Preferential Myosin Heavy Chain Isoform B Expression May Contribute to the Faster Velocity of Contraction in Veins versus Arteries

Smooth muscle myosin heavy chains occur in 2 isoforms, SMA (slow) and SMB (fast). We hypothesized that the SMB isoform is predominant in the faster-contracting rat vena cava compared to thoracic aorta. We compared the time to half maximal contraction in response to a maximal concentration of endothelin-1 (ET-1; 100 nM), potassium chloride (KCl; 100 mM) and norepinephrine (NE; 10 µM). The time to half maximal contraction was shorter in the vena cava compared to aorta (aorta: ET-1 = 235.8 ± 13.8 s, KCl = 140.0 ± 33.3 s, NE = 19.8 ± 2.7 s; vena cava: ET-1 = 121.8 ± 15.6 s, KCl = 49.5 ± 6.7 s, NE = 9.0 ± 3.3 s). Reverse-transcription polymerase chain reaction supported the greater expression of SMB in the vena cava compared to aorta. SMB was expressed to a greater extent than SMA in the vessel wall of the vena cava. Western analysis determined that expression of SMB, relative to total smooth muscle myosin heavy chains, was 12.5 ± 4.9-fold higher in the vena cava compared to aorta, while SMA was 4.9 ± 1.2-fold higher in the aorta than vena cava. Thus, the SMB isoform is the predominant form expressed in rat veins, providing one possible mechanism for the faster response of veins to vasoconstrictors

Does Circularizing Source-Separated Food Waste Present A Risk To Our Food?

About a third of the food produced annually is wasted. Food waste recycling can be a way to close the loop and attain a more sustainable food system, however, the system must be carefully monitored and managed to avoid the introduction and build-up of contaminants. To study the potential presence of contaminants in food waste, source-separated food waste was collected and screened for five classes of contaminants (physical contaminants, heavy metals, halogenated organic contaminants, pathogens, and antibiotic resistance genes) from two separate regulatory environments (voluntary vs mandated food separation). The regulatory environment did not affect the level of contamination, except there was more physical contamination in Maine, where food waste diversion is not mandated. Fifty-seven percent of samples had some form of non-compostable waste. Most of the heavy metals tested were not detected. Copper and zinc were detected in most samples but were always below the most stringent global standards for compost. Some samples had detectable halogenated organics, which is cause for concern because some are known to accumulate in the food chain. Foodborne pathogens were seldom detected and should be killed during treatment, but this could pose a risk to collectors and haulers. Antibiotic resistance genes were detected in most samples. This could jeopardize the utility of antibiotics used to fight infections. More research is needed to determine the fate of antibiotic resistance genes and halogenated organics during treatment, and the risk of their accumulation in a circular food system

Investigation on the prevalence of antibiotic resistance genes in aquatic environments of the Kathmandu Valley, Nepal

University of Yamanashi (山梨大学)博士(工学)application/pdf医工農博甲第90号thesi

The challenges Nepali women face in pastoral roles : strategies for women leaders to flourish

https://place.asburyseminary.edu/ecommonsatsdissertations/2623/thumbnail.jp

Bioavailability in soils

The consumption of locally-produced vegetables by humans may be an important exposure pathway for soil contaminants in many urban settings and for agricultural land use. Hence, prediction of metal and metalloid uptake by vegetables from contaminated soils is an important part of the Human Health Risk Assessment procedure. The behaviour of metals (cadmium, chromium, cobalt, copper, mercury, molybdenum, nickel, lead and zinc) and metalloids (arsenic, boron and selenium) in contaminated soils depends to a large extent on the intrinsic charge, valence and speciation of the contaminant ion, and soil properties such as pH, redox status and contents of clay and/or organic matter. However, chemistry and behaviour of the contaminant in soil alone cannot predict soil-to-plant transfer. Root uptake, root selectivity, ion interactions, rhizosphere processes, leaf uptake from the atmosphere, and plant partitioning are important processes that ultimately govern the accumulation ofmetals and metalloids in edible vegetable tissues. Mechanistic models to accurately describe all these processes have not yet been developed, let alone validated under field conditions. Hence, to estimate risks by vegetable consumption, empirical models have been used to correlate concentrations of metals and metalloids in contaminated soils, soil physico-chemical characteristics, and concentrations of elements in vegetable tissues. These models should only be used within the bounds of their calibration, and often need to be re-calibrated or validated using local soil and environmental conditions on a regional or site-specific basis.Mike J. McLaughlin, Erik Smolders, Fien Degryse, and Rene Rietr

Nonparametric Methods for Road Safety Analysis

Crash models for predicting long-term crash risk at some specific components of a road network are fundamental to road safety analyses such as network screening and countermeasure studies. These models are often calibrated using historical crash data from the sites of interest, aiming at capturing the underlying relationship between crash risk and various risk factors. Based on how the relationships are determined, crash models can be classified into two types: parametric or nonparametric. Parametric models represent the state of the art and practice methodology for road safety analyses. While this approach provides an easy-to-implement and easy-to-interpret tool, they come at the cost of the need for pre-selection of model forms, which, without knowing the true relation of crash and risk factors, could easily lead to misspecifications and biased estimations. In contrast, a nonparametric approach does not pre-specify a model structure but instead determines the structure from data, thereby providing greater flexibility to capture underlying complex relations. Despite this advantage of being a specification free approach, nonparametric models have not yet been accepted as part of the mainstream methodologies for road safety analyses. Little were known about their relative performance in comparison to parametric models and the practical implications of their applications for the common road safety analysis tasks such as network screening and countermeasure effectiveness estimation. Furthermore, crash data for road safety analysis and modeling are growing steadily in size and completeness with the advancement in information and sensor technologies. It is, however, unclear what implications this increased data availability has for road safety analyses in general and crash modeling in specific. Will a data-driven nonparametric technique become a more attractive alternative for addressing the complex problem of crash modeling in this era of Big Data? In this thesis, we have introduced one of the most popular nonparametric techniques - kernel regression (KR) - as an alternative for crash modeling. One of the uniqueness of this method is that it takes a fully data-driven approach in determining the relationship between crash frequency and risk factors. Compared to other nonparametric methods, it does not contain any hidden structures to train. Therefore, when a new crash dataset is available, it can be used directly in updating crash prediction without re-calibrating the underlying models. We made two methodological contributions to facilitate the application of a nonparametric model for road safety analyses. We first extended the KR method, similar to Empirical Bayesian (EB) method using parametric models, to account for the site-specific crash history in predicting risk. We then developed a bootstrap-based algorithm for identifying the important variables to be included in a nonparametric model. The research also made significant knowledge contributions to the practice field related to applications of nonparametric models for road safety analyses. First, we benchmarked the crash prediction performance of the KR model against the mainstream model – Negative Binomial (NB) model. Using three large crash datasets, we investigated the performance of the KR and NB models as a function of the amount of training data. Through a rigorous bootstrapping validation process, we found that the two approaches exhibit strikingly different patterns, especially in terms of sensitivity to data size. While the performance of the KR method improved significantly with increase in data size, the NB model showed less sensitivity. Meanwhile, the KR method outperformed the NB model in terms of predictive performance, and that performance advantage increased noticeably by data size. Secondly, we compared the two approaches in their ability to capture the underlying complex relationships between crash frequency and predicting variables. The KR method was shown to yield more sensible results on the effects of various risk factors in both case studies as compared to the NB model. Our other main contribution comes from the investigation on the practical implications of applying the KR models for two critical road safety analyses tasks – network screening and countermeasure study. Both KR method and NB model were employed in a case study under the two popular network screening frameworks, i.e., regression-based and EB-based. Their performances were compared in terms of site ranking and identification of crash hotspots. The two approaches were found to yield more similar rankings when applied in the EB-based framework, irrespective of the ranking measures (i.e., crash frequency or crash rate), than in the regression-based framework. Similar comparative results were obtained in locating the crash hotspots. Likewise, for countermeasure studies, the two popular approaches – the before-after EB study and the cross-sectional study – were considered in case studies using both KR and NB crash prediction models. As expected, the two different crash modeling techniques showed significant differences in their estimates on crash modification factors (CMF). Different from the NB model based approach, the KR-based method was able to capture the sensitivity of CMFs to traffic levels as well as combine the effect of multiple countermeasures without requiring any assumptions on the interaction between the countermeasures

Flood Risk Assessment Using the Updated FEMA Floodplain Standard in the Ellicott City, Maryland, United States

Every Year, flooding causes a calamitous impact on the people, economy, and environment all over the world. In recent years, the flood-related damages have been increasing in the United States regardless of several investments in the flood control measures. Floodplain mapping is an important tool for management that aids in the planning of infrastructures within the floodplain zone. With the magnifying effects of climate change on the hydrological cycle the study of floodplain is becoming a key tool in the water management. Federal Emergency Management Agency has recently updated their floodplain standard as per the presidential executive order 2015 on the Federal Flood Risk Management Standard. This study incorporates the newly updated floodplain mapping standard in the flood risk assessment of approximately 11.2 km stretch of the Patapsco River near Ellicott City. Hydrologic Engineering Center\u27s River Analysis System (HEC-RAS) with the conjunction of geographical information systems were used in the floodplain analysis. The different return period flows (2, 5, 10, 25, 50, 100, and 500) were used from the frequency analysis. These flows were routed through the selected reach of Patapsco River and the vulnerability assessment of the nearby existing infrastructures was conducted. This study can assist the decision makers and planner for the implementation of flood protection measures near the Ellicott City