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• Crash modification factors are estimated for adaptive traffic signal control technology at individual intersections. • CMFs estimated with both empirical bayes before-after methodology with propensity scores-potential outcomes framework. • CMFs are consistent between the two approaches when the same sample is used. Introduction: The main objective of this paper is to compare the safety effectiveness estimates obtained using the empirical Bayes (EB) before-after and propensity scores-potential outcomes (PSPO) methods. Method: The dataset employed in this study consisted of 338 intersections where adaptive traffic signal control (ATSC) technology was deployed in Pennsylvania. Results: The results revealed that the EB and PSPO methods produce Crash Modification Factors (CMFs) that are not statistically different from each other, which suggests that the two methods provide consistent results in the present study. Nevertheless, there are still some minor differences between the CMF values obtained from the EB method and the CMF values obtained using PSPO. These differences may be attributed to the different statistical basis and different methodological focus between the methods. Overall, the CMF values indicate a slightly higher expected crash frequency of various injury severity levels and for different intersection types (3-leg vs. 4-leg) associated with the implementation of ATSC; however, some of these changes were not statistically significant. [ABSTRACT FROM AUTHOR]

• Propose model to consider probabilistic preliminary preference (PP) in unlabeled choice context. • Both mixture and latent class structure considered. • Simulation analysis confirms parameters can be estimated using MLE. • Proposed model applied to parking choice dataset and has better fit to data than existing models. • Analysis reveals loss aversion and inertia (or variety seeking) are present in parking choice data. When selecting among a set of alternatives, individuals often compare each alternative with reference to some preliminary preference (PP). The preliminary preference could be the individual's preferred global option (i.e., their ideal choice) or the most ideal option within the current choice set. Previous research has shown that loss of an attribute relative to that of the PP has a stronger impact on the individual's choice compared to gain of that attribute—a phenomenon commonly known as loss aversion. To incorporate this loss aversion into choice modeling, the PP of the individuals must then be known; however, such information is generally not available when observing individual's choice behavior. To overcome the lack of knowledge of an individual's PP, this study proposes a probabilistic approach where all alternatives in the choice set have some potential of being an individual's PP. A multinomial logit (MNL) based methodology, named the latent preliminary preference (LPP) model, is used to estimate the PP probabilities entirely as a function of the decision-maker characteristics and each alternative's attributes, which overcomes the complexity that arises when choices are unlabeled. In addition, the methodology accommodates inertia that some individual's may have toward their PP, as well as the desire to try new options. Moreover, this study proposes randomizing the parameters of LPP model to account for random taste heterogeneity of individuals that would arise in a panel dataset, giving rise to random parameter latent preliminary preference (RPLPP) model. The ability of maximum likelihood inference method to retrieve the parameters of fixed parameters LPP model is demonstrated on synthetic data. The statistical significance and bias in estimated parameters are also demonstrated. The proposed model is then applied on a parking preference survey to examine the presence of loss aversion and inertia/variety seeking behavior among the respondents. [ABSTRACT FROM AUTHOR]

• Applies propensity scores-potential outcomes framework to estimate CMFs for rumble strips applied to curve sections of two-lane rural roads. • Propensity scores matching better balances covariates in with and without sample. • CMFs estimated for total crash frequency, fatal and injury crash frequency, and several target crash type combinations. Horizontal curves are known to be more crash-prone than tangent sections particularly with respect to roadway departure crashes. Rumble strips are an effective countermeasure to mitigate various types of roadway departure crashes. While existing studies on the safety effectiveness of rumble strips have primarily used before-after study designs or cross-sectional methods for crash modification factor (CMF) estimation, these methods often suffer from imbalanced datasets and larger standard errors, especially when the sample size is small. To address this, this study applies the propensity score potential outcome (PSPO) framework to estimate CMFs for centerline rumble strips, shoulder rumble strips, and their combined application on horizontal curves. In addition to contributing to the development of CMFs by crash severity, this study also examines the effects of rumble strips on collision types, highlighting their impact on vehicle maneuvering and collision characteristics. The analysis is conducted on horizontal curves on two-lane rural roads in Pennsylvania, utilizing crash data from 2017 to 2021. The PSPO method effectively reduces bias between sites with and without rumble strips, and the resulting statistical models align with engineering judgment. The findings indicate that centerline rumble strips reduce opposite direction sideswipe and head-on crashes but increase run off the road and hit fixed object crashes. Shoulder rumble strips, either alone or in combination with centerline rumble strips, decrease crash frequencies for most types except opposite direction sideswipe and head-on crashes. However, shoulder rumble strips alone are more effective at reducing crash frequencies on horizontal curves than when combined with centerline rumble strips. [ABSTRACT FROM AUTHOR]

• Proposes extension to crash frequency models that consider misclassification (MC) across crash type/severities. • Both Poisson and Negative Binomial (NB) regression model considered. • Proposed method extended to reformulated count model for NB regression to directly consider MC impact on overdispersion. • Ability of proposed models to estimate model parameters tested in simulation environment. • Proposed models shown to outperform traditional models using empirical data. Crash misclassification (MC) – e.g., a crash of one type or severity being mistakenly miscategorized as another – is a relatively common problem in transportation safety. Crash frequency models for individual crash categories estimated using datasets with MC errors could result in biased parameter estimates and thus lead to ineffective countermeasure planning. This study proposes a novel methodological formulation to directly account for this MC error and incorporates it into the two most common count data models used for crash frequency prediction: Poisson and Negative Binomial (NB) regression. The proposed framework introduces probabilistic MC rates among different crash types and modifies the likelihood function of the count models accordingly. The paper also demonstrates how this approach can be integrated into reformulated models that express each count model as a discrete choice model. The capability of the proposed models to estimate true parameters, given the existence of MC error, is examined via simulation analysis. Then, the proposed models are applied to empirical data to examine the presence of MC in crash data and further examine the robustness of the proposed models. Although the MC rates are found to be very low in the empirical data, the fit of proposed models are found to be better compared to the models that ignore MC error and thus likely provide more reliable parameter estimates. [ABSTRACT FROM AUTHOR]

Aspergillus oryzae can metabolize xylan to d-xylose and d-xylose to xylitol. However, accumulation of xylitol is controlled by dehydrogenases, such as xylitol dehydrogenase (XDH) and l-arabitol-4-dehydrogenase (LAD), and fluxed into the pentose phosphate pathway. In A. oryzae, XDH and LAD are encoded by xdhA and ladA, respectively. We disrupted the xdhA and ladA genes individually in an attempt to increase xylitol production. The xdhA- and ladA-disrupted mutants were constructed by homologous transformation into A. oryzae P5 (ΔpyrG), and pyrG was used as a selectable marker. The mutants were grown on different carbohydrate-containing media, colony diameters of mutants were measured, and gene disruption was confirmed by PCR. The xylitol productivity of the mutants was measured using d-xylose and oat spelt xylan as the sole sources of carbohydrates. The xdhA-disrupted mutant xdhA2-1 produced 16.6 g/L xylitol at a yield of 0.43 g/g d-xylose and productivity of 0.248 g/L. h from d-xylose, while 10.2 g/L xylitol was produced at a yield of 0.204 g/g xylan from oat spelt xylan. [Copyright &y& Elsevier]

• Examines the robustness of a joint NB-MFS model for micro-level crash analysis. • Proposes NB-MNL model for the same purpose. • Compares the prediction performance of the proposed model with traditional practices- collision-specific NB model and multivariate NB model. • NB-MNL model performs better than other models in most cases in terms of RMSE and MPB. • Prediction of collision type proportions from MNL model is better than that of MFS model. Individual collision types have different underlying causes and thus the relationships between roadway/traffic characteristics and crash frequency are likely to differ across unique collision types. One way these different influences have been studied is by developing separate statistical models for each collision type. While this is the most straightforward approach, developing collision-specific models can be very tedious and can produce unreliable estimates for collision types that are less frequently observed. Moreover, ignoring correlations between different collision types may result in biased and inefficient parameter estimation. To overcome these limitations, researchers have adopted a multivariate approach that explicitly accounts for the correlation among individual collision types. As an alternative to multivariate approach, two-stage approaches have been proposed in which one model is estimated to predict total crash frequency and its prediction is combined with another model, used to predict the proportions of different collision types. More efficient one-stage joint models, in which both the frequency and proportion models are estimated simultaneously and predictions are provided more directly, have also been proposed for macro-level analysis. This study investigates the performance of this joint model paradigm in analyzing unique collision type frequencies on individual road segments. For this, a joint negative binomial-multinomial fractional split (NB-MFS) model is used. Moreover, this study also proposes the use of a multinomial logit (MNL) model to estimate the proportion of different collision types. As total crash frequency NB model and MNL model utilize different datasets, a two-stage estimation process is required, which leads to the two-stage NB-MNL model proposed here. The performance of proposed model is compared with that of collision-specific NB models, multivariate negative binomial (MVNB) model, and NB-MFS model in predicting crash frequency by collision type on two-way two-lane urban-suburban collector roadway segments in Pennsylvania. The goodness of fit statistics show that the NB-MNL model performs better than collision-specific NB models, MVNB model and joint NB-MFS model and is thus a promising approach in predicting crash frequency by collision type. [ABSTRACT FROM AUTHOR]

• Non-healing wounds following surgery or trauma are suggestive of Nocardiosis. • Multiple discharging sinuses around a surgical wound indicates Nocardiosis. • Granulomatous lesions need exploration of Nocardiosis prior to anti-TB treatment. • Specimen collection after antimicrobial stoppage is vital. • Special attention during microscopy and culture is necessary. Nocardia can be introduced accidentally causing non-healing surgical wounds. From February 2017 to January 2021 samples from wounds were collected. Nocardia identification and susceptibility testing were carried out by standard procedure. Seventeen (35.4 %) Nocardia spp. and 20 other pathogens (41.7%) were recovered by culture. Drug susceptibility among Nocardia was >70% to amikacin, amoxicillin-clavulanic acid, levofloxacin, linezolid, and imipenem, 47% to cephalosporins and 41% to trimethoprim/sulfamethoxazole. Infections with Nocardia spp. should be considered in non-healing surgical wounds. [ABSTRACT FROM AUTHOR]

D614G genotype of SARS-CoV-2 virus is highly infectious and responsible for almost all infection for 2nd wave. However, there are currently no reports with D614G as vaccine candidate. Here we report the development of an mRNA-LNP vaccine with D614G variant and characterization in animal model. We have used special mRNA-architecture and formulation that provides suitable response of the product. The surface plasmon resonance (SPR) data with spike protein (S) revealed that immunization generated specific antibody pools against the whole extracellular domain (RBD and S2) of the spike protein. The anti-sera and purified IgGs from immunized mice neutralized SARS-CoV-2-pseudoviruses in ACE2-expressing HEK293 cells in a dose dependent manner. Importantly, single-dose immunization protected mice-lungs from homotypic-pseudovirus entry and cytopathy. The immunologic responses have been implicated by a balanced and stable population of CD4+ cells with a Th1 bias. The data suggested great promise for immediate translation of the technology to the clinic. [ABSTRACT FROM AUTHOR]

• Curbing the high LTBI burden in Asia is crucial for achieving WHO 'End TB' targets. • WHO LTBI guideline adoption in Asia is variable, based on regional needs/resources. • Main challenges are lack of awareness and scale-up of LTBI testing/treatment. • Diabetics and HCWs may be considered as risk groups for LTBI testing/treatment. • Improved awareness and access to tests/drugs may help optimize LTBI management. Asia has the highest burden of tuberculosis (TB) and latent TB infection (LTBI) in the world. Optimizing the diagnosis and treatment of LTBI is one of the key strategies for achieving the WHO 'End TB' targets. We report the discussions from the A sia L atent T ub ER culosis (ALTER) expert panel meeting held in 2018 in Singapore. In this meeting, a group of 13 TB experts from Bangladesh, Cambodia, Hong Kong, India, Indonesia, Malaysia, Myanmar, the Philippines, Singapore, Taiwan, Thailand and Vietnam convened to review the literature, discuss the barriers and propose strategies to improve the management of LTBI in Asia. Strategies for the optimization of risk group prioritization, diagnosis, treatment, and research of LTBI are reported. The perspectives presented herein, may help national programs and professional societies of the respective countries enhance the adoption of the WHO guidelines, scale-up the implementation of national guidelines based on the regional needs, and provide optimal guidance to clinicians for the programmatic management of LTBI. [ABSTRACT FROM AUTHOR]