Coupling microbial and abiotic amendments accelerates in situ remediation of bauxite residue at field scale.

Bauxite residue is a highly saline-sodic tailings material formed as a by-product of the Bayer process for alumina production. In situ remediation of bauxite residue has the potential to provide an effective means for accelerated rehabilitation of residue storage areas. However, previous work has predominantly only used chemical and physical amendments to date, limiting rates of pH neutralisation and extent of remediation. Combining these abiotic amendments with recently developed microbial biotechnology for pH neutralisation may transform bauxite residue into a productive soil material in a shorter timeframe. Here we investigated the effects of microbial and abiotic amendments (compost plus tillage), both in isolation and combined, on remediation of key bauxite residue properties in field scale trials (10 × 15 m × 2 m deep field plots). Triplicate residue samples were collected to 30 cm depth from each plot in quarterly field sampling campaigns. Changes in chemical and physical properties were monitored to assess remediation performance under different amendments. After one year, field plots amended with a microbial treatment had significantly (p < 0.05) lower average pH (8.99-9.46) in the upper 20 cm than the control (10.3). The combined microbial-abiotic treatment also had improved physical structure, higher organic C and lower electrical conductivity than the microbial treatment alone. The strong performance of the microbial-abiotic treatment is attributed to the combined benefits of bioneutralisation from microbial fermentation products, enhanced leaching of alkaline pore water and salts due to tillage and compost, and addition of highly stable C and N in compost. Combining novel microbial biotechnology with common abiotic amendments is therefore suggested for accelerating in situ remediation progress towards a material amenable for plant growth.

Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities.

COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.

Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function.

Sensor-based remote monitoring could help better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = -0.62), and gait (⍴ = -0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75-0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d = 0.19-0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.

Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling.

BACKGROUND: Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. OBJECTIVE: This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. METHODS: A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). RESULTS: A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). CONCLUSIONS: This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

A holistic approach for suppression of COVID-19 spread in workplaces and universities.

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the "Community-Workplace" model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and physical distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

Erratum: Author Correction: Deep learning for automated sleep staging using instantaneous heart rate.

[This corrects the article DOI: 10.1038/s41746-020-0291-x.].

Deep learning for automated sleep staging using instantaneous heart rate.

Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

Aspergillus nidulans protein kinase C forms a complex with the formin SepA that is involved in apical growth and septation.

The Aspergillus nidulans orthologue of Protein kinase C (PkcA) and the A. nidulans formin SepA participate in polarized growth. PkcA localizes to growing hyphal apices and septation sites, and amino acid sequences within PkcA that are required for PkcA to localize to these sites of cell wall synthesis have been identified. SepA is associated with the contractile actomyosin ring (CAR), and it localizes at hyphal tips in association with the Spitzenkörper (SPK) and as an apical dome. A mutation in the sepA gene (sepA1) renders A. nidulans aseptate at elevated temperature. Progress towards understanding the spatiotemporal relationship between PkcA and SepA during polarized growth is presented here. Fluorescent chimeras of PkcA and SepA strongly overlapped in some hyphal tips in a dome pattern, while other tips displayed SepA SPK and PkcA dome localization within the same tip. At septation sites PkcA and SepA consistently colocalized through late stages of CAR constriction. Bimolecular fluorescence complementation experimental results provide evidence that SepA and PkcA are both present in complexes at both hyphal tip domes and at cortical rings. A Gal4-based yeast two-hybrid analysis confirmed the physical interaction between SepA and PkcA, and indicted that the FH2 domain of SepA is involved in its physical interaction with PkcA. A functional interaction between PkcA and SepA was shown through complementation of the pkcA calC2 mutant's hypersensitivity to cell wall perturbing agents by overexpressed sepA and by the ability of the sepA1 mutation to block PkcA's ability to form cortical rings. Taken together these results suggest that a PkcA/SepA complex is involved in polarized growth. Through experiments using the actin disrupter latrunculin B, evidence is presented suggesting that actin plays a role in the PkcA/SepA complex.

Dynamic capsule restructuring by the main pneumococcal autolysin LytA in response to the epithelium.

Bacterial pathogens produce complex carbohydrate capsules to protect against bactericidal immune molecules. Paradoxically, the pneumococcal capsule sensitizes the bacterium to antimicrobial peptides found on epithelial surfaces. Here we show that upon interaction with antimicrobial peptides, encapsulated pneumococci survive by removing capsule from the cell surface within minutes in a process dependent on the suicidal amidase autolysin LytA. In contrast to classical bacterial autolysis, during capsule shedding, LytA promotes bacterial survival and is dispersed circumferentially around the cell. However, both autolysis and capsule shedding depend on the cell wall hydrolytic activity of LytA. Capsule shedding drastically increases invasion of epithelial cells and is the main pathway by which pneumococci reduce surface bound capsule during early acute lung infection of mice. The previously unrecognized role of LytA in removing capsule to combat antimicrobial peptides may explain why nearly all clinical isolates of pneumococci conserve this enzyme despite the lethal selective pressure of antibiotics.

Coherence between motor unit discharges in response to shared neural inputs.

Coherence analysis is widely employed to study the correlation between discharge times of simultaneously active motor units. Despite the widespread application of the technique, it has not been fully established how the characteristics of the observed coherence are related to the properties of the shared motoneuron inputs. In addition, the exact relationship between coherence and traditional measures of motor unit synchronization remains unclear. The purpose of this study was to examine the influence of shared motoneuron inputs on coherence between motor unit discharge patterns using computer simulations. Although less sensitive to motor unit firing rates than traditional synchronization-based indices, coherence tended to decrease with increasing frequency of the common input and to increase slightly when the common input frequency was close to the motor unit firing rates. In addition, coherence tended to be highest between motor units with similar firing rates. A linear association was observed between synchronization and coherence in the 15-30 Hz range and between 'common drive' and coherence in the 0-5 Hz range. The results suggest that caution should be taken when interpreting differences in coherence observed between motor units with significantly different firing properties or when comparing data with coherence in different frequency ranges.

Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization.

MOTIVATION: Genotype-phenotype modeling problems are often overcomplete, or ill-posed, since the number of potential predictors-genes, proteins, mutations and their interactions-is large relative to the number of measured outcomes. Such datasets can still be used to train sparse parameter models that generalize accurately, by exerting a principle similar to Occam's Razor: When many possible theories can explain the observations, the most simple is most likely to be correct. We apply this philosophy to modeling the drug response of Type-1 Human Immunodeficiency Virus (HIV-1). Owing to the decreasing expense of genetic sequencing relative to in vitro phenotype testing, a statistical model that reliably predicts viral drug response from genetic data is an important tool in the selection of antiretroviral therapy (ART). The optimization techniques described will have application to many genotype-phenotype modeling problems for the purpose of enhancing clinical decisions. RESULTS: We describe two regression techniques for predicting viral phenotype in response to ART from genetic sequence data. Both techniques employ convex optimization for the continuous subset selection of a sparse set of model parameters. The first technique, the least absolute shrinkage and selection operator, uses the l(1) norm loss function to create a sparse linear model; the second, the support vector machine with radial basis kernel functions, uses the epsilon-insensitive loss function to create a sparse non-linear model. The techniques are applied to predict the response of the HIV-1 virus to 10 reverse transcriptase inhibitor and 7 protease inhibitor drugs. The genetic data are derived from the HIV coding sequences for the reverse transcriptase and protease enzymes. When tested by cross-validation with actual laboratory measurements, these models predict drug response phenotype more accurately than models previously discussed in the literature, and other canonical techniques described here. Key features of the methods that enable this performance are the tendency to generate simple models where many of the parameters are zero, and the convexity of the cost function, which assures that we can find model parameters to globally minimize the cost function for a particular training dataset. AVAILABILITY: Results, tables and figures are available at ftp://ftp.genesecurity.net. SUPPLEMENTARY INFORMATION: An Appendix to accompany this article is available at Bioinformatics online.

Effective method for quantifying respiratory effective method for quantifying respiratory subsequent marker of anxiety.

Ambulatory respiratory data was gathered using inductive lethysmography technology with synchronous ECG(LifeShirte , VivoMetrics, Ventura, CA) during a study to evaluate the effect of an anxiolytic on heart rate variability and respiratory pattern as indicators of anxiety state. Positive control (PCR; post-marketing, broadly prescribed anxiolytic)and placebo (PBO) data was included in the analysis. Tidal volume waveforms were the result of a weighted sum of the abdominal and rib cage IP bands according to the qualitative diagnostic calibration method. A breath detection algorithm was run to identify the beginning and end of inhalation in these waveforms. Several types of respiratory artifact are common with ambulatory, non-controlled recordings and a consistent and reliable means is necessary to identify and manage such artifacts. An automated approach was adopted to define a reliable breathing index for each breath that labels that breath as contaminated by artifact or not. The root mean square of successive differences (RMSSD) were computed on the tidal inspiratory volumes and total breath times for each epoch, both for all breaths and for only those breaths that were labeled as reliable. The results indicate that when a priori automated artifact detection is included, there is a significant linear decrease in both the volume and time indices for the PCR, whilst no significant differences were noted in the PBO group. Analyzing the data without prior marking of reliable breaths showed no significant results for either group. This study demonstrates the validity of ambulatory respiratory measurements as a means to assess anxiety and establishes the need to first identify reliable breathing periods prior to the analysis of ambulatory respiratory data.

Use of the l1 norm for selection of sparse parameter sets that accurately predict drug response phenotype from viral genetic sequences.

We describe the use of the l1 norm for selection of a sparse set of model parameters that are used in the prediction of viral drug response, based on genetic sequence data of the Human Immunodeficiency Virus (HIV) reverse-transcriptase enzyme. We discuss the use of the l1 norm in the Least Absolute Selection and Shrinkage Operator (LASSO) regression model and the Support Vector Machine model. When tested by cross-validation with laboratory measurements, these models predict viral phenotype, or resistance, in response to Reverse-Transcriptase Inhibitors (RTIs) more accurately than other known models. The l1 norm is the most selective convex function, which sets a large proportion of the parameters to zero and also assures that a single optimal solution will be found, given a particular model formulation and training data set. A statistical model that reliably predicts viral drug response is an important tool in the selection of Anti-Retroviral Therapy. These techniques have general application to modeling phenotype from complex genetic data.

Time and frequency domain methods for quantifying common modulation of motor unit firing patterns.

BACKGROUND: In investigations of the human motor system, two approaches are generally employed toward the identification of common modulating drives from motor unit recordings. One is a frequency domain method and uses the coherence function to determine the degree of linear correlation between each frequency component of the signals. The other is a time domain method that has been developed to determine the strength of low frequency common modulations between motor unit spike trains, often referred to in the literature as 'common drive'. METHODS: The relationships between these methods are systematically explored using both mathematical and experimental procedures. A mathematical derivation is presented that shows the theoretical relationship between both time and frequency domain techniques. Multiple recordings from concurrent activities of pairs of motor units are studied and linear regressions are performed between time and frequency domain estimates (for different time domain window sizes) to assess their equivalence. RESULTS: Analytically, it may be demonstrated that under the theoretical condition of a narrowband point frequency, the two relations are equivalent. However practical situations deviate from this ideal condition. The correlation between the two techniques varies with time domain moving average window length and for window lengths of 200 ms, 400 ms and 800 ms, the r2 regression statistics (p < 0.05) are 0.56, 0.81 and 0.80 respectively. CONCLUSIONS: Although theoretically equivalent and experimentally well correlated there are a number of minor discrepancies between the two techniques that are explored. The time domain technique is preferred for short data segments and is better able to quantify the strength of a broad band drive into a single index. The frequency domain measures are more encompassing, providing a complete description of all oscillatory inputs and are better suited to quantifying narrow ranges of descending input into a single index. In general the physiological question at hand should dictate which technique is best suited.