The interplay between external residue addition, and soil organic carbon dynamics and mineralization kinetics: Experiences from a 12-year old conservation agriculture.

Maintaining soil carbon is vital under changing climate. Conservation agriculture (CA) is reported to have potential to store soil organic carbon (SOC). The impact of carbon inputs on SOC dynamics and mineralization kinetics, and the priming effect of residue addition under long-term CA in subtropical regions, however, are not clear or adequately evaluated. Therefore, we studied these under a 12-year-old CA-based pigeon pea-wheat cropping system with permanent broad bed with residue (CA-PBB), permanent flatbed with residue (CA-PFB), permanent narrow bed with residue (CA-PNB), and conventional till (CT) treatments. Also, an incubation study was undertaken to understand better the processes involved. Results showed that CA treatments significantly enhanced the total SOC compared to CT practice, and, among them, the CA-PFB exhibited highest total SOC with 36.6% and 35.8% higher values at 0-5 and 5-15 cm depths, respectively. The CA-PFB followed by CA-PBB and CA-PNB had significantly higher carbon management index and carbon retention efficiency than CT. The CA-PFB also showed higher carbon sequestration rates of 68.4 and 188.8 kg ha-1 year-1, surpassing values of 8.4 and 52.9 kg ha-1 year-1 under CT at 0-5 and 5-15 cm depth, respectively. Furthermore, soil incubation study revealed that the CA systems had higher cumulative mineralization values at 0-5 cm soil layer but lower at 5-15 cm soil compared to CT, indicating a considerable improvement in SOC at 5-15 cm soil depth. On the contrary, the SOC decay rate was higher under CA than CT, and at 35 °C than at 15 °C. A positive priming effect was also observed, depending on the substrate type, pigeon pea residue exhibiting higher priming effect than wheat residue. Thus, these studies show that residue input increases cumulative mineralization and SOC decay rate vis-à-vis helps to sequester carbon in the recalcitrant fraction, leading to higher stable carbon in soil.

Bayesian time-varying autoregressive models of COVID-19 epidemics.

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.

YAP at the Crossroads of Biomechanics and Drug Resistance in Human Cancer.

Biomechanical forces are of fundamental importance in biology, diseases, and medicine. Mechanobiology is an emerging interdisciplinary field that studies how biological mechanisms are regulated by biomechanical forces and how physical principles can be leveraged to innovate new therapeutic strategies. This article reviews state-of-the-art mechanobiology knowledge about the yes-associated protein (YAP), a key mechanosensitive protein, and its roles in the development of drug resistance in human cancer. Specifically, the article discusses three topics: how YAP is mechanically regulated in living cells; the molecular mechanobiology mechanisms by which YAP, along with other functional pathways, influences drug resistance of cancer cells (particularly lung cancer cells); and finally, how the mechanical regulation of YAP can influence drug resistance and vice versa. By integrating these topics, we present a unified framework that has the potential to bring theoretical insights into the design of novel mechanomedicines and advance next-generation cancer therapies to suppress tumor progression and metastasis.

Risk factors and outcomes associated with external ventricular drain related hemorrhage.

OBJECTIVE: The objective of this study was to determine risk factors predictive of external ventricular drain (EVD)-related hemorrhage and the association of such hemorrhages with mortality, discharge disposition, length of stay (LOS), and total cost. METHODS: After Institutional Review Board approval, data was collected retrospectively for adult patients requiring EVD placement from 2015 to 2018 at the authors' institution. Collected data included demographic patient information, peri-procedural factors, and relevant post-procedural measures. Computerized tomography (CT) images and associated radiologic reports were independently reviewed, identifying hemorrhages accompanying EVD placement. RESULTS: From this 487-patient sample, 85 (17.5 %) patients had hemorrhages, including asymptomatic hemorrhages identified on imaging alone. A univariable analysis of patient parameters in the overall cohort was performed to identify possible predictors of hemorrhage. Age (p = 0.002), Charlson Comorbidity Index (CCI) (p < 0.001), platelet count (p = 0.002), presence of uremia (p = 0.035), and the number of times the EVD was replaced (p < 0.001) were associated with hemorrhage in univariable models. The experience of the resident surgeon based on post-graduate year (PGY level) and the number of attempts/passes needed for EVD placement were not associated with hemorrhage risk. Significant predictor of hemorrhage confirmed in a multivariable analysis only included the number of times the EVD was replaced (OR = 2.78, adjusted p < 0.001). Outcomes between EVD-related hemorrhage versus no hemorrhage groups, including mortality, discharge disposition, LOS, and cost, were compared. EVD-related hemorrhage was found to be associated with increased mortality (OR = 3.58, adjusted p < 0.001) and decreased likelihood of discharge home (OR = 0.13, adjusted p = 0.030) in the associated multivariable regressions. CONCLUSION: The number of times an EVD was replaced was associated with EVD-related hemorrhage outcome. EVD-related hemorrhage is associated with increased mortality and a decreased likelihood of being discharged home.

Resting state brain network segregation is associated with walking speed and working memory in older adults.

Older adults exhibit larger individual differences in walking ability and cognitive function than young adults. Characterizing intrinsic brain connectivity differences in older adults across a wide walking performance spectrum may provide insight into the mechanisms of functional decline in some older adults and resilience in others. Thus, the objectives of this study were to: (1) determine whether young adults and high- and low-functioning older adults show group differences in brain network segregation, and (2) determine whether network segregation is associated with working memory and walking function in these groups. The analysis included 21 young adults and 81 older adults. Older adults were further categorized according to their physical function using a standardized assessment; 54 older adults had low physical function while 27 were considered high functioning. Structural and functional resting state magnetic resonance images were collected using a Siemens Prisma 3T scanner. Working memory was assessed with the NIH Toolbox list sorting test. Walking speed was assessed with a 400 m-walk test at participants' self-selected speed. We found that network segregation in mobility-related networks (sensorimotor, vestibular, and visual networks) was higher in younger adults compared to older adults. There were no group differences in laterality effects on network segregation. We found multivariate associations between working memory and walking speed with network segregation scores. Higher right anterior cingulate cortex network segregation was associated with higher working memory function. Higher right sensorimotor, right vestibular, right anterior cingulate cortex, and lower left anterior cingulate cortex network segregation was associated with faster walking speed. These results are unique and significant because they demonstrate higher network segregation is largely related to higher physical function and not age alone.

Prefrontal cortical activity during uneven terrain walking in younger and older adults.

Introduction: Walking in complex environments increases the cognitive demand of locomotor control; however, our understanding of the neural mechanisms contributing to walking on uneven terrain is limited. We used a novel method for altering terrain unevenness on a treadmill to investigate the association between terrain unevenness and cortical activity in the prefrontal cortex, a region known to be involved in various cognitive functions. Methods: Prefrontal cortical activity was measured with functional near infrared spectroscopy while participants walked on a novel custom-made terrain treadmill surface across four different terrains: flat, low, medium, and high levels of unevenness. The assessments were conducted in younger adults, older adults with better mobility function and older adults with worse mobility function. Mobility function was assessed using the Short Physical Performance Battery. The primary hypothesis was that increasing the unevenness of the terrain would result in greater prefrontal cortical activation in all groups. Secondary hypotheses were that heightened prefrontal cortical activation would be observed in the older groups relative to the younger group, and that prefrontal cortical activation would plateau at higher levels of terrain unevenness for the older adults with worse mobility function, as predicted by the Compensation Related Utilization of Neural Circuits Hypothesis. Results: The results revealed a significant main effect of terrain, indicating a significant increase in prefrontal cortical activation with increasing terrain unevenness during walking in all groups. A significant main effect of group revealed that prefrontal cortical activation was higher in older adults with better mobility function compared to younger adults and older adults with worse mobility function in all pooled terrains, but there was no significant difference in prefrontal cortical activation between older adults with worse mobility function and younger adults. Contrary to our hypothesis, the older group with better mobility function displayed a sustained increase in activation but the other groups did not, suggestive of neural compensation. Additional findings were that task-related increases in prefrontal cortical activation during walking were lateralized to the right hemisphere in older adults with better mobility function but were bilateral in older adults with worse mobility function and younger adults. Discussion: These findings support that compared to walking on a flat surface, walking on uneven terrain surfaces increases demand on cognitive control resources as measured by prefrontal cortical activation.

Clustering single-cell multimodal omics data with jrSiCKLSNMF.

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

Co-Application of Silicate and Low-Arsenic-Accumulating Rice Cultivars Efficiently Reduces Human Exposure to Arsenic-A Case Study from West Bengal, India.

We investigated the effect of practically realizable doses of silicate on arsenic (As) uptake by differential-As-accumulating rice cultivars grown on geogenically As-polluted soil. The possible health risk from the dietary ingestion of As through rice was also assessed. In addition, a solution culture experiment was conducted to examine the role of root-secreted weak acids in differential As acquisition by rice cultivars. When grown without silicate, Badshabhog accumulated a much smaller amount of As in grain (0.11 mg kg-1) when compared to the other three varieties. Satabdi, IR-36, and Khitish accumulated As in grain beyond the permissible limit (0.2 mg kg-1) for human consumption. The application of silicate effectively reduced the As content in the grain, husk, and straw of all of the cultivars. The grain As content fell to 17.2 and 27.6% with the addition of sodium metasilicate at the rates of 250 and 500 mg kg-1, respectively. In the case of Khitish, the grain As content was brought down within permissible limits by the applied silicate (500 mg kg-1). The integrated use of low-As-accumulating cultivars and silicate has great potential to reduce the public health risks associated with As. A positive correlation between root-secreted total weak acid and grain As content could explain the different rice cultivars' differential As acquisition capacity.

Uneven terrain versus dual-task walking: differential challenges imposed on walking behavior in older adults are predicted by cognitive and sensorimotor function.

Aging is associated with declines in walking function. To understand these mobility declines, many studies have obtained measurements while participants walk on flat surfaces in laboratory settings during concurrent cognitive task performance (dual-tasking). This may not adequately capture the real-world challenges of walking at home and around the community. Here, we hypothesized that uneven terrains in the walking path impose differential changes to walking speed compared to dual-task walking. We also hypothesized that changes in walking speed resulting from uneven terrains will be better predicted by sensorimotor function than cognitive function. Sixty-three community-dwelling older adults (65-93 yrs old) performed overground walking under varying walking conditions. Older adults were classified into two mobility function groups based on scores of the Short Physical Performance Battery. They performed uneven terrain walking across four surface conditions (Flat, Low, Medium, and High unevenness) and performed single and verbal dual-task walking on flat ground. Participants also underwent a battery of cognitive (cognitive flexibility, working memory, inhibition) and sensorimotor testing (grip strength, 2-pt discrimination, pressure pain threshold). Our results showed that walking speed decreased during both dual-task walking and across uneven terrain walking conditions compared to walking on flat terrain. Participants with lower mobility function had even greater decreases in uneven terrain walking speeds. The change in uneven terrain speed was associated with attention and inhibitory function. Changes in both dual-task and uneven terrain walking speeds were associated with 2-point tactile discrimination. This study further documents associations between mobility, executive functions, and somatosensation, highlights the differential costs to walking imposed by uneven terrains, and identifies that older adults with lower mobility function are more likely to experience these changes to walking function.

Impacts of salinity stress on crop plants: improving salt tolerance through genetic and molecular dissection.

Improper use of water resources in irrigation that contain a significant amount of salts, faulty agronomic practices such as improper fertilization, climate change etc. are gradually increasing soil salinity of arable lands across the globe. It is one of the major abiotic factors that inhibits overall plant growth through ionic imbalance, osmotic stress, oxidative stress, and reduced nutrient uptake. Plants have evolved with several adaptation strategies at morphological and molecular levels to withstand salinity stress. Among various approaches, harnessing the crop genetic variability across different genepools and developing salinity tolerant crop plants offer the most sustainable way of salt stress mitigation. Some important major genetic determinants controlling salinity tolerance have been uncovered using classical genetic approaches. However, its complex inheritance pattern makes breeding for salinity tolerance challenging. Subsequently, advances in sequence based breeding approaches and functional genomics have greatly assisted in underpinning novel genetic variants controlling salinity tolerance in plants at the whole genome level. This current review aims to shed light on physiological, biochemical, and molecular responses under salt stress, defense mechanisms of plants, underlying genetics of salt tolerance through bi-parental QTL mapping and Genome Wide Association Studies, and implication of Genomic Selection to breed salt tolerant lines.

Uneven terrain treadmill walking in younger and older adults.

We developed a method for altering terrain unevenness on a treadmill to study gait kinematics. Terrain consisted of rigid polyurethane disks (12.7 cm diameter, 1.3-3.8 cm tall) which attached to the treadmill belt using hook-and-loop fasteners. Here, we tested four terrain unevenness conditions: Flat, Low, Medium, and High. The main objective was to test the hypothesis that increasing the unevenness of the terrain would result in greater gait kinematic variability. Seventeen younger adults (age 20-40 years), 25 higher-functioning older adults (age 65+ years), and 29 lower-functioning older adults (age 65+ years, Short Physical Performance Battery score < 10) participated. We customized the treadmill speed to each participant's walking ability, keeping the speed constant across all four terrain conditions. Participants completed two 3-minute walking trials per condition. Using an inertial measurement unit placed over the sacrum and pressure sensors in the shoes, we calculated the stride-to-stride variability in step duration and sacral excursion (coefficient of variation; standard deviation expressed as percentage of the mean). Participants also self-reported their perceived stability for each condition. Terrain was a significant predictor of step duration variability, which roughly doubled from Flat to High terrain for all participant groups: younger adults (Flat 4.0%, High 8.2%), higher-functioning older adults (Flat 5.0%, High 8.9%), lower-functioning older adults (Flat 7.0%, High 14.1%). Similarly, all groups exhibited significant increases in sacral excursion variability for the Medium and High uneven terrain conditions, compared to Flat. Participants were also significantly more likely to report feeling less stable walking over all three uneven terrain conditions compared to Flat. These findings support the hypothesis that altering terrain unevenness on a treadmill will increase gait kinematic variability and reduce perceived stability in younger and older adults.

Kinematic analysis of speed transitions within walking in younger and older adults.

The ability to adapt to environmental and task demands while walking is critical to independent mobility outside the home and this ability wanes with age. Such adaptability requires individuals to acutely change their walking speed. Regardless of age, changes between walking speeds are common in daily life, and are a frequent type of walking adaptability. Here, we report on older and younger adults when transitioning from preferred walking speed overground to either slower or faster walking. Specifically, we evaluated biomechanical parameters prior to, during, and post transition. Individuals approached the walking speed transition similarly, independent of whether the transition was to slower or faster walking. Regardless of age or walking speed, the step during which a walking speed transition occurred was distinct from those prior- and post- transition, with on average 0.15 m shorter step lengths, 3.6° more hip flexion, and 3.3° more dorsiflexion during stance. We also found that peak hip flexion occurred 22% later, and peak hip extension (39%), knee flexion (26%), and dorsiflexion (44%) occurred earlier in stance for both typical to slower and typical to faster walking. Older adults had altered timing of peak joint angles compared with younger adults across both acceleration and deceleration conditions, indicating age-dependent responses to changing walking speed. Our findings are an important first step in establishing values for kinematics during walking speed transitions in younger and typical older adults.

Bayesian time-aligned factor analysis of paired multivariate time series.

Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of data in static cases, but only a few approaches are available for dynamic settings. To address this gap, we consider novel models and inference methods for pairs of matrices in which the columns correspond to multivariate observations at different time points. In order to characterize common and individual features, we propose a Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) that includes uncertainty in time alignment through an unknown warping function. We provide theoretical support for the proposed model, showing identifiability and posterior concentration. The structure enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm. We show excellent performance in simulations, and illustrate the method through application to a social mimicry experiment.

PERTURBED FACTOR ANALYSIS: ACCOUNTING FOR GROUP DIFFERENCES IN EXPOSURE PROFILES.

In this article we investigate group differences in phthalate exposure profiles using NHANES data. Phthalates are a family of industrial chemicals used in plastics and as solvents. There is increasing evidence of adverse health effects of exposure to phthalates on reproduction and neurodevelopment and concern about racial disparities in exposure. We would like to identify a single set of low-dimensional factors summarizing exposure to different chemicals, while allowing differences across groups. Improving on current multigroup additive factor models, we propose a class of Perturbed Factor Analysis (PFA) models that assume a common factor structure after perturbing the data via multiplication by a group-specific matrix. Bayesian inference algorithms are defined using a matrix normal hierarchical model for the perturbation matrices. The resulting model is just as flexible as current approaches in allowing arbitrarily large differences across groups but has substantial advantages that we illustrate in simulation studies. Applying PFA to NHANES data, we learn common factors summarizing exposures to phthalates, while showing clear differences across groups.

Nonparametric graphical model for counts.

Although multivariate count data are routinely collected in many application areas, there is surprisingly little work developing flexible models for characterizing their dependence structure. This is particularly true when interest focuses on inferring the conditional independence graph. In this article, we propose a new class of pairwise Markov random field-type models for the joint distribution of a multivariate count vector. By employing a novel type of transformation, we avoid restricting to non-negative dependence structures or inducing other restrictions through truncations. Taking a Bayesian approach to inference, we choose a Dirichlet process prior for the distribution of a random effect to induce great flexibility in the specification. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for posterior computation. We prove various theoretical properties, including posterior consistency, and show that our COunt Nonparametric Graphical Analysis (CONGA) approach has good performance relative to competitors in simulation studies. The methods are motivated by an application to neuron spike count data in mice.