Maximally localized Wannier functions, interaction models, and fractional quantum anomalous Hall effect in twisted bilayer MoTe2.

We investigate the moiré band structures and the strong correlation effects in twisted bilayer MoTe[Formula: see text] for a wide range of twist angles, employing a combination of various techniques. Using large-scale first-principles calculations, we pinpoint realistic continuum modeling parameters, subsequently deriving the maximally localized Wannier functions for the top three moiré bands. Simplifying our model with reasonable assumptions, we obtain a minimal two-band model, encompassing Coulomb repulsion, correlated hopping, and spin exchange. Our minimal interaction models pave the way for further exploration of the rich many-body physics in twisted MoTe[Formula: see text]. Furthermore, we explore the phase diagrams of the system through Hartree-Fock approximation and exact diagonalization (ED). Our two-band ED analysis underscores significant band-mixing effects in this system, which enlarge the optimal twist angle for fractional quantum anomalous Hall states.

Critical windows of perinatal particulate matter (PM2.5) exposure and preadolescent kidney function.

Air pollution exposure, especially particulate matter ≤2.5 µm in diameter (PM2.5), is associated with poorer kidney function in adults and children. Perinatal exposure may occur during susceptible periods of nephron development. We used distributed lag nonlinear models (DLNMs) to examine time-varying associations between early life daily PM2.5 exposure (periconceptional through age 8 years) and kidney parameters in preadolescent children aged 8-10 years. Participants included 427 mother-child dyads enrolled in the PROGRESS birth cohort study based in Mexico City. Daily PM2.5 exposure was estimated at each participant's residence using a validated satellite-based spatio-temporal model. Kidney function parameters included estimated glomerular filtration rate (eGFR), serum cystatin C, and blood urea nitrogen (BUN). Models were adjusted for child's age, sex and body mass index (BMI) z-score, as well as maternal education, indoor smoking report and seasonality (prenatal models were additionally adjusted for average first year of life PM2.5 exposure). We also tested for sex-specific effects. Average perinatal PM2.5 was 22.7 µg/m3 and ranged 16.4-29.3 µg/m3. Early pregnancy PM2.5 exposures were associated with higher eGFR in preadolescence. Specifically, we found that PM2.5 exposure between weeks 1-18 of gestation was associated with increased preadolescent eGFR, whereas exposure in the first 14 months of life after birth were associated with decreased eGFR. Specifically, a 5 µg/m3 increase in PM2.5 during the detected prenatal window was associated with a cumulative increase in eGFR of 4.44 mL/min/1.732 (95%CI: 1.37, 7.52), and during the postnatal window we report a cumulative eGFR decrease of -10.36 mL/min/1.732 (95%CI: -17.68, -3.04). We identified perinatal windows of susceptibility to PM2.5 exposure with preadolescent kidney function parameters. Follow-up investigating PM2.5 exposure with peripubertal kidney function trajectories and risk of kidney disease in adulthood will be critical.

Examining Changes in Food Security, Perceived Stress, and Dietary Intake in a Cohort of Low-Wage Workers Experiencing an Increase in Hourly Wage.

OBJECTIVE: To determine whether an increase in hourly wages was associated with changes in food security and perceived stress among low-wage workers. We also determined whether changes in food security and stress were associated with changes in diet. SETTING: Wages is a prospective cohort study following 974 low-wage workers in Minneapolis, MN, where an ordinance is incrementally increasing minimum wage to US$15/hr from 2018 to 2022, and a comparison community with no minimum wage ordinance (Raleigh, NC). Interaction models were estimated using generalized estimating equations. PARTICIPANTS: Analyses used two waves of data (2018 [baseline], 2019) and included 219 and 321 low-wage workers in Minneapolis and Raleigh (respectively). RESULTS: Average hourly wages increased from US$9.77 (SD US$1.69) to US$11.67 (SD US$4.02). Changes in wages were not associated with changes in food security (odds ratio = 1.05, 95% confidence interval [CI] [0.89, 1.23], p = .57) or stress (ß = -0.01, 95% CI [-0.04, 0.03], p = .70) after 1 year of policy implementation. Changes in food security were not associated with changes in diet. However, we found significant changes in the frequency of fruit and vegetable intake across time by levels of stress, with decreased intake from Wave 1 to 2 at low levels of stress, and increased intake at high levels of stress (incidence rate ratio = 1.17, 95% CI [1.05, 1.31], p = .01). CONCLUSIONS: Changes in wages were not associated with changes in food security or stress in a sample of low-wage workers. Future research should examine whether full implementation of a minimum wage increase is associated with changes in these outcomes.

Identifying critical windows of prenatal particulate matter (PM2.5) exposure and early childhood blood pressure.

BACKGROUND: Exposure to air pollution is associated with increased blood pressure (BP) in adults and children. Some evidence suggests that air pollution exposure during the prenatal period may contribute to adverse cardiorenal health later in life. Here we apply a distributed lag model (DLM) approach to identify critical windows that may underlie the association between prenatal particulate matter ≤ 2.5 µm in diameter (PM2.5) exposure and children's BP at ages 4-6 years. METHODS: Participants included 537 mother-child dyads enrolled in the Programming Research in Obesity, GRowth Environment, and Social Stress (PROGRESS) longitudinal birth cohort study based in Mexico City. Prenatal daily PM2.5 exposure was estimated using a validated satellite-based spatio-temporal model and BP was measured using the automated Spacelabs system with a sized cuff. We used distributed lag models (DLMs) to examine associations between daily PM2.5 exposure and systolic and diastolic BP (SBP and DBP), adjusting for child's age, sex and BMI, as well as maternal education, preeclampsia and indoor smoking report during the second and third trimester, seasonality and average postnatal year 1 PM2.5 exposure. RESULTS: We found that PM2.5 exposure between weeks 11-32 of gestation (days 80-226) was significantly associated with children's increased SBP. Similarly, PM2.5 exposure between weeks 9-25 of gestation (days 63-176) was significantly associated with increased DBP. To place this into context, a constant 10 µg/m3 increase in PM2.5 sustained throughout this critical window would predict a cumulative increase of 2.6 mmHg (CI: 0.5, 4.6) in SBP and 0.88 mmHg (CI: 0.1, 1.6) in DBP at ages 4-6 years. In a stratified analysis by sex, this association persisted in boys but not in girls. CONCLUSIONS: Second and third trimester PM2.5 exposure may increase children's BP in early life. Further work investigating PM2.5 exposure with BP trajectories later in childhood will be important to understanding cardiorenal trajectories that may predict adult disease. Our results underscore the importance of reducing air pollution exposure among susceptible populations, including pregnant women.

Solvable Model of a Generic Driven Mixture of Trapped Bose-Einstein Condensates and Properties of a Many-Boson Floquet State at the Limit of an Infinite Number of Particles.

A solvable model of a periodically driven trapped mixture of Bose-Einstein condensates, consisting of N1 interacting bosons of mass m1 driven by a force of amplitude fL,1 and N2 interacting bosons of mass m2 driven by a force of amplitude fL,2, is presented. The model generalizes the harmonic-interaction model for mixtures to the time-dependent domain. The resulting many-particle ground Floquet wavefunction and quasienergy, as well as the time-dependent densities and reduced density matrices, are prescribed explicitly and analyzed at the many-body and mean-field levels of theory for finite systems and at the limit of an infinite number of particles. We prove that the time-dependent densities per particle are given at the limit of an infinite number of particles by their respective mean-field quantities, and that the time-dependent reduced one-particle and two-particle density matrices per particle of the driven mixture are 100% condensed. Interestingly, the quasienergy per particle does not coincide with the mean-field value at this limit, unless the relative center-of-mass coordinate of the two Bose-Einstein condensates is not activated by the driving forces fL,1 and fL,2. As an application, we investigate the imprinting of angular momentum and its fluctuations when steering a Bose-Einstein condensate by an interacting bosonic impurity and the resulting modes of rotations. Whereas the expectation values per particle of the angular-momentum operator for the many-body and mean-field solutions coincide at the limit of an infinite number of particles, the respective fluctuations can differ substantially. The results are analyzed in terms of the transformation properties of the angular-momentum operator under translations and boosts, and as a function of the interactions between the particles. Implications are briefly discussed.

Constructing treatment selection rules based on an estimated treatment effect function: different approaches to take stochastic uncertainty into account have a substantial effect on performance.

BACKGROUND: Today we are often interested in the predictive value of a continuous marker with respect to the expected difference in outcome between a new treatment and a standard treatment. We can investigate this in a randomized control trial, allowing us to assess interactions between treatment and marker and to construct a treatment selection rule. A first step is often to estimate the treatment effect as a function of the marker value. A variety of approaches have been suggested for the second step to define explicitly the rule to select the treatment, varying in the way to take uncertainty into account. Little is known about the merits of the different approaches. METHODS: Four construction principles for the second step are compared. They are based on the root of the estimated function, on confidence intervals for the root, or on pointwise or simultaneous confidence bands. All of them have been used implicitly or explicitly in the literature. As performance characteristics we consider the probability to select at least some patients, the probability to classify patients with and without a benefit correctly, and the gain in expected outcome at the population level. These characteristics are investigated in a simulation study. RESULTS: As to be expected confidence interval/band based approaches reduce the risk to select patients who do not benefit from the new treatment, but they tend to overlook patients who can benefit. Simply using positivity of the estimated treatment effect function for selection implies often a larger gain in expected outcome. CONCLUSIONS: The use of 95% confidence intervals/bands in constructing treatment selection rules is a rather conservative approach. There is a need for better construction principles for treatment selection rules aiming to maximize the gain in expected outcome at the population level. Choosing a confidence level of 80% may be a first step in this direction.

Association between prenatal particulate air pollution exposure and telomere length in cord blood: Effect modification by fetal sex.

INTRODUCTION: In utero particulate matter exposure produces oxidative stress that impacts cellular processes that include telomere biology. Newborn telomere length is likely critical to an individual's telomere biology; reduction in this initial telomere setting may signal increased susceptibility to adverse outcomes later in life. We examined associations between prenatal particulate matter with diameter ≤2.5 µm (PM2.5) and relative leukocyte telomere length (LTL) measured in cord blood using a data-driven approach to characterize sensitive windows of prenatal PM2.5 effects and explore sex differences. METHODS: Women who were residents of Mexico City and affiliated with the Mexican Social Security System were recruited during pregnancy (n = 423 for analyses). Mothers' prenatal exposure to PM2.5 was estimated based on residence during pregnancy using a validated satellite-based spatio-temporally resolved prediction model. Leukocyte DNA was extracted from cord blood obtained at delivery. Duplex quantitative polymerase chain reaction was used to compare the relative amplification of the telomere repeat copy number to single gene (albumin) copy number. A distributed lag model incorporating weekly averages for PM2.5 over gestation was used in order to explore sensitive windows. Sex-specific associations were examined using Bayesian distributed lag interaction models. RESULTS: In models that included child's sex, mother's age at delivery, prenatal environmental tobacco smoke exposure, pre-pregnancy BMI, gestational age, birth season and assay batch, we found significant associations between higher PM2.5 exposure during early pregnancy (4-9 weeks) and shorter LTL in cord blood. We also identified two more windows at 14-19 and 34-36 weeks in which increased PM2.5 exposure was associated with longer LTL. In stratified analyses, the mean and cumulative associations between PM2.5 and shortened LTL were stronger in girls when compared to boys. CONCLUSIONS: Increased PM2.5 during specific prenatal windows was associated with shorter LTL and longer LTL. PM2.5 was more strongly associated with shortened LTL in girls when compared to boys. Understanding sex and temporal differences in response to air pollution may provide unique insight into mechanisms.

[Research progress of interaction models between intestinal microflora and host cells].

Intestinal microbes play an important role in human health. The development of various clinical diseases, such as obesity, diabetes and cardiovascular disease, is closely related to the imbalance of intestinal microflora. With the development of high-throughput sequencing technology, there has been a breakthrough in the understanding of intestinal microorganism. The interaction between intestinal epithelial cells and intestinal microbes has become one of the hotspots and difficulties of current research. Because of the constraints of ethical review and experimental cost, people are more interested in the development of interaction models between the intestinal microflora and the host cells. In this paper, interaction models between intestinal microflora and host cells, and its working principle and application prospect are reviewed, hoping to provide new techniques and new ideas for studying functions of intestinal microbes.

Age-space-time CAR models in Bayesian disease mapping.

Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio-temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space-time, space-age, and age-time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986-2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate-specific antigen) test and better treatment of early-stage disease. The decline is not clearly observed in the oldest age groups. Copyright © 2016 John Wiley & Sons, Ltd.

Predicting severity of pathological scarring due to burn injuries: a clinical decision making tool using Bayesian networks.

It is important for clinicians to understand which are the clinical signs, the patient characteristics and the procedures that are related with the occurrence of hypertrophic burn scars in order to carry out a possible prognostic assessment. Providing clinicians with an easy-to- use tool for predicting the risk of pathological scars. A total of 703 patients with 2440 anatomical burn sites who were admitted to the Department of Plastic and Reconstructive Surgery, Burn Center of the Traumatological Hospital in Torino between January 1994 and May 2006 were included in the analysis. A Bayesian network (BN) model was implemented. The probability of developing a hypertrophic scar was evaluated on a number of scenarios. The error rate of the BN model was assessed internally and it was equal to 24·83%. While classical statistical method as logistic models can infer only which variables are related to the final outcome, the BN approach displays a set of relationships between the final outcome (scar type) and the explanatory covariates (patient's age and gender, burn surface area, full-thickness burn surface area, burn anatomical area and wound-healing time; burn treatment options such as advanced dressings, type of surgical approach, number of surgical procedures, type of skin graft, excision and coverage timing). A web-based interface to handle the BN model was developed on the website www.pubchild.org (burns header). Clinicians who registered at the website could submit their data in order to get from the BN model the predicted probability of observing a pathological scar type.

Decomposing scale, technique and composition effects of foreign direct investment on environmental quality.

This study aimed to investigate how the decomposing scale effect, technique effect and composition effect of foreign direct investment (FDI) impact on carbon dioxide (CO2) emissions for 115 nations spanning 1999 to 2019 by employing Generalised Method of Moments (GMM) model. The results indicated that FDI, real GDP per capita, capital-labor ratio, institutional quality and urbanization increase CO2 emissions while the square of real GDP per capita and trade openness contributed to reducing CO2 emissions. Also, our findings fail to support Environmental Kuznets Curve (EKC) theory. The outcomes of this research illustrated that scale effect dominates composition effect and followed by technique effect. The interaction effect of FDI and technique effect has the least influence on CO2 levels in reducing the harmful effects of FDI on CO2. Furthermore, it should be highlighted that although FDI increases CO2 emissions, its detrimental impact on CO2 emissions is moderately mitigated by its interactions with three economic mechanisms. Therefore, it is necessary to enhance the technical processes of production as well as the development of modern technologies. We recommended that policymakers balance sustainable economic development with environmental sustainability by considering the indirect effects of factors on CO2 emissions.

Effects of municipal boundaries measured by combining urban scaling and spatial interaction.

Urban scaling, the superlinear increase of socio-economic measures with increasing population, is a well-researched phenomenon. This article is focused on socio-economic performance scaling, which could possibly be driven by increasing returns of the size and density of interaction networks. If this is indeed the case, we should also find that spatial barriers to interaction affect scaling and cause local performance deviations. Possible barring effects of municipal boundaries are important from the perspective of urban and regional governance. We test the hypothesis of barring effects by correlating municipal boundaries with the structure of commuter networks within a large densely urbanized region, the Randstad in The Netherlands. The measured impacts of these boundaries are correlated with local employment-scaling deviations. Applying spatially weighted modelling techniques, we find that municipal borders have significant effects on inter-municipal commuting and indicate these effects on the map. The results show particularly significant correlations along dividing lines between large urban agglomerations and rural communities. The southern part of the Randstad is more fragmented by such dividing lines than the northern part, which could partly explain the diverging economic development between the two parts.

Moderating effects of uric acid and sex on cognition and psychiatric symptoms in asymmetric Parkinson's disease.

BACKGROUND: Non-motor symptoms are an important early feature of Parkinson's disease (PD), encompassing a variety of cognitive and psychiatric symptoms that seem to manifest differently depending on motor symptom asymmetry. Different factors, such as uric acid (UA) and sex, seem to influence cognitive and psychiatric expression in PD, however their interplay remains to be better understood. METHODS: Participants taking part in the Parkinson's Progression Marker Initiative were studied based on the side of motor symptom asymmetry and sex. Three-way interaction modeling was used to examine the moderating effects of sex and UA on cognitive functions and psychiatric symptoms. RESULTS: Significant three-way interactions were highlighted at 1-year follow-up between motor symptom asymmetry, UA and sex for immediate and long-term memory in female patients exhibiting predominantly left-sided motor symptoms, and for processing speed and sleepiness in female patients exhibiting predominantly right-sided motor symptoms. No significant interactions were observed for male patients. Moreover, female patients exhibiting predominantly right-sided motor symptoms demonstrated lower serum UA concentrations and had overall better outcomes, while male patients with predominantly right-sided motor symptoms demonstrated particularly poor outcomes. CONCLUSIONS: These findings suggest that in the earliest stages of the disease, UA and sex moderate cognitive functions and psychiatric symptoms differently depending on motor asymmetry, holding important clinical implications for symptom management in patients.

Parkinson's disease is characterized by motor symptoms that usually manifest in an asymmetrical fashion. Given this motor symptom asymmetry, it is possible to distinguish patients that exhibit predominantly right-sided motor symptoms from those that exhibit predominantly left-sided motor symptoms. Patients also often develop non-motor symptoms, such as cognitive and psychiatric complaints. Recent studies have found that non-motor symptoms can manifest differently depending on motor symptom asymmetry. Furthermore, different factors, such as uric acid, a natural antioxidant in the human body, and the patient's sex seem to influence cognitive and psychiatric manifestations, however their interplay remains to be better understood. The present study aimed to examine the interactions between motor symptom asymmetry, serum uric acid and patient's sex on the manifestation of cognitive and psychiatric symptoms. Using regression models, it was found that at 1 year from diagnosis, uric acid and sex moderated cognitive and psychiatric symptoms differently according to motor symptom asymmetry. Indeed, female patients with predominantly left-sided motor symptoms had better memory performances with lower concentrations of serum uric acid, whereas female patients with predominantly right-sided symptoms presented better psychomotor speed and less sleepiness with higher concentrations of uric acid. Moreover, female patients with predominantly right-sided motor symptoms had overall better outcomes, while male patients with predominantly right-sided motor symptoms demonstrated particularly poor clinical outcomes. These findings suggest that in the earliest stages of the disease, uric acid and sex moderate cognitive and psychiatric symptoms differently depending on motor asymmetry, holding important clinical implications for symptom management in patients.

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

A mathematical model to predict the antilisteria bioprotective effect of Latilactobacillus sakei CTC494 in vacuum packaged cooked ham.

Biopreservation is a strategy that has been extensively covered by the scientific literature from a variety of perspectives. However, the development of quantitative modelling approaches has received little attention, despite the usefulness of these tools for the food industry to assess the performance and to set the optimal application conditions. The objective of this study was to evaluate and model the interaction between the antilisteria strain Latilactobacillus sakei CTC494 (sakacin K producer) and Listeria monocytogenes in vacuum-packaged sliced cooked ham. Cooked ham was sliced under aseptic conditions and inoculated with L. monocytogenes CTC1034 and/or L. sakei CTC494 in monoculture and coculture at 10:10, 10:103 and 10:105 cfu/g ratios of pathogen:bioprotective cultures. Samples were vacuum packaged and stored at isothermal temperature (2, 5, 10 and 15 °C). The growth of the two bacteria was monitored by plate counting. The Logistic growth model was applied to estimate the growth kinetic parameters (N0, λ, µmax, Nmax). The effect of storage temperature was modelled using the hyperbola (λ) and Ratkowsky (µmax) models. The simple Jameson-effect model, its modifications including the Ncri and the interaction γ factor, and the predator-prey Lotka Volterra model were used to characterize the interaction between both microorganisms. Two additional experiments at non-isothermal temperature conditions were also carried out to assess the predictive performance of the developed models through the Acceptable Simulation Zone (ASZ) approach. In monoculture conditions, L. monocytogenes and L. sakei CTC494 grew at all temperatures. In coculture conditions, L. sakei CTC494 had an inhibitory effect on L. monocytogenes by lowering the Nmax, especially with increasing levels of L. sakei CTC494 and lowering the storage temperature. At the lowest temperature (2 °C) L. sakei CTC494 was able to completely inhibit the growth of L. monocytogenes when added at a concentration 3 and 5 Log higher than that of the pathogen. The inhibitory effect of the L. sakei CTC494 against L. monocytogenes was properly characterized and modelled using the modified Jameson-effect with interaction γ factor model. The developed interaction model was tested under non-isothermal conditions, resulting in ASZ values ≥83%. This study shows the potential of L. sakei CTC494 in the biopreservation of vacuum-packaged cooked ham against L. monocytogenes. The developed interaction model can be useful for the industry as a risk management tool to assess and set biopreservation strategies for the control of L. monocytogenes in cooked ham.

The Scaling and Modeling of Pay and the Robustness of the Effect of Core Self Evaluations on Career Success.

A number of recent studies used nominal pay in estimating the effects of individual differences, particularly core-self-evaluation, on career success. We show that this practice may lead to results that are substantively different from the results when the logarithm of pay is used. We conduct three constructive replications of previous studies, and argue that substantive conclusion based on the results of nominal pay are misleading.

Does diabetes modify the effect of heparin on plasma proteins? - A proteomic search for plasma protein biomarkers for diabetes-related endothelial dysfunction.

AIM: Heparin administration affects the concentrations of many plasma proteins through their displacement from the endothelial glycocalyx. A differentiated protein response in diabetes will therefore, at least partly, reflect glycocalyx changes. This study aims at identifying biomarkers of endothelial dysfunction in diabetes by statistical exploration of plasma proteome data for interactions between diabetes status and heparin treatment. METHODS: Diabetes-by-heparin interactions in relation to protein levels were inspected by regression modelling in plasma proteome data from 497 patients admitted for acute angiography. Analyses were conducted separately for all 273 proteins and as set-based analyses of 44 heparin-relevant proteins identified by gene ontology analysis and 42 heparin-influenced proteins previously reported. RESULTS: Seventy-five patients had diabetes and 361 received heparin before hospitalization. The proteome-wide analysis displayed no proteins with diabetes-heparin interaction to pass correction for multiple testing. The overall set-based analyses revealed significant association for both protein sets (p-values<2*10-4), while constraining on opposite directions of effect in diabetics and none-diabetics was insignificant (p-values = 0.11 and 0.17). CONCLUSIONS: Our plasma proteome-wide interaction approach supports that diabetes influences heparin effects on protein levels, however the direction of effects and individual proteins could not be definitively pinpointed, likely reflecting a complex protein-basis for glycocalyx dysfunction in diabetes.

Drought and Nitrogen Application Modulate the Morphological and Physiological Responses of Dalbergia odorifera to Different Niche Neighbors.

Mixed stands can be more productive if growth facilitation via niche segregation occurs. Dalbergia odorifera T. Chen, a tropical tree species endemic to Hainan Island with great economic values, belongs to the family Leguminosae. However, selecting mixed species with suitable ecological niches to efficiently construct mixed forests of D. odorifera in the context of abiotic stress [drought, nitrogen (N) deposition] remained obscure. In the present study, the target plant D. odorifera was planted with the same species D. odorifera, heterogeneous but the same family Delonix regia and non-Leguminous Family Swietenia mahagoni in the root interaction and isolated models under two watering regimes [100% and 30% field capacity (FC)] and two N applications (application, non-application), respectively. Principle component analysis based on the performances of growth, phenotype, and physiology was performed to identify the main factors affected by the treatments and the most discriminatory effects of water, N level, and species interaction models. Both comprehensive evaluation values and comprehensive index values were calculated to evaluate the influences of different niche neighbors on D. odorifera. Results showed that D. odorifera was benefited from S. mahagoni but inhibited from D. odorifera in all treatments under root system interaction. Drought stress aggravated the inhibitory effects on D. odorifera from D. odorifera. N application stimulated the promoted effects on D. odorifera from S. mahagoni but enhanced competition intensity of D. odorifera from D. regia under the 100% FC condition. N application alleviated the inhibitory effect of drought stress on D. odorifera from D. odorifera and S. mahagoni. Furthermore, the responses of D. odorifera to different niche neighbors were dominated by belowground interaction rather than the negligible aboveground one. Therefore, the feasibility of niche segregation as the criterion for selecting neighbors to construct D. odorifera mixed stands was confirmed. In addition, water level and N application could alter responses of D. odorifera to different niche neighbors under the root system interaction. Appropriate N application could alleviate the inhibitory effect of drought stress on D. odorifera in its mixed forests. A mixture with S. mahagoni under appropriate N application could be the optimal planting model.

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples.

The aim of this study was to obtain the growth parameters of specific spoilage micro-organisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono- and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO2 30%/O2 70%)] at three isothermal conditions (4, 8, and 12°C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey-predator) model. The modified Jameson-effect model showed slightly better performances, with 40-86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14-48% for the prey-predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three- or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.

Distributed Lag Interaction Models with Two Pollutants.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on a health outcome of interest such as mortality and morbidity. Most previous DLM approaches only consider one pollutant at a time. In this article, we propose distributed lag interaction model (DLIM) to characterize the joint lagged effect of two pollutants. One natural way to model the interaction surface is by assuming that the underlying basis functions are tensor products of the basis functions that generate the main-effect distributed lag functions. We extend Tukey's one-degree-of-freedom interaction structure to the two-dimensional DLM context. We also consider shrinkage versions of the two to allow departure from the specified Tukey's interaction structure and achieve bias-variance tradeoff. We derive the marginal lag effects of one pollutant when the other pollutant is fixed at certain quantiles. In a simulation study, we show that the shrinkage methods have better average performance in terms of mean squared error (MSE) across different scenarios. We illustrate the proposed methods by using the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data to model the joint effects of PM10 and O3 on mortality count in Chicago, Illinois, from 1987 to 2000.