234 research outputs found
Local labor markets and the persistence of population shocks
The research in this paper was funded by Deutsche Forschungsgemeinschaft (grant no. BR 4979/1-1, āDie volkswirtschaftlichen Effekte der Vertriebenen und ihre Integration in Westdeutschland, 1945-70ā).This paper studies the persistence of a large, unexpected, and regionally very unevenly distributed population shock, the inflow of eight million ethnic Germans from Eastern Europe to West Germany after World War II. Using detailed census data from 1939 to 1970, we show that the shock had a persistent effect on the distribution of population within local labor markets, but only a temporary effect on the distribution between labor markets. These results suggest that locational fundamentals determine population patterns across but not within local labor markets, and they can help to explain why previous studies on the persistence of population shocks reached such different conclusions.Publisher PD
Investigation of Senders' and Couriers' Preferences in a Two-sided Crowdshipping Market
Crowdshipping (CS), as an innovative logistics service, is still in its infancy and many aspects of this system have not been fully investigated and understood. To bridge this gap, this thesis comprehensively investigates the service mainly from the CS sendersā preferences on the demand side, the CS couriersā preferences on the supply side, and the core matching problem between the senders and the couriers. Specifically, what are the characteristics of senders in using the CS service and how to accurately predict sendersā delivery requests (demannds) in an area and a short time period, what are couriersā delivery preferences and behaviors in bidding sendersā requests, and how to match sendersā requests and couriers based on their preferences equitably and satisfactorily. The used data comes from a real CS service in U.S. between April 2015 and August 2018. A series of data descriptive analyses are conducted firstly, and find that the CS has a price advantage over FedEx in the same-day or express service, extra-large, and huge size package delivery, as well as medium size packages of short to medium delivery distance. Critically, there are discrepancies in preferences between the senders and couriers on the package size, delivery time window, delivery distance, and delivery fee. Two classes of popular deep learning (DL) methods are then applied to predict sendersā delivery requests. One class only captures the temporal feature of the data, and the other captures both spatial and temporal features. The results find that a DL method capturing both spatial and temporal features correlations inherently in the CS dataset achieves the best performance. For couriersā bidding behavior in the CS service, this study uses popular machine learning (ML) methods to explore how features of CS delivery requests influence couriersā bidding preferences as well as the delivery status of a requests (delivered versus undelivered). As a result, most characteristics of requests and the created discrepancy related features significantly influence the prediction targets. Importantly, this study also demonstrates the feature impacts in ML models are consistent with the results of traditional logit models. For the sender-courier matching problem, this study proposes a practical matching mechanism that equitably considers both sendersā and couriersā preferences, and ensures all agents are satisfied with their matching results and no pair of agents prefer each other to their current match. The proposed equitable and stable two-sided matching (ESTM) algorithm is also suitable to other two-sided markets. The results show that ESTM achieves good matching rate, equity metrics (e.g., egalitarian cost, side equality cost, and pair equality cost), sendersā benefits, and social welfare in our designed CS market. As goods transportation becomes more popular with outbreaking the COVID-19 pandemic. Finally, this research also analyzes peopleās online grocery shopping (OGS) choice before, during, and after COVID-19 by descriptive analysis and logit models. The used data is collected by an online survey. The results show a significant shift from physical grocery shopping (PGS) to OGS due to the pandemic. Many socio-economic characteristics, health constraints, concern of the pandemic, and grocery shopping choice behavior before COVID-19 would influence people to choose OGS during and after COVID-19
Table_2.XLSX
<p>Metabolic engineering of microalgae to accumulate high levels of medium chain length fatty acids (MCFAs) has met with limited success. Traditional approaches employ single introduction of MCFA specific acyl-ACP thioesterases (TEs), but our current research in transgenic Dunaliella tertiolecta line has highlighted that, there is no single rate-limiting approach that can effectively increase MCFA levels. Here, we explore the accumulation of MCFAs in D. tertiolecta after transgenic expression of myristic acid biased TE (C14TE). We observe that the MCFA levels were negatively correlated to the fatty acid (FA) synthesis genes, ketoacyl-ACP synthase II (KASII), stearoyl-CoA-9-desaturase (Ī9D), and oleoyl-CoA-12-desaturase (Ī12D). To further examine the molecular mechanism of MCFA accumulation in microalgae, we investigate the transcriptomic dynamics of the MCFA producing strain of D. tertiolecta. At the transcript level, enhanced MCFA accumulation primarily involved up-regulation of photosynthetic genes and down-regulation of genes from central carbon metabolic processes, resulting in an overall decrease in carbon precursors for FA synthesis. We additionally observe that MCFA specific peroxisomal Ī²-oxidation gene (ACX3) was greatly enhanced to prevent excessive build-up of unusual MCFA levels. Besides, long chain acyl-CoA synthetase gene (LACS) was down-regulated, likely in attempt to control fatty acyl supply flux to FA synthesis cycle. This article provides a spatial regulation model of unusual FA accumulation in microalgae and a platform for additional metabolic engineering targeting pathways from FA synthesis, FA transport, and peroxisomal Ī²-oxidation to achieve microalgae oils with higher levels of MCFAs.</p
Table_1.DOCX
<p>Metabolic engineering of microalgae to accumulate high levels of medium chain length fatty acids (MCFAs) has met with limited success. Traditional approaches employ single introduction of MCFA specific acyl-ACP thioesterases (TEs), but our current research in transgenic Dunaliella tertiolecta line has highlighted that, there is no single rate-limiting approach that can effectively increase MCFA levels. Here, we explore the accumulation of MCFAs in D. tertiolecta after transgenic expression of myristic acid biased TE (C14TE). We observe that the MCFA levels were negatively correlated to the fatty acid (FA) synthesis genes, ketoacyl-ACP synthase II (KASII), stearoyl-CoA-9-desaturase (Ī9D), and oleoyl-CoA-12-desaturase (Ī12D). To further examine the molecular mechanism of MCFA accumulation in microalgae, we investigate the transcriptomic dynamics of the MCFA producing strain of D. tertiolecta. At the transcript level, enhanced MCFA accumulation primarily involved up-regulation of photosynthetic genes and down-regulation of genes from central carbon metabolic processes, resulting in an overall decrease in carbon precursors for FA synthesis. We additionally observe that MCFA specific peroxisomal Ī²-oxidation gene (ACX3) was greatly enhanced to prevent excessive build-up of unusual MCFA levels. Besides, long chain acyl-CoA synthetase gene (LACS) was down-regulated, likely in attempt to control fatty acyl supply flux to FA synthesis cycle. This article provides a spatial regulation model of unusual FA accumulation in microalgae and a platform for additional metabolic engineering targeting pathways from FA synthesis, FA transport, and peroxisomal Ī²-oxidation to achieve microalgae oils with higher levels of MCFAs.</p
Age and CD161 Expression Contribute to Inter-Individual Variation in Interleukin-23 Response in CD8+ Memory Human T Cells
<div><p>The interleukin-23 (IL-23) pathway plays a critical role in the pathogenesis of multiple chronic inflammatory disorders, however, inter-individual variability in IL-23-induced signal transduction in circulating human lymphocytes has not been well-defined. In this study, we observed marked, reproducible inter-individual differences in IL-23 responsiveness (measured by STAT3 phosphorylation) in peripheral blood CD8+CD45RO+ memory T and CD3+CD56+ NKT cells. Age, but not gender, was a significant (Pearsonās correlation coefficient, rā=āā0.37, pā=ā0.001) source of variability observed in CD8+CD45RO+ memory T cells, with IL-23 responsiveness gradually decreasing with increasing age. Relative to cells from individuals demonstrating low responsiveness to IL-23 stimulation, CD8+CD45RO+ memory T cells from individuals demonstrating high responsiveness to IL-23 stimulation showed increased gene expression for IL-23 receptor (IL-23R), RORC (RORĪ³t) and CD161 (KLRB1), whereas RORA (RORĪ±) and STAT3 expression were equivalent. Similar to CD4+ memory T cells, IL-23 responsiveness is confined to the CD161+ subset in CD8+CD45RO+ memory T cells, suggesting a similar CD161+ precursor as has been reported for CD4+ Th17 cells. We observed a very strong positive correlation between IL-23 responsiveness and the fraction of CD161+, CD8+CD45RO+ memory T cells (rā=ā0.80, p<0.001). Moreover, the fraction of CD161+, CD8+CD45RO+ memory T cells gradually decreases with aging (rā=āā0.34, pā=ā0.05). Our data define the inter-individual differences in IL-23 responsiveness in peripheral blood lymphocytes from the general population. Variable expression of CD161, IL-23R and RORC affects IL-23 responsiveness and contributes to the inter-individual susceptibility to IL-23-mediated defenses and inflammatory processes.</p> </div
Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
<div><p>The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8ā79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' ābrain agesā from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.</p> </div
Age-related changes in interregional functional connectivity displayed on (A) a surface rendering of the brain and (B) a schematic diagram.
<p>Connections with positive linear, negative linear, positive quadratic and negative quadratic developmental trajectories are shown in green, blue, magenta, and red, respectively. Also displayed are the brain regions scaled by their weights (sum of the T statistics for all the connections passing through that brain region).</p
Areas, amplitudes and dynamic features for sNBR and PBR.
<p>āRā denotes right hand data, āLā denotes left hand data.</p
Characteristics of different age groups in this study.
a<p>in the 71+ group, the handedness of one subject is ambidextrous.</p
Comparison of prediction performance using different SVR algorithms.
<p>MAE denotes the mean absolute error between the predicted ages and chronological ages. CS(<i>j</i>) denotes the percent of test samples with an absolute error no higher than <i>j</i> years. Different local adjustment ranges (4, 8, and 16) were tried for LASVR.</p
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