Characteristic of Stomatal Conductance and Optimal Stomatal Behaviour in an Arid Oasis of Northwestern China

Stomatal conductance (gs), the process that governs plant carbon uptake and water loss, is fundamental to most Land Surface Models (LSMs). With global change accelerating, more attention should be paid to investigating stomatal behavior, especially in extremely arid areas. In this study, gas exchange measurements and environmental/biological variables observations during growing seasons in 2016 and 2017 were combined to investigate diurnal and seasonal characteristics of gs and the applicability of the optimal stomatal conductance model in a desert oasis vineyard. The results showed that the responses of gs to environmental factors (photosynthesis active radiation, PAR; vapor pressure deficit, VPD; and temperature, T) formed hysteresis loops in the daytime. The stomatal conductance slope, g1, a parameter in the unified stomatal optimal model, varied in different growing seasons and correlated with the soil-to-leaf hydraulic conductance (KL). These results indicated the potential bias when using a constant g1 value to simulate gs and highlighted that the water-use strategy of oasis plants might not be consistent throughout the entire growing season. Our findings further help to achieve a better understanding of stomata behavior in responding to climate change and encourage future efforts toward a more accurate parameterization of gs to improve the modeling of LSMs.

When Shape Matters: Using a Simple Mathematical Model to Estimate Critical Area Sizes in Conservation

In the analysis of anthropogenic impact on the environment arises the question of whether the shapes of preserved habitat fragments play an important role in the conservation of wild species. In this work we use a very simple mathematical model based on a reaction-diffusion equation to analyze the effects of geometric shape and area on the permanence of populations in habitat fragments. Our results indicate that a dimensionless quantity calculated from a combination of biological variables is the main component that determines if the species survives in the preserved fragment and whether its geometric shape is important. We provide a methodology to calculate critical area sizes for which population size is most affected by fragment shape. The calculation is based on four quantitative variables: maximum per capita reproduction rate, per capita mortality rate outside the fragment, carrying capacity in the conserved environment and mobility in the disturbed environment. The methodology is illustrated by a preliminary study, in which the model is used to estimate threshold area sizes for habitat fragments for the threatened species Sapajus xanthosternos .

Interpretable machine learning for high-dimensional trajectories of aging health

We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables as well as clusters of strongly connected health variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than multiple dedicated linear models for health outcomes and survival. We compare our model with flexible lower-dimensional latent-space models to explore the dimensionality required to accurately model aging health outcomes. Our DJIN model can be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given arbitrary initial health states.

Parent/Caregiver Views of the Effectiveness of Speech-Language Pathology for Children Born With Cleft Palate Delivered via Telemedicine During COVID-19

Purpose: The purpose of this study was to describe and examine parent views of speech-language pathology (SLP) for children born with cleft palate delivered via telemedicine during the COVID-19 pandemic in the United Kingdom (UK). Method: Parents were asked whether they found this method of delivery “very effective,” “somewhat effective,” or “not at all effective.” Free text was then invited. There were 212 responses. Ordinal chi-square, Kruskal–Wallis, or Fisher's exact tests examined associations between parent views of effectiveness and biological variables and socioeconomic status. Free text responses were analyzed using qualitative content analysis. Results: One hundred and forty (66.0%) respondents reported that SLP delivered via telemedicine was “somewhat effective,” 56 (26.4%) “very effective,” and 16 (7.6%) “not at all effective.” There was no evidence of an association between parent reported effectiveness and any of the explanatory variables. Parent-reported challenges impacting on effectiveness included technology issues and keeping their children engaged with sessions. Importantly, telemedicine was viewed as “better than nothing.” Conclusions: Most parents reported that they felt SLP delivered via telemedicine during the first few months of the COVID-19 pandemic in the UK was at least “somewhat effective.” It is important to interpret this in the context of there being no other method of service delivery during this time and that this study only represents families who were able to access SLP delivered via telemedicine. Further work is needed to identify which children with cleft palate might benefit from SLP delivered via telemedicine to inform postpandemic service provision.

Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

BackgroundSoft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS.MethodsWe retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS).ResultsThirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients’ age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies.ConclusionICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.

Interface between obesity with dysfunctional metabolism and inflammation, and the triple-negative breast cancer in African American women

Obesity has dramatically increased over the past fifty years. In the last decade, it has been noted that augmented body mass, metabolic abnormalities, and the relevant “obese” tumor microenvironment (TME) are connected with signaling molecular networks, which in turn, may contribute to aggressive tumor biology in some patients with breast malignancies. This article presents the associations between obesity, metabolic derangements, inflammatory processes in the adipose tissue or TME, and aggressive behavior of triple-negative breast cancer (TNBC) in African American (AA) women. It also describes some abnormal molecular signaling patterns in the “obese” TME with relevance to TNBC biology. Ethnic disparities in TNBC can be due to a variety of biological features (e.g., genetic mutations and tumor heterogeneity), comorbidities (e.g., cardio-metabolic diseases, including diabetes mellitus), and reproductive factors (e.g., multiparty or short breastfeeding period). Such a constellation of biological variables potentially leads to the association between obesity, metabolic derangements, inflammatory processes in the adipose tissue or TME, and aggressive behavior of TNBC in AA women. Since the TNBC and its TME can display very aggressive behavior, it is crucial that the afflicted AA women make efforts to maintain healthy body weight, “flexible” metabolism, and a well-functioning immune system. Further studies are merited to explore the multi-disciplinary factors that can affect TNBC prevention, management, and outcomes to optimize treatment strategies and survival among AA women.

Several Theoretical and Applied Problems of Human Extreme Physiology: Mathematical Modeling

Human cardiovascular system (CVS) and hemodynamics are critically sensitive to essential alterations of mechanical inertial forces in directions of head-legs (+Gz) or legs-head (-Gz). Typically, such alterations appear during pilotage maneuvers of modern high maneuverable airspace vehicles (HMAV).The vulnerability of pilots or passengers of HMAV to these altering forces depends on their three main characteristics: amplitude, dynamics, and duration. Special protections, proposed to minimize this vulnerability, should be improved in parallel with the increasing of these hazardous characteristics of HMAVs. Empiric testing of novel protection methods and tools is both expensive and hazardous. Therefore computer simulations are encouraged. Autonomic software (AS) for simulating and theoretical investigating of the main dynamic responses of human CVS to altering Gz is developed. AS is based on a system of quantitative mathematical models (QMM) consisting of about 1300 differential and algebraic equations. QMM describes the dynamics of both CVS (the cardiac pump function, baroreceptor control of parameters of cardiovascular net presented by means of lumped parameter vascular compartments) and non-biological variables (inertial forces, and used protections). The main function of AS is to provide physiologist-researcher by visualizations of calculated additional data concerning characteristics of both external and internal environments under high sustained accelerations and short-time microgravity. Additionally, AS can be useful as an educational tool able to show both researchers and young pilots the main hemodynamic effects caused by accelerations and acute weightlessness with and without use of different protection tools and technics. In this case, AS does help users to optimize training process aimed to ensure optimal-like human tolerance to the altered physical environment. Main physiological events appearing under different scenarios of accelerations and microgravity have been tested.

A Novel Approach for the Assessment of Cities through Ecosystem Integrity

To tackle urban heterogeneity and complexity, several indices have been proposed, commonly aiming to provide information for decision-makers. In this study, we propose a novel and customizable procedure for quantifying urban ecosystem integrity. Based on a citywide approach, we developed an easy-to-use index that contrasts physical and biological variables of urban ecosystems with a given reference system. The Urban Ecosystem Integrity Index (UEII) is the sum of the averages from the variables that make up its intensity of urbanization and biological components. We applied the UEII in a Mexican tropical city using land surface temperature, built cover, and the richness of native plants and birds. The overall ecosystem integrity of the city, having montane cloud, tropical dry, and temperate forests as reference systems, was low (−0.34 ± SD 0.32), showing that, beyond its biodiverse greenspace network, the built-up structure highly differs from the ecosystems of reference. The UEII showed to be a flexible and easy-to-calculate tool to evaluate ecosystem integrity for cities, allowing for comparisons between or among cities, as well as the sectors/regions within cities. If used properly, the index could become a useful tool for decision making and resource allocation at a city level.