This study evaluated the impact of the COVID-19 pandemic on the Massachusetts freight network and freight planning. The study included an analysis of existing conditions, an assessment of the data that outlined the impact of the pandemic on the freight network, and recommendations.
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In March 2020, MassDOT completed the I-90 Interchange Study. The planning study evaluated the feasibility of a new interchange on Interstate 90 (I-90) between the existing exits in the Town of Lee and the City of Westfield. Civic, business, and political leaders in the area have been advocating for the study of a new interchange for many years.The study examined alternatives that could provide better access to and from I-90 for study area communities. It also sought to mitigate traffic bound for I-90 away from Lee and Westfield. It examined alternatives in the context of vehicular, bicycle, pedestrian, and land use. It also considered cost, as well as resulting economic, social, and cultural impacts. A final report detailed findings of the analysis, recommendations, and next steps.
In 2018, MassDOT conducted analysis to revisit assumptions from the 2010 Draft Environmental Impact Report (DEIR) for a project to extend the Blue Line from Bowdoin to Charles/MGH under Cambridge Street to create a transfer to the Red Line. The analyses posted here focused on constructability of the tunnel and the changes in land use, demographics, and ridership.
As required by state law MassDOT completed an analysis of the potential safety impacts of ethanol transport by rail through the cities of Boston, Cambridge, Chelsea, Everett, Revere, and Somerville. The Ethanol Safety Study was carried out in response to a proposal by the terminal operator Global Petroleum to have ethanol delivered by rail to its facility on the Revere - East Boston line on the East Boston Branch.
The Massachusetts Travel Survey was a large-scale effort to collect information on residents' travel patterns, preferences, and behavior to help build a more complete and accurate picture of statewide transportation needs. Between June 2010 and November 2011, MassDOT contractors asked over 15,000 households to identify where and how they traveled on a specific, designated travel day (24 hours). In order to ensure a sample that was representative of the Massachusetts population, each household was asked a series of detailed questions about their socioeconomic characteristics and access to transportation.
The Roxbury/Dorchester/Mattapan Transit Needs Study fulfilled a MassDOT commitment to complete a community-driven public transit needs assessment in this large section of Boston currently underserved by direct rapid transit. The study made a series of 19 recommendations for improving the customer experience and efficiency of MBTA service in these neighborhoods. These recommendations ranged from short-term improvements that could be implemented at low or no cost, to long-term aspirational goals that will require both an increase in system-wide funding for public transit and project-specific planning and design efforts in order to become reality.
A recommended plan of near, mid, and long-term transportation improvements based on the alternatives analysis, is included in the final report. For additional information, please contact: Bob Frey, Director of Project-Oriented Planning.
We performed PICRUSt analysis to predict the relative abundance of transporter genes because an altered diet [2] and antibiotic treatment [31] have been reported to be among the most powerful factors that affect the gut microbiota. Clustering based on the relative abundance of the predicted transporters showed that subjects were divided into two age-related groups, the adult-enriched and infant/elderly-enriched clusters (Additional file 13). For example, there was a lower abundance of a predicted xylose transporter (KEGG module: M00215) in pre-weaned infants, probably reflecting the different dietary habits of subjects in each segmented age group (Additional file 14). Interestingly, all drug transporters based on KEGG Orthology groups were found in the infant/elderly-enriched cluster (Additional files 13 and 15).
Although there are differences among individuals, our analysis of the phylum composition of gut microbiota in each age group showed a significant shift in the relative abundance of Actinobacteria in infants from before to after weaning. The compositional pattern of gut microbiota during childhood has been thought to impact health later in life [11, 25], but children older than 2 years have not been sufficiently investigated. Our data show that some genera belonging to Bacteroides, Lachnospiraceae and Bifidobacterium CAGs and the alpha diversity of gut microbiota continued to change sequentially with age in subjects younger than twenty, reflecting the human gut microbiota maturation process. However, children younger than 20 years fell into both the infant and adult clusters, regardless of their age, when clustered based on the abundance of genus-like groups, thus illustrating the individual differences in the gut microbiota maturation process.
We performed in vitro assays to investigate bacterial interactions, including those between Enterobacteriaceae and Blautia. These results were in accordance with the Wiggum plot results. However, these results might have some biases from the different of environmental condition between in human gut and in vitro assay. In addition, it is uncertain that all bacterial interactions are consistent with the relationships in the Wiggum plot, because genera with positive relationships in the Wiggum plot might grow well under the same environmental conditions without a mutualistic relationship. In contrast, some genera combinations have been reported to exhibit mutualistic relationships,although the absolute values of the correlation coefficients were below 0.3 (no visible relationship in the Wiggum plot). Pande et al. revealed that Acinetobacter baylyi and Escherichia coli reciprocally exchange essential amino acids [31]. It has also been reported that Bifidobacterium populations can be stimulated efficiently with a concomitant decrease in Enterobacteriaceae [32, 33]. Acetate, one of the main fermentation products of Bifidobacterium, was reported to promote the growth of butyrate-producing bacteria and the in vitro production of butyrate [34, 35]. Furthermore, Bifidobacterium longum has been reported to alter gut luminal metabolism via interactions with Bacteroides caccae and Eubacterium rectale [36]. Considering these reports, the correlations between gut microbiota members might be more complicated than shown in the Wiggum plot. Our computational analysis results must be interpreted cautiously because they are based on a limited data set. An advanced culture method is needed to clarify the relationships among gut microbiota.
PICRUSt analysis was performed to predict the relative abundance of transporter genes [61]. Independent of the taxonomic analysis, 97 % of the OTUs were picked using a closed-reference OTU picking protocol (QIIME 1.8.0 [51, 52]) and the Greengenes database (database/13_8) [54] pre-clustered at 97 % identity. The obtained OTU table was normalized by 16S rRNA copy number, and functional genes were predicted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) catalogue [62].
TO is the corresponding author; he contributed to study conception and design, conducted most of the experiments, analyzed and interpreted the data, and wrote the manuscript. KK performed most of the experiments and data interpretation. HS conducted some of the laboratory analyses and data interpretation. NH and SH performed some of the laboratory analyses. JX contributed to study conception and design and reviewed the manuscript. FA contributed to study conception and design and data interpretation. RO designed and supervised the entire project and data interpretation as well as corrected the manuscript. All the authors read and approved the final manuscript.
Conclusions and Relevance In this systematic review and meta-analysis, the risk of subsequent stroke among patients who were evaluated in a TIA clinic was not higher than those hospitalized. Patients who received treatment in EDs without further follow-up had a higher risk of subsequent stroke. These findings suggest that TIA clinics can be an effective component of the TIA care component pathway.
We distinguished four categories of decreasing health (see table 1). Subjects categorized as A were completely healthy and were considered as presenting no particular risk for any kind of physical exercise. An additional distinction can be made between those using no medication (A1) and those using preventive medication only (A2). This subdivision might be important in specific clinical contexts (e.g. assessment of Vitamin D levels in elderly); in the context of our study the distinction between health categories A1 and A2 is less relevant and therefore these participants will be considered together in all statistical analysis. Category B consisted of participants who were functioning normally, presented no major medical restrictions, but could be in need of special instructions for exercising due to their health status. Category B1 was accorded to participants having a disease that was non-cardiovascular and stable. Category B2 was given to participants using medication having cardiovascular effects. Subjects in category C had cardiovascular pathology or a history thereof; they were considered as having an increased risk of cardiovascular complications during exercise. Those belonging to category D were found to present signs of acute disease or exacerbation of chronic disease. If combinations of health conditions existed, subjects were classified in the worst health category.
Statistical analysis was performed using the SPSS (for Windows, release 11.5.1) software package. All data subsets were assessed for the presence of a normal distribution (Kolmogorov-Smirnov Goodness of Fit Test p > 0.05) before using parametric analysis. Correlations between data subsets with a normal distribution were performed using Pearson's Correlation Coefficient; non-normally distributed datasets were analyzed for correlation using Kendall's Correlation Coefficient. Differences between data subsets were analyzed using Analysis of Variance (ANOVA), Analysis of Co-Variance (ANCOVA) and Students t-test. Bonferroni post-hoc test was performed to detect significant differences between subgroups. A multiple linear regression model was designed in order to explain the variability of the 6MWT-distance. Significance level was set at p 2ff7e9595c
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