Search

There are many types of natural plants in nature that contain a variety of effective and complex chemical components. These constituents can be categorized as organic acids, volatile oils, coumarins, steroids, glycosides, alkaloids, carbohydrates, phytochromes, etc., all of which play important roles in the fields of pharmaceuticals, food, nutraceuticals, and cosmetics. The study of extraction and chemical composition analysis of natural products is important for the discovery of these active ingredients and their precursors. Therefore, the aim of this article is to review the status of research on the extraction, separation and purification, and structural identification of natural products, to provide a reference for the study of natural products. [ABSTRACT FROM AUTHOR]

Objective. The present study was designed to establish and evaluate an early prediction model of epilepsy after encephalitis in childhood based on electroencephalogram (ECG) and clinical features. Methods. 255 patients with encephalitis were randomly divided into training and verification sets and were divided into postencephalitic epilepsy (PE) and no postencephalitic epilepsy (no-PE) according to whether epilepsy occurred one year after discharge. Univariate and multivariate logistic regression analyses were used to screen the risk factors for PE. The identified risk factors were used to establish and verify a model. Results. This study included 255 patients with encephalitis, including 209 in the non-PE group and 46 in the PE group. Univariate and multiple logistic regression analysis showed that hemoglobin (OR = 0.968, 95% CI = 0.951–0.958), epilepsy frequency (OR = 0.968, 95% CI = 0.951–0.958), and ECG slow wave/fast wave frequency (S/F) in the occipital region were independent influencing factors for PE (P < 0.05).The prediction model is based on the above factors: −0.031 × hemoglobin −2.113 × epilepsy frequency + 7.836 × occipital region S/F + 1.595. In the training set and the validation set, the area under the ROC curve (AUC) of the model for the diagnosis of PE was 0.835 and 0.712, respectively. Conclusion. The peripheral blood hemoglobin, the number of epileptic seizures in the acute stage of encephalitis, and EEG slow wave/fast wave frequencies can be used as predictors of epilepsy after encephalitis. [ABSTRACT FROM AUTHOR]

• We study the effects of an episode of extensive news coverage of cadmium contamination of rice in China in 2013. • The cadmium rice event reduced the incidence of joint or muscle pain (JMP) by 2% in Hunan Province. • Hunan residents switched away from unsafe drinking water sources such as surface water to deep groundwater, which contributed to the reduction in JMP incidence. • Hunan residents also reduced their consumption of rice in response to the event. • Strong evidence is in support of news and public information as supplemental approaches to formal environmental regulation. The paper studies the effects of news media coverage of the 2013 cadmium rice event in China on consumer health outcomes and avoidance behavior. A difference-in-differences analysis shows that the event caused a 2% decrease in the incidence of joint or muscle pain (JMP), a major health outcome of cadmium pollution, in high-cadmium-pollution regions relative to other regions. Individuals with higher income or with more access to traditional TV media or digital media experienced a greater level of JMP reduction. To defend against cadmium pollution, residents in cadmium-polluted regions switched their drinking water from unsafe sources such as surface water to the safe source of deep groundwater and significantly reduced their consumption of rice. Our findings demonstrate the importance of information provision as an informal approach to complement formal environmental regulation and the critical role of news media in environmental regulation and public health. [ABSTRACT FROM AUTHOR]

Synthetic biology seeks to reprogram microbial cells for efficient production of value-added compounds from low-cost renewable substrates. A great challenge of chemicals biosynthesis is the competition between cell metabolism and target product synthesis for limited cellular resource. Dynamic regulation provides an effective strategy for fine-tuning metabolic flux to maximize chemicals production. In this work, we created a tunable growth phase-dependent autonomous bifunctional genetic switch (GABS) by coupling growth phase responsive promoters and degrons to dynamically redirect the carbon flux for metabolic state switching from cell growth mode to production mode, and achieved high-level GABA production from low-value glycerol in Corynebacterium glutamicum. A ribosome binding sites (RBS)-library-based pathway optimization strategy was firstly developed to reconstruct and optimize the glycerol utilization pathway in C. glutamicum , and the resulting strain CgGly2 displayed excellent glycerol utilization ability. Then, the initial GABA-producing strain was constructed by deleting the GABA degradation pathway and introducing an exogenous GABA synthetic pathway, which led to 5.26 g/L of GABA production from glycerol. In order to resolve the conflicts of carbon flux between cell growth and GABA production, we used the GABS to reconstruct the GABA synthetic metabolic network, in which the competitive modules of GABA biosynthesis, including the tricarboxylic acid (TCA) cycle module and the arginine biosynthesis module, were dynamically down-regulated while the synthetic modules were dynamically up-regulated after sufficient biomass accumulation. Finally, the resulting strain G7-1 accumulated 45.6 g/L of GABA with a yield of 0.4 g/g glycerol, which was the highest titer of GABA ever reported from low-value glycerol. Therefore, these results provide a promising technology to dynamically balance the metabolic flux for the efficient production of other high value-added chemicals from a low-value substrate in C. glutamicum. • A RBS-library-based pathway optimization strategy was designed to reconstruct glycerol utilization pathway in C. glutamicum. • A tunable growth phase-dependent autonomous bifunctional switch was designed to dynamically orchestrate the carbon flux. • Growth-coupled regulation network was rebuilt using GABS to achieve a desirable metabolic state switching. • The engineered strain showed the highest titer and yield of GABA production from glycerol ever reported. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning is utilized to train a global data set of major-minor elements of Cr-spinels. • Ti, Cr, and Al are critical elements for distinguishing Cr-spinel forming-environments. • The new model is successfully applied to reveal tectonic settings of magmatic sulfide deposits. This study uses the random forest machine learning algorithm to classify and predict Cr-spinel formation environments in mafic–ultramafic rocks. Cr-spinel is an early-crystallized oxide phase in these rocks and exhibits significant compositional variations that provide valuable insights into their formation environments. Traditionally, empirical diagrams have been used to classify the origins of Cr-spinel based on major and trace element compositions; however, overlap exists among spinel from different formation environments. To overcome this limitation, we develop a random forest model using a compiled dataset of Cr-spinel compositions from various tectonic settings, including small intrusions from continental collisional zones, layered intrusions and small intrusions from intra-continental plates (including large igneous provinces and intra-continental rifts), Alaskan-type complexes from oceanic subduction zones, and ophiolites from oceanic spreading centers. The model is trained, validated, and optimized using cross-validation and hyperparameter tuning. The results show high accuracy in predicting the Cr-spinel formation environments, as demonstrated by high precision, recall, and F1 scores. Feature importance analyses reveal that elements, such as Ti, Cr, and Al, play a more significant role in the prediction. Visualization techniques, i.e., the t-distributed stochastic neighbor embedding (TSNE) algorithm, are applied to reduce the dimensionality of the dataset and create decision boundaries for better interpretation. The model is validated using an independent Cr-spinel composition dataset obtained from an area with known geological settings, to confirm the model's reliability. The model is applied successfully to reveal the heterogeneous sources of the rifting-related Daxueshan magmatic Ni-Cu sulfide deposit in the Baoshan block in the eastern part of the Tethyan orogenic belt. The machine learning algorithm can accurately distinguish different Cr-spinel types using large and diverse datasets to provide valuable insights into geological formation conditions. This approach is a powerful tool for understanding Earth's geological processes, improving exploration models of magmatic sulfide deposit and reducing mineral exploration costs. [ABSTRACT FROM AUTHOR]

• The effects and applications of essential oils were summarized. • LPME and SPME microextraction applied in EOs analysis have been reviewed. • The combination of liquid-phase with solid-phase microextraction novel technique is prospected. Liquid phase microextraction (LPME) and solid phase microextraction (SPME) are popular extraction techniques for sample preparation due to their green and highly efficient single-step extraction efficiency. With the increasing attention to essential oils, their evaluation and analysis are significant in analytical sciences. In this review, starting from a brief description of the recent advances in the last decade, the attention has been focused on the up-to-date research works and applications based on liquid and solid phase microextraction for essential oil analyses. Particular attention has been given to the approaches using ionic liquids, eutectic solvents, gas flow assisted, and novel composite materials. In the end, the technological convergence of novel microextraction of essential oils in the future has been prospected. [ABSTRACT FROM AUTHOR]

Given the rapid rise of remote work, there is an opportunity for new shared mobility services designed to meet the needs of passengers with multiple possible work locations. This paper develops a new optimization model to enable shared mobility systems to match drivers and passengers when passengers have flexible destinations. Constraints representing employer policies, such as mandatory co-location of colleagues and limited capacity of satellite offices are introduced in order to explore the impact of employer remote work policies on travel demand. A case study using historical demand data demonstrates that incorporating flexible work locations can increase ride-pooling participation by up to 6.7% and reduce vehicle-kilometers traveled by 4.9%. Outcomes are found to be significantly affected by employer policies. The implications of the results for shared mobility business models, employer remote work plans and local transportation policy are discussed. [ABSTRACT FROM AUTHOR]

Despite extensive efforts to understand the tectonic evolution of the Jiangnan Orogen in South China, the orogenic process and its mechanism remain a matter of dispute. Previous geodynamic studies have mostly focused on collisional orogeny, which is commonly invoked to explain the Jiangnan Orogen. However, it is difficult for such hypotheses to reconcile all the geological and geophysical data, especially the absence of ultrahigh‐pressure metamorphic rocks. Based on the magnetotelluric data, we present a group of resistivity models produced through the combination of two ‐dimensional and three‐dimensional inversions, revealing the geo‐electrical structures of Jiangnan and a typical collisional orogen. In our models, the resistive crust is separated into three parts by a prominent conductive layer with opposite dipping directions on both sides. A special thrust‐nappe system, which is different from that developed in a typical collisional process, is revealed in the Jiangnan Orogen. This structure suggests a process different from the simple collisional orogeny. To interpret our observations, an 'intra‐continental orogeny' is proposed to address the development of the Jiangnan Orogen in the Mesozoic. Furthermore, this 'reworked' process may contain at least two stages caused by the decoupling of the lithosphere, which is revealed by an extra conductive layer beneath Jiangnan. [ABSTRACT FROM AUTHOR]

• This paper studies the Maximum-Weight Bounded-Degree Rooted Tree Problem from the computational aspect. • Basic models and enhanced constraints are introduced for compact and extended models. • Separation problems and algorithms are studied for each family of constraints. • Several branch-and-cut frameworks are proposed and tested to compare the performance. • Enhanced formulations show superior performance in computational simulation. This paper contributes to the computational study of the Maximum-Weight Bounded-Degree Rooted Tree Problem. Based on previous work, two types of formulations are introduced for the problem, along with some newly discovered constraints that can enhance the formulations. The separation problem for each family of constraints are studied in terms of their complexity and associated algorithms. We then compare the performance of four branch-and-cut frameworks in extensive computational simulations, especially the performance difference between original models and enhanced models with newly discovered constraints. Results show that the enhanced models have a significantly better performance than the original ones. [ABSTRACT FROM AUTHOR]

• Attitude towards watching videos on phones while driving predicts intention. • Perception about state law doesn't determine watching videos while driving. • State law on distracted driving doesn't determine watching videos while driving. • Drivers think entertainment is the biggest benefit of watching videos while driving. • Drivers should be required to pass a course on distracted driving. This is a two-part mixed methods study that investigated motivations for watching videos on mobile phones while driving. We make three theoretical contributions in this paper. First, we specifically examine watching videos on mobile phones while driving, whereas previous studies examine calling, texting, monitoring messages, and using apps. Second, we specifically focus on waiting at intersections and driving in parking lots; parking lots have not been studied in previous studies. Third, we incorporate perception about the law into the Theory of Planned Behavior model as a predictor of intention. The quantitative survey yielded responses measuring each variable in our extended Theory of Planned Behavior model. We found that although people don't watch videos on their phones while driving as much as they text or converse, the rates of watching are disturbingly high (41% watch at intersections). The intention to watch while driving is a significant predictor of behavior, and attitude is a significant predictor of intention in both scenarios. Moral norms are significant predictors of intention in the parking lot scenario. Nearly half (48%) of drivers don't know their state laws regarding mobile phone use while driving. Neither knowledge about state law with respect to watching videos on mobile phones while driving nor the actual state law about using handheld devices while driving is a significant predictor of the intention to watch videos while driving. The qualitative survey yielded open-ended responses on drivers' salient beliefs about watching videos on mobile phones while driving. We propose using a multitude of laws to reduce mobile phone use while driving, and requiring drivers to take a short course on distracted driving. [ABSTRACT FROM AUTHOR]

The size uniformity and spatial dispersion of nanoparticles (NPs) formed by ion implantation must be further improved due to the characteristics of the ion implantation method. Therefore, specific swift heavy ion irradiation and thermal annealing are combined in this work to regulate the size and spatial distributions of embedded Au NPs formed within LiTaO3 crystals. Experimental results show that small NPs migrate to deeper depths induced by 656 MeV Xe35+ ion irradiation. During thermal annealing, the growth of large Au NPs is limited due to the reductions in the number of small Au NPs, and the migrated Au NPs aggregate at deeper depths, resulting in a more uniform size distribution and an increased spatial distribution of Au NPs. The present work presents a novel method to modify the size and spatial distributions of embedded NPs. [ABSTRACT FROM AUTHOR]

Metabolic engineering seeks to rewire the metabolic network of cells for the efficient production of value-added compounds from renewable substrates. However, it remains challenging to evaluate and identify strains with the desired phenotype from the vast rational or random mutagenesis library. One effective approach to resolve this bottleneck is to design an efficient high-throughput screening (HTS) method to rapidly detect and analyze target candidates. L-cysteine is an important sulfur-containing amino acid and has been widely used in agriculture, pharmaceuticals, cosmetics, and food additive industries. However, HTS methods that enable monitoring of L-cysteine levels and screening of the enzyme variants and strains to confer superior L-cysteine biosynthesis remain unavailable, greatly limiting the development of efficient microbial cell factories for L-cysteine production at the industrial scale. Here, we took advantage of the L-cysteine-responsive transcriptional regulator CcdR to develop a genetically encoded biosensor for engineering and screening the L-cysteine overproducer. The in vivo L-cysteine-responsive assays and in vitro electrophoretic mobility shift assay (EMSA) and DNase I footprint analysis indicated that CcdR is a transcriptional activator that specifically interacts with L-cysteine and binds to its regulatory region to induce the expression of target genes. To improve the response performance of the L-cysteine biosensor, multilevel optimization strategies were performed, including regulator engineering by semi-rational design and systematic optimization of the genetic elements by modulating the promoter and RBS combination. As a result, the dynamic range and sensitivity of the biosensor were significantly improved. Using this the excellent L-cysteine biosensor, a HTS platform was established by coupling with fluorescence-activated cell sorting (FACS) and was successfully applied to achieve direct evolution of the key enzyme in the L-cysteine biosynthetic pathway to increase its catalytic performance and to screen the high L-cysteine-producing strains from the random mutagenesis library. These results presented a paradigm of design and optimization of biosensors to dynamically detect metabolite concentrations and provided a promising tool enabling HTS and metabolic regulation to construct L-cysteine hyperproducing strains to satisfy industrial demand. • The regulation mechanism of a L-cysteine-responsive transcriptional factor CcdR was firstly investigated. • The dynamic range and sensitivity of the L-cysteine biosensor were significantly improved using regulator engineering. • A biosensor-based high-throughput screening (HTS) platform was constructed by coupling with FACS. • The biosensor-based HTS platform was applied in direction evolution of the key enzyme CysE to increase its activity. • The developed HTS platform allowed us to identify the efficient L-cysteine-producing strains from a large-scale mutagenesis library. [ABSTRACT FROM AUTHOR]

The use of anti-pollution facemasks (APFs) in defense against particulate matter (PM) pollution is subject to debate as air pollution and wildfire events intensify. Inward leakage due to imperfect fitting and the Peltzman effect of people spending more time outdoors when wearing masks have led to mixed evidence regarding the effectiveness of APFs, which in turn has contributed to conflicting public messages about APFs with potentially large public health costs. We conduct a large-scale randomized field study on individuals' daily outdoor time and mask wearing behaviors and the associated health outcomes during an entire winter heating season in a provincial capital city in Northeastern China. We find that APFs used in everyday life achieved an overall efficiency of 80% in reducing respiratory or cardiovascular disease related doctor visits. Mask wearing, due to its discomfort, reduced outdoor time. However, the added protection provided by masks against PM led respondents to spend more time outdoors on smog days, and this relative Peltzman effect wiped out about 12% of APFs' health benefits. Taken together, APFs' health benefits far exceed their financial costs. These findings call for affirmatory but careful messaging to the public about using APFs as personal protection against PM pollution. [ABSTRACT FROM AUTHOR]

Background: Although the association between ambient temperature and mortality of respiratory diseases was numerously documented, the association between various ambient temperature levels and respiratory emergency department (ED) visits has not been well studied. A recent investigation of the association between respiratory ED visits and various levels of ambient temperature was conducted in Beijing, China. Methods: Daily meteorological data, air pollution data, and respiratory ED visits data from 2017 to 2018 were collected in Beijing. The relationship between ambient temperature and respiratory ED visits was explored using a distributed lagged nonlinear model (DLNM). Then we performed subgroup analysis based on age and gender. Finally, meta-analysis was utilized to aggregate the total influence of ambient temperature on respiratory ED visits across China. Results: The single-day lag risk for extreme cold peaked at a relative risk (RR) of 1.048 [95% confidence interval (CI): 1.009, 1.088] at a lag of 21 days, with a long lag effect. As for the single-day lag risk for extreme hot, a short lag effect was shown at a lag of 7 days with an RR of 1.076 (95% CI: 1.038, 1.114). The cumulative lagged effects of both hot and cold effects peaked at lag 0–21 days, with a cumulative risk of the onset of 3.690 (95% CI: 2.133, 6.382) and 1.641 (95% CI: 1.284, 2.098), respectively, with stronger impact on the hot. Additionally, the elderly were more sensitive to ambient temperature. The males were more susceptible to hot weather than the females. A longer cold temperature lag effect was found in females. Compared with the meta-analysis, a pooled effect of ambient temperature was consistent in general. In the subgroup analysis, a significant difference was found by gender. Conclusions: Temperature level, age-specific, and gender-specific effects between ambient temperature and the number of ED visits provide information on early warning measures for the prevention and control of respiratory diseases. Highlights: 1. Cold weather had a longer lag time and hot weather had a shorter lag time but a stronger effect. 2. Extreme temperatures had a greater effect on emergency room visits for respiratory disease than moderate temperatures. 3. The elderly population were more sensitive to cold and hot weather. [ABSTRACT FROM AUTHOR]

Hydrophilic trioctylammonium triflate ([TOA][OTf]), a protic organic ionic plastic crystal (POIPC), is studied by differential scanning calorimetry (DSC) and broadband dielectric spectroscopy (BDS) in a temperature range from −100°C to 120 °C. Below the melting temperature, ${T}_{m}~\approx ~18 ^{\circ} {\mathrm {C}}$ , [TOA][OTf] shows five solid phases, separated by sharp thermal transitions as detected by DSC. BDS spectra are analyzed using various strategies for data analysis. Phase changes are discernible in temperature-dependent permittivity and dielectric loss data; however, the derivatives ${{\partial \epsilon ^{\prime }}}/ {{\partial }\text {ln}{\omega }}$ and ${{\partial }\text {ln}{\varepsilon ^{\prime }}}/ {{\partial {T}}}$ appear to be much more sensitive to confirm all calorimetric phase transitions consistently. Fitting of ${{\partial \epsilon ^{\prime }}} /{{\partial }\text {ln}{\omega }}$ spectra with symmetric Cole–Cole functions reveals two conductivity relaxation processes, the relaxation time of which (${\tau }_{\text {fast}}$ and ${\tau }_{\mathrm {slow}}$) is separated by about two decades over the whole temperature range. The dc conductivity (${\sigma }_{\mathrm {dc}}$) is computed from ${\varepsilon ^{\prime \prime }}$ spectra for the solid phases I–IV and in the melt. Within all solid phases, ${\sigma }_{\text {dc}}$ shows Arrhenius behavior with typically decreasing activation energy toward higher temperature except for phase III. In the melt, ${\sigma }_{\text {dc}}$ obeys Vogel–Fulcher–Tammann (VFT) behavior as expected favoring a “vehicle” mechanism for ionic charge transport. Surprisingly, the ratio ${\tau }_{\text {fast}}/ {\tau }_{\text {slow}}$ shows an exceptional sensitivity to phase changes, either by stepwise changes or changes in its temperature dependence. From the presence of two distinct conductivity relaxation modes, we conclude that the crossover from subdiffusive to diffusive charge transport proceeds via two characteristic length scales, where the longer one likely relates to apolar aggregates of nanophase-separated cation tails as proposed by various authors. Contact angle measurements support this hypothesis. Finally, we discuss the temperature dependence of ${\sigma \tau {T}}$ and ${\sigma \tau }$ in the framework of the Debye–Stokes–Einstein (DSE) and Barton–Nakajima–Namikawa (BNN) relation. Here, ${\sigma }_{\text {dc}}{\tau }_{\text {slow}}{T}$ exclusively “sees” the transition III $\to $ II, which implies that this transition originates from a reorganization of the nanophase-separated apolar aggregates. [ABSTRACT FROM AUTHOR]

Extensive studies have reported that interaction of α-synuclein amyloid species with neurons is a crucial mechanistic characteristic of Parkinson's disease (PD) and small molecules can downregulate the neurotoxic effects induced by protein aggregation. However, the exact mechanism(s) of these neuroprotective effects by small molecules remain widely unknown. In the present study, α-synuclein samples in the amyloidogenic condition were aged for 120 h with or without different concentrations of mitoquinone (MitoQ) as a quinone derivative compound and the amyloid characteristics and the relevant neurotoxicity were evaluated by Thioflavin T (ThT)/Nile red fluorescence, Congo red absorption, circular dichroism (CD), transmission electron microscopy (TEM), cell viability, lactate dehydrogenase (LDH), reactive oxygen species (ROS), reactive nitrogen species (RNS), malondialdehyde (MDA), superoxide dismutase (SOD), and caspase-9/-3 activity assays. Results clearly showed the capacity of MitoQ on the inhibition of the formation of α-synuclein fibrillation products through modulation of the aggregation pathway by an effect on the kinetic parameters. Also, it was shown that α-synuclein samples aged for 120 h with MitoQ trigger less neurotoxic effects against SH-SY5Y cells than α-synuclein amyloid alone. Indeed, co-incubation of α-synuclein with MitoQ reduced the membrane leakage, oxidative and nitro-oxidative stress, modifications of macromolecules, and apoptosis. [ABSTRACT FROM AUTHOR]

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver–customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver–customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model. • Introduce a matching-integrated vehicle rebalancing (MIVR) model for generating better rebalancing decisions. • A robust MIVR model incorporates inherent demand uncertainty in the ride-hailing system. • Real-world simulations show the performance improvements over two benchmark models. • Compare the robust and normal MIVR models under multiple demand scenarios. [ABSTRACT FROM AUTHOR]

Facing rapid motorization, many Chinese municipalities are implementing policies that restrict car ownership or use. However, there is significant variation in terms of which cities adopt these policies and when. This research systematically investigates what factors prompt local governments in China to adopt these car restriction policies. We collect a database of car restriction policies as well as economic, demographic, land use, and transportation indicators for 287 Chinese municipalities from 2001 to 2014. We adopt a mixed methods approach that combines a qualitative investigation of stated objectives and legislative precedent within policy documents with a quantitative duration model of policy adoption. We find that the adoption of comprehensive car ownership and use restriction policies across Chinese cities primarily responds to local air pollution and secondarily to car ownership and congestion. Policy adoption additionally responds to local subway line constructions. Local economic power and population size do not effectively explain policy adoption. Idiosyncratic effects at provincial or city levels are important, although the underlying mechanisms by which these network effects manifest remain unclear. Broadly, our findings suggest that problem solving and network effects both contribute to the adoption of car restriction policies across China's cities and that the legal policy documents reliably illustrate the motivations of these policies. [ABSTRACT FROM AUTHOR]

A great amount of new S‐wave data has been combined to image the mantle structure down to lower mantle depth near the southeastern margin of Eurasia and understand the tectonics in South China since the Mesozoic. Our results reveal a large‐scale structure beneath the South China Sea, with a prominent, broad low‐velocity feature of at least 1500‐km wide in and below the mantle transition zone (MTZ) and a pronounced low‐velocity feature of nearly 500‐km wide in the lowermost mantle. Together these features may represent the head‐and‐tail of an upwelling mantle plume that has interacted with and deformed by the subducting plates in the region. The Andaman–Sumatra plate is seen subducting steeply without stagnation in the MTZ over the large‐scale low‐velocity structure, suggesting that the upwelling mantle may have exerted significant influence on the overlying plate tectonics. Our results suggest a long‐term extensional regime near the southeastern margin of Eurasia, which may help to understand the formation of the large‐scale intra‐plate magmatism and mineralization with accumulation of huge amount of rare metal elements in South China during the Mesozoic, and the continental breakup, the southeastward extrusion of Indochina, and the opening of the South China Sea during the Cenozoic. [ABSTRACT FROM AUTHOR]

An effective approach for monodesulfonation of polyphenol sulfonate under ultrasonic irradiation was reported. Series of substituted ortho‐, meta‐ or para‐hydroxylphenol sulfonates or 5‐hydroxy‐1,3‐phenylene bis(4‐methylbenzenesulfonate) were obtained specifically in the presence of sodium hydroxide in acetonitrile. With treatment of potassium carbonate in alcohol, alkyloxylphenol sulfonates was provided via one‐pot monodesulfanation followed by alkylation under ultrasonicated solvent‐free conditions. The alcohol had been regarded as green alkylation reagent. The results of the exploration of the scalability of monodesulfonation and one‐pot monodesulfonation‐alkylation of resorcinol bissulfonate indicated that such modes had the potential application value in scale production. [ABSTRACT FROM AUTHOR]

In order to illuminate the hindering effects induced by grain boundaries on the propagation behavior of crack with respect to 15‐5 PH stainless steel, interactions between propagated crack and typical grain boundaries, including the parent austenite grain boundary (PAGB), coincidence site lattice Σ3 grain boundary (CSL Σ3), packet grain boundary (PGB), and block grain boundary (BGB) are investigated at the atomic scale. Results indicate that the higher stress in the crack initiation (CI) stage and crack and grain boundary interaction (CGB) stage is required to drive the propagating crack in terms of the PAGB model compared to the other models, and the longest interaction time between propagated crack and established grain boundary is needed for the crack from the crack propagation (CP) stage to the CGB stage in the case of the PGB model. The PAGB and PGB are determined to be the most effective in hindering crack propagation in 15‐5 PH stainless steel. [ABSTRACT FROM AUTHOR]

Purpose: The prognostic value of interim 18F-FDG PET/CT (I-PET) for follicular lymphoma (FL) is controversial, and may be related to the lack of strict standards in terms of age, chemotherapy regimen, and evaluation criteria in previous studies. This study aimed to investigate the prognostic value of I-PET in adult FL patients treated with R-CHOP. Methods: I-PET was performed in 30 adult FL patients after treatment with 3–5 cycles of R-CHOP. PET/CT images were assessed using the Deauville 5-point scale (D-5PS) criteria. Baseline PET/CT (B-PET) was performed in 24 of the patients with FL before treatment. The PET/CT image parameters, such as the SUVmax, TLG, and tMTV, were recorded. The prognostic values of sex, age, grade, Ann Arbor stage, LDH level, and I-PET were evaluated. Results: Kaplan-Meier analysis and Cox regression showed that sex, age, grade, Ann Arbor stage, LDH, and I-PET using the D-5PS criteria could not predict the PFS of adult patients with FL treated with R-CHOP (P>0.05). ROC curve analysis evaluated the predictive values of SUVmax, TLG, and tMTV in B-PET and I-PET and showed that none of them was predictive of PFS in adult FL patients (P>0.05). However, the variation in SUVmax (∆SUVmax) was predictive of PFS in adult FL patients (AUC=0.83, P=0.040), and the cutoff threshold was 4.85. Conclusions: I-PET using the D-5PS criteria cannot predict the PFS of adult FL patients treated with R-CHOP. However, the ∆SUVmax between B-PET and I-PET is applicable for the prognosis of adult patients with FL. [ABSTRACT FROM AUTHOR]

This paper proposes an ex post path choice estimation framework for urban rail systems using an aggregated time-space hypernetwork approach. We aim to infer the actual passenger flow distribution in an urban rail system for any historical day using the observed automated fare collection (AFC) data. By incorporating a schedule-based dynamic transit network loading (SDTNL) model, the framework captures the crowding correlation among stations and the interaction between the path choice and passenger left behind, which is important for the path choice estimation in a "near-capacity" operated urban rail system. The path choice estimation is formulated as an optimization problem, which aims to minimize the difference between the model-derived and observed information with path choice parameters as decision variables. The original problem is intractable because of nonlinear (logit model) and nonanalytical (SDTNL) constraints. A solution procedure is proposed to decompose the original problem into three tractable subproblems, which can be solved efficiently. Solving the decomposed problem is equivalent to finding a fixed point. We prove that the solution to the original problem is the same as the decomposed problem (i.e., the fixed point) when passenger path choices follow the predefined behavior model. If this condition does not hold, the solution of the original problem is proved to be an "almost fixed point" for the decomposed problem. The model is validated using both synthetic and real-world AFC data from a major urban railway system. The analysis with synthetic data validates the model's effectiveness in estimating path choice parameters and left behind probabilities, which outperforms state-of-art simulation-based optimization methods and probabilistic models in both accuracy and efficiency. The analysis using actual data shows that the estimated path shares are more reasonable than the baseline uniform path shares and survey-derived path shares. The model estimation is robust to different initial parameter values and AFC data from various dates. Funding: The project is partially funded by the TRENoP Strategic Research funding at KTH. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1177. [ABSTRACT FROM AUTHOR]

When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (−20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand. • A transit path recommendation model for minimizing system travel times under disruptions. • Using robust optimization to consider demand uncertainty. • Propose a simulation-based first-order approximation for the non-tractable objective function. • A case study in the Chicago public transit system with a real-world disruption. • The proposed model can reduce the incident-line passengers' travel time by 20%. [ABSTRACT FROM AUTHOR]

Novel strategies for diagnosing triple-negative breast cancer (TNBC) are essential for effective clinical treatment. Vascular endothelial growth factor receptor 2 (VEGFR2) and neuropilin-1 (NRP-1) are potential targets for tumor imaging agents. We designed and synthesized [99mTc]Tc-labeled heptapeptide (A7R) and its D-type peptide (DA7R) with high affinity and specificity for VEGFR2 and NRP-1 as novel single-photon emission computed tomography (SPECT) probes for TNBC imaging. The specificities of A7R and DA7R were first evaluated in vitro using flow cytometry and confocal microscopy and ex vivo using fluorescence imaging. Subsequently, A7R and DA7R were labeled with [99mTc]Tc through 6-hydrazino nicotinamide (HYNIC), and their radiochemical purities (RCPs) and stability in vitro were assessed. The imaging performance and biodistribution of [99mTc]Tc-HYNIC-A7R and [99mTc]Tc-HYNIC-DA7R were evaluated in TNBC mouse models. A7R and DA7R exhibited good TNBC cell-targeting abilities and were readily labeled with [99mTc]Tc through HYNIC. Both [99mTc]Tc-HYNIC-A7R and [99mTc]Tc-HYNIC-DA7R had high RCPs and stability in vitro, and their accumulation in tumor tissues in the TNBC models was evident, with fast blood clearance and favorable biodistribution. More importantly, [99mTc]Tc-HYNIC-DA7R showed better tumor-to-muscle ratios and lower renal uptake than [99mTc]Tc-HYNIC-A7R. These results suggest that both [99mTc]Tc-HYNIC-DA7R and [99mTc]Tc-HYNIC-A7R have substantial potential as probes for targeted SPECT imaging of TNBC, and the former may be considered for future clinical translation owing to its superior tumor imaging performance. [ABSTRACT FROM AUTHOR]

Objectives: To validate a total-body PET-guided deep progressive learning reconstruction method (DPR) for low-dose 18F-FDG PET imaging. Methods: List-mode data from the retrospective study (n = 26) were rebinned into short-duration scans and reconstructed with DPR. The standard uptake value (SUV) and tumor-to-liver ratio (TLR) in lesions and coefficient of variation (COV) in the liver in the DPR images were compared to the reference (OSEM images with full-duration data). In the prospective study, another 41 patients were injected with 1/3 of the activity based on the retrospective results. The DPR images (DPR_1/3(p)) were generated and compared with the reference (OSEM images with extended acquisition time). The SUV and COV were evaluated in three selected organs: liver, blood pool and muscle. Quantitative analyses were performed with lesion SUV and TLR, furthermore on small lesions (≤ 10 mm in diameter). Additionally, a 5-point Likert scale visual analysis was performed on the following perspectives: contrast, noise and diagnostic confidence. Results: In the retrospective study, the DPR with one-third duration can maintain the image quality as the reference. In the prospective study, good agreement among the SUVs was observed in all selected organs. The quantitative results showed that there was no significant difference in COV between the DPR_1/3(p) group and the reference, while the visual analysis showed no significant differences in image contrast, noise and diagnostic confidence. The lesion SUVs and TLRs in the DPR_1/3(p) group were significantly enhanced compared with the reference, even for small lesions. Conclusions: The proposed DPR method can reduce the administered activity of 18F-FDG by up to 2/3 in a real-world deployment while maintaining image quality. [ABSTRACT FROM AUTHOR]

This study investigates the influence of EGR (exhaust gas recirculation) coupled with injection pressure on the combustion and emission characteristics of an engine fueled with methanol-gasoline blends. Increasing the methanol blending ratio can improve the knocking phenomenon, BTE (brake thermal efficiency) and regulated emissions. As the methanol blending ratio increases, the optimal fuel injection pressure for achieving the optimal combustion process, BTE and CO (carbon monoxide) emissions increases. The optimal EGR rate for achieving the highest BTE also increases. As the methanol blending ratio increases, the optimal injection pressure for achieving the lowest TPN (total particle number) and NPN (nucleation mode particle number) also increases. Increasing the fuel injection pressure leads to a decrease in APN (accumulation mode particle number). Increasing the methanol blending ratio and EGR rate can reduce TPN and NPN. With increasing methanol blending ratio, APN initially increases and then decreases. When using a lower methanol blending ratio, increasing the EGR rate leads to a higher proportion of APN to TPN. However, when using a higher methanol blending ratio, the opposite is true. The optimal engine performance can be achieved by using M100 fuel with a 35 MPa injection pressure and a 30% EGR rate. • Investigation of the effects of EGR dilution and injection pressure on engine performance with a methanol/gasoline blend. • Blending methanol fuel reduces total particles and increases accumulation mode particles. • Increasing EGR reduces total particles and increases accumulation mode particles. • Appropriate increase in injection pressure reduces total particles and accumulation mode particles. • Increasing EGR, injection pressure, and methanol blending ratio reduces knock intensity. [ABSTRACT FROM AUTHOR]

• Multi-class pesticides were simultaneously detected by UAE-GLME. • The cleanup performance of GLME was verified in potato, lentil, corn and soybean. • Matrix effects of 41 pesticides showed weak or moderate matrix effects in different matrices. • 20 min of pretreatment time by UAE-GLME is proved feasible for high throughput analysis. A low matrix effect pretreatment method by Gas-liquid microextraction technique (GLME) combined with ultrasound assisted extraction (UAE-GLME) has been developed for fast analysis of 41 multi-class pesticides in different crop samples. Extraction and cleanup procedures were completed within 20 min, and the impact of matrix effects in potato, lentil, corn and soybean matrices were systematically evaluated. As high as 97.6% pesticides treated by UAE-GLME experienced low matrix effect, and up to 99% pesticides except parathion-methyl in lentil samples were categorized as medium matrix effect. Good trueness (recoveries of 61.3–115.8%) and precision (RSD≤20%) were demonstrated by the UAE-GLME method, with MLOQs ranged from 0.005 to 0.02 mg kg−1. This method detected 2-phenylphenol, pyrimethanil, triadimefon and tebuconazole in the range of 0.008–0.012 mg kg−1 in real food samples. The multiresidue analysis feature of GLME has been validated, which displays further potential for high-throughput determination of organic pollutants in foods with distinctive properties. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma (CCA) is a rare biliary adenocarcinoma related to poor clinical prognosis. Crowberry is an herbal medicine used to control inflammatory diseases and reestablish antioxidant enzyme activity. Although crowberry shows significant therapeutic efficacy in various tumors and diseases, its anticancer effects and specific molecular mechanisms in CCA are poorly understood. Aim of the study: This study was conducted to characterize crowberry effects on CCA cells behavior. Materials and methods: The chemical profiles of crowberry extract was qualitatively analyzed by high-performance liquid chromatography (HPLC) and HPLC–tandem mass spectrometry. MTT, colony formation and EdU assays were performed to measure cell proliferation. The effect of crowberry treatment on CCA cell migration was assessed by wound healing and migration assays. Moreover, Hoechst staining assay and flow cytometry were performed to assess the cell apoptosis rate. Western blotting was used to assess the protein expression levels of key factors associated with apoptosis, the Akt signaling pathway, and the epithelial-mesenchymal transition. A xenograft model was established and immunohistochemical and H&E staining was performed to assess crowberry antitumor effects in vivo. Results: Crowberry clearly inhibited CCA cells proliferation and migration in a dose-dependent manner and induced apoptosis in vitro. Crowberry inactivated the PI3K/Akt signaling pathway by regulating DEK in vitro and significantly inhibited tumor growth by downregulating the DEK expression in xenograft models. Conclusion: Crowberry inhibits CCA cells proliferation and migration through a molecular mechanism that includes inhibition of DEK and Akt signaling pathway inhibition in vitro and in vivo. Highlights: Crowberry alterd expression levels of key mediators in PI3K/Akt signaling pathway. Crowberry alterd expression levels of key mediators in PI3K/Akt signaling pathway. Crowberry suppressed the expression of the proto-oncogene DEK in vivo and in vitro. Crowberry inhibited CCA progression and migration through a molecular mechanism that includes inhibition of DEK and the Akt signaling pathway in vivo and in vitro. [ABSTRACT FROM AUTHOR]

Certain receptors are often overexpressed during tumor occurrence and development and closely correlate with carcinogenesis. Owing to its overexpression on the cell membrane and cytoplasm of various tumors, plectin, which is involved in tumor proliferation, migration, and invasion, has been viewed as a promising target for cancer imaging. Hence, plectin-targeting agents have great potential as imaging probes for tumor diagnosis. In this study, we developed a [99mTc]Tc-labeled plectin-targeted peptide (PTP) as a novel single-photon emission computed tomography (SPECT) probe for tumor imaging and investigated its pharmacokinetics, biodistribution, and targeting ability in several types of tumor-bearing mouse models. The PTP had good biocompatibility and targeting ability to tumor cells in vitro and could be readily labeled with [99mTc]Tc after modification with the bifunctional chelator 6-hydrazino nicotinamide (HYNIC). Furthermore, the prepared [99mTc]Tc-labeled PTP ([99mTc]Tc-HYNIC-PTP) showed high radiochemical purity and excellent stability in vitro. In addition, favorable biodistribution, fast blood clearance, and clear accumulation of [99mTc]Tc-HYNIC-PTP in several types of tumors were observed, with a good correlation between tumor uptake and plectin expression levels. These results indicate the potential of [99mTc]Tc-HYNIC-PTP as a novel SPECT probe for tumor imaging. [ABSTRACT FROM AUTHOR]

Fucan sulfate I (FSI) from the sea cucumber Holothuria fuscopunctata was purified and its structure was clarified based on a bottom-up strategy. The unambiguous structures of a series of oligosaccharides including disaccharides, trisaccharides, and tetrasaccharides, which were released from mild acid hydrolysis of FSI, were identified by one-dimensional (1D)/two-dimensional (2D) nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis. All the glycosidic bonds in these oligosaccharides were presented as α1,3 linkages confirmed by correlated signals from their 1H-1H ROESY and 1H-13C HMBC spectra. The structural sequence of these oligosaccharides formed by Fuc2S4S, Fuc2S, and non-sulfated ones (Fuc0S), along with the general structural information of FSI, indicated that the structure of FSI could be elucidated as: [-L-Fuc2S4S-α1,3-L-Fuc(2S)-α1,3-L-Fuc2S-α1,3-L-Fuc0S-α1,3-1-]n. Moreover, the L-Fuc0S-α1,3-L-Fuc2S4S linkage in FSI was susceptible to be cleaved by mild acid hydrolysis. The antioxidant activity assays in vitro showed that FSI and the depolymerized product (dFSI′) had potent activities for superoxide radical scavenging activity with IC50 of 65.71 and 83.72 μg/mL, respectively, while there was no scavenging effect on DPPH, hydroxyl and ABTS radicals. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease (PD), also known as paralysis tremor, is a chronic disease of the central nervous system. It has been reported that hepatocyte nuclear factor 4 alpha (HNF4A) is upregulated in PD, but its specific function has not been well explored. Methods: We established an in vitro PD model in SH‐SY5Y cells stimulated with 1‐methyl‐4‐phenylpyridinium (MPP+). Meanwhile, the effect of HNF4A on MPP+‐treated SH‐SY5Y cell behavior was monitored by functional assays. Mechanism assays were conducted to verify the relationship among LINC00667/miR‐34c‐5p/HNF4A. Rescue experiments validated the regulatory mechanism in PD model. Results: The results revealed that depletion of HNF4A suppressed cell cytotoxicity and apoptosis caused by MPP+. Knockdown of HNF4A recovered MPP+‐stimulated oxidative stress and neuroinflammation. Mechanically, HNF4A was targeted and inhibited by miR‐34c‐5p. Furthermore, we found that LINC00667 positively modulated HNF4A expression via sequestering miR‐34c‐5p in MPP+‐stimulated SH‐SY5Y cells. Interestingly, the data indicated that HNF4A could transcriptionally activate LINC00667 expression. Rescue experiments presented that miR‐34c‐5p interference or HNF4A overexpression could mitigate the effects of LINC00667 knockdown on cell viability, cytotoxicity, cell apoptosis, oxidative stress, and neuroinflammation in MPP+‐treated SH‐SY5Y cells. Conclusion: Our study first proved LINC00667, miR‐34c‐5p, and HNF4A constructed a positive feedback loop in MPP+‐treated SH‐SY5Y cells, enriching our understanding of PD. [ABSTRACT FROM AUTHOR]

This study proposes a probabilistic framework to infer passengers' responses to unplanned urban rail service disruptions using smart card data in tap-in-only public transit systems. We first identify 19 possible response behaviors that passengers may have based on their decision-making times and locations (i.e, the stage of their trips when an incident happened), including transferring to a bus line, canceling trips, waiting, delaying departure time, etc. A probabilistic model is proposed to estimate the mean and variance of the number of passengers in each of the 19 behavior groups using passengers' smart card transactions. The 19 behavioral responses can be categorized from two aspects. From the behavioral aspect, they can be grouped into 5 aggregated response behaviors including using bus, using rail (changing or not changing route), not using public transit, and not being affected. The inference of the 19 behaviors can be classified into four cases based on the information used (historical trips vs. subsequent trips) and the context of the observed transactions (direct incident-related vs. indirect incident-related). The public transit system (bus and urban rail) of the Chicago Transit Authority (CTA) is used as a case study based on a real-world rail disruption incident. The model is applied with both synthetic data and real-world data. Results with synthetic data show that the proposed approach can estimate passengers' behavior well. The mean absolute percentage error (MAPE) for the estimated expected number of passengers in each behavior group is 20.5%, which outperforms the rule-based benchmark method (60.3%). The estimation results with real-world data are consistent with the incident's context. An indirect model validation method using demand change information and incident log data demonstrates the reasonableness of the results. • A probabilistic framework to infer passenger's response during disruption. • Estimate the mean and variance of the number of passengers with a specific behavior. • A case study in Chicago public transit systems using smart card data. • The probabilistic model significantly outperforms typical rule-based methods. • Results can help to improve public transit operating strategies during incidents. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods: Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results: The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions: The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. [ABSTRACT FROM AUTHOR]

• A group-level selection dynamic induced by the implementation of an individual-level selection is proposed. • The existence of an independent cooperator group is beneficial to the rise of cooperation in the population with multiple groups. • Mutation and migration will inhibit the prevalence of cooperation by destroying the independence and purity of cooperator groups. • A special kind of multi-person migration, group division, will inhibit the anti-cooperation effect of the mutation. It is a challenge to understand why cooperation is prevalent in human society, especially without a mechanism that benefits cooperators such as direct, indirect and strong reciprocity. We study an evolutionary process of public goods game among individuals in multiple groups in a population. We find that the existence of an independent cooperator group is beneficial to the rise of cooperation in the population, which is applicable to both Moran process and a so-called disaster update process proposed in this paper. However, the introduction of mutation can completely overturn the emerging trend of cooperation, and even let the defectors occupy the whole population for a short period of time, by continuously reducing the number of cooperator groups. In addition, the migration of individuals between groups can also inhibit cooperation. More specifically, a higher frequency of migration can reduce the probability of cooperators occupying the population, and extend the time it takes for the population to reach the stationary state. Furthermore, we find that a special kind of multi-individual migration, namely group division, can resist the anti-cooperation effect of mutation to some extent, mainly by maintaining a certain number of cooperator groups in the population. The aforementioned results suggest that cooperation can rise without strengthening the ability of cooperators to directly confront the defectors in a population composed of multiple groups. [ABSTRACT FROM AUTHOR]

• Investigated computational fairness in travel behavior modeling. • Operationalized computational fairness by equality of opportunity. • Revealed prediction disparity for race, income, gender, health and region. • Adopted a bias mitigation method to improve fairness in travel behavior prediction. • Demonstrated the accuracy-fairness tradeoff. Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. It highlights the accuracy-fairness tradeoff instead of the single dimensional focus on prediction accuracy in the contexts of deep neural network (DNN) and discrete choice models (DCM). We first operationalize computational fairness by equality of opportunity , then differentiate between the bias inherent in data and the bias introduced by modeling. The models inheriting the inherent biases can risk perpetuating the existing inequality in the data structure, and the biases in modeling can further exacerbate it. We then demonstrate the prediction disparities in travel behavior modeling using the 2017 National Household Travel Survey (NHTS) and the 2018–2019 My Daily Travel Survey in Chicago. Empirically, DNN and DCM reveal consistent prediction disparities across multiple social groups: both over-predict the false negative rate of frequent driving for the ethnic minorities, the low-income and the disabled populations, and falsely predict a higher travel burden of the socially disadvantaged groups and the rural populations than reality. Comparing DNN with DCM, we find that DNN can outperform DCM in prediction disparities because of DNN's smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with synthetic and real-world data. The results demonstrate the prevalence of prediction disparities in travel behavior modeling, and the disparities still persist regarding a variety of model specifics such as the number of DNN layers, batch size and weight initialization. Since these prediction disparities can exacerbate social inequity if prediction results without fairness adjustment are used for transportation policy making, we advocate for careful consideration of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions. [ABSTRACT FROM AUTHOR]

The elucidation of the precise structure of fucan sulfate is essential for understanding the structure-activity relationship and promoting potential biomedical applications. In this work, the structure of a distinct fucan sulfate fraction V (PmFS in Ref 15 and FSV in Ref 16 → PFV) from Pattalus mollis was investigated using an oligosaccharide mapping approach. Six size-homogeneous fractions were purified from the mild acid hydrolyzed PFV and identified as fucitols, disaccharides and trisaccharides by 1D/2D NMR and MS analysis. Significantly, the sulfation pattern, glycosidic linkages, and sequences of all the oligosaccharides were unambiguously identified. The common 2-desulfation of the reducing end residue of the oligosaccharides was observed. Overall, the backbone of PFV was composed of L-Fuc 2S (major) and L-Fuc 3S (minor) linked by α1,4 glycosidic bonds. Importantly, the branches contain both monosaccharide and disaccharide linked to the backbone by α1,3 glycosidic linkages. Thus, the tentative structure of natural PFV was shown to be {-(R -α1,3)-L-Fuc 2S -α1,4-(L-Fuc 2S/3S -α1,4) x -} n , where R is L-Fuc (2S)4S -α1,3/4-L-Fuc 4S(0S) - or L-Fuc (2S)4S -. Our results provide insight into the heterogeneous structure of the fucan sulfate found in sea cucumbers. Additionally, PFV and its fractions showed strong anticoagulant and anti-iXase activities, which may be related to the distinct structure of PFV. [Display omitted] • A distinct fucan sulfate was investigated using an oligosaccharide mapping approach. • The structures of oligosaccharides were unambiguously identified. • The structure of the mainchain and the branches of PFV has been revised. • PFV displayed strong anticoagulant and anti-iXase activities. [ABSTRACT FROM AUTHOR]

• Dockless bike-sharing mobility presents a high degree of spatiotemporal regularity. • Use Poisson regression to analyze the trip generation/attraction factors. • A majority of trips are started from or ended at metro stations. • Flow imbalance is observed with more trips towards metro stations. • Cycling "communities" are locally clustered and consistent across the four months. Many cities around the world have adopted dockless bike-sharing programs with the hope that this new service could enhance last-mile public transit connections. However, our understanding of the travel patterns using dockless bike sharing is still limited. To advance the knowledge on the new service, this study investigates mobility patterns of dockless bike sharing in Singapore using a four-month dataset. An exploratory spatiotemporal analysis is conducted to show daily travel patterns, while community detection of networks is used to explore the spatial clusters emerged from cycling behaviors. A series of Poisson regression models are then estimated to characterize the generation, attraction and resistance factors of bike trips in different periods of a day. The proposed regression model, which considers built environment factors of origin and destination simultaneously, is proved to be effective in deciphering mobility. The empirical findings shed light on policy implications in sustainable transportation planning. [ABSTRACT FROM AUTHOR]

• We propose a real-time predictive decision support platform which addresses both, operations control and customer information needs. • The system provides crowding predictions on trains and platforms, communicates this information to passengers • Using a fixed-point formulation, the methodology accounts for the expected passenger behavior in response to the predictive crowding information • It is demonstrated through a case study that providing predictive information to passengers can potentially reduce denied boarding and lead to better utilization of train capacity. Demand for public transport has witnessed a steady growth over the last decade in many densely populated cities around the world. However, capacity has not always matched this increased demand. As such, passengers experience long waiting times and are denied boarding during the peak hours. Crowded platforms and the subsequent customer dissatisfaction and safety issues have become a serious concern. The COVID-19 pandemic has dramatically reduced passengers' willingness to board crowded trains, causing a surge in demand for real-time crowding information. In this paper, we propose a real-time predictive decision support platform which addresses both, operations control and customer information needs. The system provides crowding predictions on trains and platforms, communicates this information to passengers, and takes into account their response to it. It is demonstrated through a case study that providing predictive information to passengers can potentially reduce denied boarding and lead to better utilization of train capacity. [ABSTRACT FROM AUTHOR]

Fucosylated glycosaminoglycan (FG) from sea cucumbers has been reported to have anticoagulant effects via targeting intrinsic tenase. However, FG from natural source also potentially poses risks due to its FXIIa activation and platelet aggregating effects. Here, we found that the effect of FG on human platelet aggregation depended on both the sulfation pattern and chain length. FGs with higher content of Fuc2S4S and larger molecular weight (≥14 kD) had stronger activity. Both platelet aggregation and P-selectin expression induced by TaFG (an FG from Thelenota ananas) were decreased as the molecular weight reduced. Ticagrelor, aspirin and wortmannin completely blocked the secretion (ADP) but only partially blocked the aggregation induced by TaFG. Tirofiban an αIIbβ3 antagonist however potently inhibited both the secretion and aggregation, with IC50 of 6.01 ± 1.1.97 nM. Furthermore, TaFG could bind to human αIIbβ3 with high affinity, and the affinities of two FGs were paralleled with their activity in platelet aggregation or activation. These results indicated that αIIbβ3 played an important role in TaFG-induced platelet aggregation which was mediated by PI3K, and that platelet secretion was required for the amplification of aggregation. [ABSTRACT FROM AUTHOR]

• Car users informed of cars' emissions externality more likely to try green modes. • This effect persists but is dampened for car users in cities with polluted air. • Green travel subsidy is stronger boost to green modes, persists in polluted cities. • Green mobility policy more effective when combined with greening of other sectors. • Study uses stated preference experiment in Zhengzhou, China. Urban leaders in areas with high air pollution often face the dual task of reducing pollution levels while educating the public about the health impacts of pollution and preventive measures. Transportation policies to cut motorized personal vehicle use are often a key part of pollution reduction efforts. One type of these policies is information interventions educating commuters of the higher emissions impact of cars. This study evaluates the impact of such an information intervention on car commuters' intention to switch from car use to transit, biking, or walking in Zhengzhou, China. Further, it tracks how this greening impact evolves as drivers are given additional public health information regarding Zhengzhou's severe air pollution level, its health effects, and the reduced pollution exposure when driving compared with transiting, biking, or walking outside in polluted air. Using a randomized controlled survey experiment with a treatment group of 281 participants and a control group of 280 participants drawn from Zhengzhou workers who typically commute by car, this study applies multinomial logit models and difference-in-difference estimation to estimate the information intervention's impact. We find that while emissions information interventions are initially highly effective in reducing intended car usage, the effect is progressively dampened as respondents learn of the level of local air pollution and the risks of outdoor pollution exposure. Some greening effects do, however, remain even at the end of the experiment. The results suggest that governments in polluted areas must be aware of how policies in different segments of their portfolio interact and support broader initiatives like mixed-use planning and transit extension, simultaneously cutting transportation emissions and creating shorter commute durations with lower outdoor pollution exposure. [ABSTRACT FROM AUTHOR]

Fucan sulfates from echinoderm possess characteristic structures and various biological activities. Herein, comprehensive methods including enzymolysis, ion-exchange chromatography and size exclusion chromatography lead to the purification of five fucan sulfates (FSI, FSII, FSIII, FSIV, FSV) from the sea cucumber Pattalus mollis. Chemical composition analysis showed that they were all composed of l -fucose. Their sulfate content was determined by a conductimetric method. The molecular weight (Mw) of FSI, FSII, FSIII, FSIV and FSV were measured as 238.3 kDa, 81.0 kDa, 82.0 kDa, 23.2 kDa and 6.12 kDa, respectively. Detailed NMR spectroscopic analysis revealed that the structural sequence of FSI and FSII was →3)- l -Fuc S -α(1→, where Fuc S were Fuc 2S4S (10%), Fuc 2S (44%), Fuc 0S (10%), Fuc 4S (36%), that of FSIII was →4)- l -Fuc 2S -(α1 → 4)- l -Fuc 2S -(α1 → 4)- l -Fuc 0S/3S -(α1→, where Fuc 0S and Fuc 3S were in equal molar, and that FSIV was →4)- l -Fuc 2S3S -(α1 → 4)- l -Fuc 2S3S -(α1 → 4)- l -Fuc 2S -(α1→4)- l -Fuc 2S -(α1 → 4)- l -Fuc 2S -(α1 → 4)- l -Fuc 2S -(α1 →. This is the first report that such a diversity of fucan sulfates were obtained from the same sea cucumber species. Biological activity showed that FSI, FSII, FSIII and FSIV exhibited potent anticoagulant by prolonging the APTT. Among them, FSII, FSIII and FSIV showed the similar potency, while FSI owned the strongest. Structure-activity relationships analysis showed that molecular weight and sulfation degree should be the crucial factors for the activity. • Five fucan sulfates were purified from sea cucumber Pattalus mollis. • The physicochemical properties of four fucan sulfates were characterized. • Structural unit of FSI and FSIII was →3)- l -Fuc S -α(1→ and →4)- l -Fuc 2S -(α1→4)- l -Fuc 2S -(α1→4)- l -Fuc 0S/3S -(α1→, respectively. • FSIV was composed of an α1,4-linked hexafucose repeating unit. • FSI exhibit strong inhibition of the intrinsic coagulation pathway. [ABSTRACT FROM AUTHOR]

Background: The development of alternative anti-angiogenesis therapy for choroidal neovascularization (CNV) remains a great challenge. Nanoparticle systems have emerged as a new form of drug delivery in ocular diseases. Here, we report the construction and characterization of arginine-glycine-aspartic acid (RGD)-conjugated polyethyleneimine (PEI) as a vehicle to load antioxidant salvianolic acid A (SAA) for targeted anti-angiogenesis therapy of CNV. In this study, PEI was consecutively modified with polyethylene glycol (PEG) conjugated RGD segments, 3-(4′-hydroxyphenyl) propionic acid-Osu (HPAO), and fluorescein isothiocyanate (FI), followed by acetylation of the remaining PEI surface amines to generate the multifunctional PEI vehicle PEI.NHAc-FI-HPAO-(PEG-RGD) (for short, RGD-PEI). The formed RGD-PEI was utilized as an effective vehicle platform to load SAA. Results: We showed that RGD-PEI/SAA complexes displayed desirable water dispersibility, low cytotoxicity, and sustainable release of SAA under different pH conditions. It could be specifically taken up by retinal pigment epithelium (RPE) cells which highly expressed ɑvβ5 integrin receptors in vitro and selectively accumulated in CNV lesions in vivo. Moreover, the complexes displayed specific therapeutic efficacy in a mouse model of laser induced CNV, and the slow elimination of the complexes in the vitreous cavity was verified by SPECT imaging after 131I radiolabeling. The histological examinations further confirmed the biocompatibility of RGD-PEI/SAA. Conclusions: The results suggest that the designed RGD-PEI/SAA complexes may be a potential alternative anti-angiogenesis therapy for posterior ocular neovascular diseases. [ABSTRACT FROM AUTHOR]

The Vessel Monitoring System (VMS) on trawlers has revolutionized our understanding of spatiotemporal fishing activities. However, the low temporal resolution of historical VMS datasets complicates a precise analysis of fishing effort distribution. One inherent challenge for precise interpolation is the stark contrast between trawler movement patterns during steaming, characterized by straight lines, and fishing, which often involves consecutive turns. In this study, we introduce HiTrip, a deep learning approach that interpolates historical VMS data from two-hour intervals down to three minutes by harnessing both VMS and marine hydrological datasets. The proposed deep learning model, integrating ResNet, LSTM, and MLP, seamlessly synthesizes spatial features from coarse fishing effort distributions, sea surface factor fields, and current fields, while accounting for the temporal relationships within trajectory segments. Evaluated on 1855 East China Sea trawler VMS records and Copernicus Climate Data Store hydrological factor data, HiTrip achieves a 0.20 km interpolation error, meeting a finery 0.005° × 0.005°spatial resolution demand for fishing effort distribution analysis. Ablation study validates the efficacy of our deep learning model integrating multi-source datasets. Moreover, when evaluated on a diverse Global Fishing Watch dataset, which includes 45 trawlers spanning various global maritime regions, HiTrip maintains a 0.40 km error, emphasizing its broad generalization ability. • This study presents a novel method for precise trawler trajectory interpolation. • The proposed deep learning model integrates MLP, ResNet, and LSTM to fuse spatial features. extracted from coarse fishing effort distributions, sea surface factors fields, and current fields. • The proposed model also captures the temporal relation among VMS records. • In a case study HiTrip achieved a remarkably small interpolation error of 0.20 km. [ABSTRACT FROM AUTHOR]

Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a theoretical framework of deep hybrid models consisting of a mixing operator and a behavioral predictor, thus integrating the numeric and imagery data for travel behavior analysis. Empirically, this framework is applied to analyze travel mode choice using the Chicago MyDailyTravel Survey as the numeric inputs and the satellite images as the imagery inputs. We found that deep hybrid models significantly outperform both classical demand models and deep learning models in predicting aggregate and disaggregate travel behavior. The deep hybrid models can reveal spatial clusters with meaningful sociodemographic associations in the latent space. The models can also generate new satellite images that do not exist in reality and compute the corresponding economic information, such as substitution patterns and social welfare. Overall, the deep hybrid models demonstrate the complementarity between the low-dimensional numeric and high-dimensional imagery data and between the traditional demand modeling and recent deep learning. They enrich the family of hybrid demand models by using deep architecture as the latent space and enabling researchers to conduct associative analysis for sociodemographics, travel decisions, and generated satellite imagery. Future research could address the limitations in interpretability, robustness, and transferability, and propose new methods to further enrich the deep hybrid models. • Designing deep hybrid models to integrate numeric and imagery data. • Deep hybrid models outperform demand models and deep learning in demand prediction. • Deep hybrid models have high-dimensional latent space for image processing and generation. • The latent space is spatially and socially meaningful. • Deep hybrid models can generate new urban imagery and derive economic information. [ABSTRACT FROM AUTHOR]