaptic inputs to the motoneuron pool, and/or a decrease in the responsiveness of the motoneuron pool to synchronous inputs at higher forces. The study highlights the importance of considering muscle activation when investigating changes in motor unit coherence or nonlinear EMG features and examines other factors that can influence coherence estimation.NEW & NOTEWORTHY Intramuscular alpha- and beta-band coherence decreased as muscle contraction force increased. Beta-band coherence was higher in groups of high-threshold motor units than in simultaneously active lower threshold units. Alterations in motor unit coherence with increases or decreases in force and with the onset of fatigue were accompanied by corresponding changes in surface electromyography sample entropy and determinism. Mixed-model analysis indicated mean firing rate and number of motor units also influenced the coherence estimate.

Application of the Teagar-Kaiser energy operator and wavelet transform for detection of finger tapping contact and release times using accelerometery.

The Teager-Kaiser energy operator (TKEO), when applied to a signal gives an estimation of the instantaneous energy of that signal. It, therefore, accentuates both frequency and amplitude changes in a signal. To date, it has been primarily used in communications systems and most popularly in electromyographic signal analysis to detect bursts of muscle activity, however, it has the potential to be used in a number of applications including accelerometer and movement data.A new algorithm was developed which used the TKEO to detect contact times during a finger tapping task from accelerometer data recorded from the index finger. The accuracy of the algorithm was assessed in 7 healthy control subjects during continuous finger tapping across a range of frequencies from 0.5Hz to 2.5Hz. The algorithm proved to be sensitive, correctly identifying at least 99% of all contacts in each of the finger tapping conditions that were tested. The mean absolute error of the contact detection is 14.7 ± 6 ms, while the mean absolute error of the release detection is 36.5 ± 36.3 ms. The proposed algorithm provides a method for the automatic detection of the temporal occurrences of the events of the finger tapping task using only a tri-axial accelerometer. The approach presented provides a means for objective assessment of finger tapping tasks for evaluation of the fine dexterity of the upper limb.

The Influence of Force Level and Motor Unit Coherence on Nonlinear Surface EMG Features Examined Using Model Simulation.

Nonlinear features extracted from surface EMG signals have been previously used to infer information on coherent or synchronous activity in the underlying motor unit discharges. However, it has not yet been assessed how these features are affected by the density of the surface EMG signal, and whether changes in the level of muscle activation can influence the effective detection of correlated motor unit firing. To examine this, a motoneuron pool model receiving a beta-band modulated cortical input was used to generate correlated motor unit firing trains. These firing trains were convolved with motor unit action potentials generated from an anatomically accurate electrophysiological model of the first dorsal interosseous muscle. The sample entropy (SampEn) and percentage determinism (%DET) of recurrence quantification analysis were calculated from the composite surface EMG signals, for signals comprised of both correlated and uncorrelated motor unit firing trains. The results show that although both SampEn and %DET are influenced by motor unit coherence, they are differentially affected by muscle activation and motor unit distribution. The results also suggest that sample entropy may provide a more accurate assessment of the underlying motor unit coherence than percentage determinism, as it is less sensitive to factors unrelated to motor unit synchrony.

Increased EMG intermuscular coherence and reduced signal complexity in Parkinson's disease.

OBJECTIVES: To investigate differences in surface electromyography (EMG) features in individuals with idiopathic Parkinson's disease (PD) and aged-matched controls. METHODS: Surface EMG was recorded during isometric leg extension in PD patients prior to, and after undergoing a locomotor training programme, and in aged-matched controls. Differences in EMG structure were quantified using determinism (%DET), sample entropy (SampEn) and intermuscular coherence. RESULTS: %DET was significantly higher, and SampEn significantly lower, in PD patients. Intermuscular coherence was also significantly higher in the PD group in theta, alpha and beta frequency bands. %DET increased and SampEn decreased with increasing Movement-Disorder-Society UPDRS scores, while theta band coherence was significantly correlated with total MDS-UPDRS scores and torque variance. Neither %DET, SampEn nor intermuscular coherence changed in response to training. CONCLUSIONS: The differences observed are consistent with increased synchrony among motor units within and across leg muscles in PD. Differences between EMG signals recorded from the PD and control groups persisted post-therapy, after improvements in walking capacity occurred. SIGNIFICANCE: These results provide insight into changes in motoneuron activity in PD, demonstrate increased beta band intramuscular coherence in PD for the first time, and support the development of quantitative biomarkers for PD based on advanced surface EMG features.

Regional Variations of Bovine and Porcine Fecal Pollution as a Function of Landscape, Nutrient, and Hydrological Factors.

The effects of manure application in agriculture on surface water quality has become a local to global problem because of the adverse consequences on public health and food security. This study evaluated (i) the spatial distribution of bovine (cow) and porcine (pig) genetic fecal markers, (ii) how hydrologic factors influenced these genetic markers, and (iii) their variations as a function of land use, nutrients, and other physiochemical factors. We collected 189 samples from 63 watersheds in Michigan's Lower Peninsula during baseflow, spring melt, and summer rain conditions. For each sample, we quantified the concentrations of bovine and porcine genetic markers by digital droplet polymerase chain reaction and measured , dissolved oxygen, pH, temperature, total phosphorus, total nitrogen, nitrate-nitrite (NO), ammonia (NH), soluble reactive phosphorus, streamflow, and watershed specific precipitation. Bovine and porcine manure markers were ubiquitous in rivers that drain agricultural and natural fields across the study region. This study provides baseline conditions on the state of watershed impairment, which can be used to develop best management practices that could improve water quality. Similar studies should be performed with higher spatial sampling density to elucidate detailed factors that influence the transport of manure constituents.

Cytotoxic oplopane sesquiterpenoids from Arnoglossum atriplicifolium.

Pale Indian plantain (Arnoglossum atriplicifolium (L.) H. Rob.) is a plant with traditional medicinal usage among the Cherokee Native American tribe for treating cancer. Two oplopane sesquiterpenoids were isolated from an extract of A. atriplicifolium from Western North Carolina. The compounds were isolated by bioassay-guided fractionation using an MCF-7 breast tumour cell line assay. The known compound (1S,6R,7R,8R)-1-acetoxy-6,7-diangeloxy-8,10-epoxy-2-oxo-oplopa-3,14Z,11,12-dien-13-al (1) had an EC50 value of 9.0 µM against MCF-7 cells, while the new compound (1S,3R,6R,7R,8R,11S)-1-acetoxy-6,7-diangeloxy-8,10,11,13-bisepoxyoplopan-2-one (2) had an EC50 value of 96 µM. The compounds were characterised by 1D and 2D NMR spectroscopy and by comparison with literature values in the case for 1. Based on NOESY analysis, a correction of the relative configuration for 1 is presented. The presence of these compounds may help to explain the folk remedy usage of this plant as an anticancer agent.

Ammonia-oxidizing bacteria and archaea in sediments of the Gulf of Mexico.

The diversity (richness and community composition) of ammonia-oxidizing archaea (AOA) and bacteria (AOB) within sediments of the Gulf of Mexico was examined. Using polymerase chain reaction primers designed to specifically target the archaeal ammonia monooxygenase-subunit (amoA) gene and bacterial amoA gene, we found AOA and AOB to be present in all three sampling sites. Archaeal amoA libraries were dominated by a few widely distributed Nitrosopumilus-like sequence types, whereas AOB diversity showed significant variation in both richness and community composition. Majority of the bacterial amoA sequences recovered belong to Betaproteobacteria and very few belong to Gammaproteobacteria. Results suggest that water depth and nutrient availability were identified as potential drivers that affected the selection of the AOA and AOB communities. Besides influencing the abundance of individual taxa, these environmental factors also had an impact on the overall richness of the overall AOA and AOB communities. The richness and diversity of AOA and AOB genes were higher at the shallowest sediments (100 m depth) and the deepest sediments (1300 m depth). The reduced diversity in the deepest sediments could be explained by much lower nutrient availability.

Linking bacterial diversity and geochemistry of uranium-contaminated groundwater.

To understand the link between bacterial diversity and geochemistry in uranium-contaminated groundwater, microbial communities were assessed based on clone libraries of 16S rDNA genes from the USDOE Oak Ridge Field Research Centre (FRC) site. Four groundwater wells (GW835, GW836, FW113-47 and FW215-49) with a wide range of pH (3 to 7), nitrate (44 to 23,400 mg L(-1)), uranium (0.73 to 60.36 mg L(-1)) and other metal contamination, were investigated. Results indicated that bacterial diversity correlated with the geochemistry of the groundwater. Microbial diversity decreased in relation to the contamination levels of the wells. The highly contaminated well (FW113-47) had lower gene diversity than less contaminated wells (FW215-49, GW835 and GW836). The high concentrations of contaminants present in well FW113-47 stimulated the growth of organisms capable of reducing uranium (Shewanella and Pseudomonas), nitrate (Pseudomonas, Rhodanobacter and Xanthomonas) and iron (Stenotrophomonas), and which were unique to this well. The clone libraries consisted primarily of sequences closely related to the phylum Proteobacteria, with FW-113-47 almost exclusively containing this phylum. Metal-reducing bacteria were present in all four wells, which may suggest that there is potential for successful bioremediation of the groundwater at the Oak Ridge FRC. The microbial community information gained from this study and previous studies at the site can be used to develop predictive multivariate and geographical information system (GIS) based models for microbial populations at the Oak Ridge FRC. This will allow for a better understanding of what organisms are likely to occur where and when, based on geochemistry, and how these organisms relate to bioremediation processes at the site.

Injuries and deformities in fish: their potential impacts upon aquacultural production and welfare.

Fish can be the recipients of numerous injuries that are potentially deleterious to aquacultural production performance and welfare. This review will employ a systematic approach that classifies injuries in relation to specific anatomical areas of the fish and will evaluate the effects of injury upon production and welfare. The selected areas include the (1) mouth, (2) eye, (3) epidermis and (4) fins. These areas cover a large number of external anatomical features that can be injured during aquacultural procedures and husbandry practices. In particular, these injuries can be diagnosed on live fish, in a farm environment. For each anatomical feature, this review addresses (a) its structure and function and (b) defines key injuries that can affect the fish from a production and a welfare perspective. Particular attention is then given to (c) defining known and potential aquacultural risk factors before (d) identifying and outlining potential short- and long-term farming practices and mitigation strategies to reduce the incidence and prevalence of these injuries. The review then concludes with an analysis of potential synergies between risk factors the type of injury, in addition to identifying potential synergies in mitigation strategies. The paper covers both aquaculture and capture-based aquaculture.