Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City.

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

MiR-26a regulated adipogenic differentiation of ADSCs induced by insulin through CDK5/FOXC2 pathway.

OBJECTIVE: Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes, since insulin can induce adipogenic differentiation of human adipose-derived stem cells (ADSCs). MiR-26a was reported to be highly expressed in ADSCs under induction and Forkhead box C2 (FOXC2), as a key substrate of cyclin-dependent kinase 5 (CDK5) could inhibit white adipocyte differentiation, which was mediated by miR-26a. However, the relationship between miR-26a and CDK5/FOXC2 during ADSCs differentiation remains unknown. We want to verify the regulated mechanism of miR-26a/CDK5/FOXC2 axis participating in the adipogenic differentiation of ADSCS. METHODS: ADSCs were isolated and verified by flow cytometry. Oil Red O staining was performed to assess the capacity for adipogenic differentiation of ADSCs. The proliferation ability of ADSCs was verified by MTT assay. The expression of miR-26a, peroxisome proliferator-activated receptors γ (PPARγ), CDK5, and FOXC2 were tested by qRT-PCR and Western blot, and the relationship between miR-26a and CDK5 was verified by dual-luciferase reporter gene assay. RESULTS: MiR-26a and PPARγ were upregulated and CDK5 and FOXC2 were downregulated during adipogenic differentiation of ADSCs. Knockdown of miR-26a or overexpression of CDK5 could inhibit adipogenic differentiation of ADSCs induced by insulin. MiR-26a could directly target CDK5 and the effect of miR-26a inhibitor on adipogenic differentiation of ADSCs could be blocked by si-CDK5. CONCLUSION: We demonstrated that miR-26a regulated insulin-induced adipogenic differentiation of ADSCs by regulating CDK5/FOXC2 pathway, which could provide the key to a comprehensive mechanistic understanding of obesity and type 2 diabetes.

Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle.

The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle-two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system.

Anion stripping as a general method to create cationic porous framework with mobile anions.

Metal-organic frameworks (MOFs) with cationic frameworks and mobile anions have many applications from sensing, anion exchange and separation, to fast ion conductivity. Despite recent progress, the vast majority of MOFs have neutral frameworks. A common mechanism for the formation of neutral frameworks is the attachment of anionic species such as F(-) or OH(-) to the framework metal sites, neutralizing an otherwise cationic scaffolding. Here, we report a general method capable of converting such neutral frameworks directly into cationic ones with concurrent generation of mobile anions. Our method is based on the differential affinity between distinct metal ions with framework anionic species. Specifically, Al(3+) is used to strip F(-) anions away from framework Cr(3+) sites, leading to cationic frameworks with mobile Cl(-) anions. The subsequent anion exchange with OH(-) further leads to a porous network with mobile OH(-) anions. New materials prepared by anion stripping can undergo ion exchange with anionic organic dyes and also exhibit much improved ionic conductivity compared to the original unmodified MOFs.

In situ preparation of a Ti³âº self-doped TiO2 film with enhanced activity as photoanode by N2H4 reduction.

A new synthetic method to fabricate Ti(3+)-modified, highly stable TiO2 photoanodes for H2O oxidation is reported. With Ti foil as both the conducting substrate and the Ti(3+)/Ti(4+) source, one-dimensional blue Ti(3+)/TiO2 crystals were grown by a one-step hydrothermal reaction. The concentration of Ti(3+) was further tuned by N2H4 reduction, leading to a greater photoelectrocatalytic activity, as evidenced by a high photocurrent density of 0.64 mA cm(-2) at 1.0 V vs RHE under simulated AM 1.5 G illumination. Electron paramagnetic resonance and Mott-Schottky plots reveal that higher charge-carrier density owing to N2H4 reduction contributes to the observed improvement. The generality of this synthesis method was demonstrated by its effectiveness in improving the performance of other types of photoanodes. By integrating the advantages of the 1D TiO2 architecture with those of Ti(3+) self-doping, this work provides a versatile tool toward the fabrication of efficient TiO2 photoanodes.

Facile synthesis of thermal- and photostable titania with paramagnetic oxygen vacancies for visible-light photocatalysis.

A novel dopant-free TiO(2) photocatalyst (V(o)(.)-TiO(2)), which is self-modified by a large number of paramagnetic (single-electron-trapped) oxygen vacancies, was prepared by calcining a mixture of a porous amorphous TiO(2) precursor, imidazole, and hydrochloric acid at elevated temperature (450 °C) in air. Control experiments demonstrate that the porous TiO(2) precursor, imidazole, and hydrochloric acid are all necessary for the formation of V(o)(.)-TiO(2). Although the synthesis of V(o)(.)-TiO(2) originates from such a multicomponent system, this synthetic approach is facile, controllable, and reproducible. X-ray diffraction, XPS, and EPR spectroscopy reveal that the V(o)(.)-TiO(2) material with a high crystallinity embodies a mass of paramagnetic oxygen vacancies, and is free of other dopant species such as nitrogen and carbon. UV/Vis diffuse-reflectance spectroscopy and photoelectrochemical measurement demonstrate that V(o)(.)-TiO(2) is a stable visible-light-responsive material with photogenerated charge separation efficiency higher than N-TiO(2) and P25 under visible-light irradiation. The V(o)(.)-TiO(2) material exhibits not only satisfactory thermal- and photostability, but also superior photocatalytic activity for H(2) evolution (115 µmol h(-1) g(-1)) from water with methanol as sacrificial reagent under visible light (λ>400 nm) irradiation. Furthermore, the effects of reaction temperature, ratio of starting materials (imidazole:TiO(2) precursor) and calcination time on the photocatalytic activity and the microstructure of V(o)(.)-TiO(2) were elucidated.

Coassembly between the largest and smallest metal chalcogenide supertetrahedral clusters.

Reported here is the first crystalline inorganic-organic hybrid material combining In, Ge, and Cu. The trimetallic system undergoes a nanoscale separation into small In-Ge-S supertetrahedral T2 clusters and large In-Cu-S T5 clusters, which are further assembled into a T2-T5 mixed layer. This material reveals the long-range crystallographically ordered assembly from supertetrahedral clusters with 3 orders of difference, the largest size mismatch known so far for mixed-supertetrahedral-cluster materials. Theoretical simulations were performed to probe the contribution to the band structure by the different-sized semiconducting clusters.

Visible light-driven α-Fe2O3 nanorod/graphene/BiV1-xMoxO4 core/shell heterojunction array for efficient photoelectrochemical water splitting.

We report the design, synthesis, and characterization of a novel heterojunction array of α-Fe(2)O(3)/graphene/BiV(1-x)Mo(x)O(4) core/shell nanorod for photoelectrochemical water splitting. The heterojunction array was prepared by hydrothermal deposition of α-Fe(2)O(3) nanorods onto Ti substrate, with subsequent coating of graphene interlayer and BiV(1-x)Mo(x)O(4) shell by photocatalytic reduction and a spin-coating approach, respectively. The heterojunction yielded a pronounced photocurrent density of ∼1.97 mA/cm(2) at 1.0 V vs Ag/AgCl and a high photoconversion efficiency of ∼0.53% at -0.04 V vs Ag/AgCl under the irradiation of a Xe lamp. The improved photoelectrochemical properties benefited from (1) the enhanced light absorption due to behavior of the "window effect" between the α-Fe(2)O(3) cores and BiV(1-x)Mo(x)O(4) shells, and (2) the improved separation of photogenerated carriers at the α-Fe(2)O(3) nanorod/graphene/BiV(1-x)Mo(x)O(4) interfaces. Our results demonstrate the advantages of the novel graphene-mediated core/shell heterojunction array and provide a valuable insight for the further development of such materials.

Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos.

Proper calibration process is of considerable importance for traffic safety evaluations using simulation models. Allowing for a pure with and without comparison under identical circumstances that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, such as connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This paper proposes a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization so that the operational and safety measures can be calibrated simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. The proposed calibration methodology is implemented using an open-source software SUMO on a simulation network of the Flatbush Avenue corridor in Brooklyn, NY. 17 key parameters are calibrated using the SPSA algorithm and are compared with the real-world traffic conflicts extracted using vehicle trajectories from 14 h' high-resolution aerial and traffic surveillance videos. Representative days are identified to create variation envelopes for performance measures. Four acceptability criteria, including control for time-variant outliers and inliers, bounded dynamic absolute and system errors are adopted for results analysis. The results show that the calibrated parameters can significantly improve the performance of the simulation model to represent real-world safety conditions (i.e., traffic conflicts) as well as operational conditions. The case study also demonstrates the usefulness of aerial imagery and the applicability of the proposed model calibration framework, so the calibrated model can be used to evaluate the safety benefits of CV applications more accurately.

Tumor Organoid Model and Its Pharmacological Applications in Tumorigenesis Prevention.

Cancer is a leading cause of death and a severe threat to global public health. Organoid, as a novel 3D in vitro model, has been applied in various tumor related studies due to its apparent advantages. The organoid is mainly constructed by Matrigel-depended 3D culture system, Air-Liquid Interface (ALI) culture, and Microfluidic culture or Organ-on-chips platform. For the application in carcinogenesis studies, the organoid model may favor depicting initiative hallmarks and identifying potential intervening targets, investigating driver genes of carcinogenesis, and identifying known or unknown risk or protective factors. In this review, we discussed different organoid construction methods and their properties. We also noted that tumor organoids can portray initiative hallmarks and identify possible intervening targets, as well as explore carcinogenesis driver genes and uncover known or unknown risks or protective factors. Organoid systems have been used to identify tumor-preventive drugs such as oligomeric proanthocyanidins, Vitamin D, n-3 PUFAs, and pomegranate. The current evidence underscores the organoid model's potential importance in developing innovative tumorprevention techniques.

Understanding the travel challenges and gaps for older adults during the COVID-19 outbreak: Insights from the New York City area.

The COVID-19 pandemic has greatly impacted lifestyles and travel patterns, revealing existing societal and transportation gaps and introducing new challenges. In the context of an aging population, this study investigated how the travel behaviors of older adults (aged 60+) in New York City were affected by COVID-19, using an online survey and analyzing younger adult (aged 18-59) data for comparative analysis. The purpose of the study is to understand the pandemic's effects on older adults' travel purpose and frequency, challenges faced during essential trips, and to identify potential policies to enhance their mobility during future crises. Descriptive analysis and Wilcoxon signed-rank tests were used to summarize the changes in employment status, trip purposes, transportation mode usage, and attitude regarding transportation systems before and during the outbreak and after the travel restrictions were lifted. A Natural Language Processing model, Gibbs Sampling Dirichlet Multinomial Mixture, was adopted to open-ended questions due to its advantage in extracting information from short text. The findings show differences between older and younger adults in telework and increased essential-purpose trips (e.g., medical visits) for older adults. The pandemic increased older adults' concern about health, safety, comfort, prices when choosing travel mode, leading to reduced transit use and walking, increased driving, and limited bike use. To reduce travel burdens and maintain older adults' employment, targeted programs improving digital skills (telework, telehealth, telemedicine) are recommended. Additionally, safe, affordable, and accessible transportation alternatives are necessary to ensure mobility and essential trips for older adults, along with facilitation of walkable communities.

Relationships of adiponectin to regional adiposity, insulin sensitivity, serum lipids, and inflammatory markers in sedentary and endurance-trained Japanese young women.

Introduction: This study aims to compare the differences in circulating adiponectin levels and their relationships to regional adiposity, insulin resistance, serum lipid, and inflammatory factors in young, healthy Japanese women with different physical activity statuses. Methods: Adipokines (adiponectin and leptin), full serum lipid, and inflammatory factors [white blood cell counts, C-reactive protein, tumor necrosis factor-α, tissue plasminogen activator inhibitor-1 (PAI-1)] were measured in 101 sedentary and 100 endurance-trained healthy Japanese women (aged 18-23 years). Insulin sensitivity was obtained through a quantitative insulin-sensitivity check index (QUICKI). Regional adiposity [trunk fat mass (TFM), lower-body fat mass (LFM), and arm fat mass (AFM)] was evaluated using the dual-energy X-ray absorptiometry method. Results: No significant difference was observed between the sedentary and trained women in terms of adiponectin levels. The LFM-to-TFM ratio and the high-density lipoprotein cholesterol (HDL-C) were the strong positive determinants for adiponectin in both groups. Triglyceride in the sedentary women was closely and negatively associated with adiponectin, as well as PAI-1 in the trained women. The QUICKI level was higher in the trained than sedentary women. However, no significant correlation between adiponectin and insulin sensitivity was detected in both groups. Furthermore, LFM was associated with a favorable lipid profile against cardiovascular diseases (CVDs) in the whole study cohort, but this association became insignificant when adiponectin was taken into account. Conclusions: These findings suggest that adiponectin is primarily associated with regional adiposity and HDL-C regardless of insulin sensitivity and physical activity status in young, healthy women. The associations among adiponectin, lipid, and inflammatory factors are likely different in women with different physical activity statuses. The correlation of LFM and a favorable lipid profile against CVD and adiponectin is likely involved in this association.

Mimicking zeolite to its core: porous sodalite cages as hangers for pendant trimeric M3(OH) clusters (M = Mg, Mn, Co, Ni, Cd).

A new class of zeolite-type porous materials in which 3D frameworks are covalently functionalized with crystallographically ordered pendant metal clusters have been synthesized. This work demonstrates a new paradigm for and the feasibility of functionalizing zeolite-type frameworks through the conversion of extraframework sites in mineral zeolites into part of the framework for occupation by dangling metal clusters in metal-organic frameworks.

Exenatide improves hepatocyte insulin resistance induced by different regional adipose tissue.

Obesity is resulted from energy surplus and is characterized by abnormal adipose tissue accumulation and/or distribution. Adipokines secreted by different regional adipose tissue can induce changes in key proteins of the insulin signaling pathway in hepatocytes and result in impaired hepatic glucose metabolism. This study aimed to investigate whether exenatide affects key proteins of IRS2/PI3K/Akt2 signaling pathway in hepatocytes altered by the different regional fat depots. Six non-obese patients without endocrine diseases were selected as the research subjects. Their subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT)were co-cultured with HepG2 cells in the transwell chamber. In the presence or absence of exenatide, adipokines content in the supernatant of each experimental group was detected by ELISA. In addition, HepG2 cells in each co-culture group with and without insulin were collected, and the expression of key proteins IRS2, p-IRS2(S731), PI3K-p85, Akt2, and p-Akt2(S473) was detected by western blotting (WB). The results showed that the adipokines IL-8, MCP-1, VEGF, and sTNFR2 in the supernatant of HepG2 cells induced by different regional adipose tissue were significantly higher than those in the HepG2 group, and VAT released more adipokines than SAT. Furthermore, these adipokines were significantly inhibited by exenatide. Importantly, the different regional fat depot affects the IRS2/PI3K/Akt2 insulin signaling pathway of hepatocytes. Exenatide can up-regulate the expression of hepatocyte proteins IRS2, PI3K-p85, p-Akt2(S731) inhibited by adipose tissue, and down-regulate the expression of hepatocyte proteins p-IRS2(S731) promoted by adipose tissue. The effect of VAT on the expression of these key proteins in hepatocytes is more significant than that of SAT. But there was no statistical difference in the expression of Akt2 protein among each experimental group, suggesting that exenatide has no influence on the expression of Akt2 protein in hepatocytes. In conclusion, exenatide may improve hepatic insulin resistance (IR) by inhibiting adipokines and regulating the expression of key proteins in the IRS2/PI3K/Akt2 pathway.

A large indium sulfide supertetrahedral cluster built from integration of ZnS-like tetrahedral shell with NaCl-like octahedral core.

An entirely new type of chalcogenide cluster and a new structural mechanism for the formation of large semiconducting tetrahedral clusters have been revealed as a result of crystallization of a templated indium sulfide consisting of an unprecedented cluster, In(38)S(65), which is the largest supertetrahedral cluster based on trivalent metal ions. At the core of this cluster is In(10)S(13), which can be considered as a fragment of the NaCl-type lattice. The In(10)S(13) cluster is coupled to four In(4)S(10) supertetrahedral T2 clusters and four In(3)S(3) hexagonal rings to give In(38)S(65), which is also the largest inorganic chalcogenide supertetrahedral cluster, superseding a supertetrahedral T5 cluster with only 35 metal sites.

Synthesis and photocatalytic properties of a new heteropolyoxoniobate compound: K10[Nb2O2(H2O)2][SiNb12O40)·12H2O.

The synthesis and photocatalytic properties of a heteropolyoxoniobate, K(10)[Nb(2)O(2)(H(2)O)(2)][SiNb(12)O(40)]·12H(2)O (1), are reported, revealing an important role of Zr(4+) additives in the crystallization. Compound 1 exhibits overall photocatalytic water splitting activity, and its photocatalytic activity is significantly higher than that of Na(10)[Nb(2)O(2)][SiNb(12)O(40)]·xH(2)O (2). Fluorescence lifetime measurements suggest that the enhanced photocatalytic activity of 1 likely results from a larger yield of longer-lived charge trapping states in 1 due to the coordination of one water molecule to the bridging Nb(5+), leading to highly unsymmetrical seven-coordinated Nb(5+) sites.

Multicomponent self-assembly of a nested Co24 @Co48 metal-organic polyhedral framework.

A tale of two polyhedra: two nested Archimedean metal-organic polyhedra, a rhombicuboctahedron (Co(48) cage) and a cuboctahedron (Co(24) cage), have been assembled from two types of cobalt dimers and two complementary ligands. Within the 3D covalent cubic array of outer Co(48) cages and framework lie encapsulated inner Co(24) cages that are linked into a separate "hidden" 3D framework.

A functional approach for characterizing safety risk of signalized intersections at the movement level: An exploratory analysis.

Safety evaluation of signalized intersections is often conducted by developing statistical and data-driven methods based on data aggregated at certain temporal and spatial levels (e.g., yearly, hourly, or per signal cycle; intersection or approach leg). However, such aggregations are subject to a major simplification that masks the underlying spatio-temporal safety risk patterns within the data aggregation levels. Consequently, high-resolution analysis such as safety risk within signal cycles and at traffic movement level cannot be performed. This study contributes to the literature by proposing a new functional data analysis (FDA) approach for a novel characterization of safety risk patterns of signalized intersections. Functional data smoothing methods that can mitigate overfitting and account for the nonnegative characteristics of safety risk are proposed to model the time series of safety risk within signal cycles at the traffic movement level. Functional analysis of variance method (FANOVA) that can compare the group level differences of functional curves is used to test differences of safety risk functions among different traffic movements. A typical signalized intersection with representative signal types and channelizations is selected as the study location and approximately 1-hour traffic video data recorded by an unmanned aerial vehicle are used to extract traffic conflicts. New movement-level safety risk patterns are characterized based on the safety risk functions that can reveal the temporal distribution of risk within signal cycles. Most of the tested traffic movements have significantly distinct functional risk patterns according to the FANOVA results while risk patterns for most of the traffic movements cannot be differentiated based on the data aggregated at the cycle and approach levels. The proposed functional approach has the potential to be used for facilitating proactive safety management, calibrating microsimulation models for safety evaluation, and optimizing signal timing while considering traffic safety at more disaggregated levels.

Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic.

Introduction: The rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between "social distancing," a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic. Methods: There are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns. Results: The results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.