A traffic-induced shift of ultrafine particle sources under COVID-19 soft lockdown in a subtropical urban area.

During the unprecedented COVID-19 city lockdown, a unique opportunity arose to dissect the intricate dynamics of urban air quality, focusing on ultrafine particles (UFPs) and volatile organic compounds (VOCs). This study delves into the nuanced interplay between traffic patterns and UFP emissions in a subtropical urban setting during the spring-summer transition of 2021. Leveraging meticulous roadside measurements near a traffic nexus, our investigation unravels the intricate relationship between particle number size distribution (PNSD), VOCs mixing ratios, and detailed vehicle activity metrics. The soft lockdown era, marked by a 20-27% dip in overall traffic yet a surprising surge in early morning motorcycle activity, presented a natural experiment. We observed a consequential shift in the urban aerosol regime: the decrease in primary emissions from traffic substantially amplified the role of aged particles and secondary aerosols. This shift was particularly pronounced under stagnant atmospheric conditions, where reduced dilution exacerbated the influence of alternative emission sources, notably solvent evaporation, and was further accentuated with the resumption of normal traffic flows. A distinct seasonal trend emerged as warmer months approached, with aromatic VOCs such as toluene, ethylbenzene, and xylene not only increasing but also significantly contributing to more frequent particle growth events. These findings spotlight the criticality of targeted strategies at traffic hotspots, especially during periods susceptible to weak atmospheric dilution, to curb UFP and precursor emissions effectively. As we stand at the cusp of widespread vehicle electrification, this study underscores the imperative of a holistic approach to urban air quality management, embracing the complexities of primary emission reductions and the resultant shifts in atmospheric chemistry.

Source-oriented risk and lung-deposited surface area (LDSA) of ultrafine particles in a Southeast Asia urban area.

Submicron and ultrafine particle (UFP) exposure may be epidemiologically and toxicologically linked to pulmonary, neurodegenerative, and cardiovascular diseases. This study explores UFP and fine particle sources using a positive matrix factorization (PMF) model based on PM2.5 chemical compositions and particle number size distributions (PNSDs). The particle chemical composition and size distribution contributions are simultaneously identified to evaluate lung deposition and excess cancer risks. High correlations between the PNSD and chemical composition apportionment results were observed. Fresh and aged traffic particles dominated the number concentrations, while heterogeneous, photochemical reactions and/or regional transport may have resulted in secondary aerosol formation. Fresh and aged road traffic particle sources mostly contributed to the lung deposition dosage in the pulmonary region (~53 %), followed by the tracheobronchial (~30.4 %) and head regions (~16.6 %). However, lung-deposited surface area (LDSA) concentrations were dominated by aged road traffic (~39.2 %) and secondary aerosol (~33.2 %) sources. The excess cancer risks caused by Cr6+, Ni, and As were also mainly contributed to by aged road traffic (~31.7 %) and secondary aerosols (~67 %). The source apportionments based on the physical and chemical properties of aerosol particles are complementary, offering a health impact benchmark of UFPs in a Southeast Asia urban city.

Machine Learning-Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation.

BACKGROUND: Early recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies. OBJECTIVE: In this study, we aimed to build a machine learning-based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident. METHODS: Audio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency-inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: Although the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments. CONCLUSIONS: A machine learning-based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

Temporal stability of associations between crash characteristics: A multiple correspondence analysis.

Understanding the associations between crash characteristics facilitates the development of traffic safety policies for improving traffic safety. This study investigates the temporal stability of associations between crash characteristics at different temporal levels using multiple correspondence analysis (MCA). For each date in 2020, crash data from the previous week, month, season, half year, one year, two years, three years, and four years are collected respectively as eight temporal levels. MCA plots and chi-square distance analysis are used to assess the temporal stability of associations between crash characteristics across dates in 2020 with data from various temporal levels. The key findings of this study demonstrate that associations between crash characteristics at lower temporal levels show notable and potential cyclical variations across dates, while more stable and long-term trend of associations between crash characteristics may be identified as the temporal level increases, especially at the two-year level and higher temporal levels at which temporal stability may be expected. The study contributes to the literature by presenting a challenge for traffic analysts in that both temporally stable and unstable associations between crash characteristics may be observed at any point in time when different temporal levels are considered as study periods. Therefore, it may serve as a foundation for future research and practical works to identify traffic safety issues and optimal policies as well as facilitate the interpretation of statistical modeling in the presence of temporally unstable data.

Chemically and temporally resolved oxidative potential of urban fine particulate matter.

Vehicle emissions are an important source of particulate matter (PM) in urban areas and have well-known adverse health effects on human health. Oxidative potential (OP) is used as a quantification metric for indexing PM toxicity. In this study, by using a liquid spot sampler (LSS) and the dithiothreitol (DTT) assay, the diurnal OP variation was assessed at a ground-level urban monitoring station. Besides, since the monitoring station was adjacent to the main road, the correlation between OP and traffic volume was also evaluated. PM components, including metals, water-soluble inorganic aerosols (WSIAs), black carbon (BC), and polycyclic aromatic hydrocarbons (PAHs), were also simultaneously monitored. The daytime and evening mean ±â€¯std volume-normalized OP (OPv) were 0.46 ±â€¯0.27 and 0.48 ±â€¯0.26 nmol/min/m3, and exhibited good correlations with PM1.0 and BC; however, these concentrations were only weakly correlated with mass-normalized OP (OPm). The mean ±â€¯std OPm was higher in the daytime (41.3 ±â€¯13.8 pmol/min/µg) than in the evening (36.1 ±â€¯11.5 pmol/min/µg). According to the PMF analysis, traffic emissions dominated the diurnal OP contribution. Organic matter and individual metals associated with non-exhaust traffic emissions, such as Mn, Fe, and Cu, contributed substantially to OP. Diurnal variations of PAH concentrations suggest that photochemical reactions could enhance OP, highlighting the importance of atmospheric aging on PM toxicity.

Early recognition of a caller's emotion in out-of-hospital cardiac arrest dispatching: An artificial intelligence approach.

AIM: This study aimed to develop an AI model for detecting a caller's emotional state during out-of-hospital cardiac arrest calls by processing audio recordings of dispatch communications. METHODS: Audio recordings of 337 out-of-hospital cardiac arrest calls from March-April 2011 were retrieved. The callers' emotional state was classified based on the emotional content and cooperative scores. Mel-frequency cepstral coefficients extracted essential information from the voice signals. A support vector machine was utilised for the automatic judgement, and repeated random sub-sampling cross validation (RRS-CV) was applied to evaluate robustness. The results from the artificial intelligence classifier were compared with the consensus of expert reviewers. RESULTS: The audio recordings were classified into five emotional content and cooperative score levels. The proposed model had an average positive predictive value of 72.97%, a negative predictive value of 93.47%, sensitivity of 38.76%, and specificity of 98.29%. If only the first 10 seconds of the recordings were considered, it had an average positive predictive value of 84.62%, a negative predictive value of 93.57%, sensitivity of 52.38%, and specificity of 98.64%. The artificial intelligence model's performance maintained preferable results for emotionally stable cases. CONCLUSION: Artificial intelligence models can possibly facilitate the judgement of callers' emotional states during dispatch conversations. This model has the potential to be utilised in practice, by pre-screening emotionally stable callers, thus allowing dispatchers to focus on cases that are judged to be emotionally unstable. Further research and validation are required to improve the model's performance and make it suitable for the general population.

Stepwise Promoter Melting by Bacterial RNA Polymerase.

Transcription initiation requires formation of the open promoter complex (RPo). To generate RPo, RNA polymerase (RNAP) unwinds the DNA duplex to form the transcription bubble and loads the DNA into the RNAP active site. RPo formation is a multi-step process with transient intermediates of unknown structure. We use single-particle cryoelectron microscopy to visualize seven intermediates containing Escherichia coli RNAP with the transcription factor TraR en route to forming RPo. The structures span the RPo formation pathway from initial recognition of the duplex promoter in a closed complex to the final RPo. The structures and supporting biochemical data define RNAP and promoter DNA conformational changes that delineate steps on the pathway, including previously undetected transient promoter-RNAP interactions that contribute to populating the intermediates but do not occur in RPo. Our work provides a structural basis for understanding RPo formation and its regulation, a major checkpoint in gene expression throughout evolution.

E. coli TraR allosterically regulates transcription initiation by altering RNA polymerase conformation.

TraR and its homolog DksA are bacterial proteins that regulate transcription initiation by binding directly to RNA polymerase (RNAP) rather than to promoter DNA. Effects of TraR mimic the combined effects of DksA and its cofactor ppGpp, but the structural basis for regulation by these factors remains unclear. Here, we use cryo-electron microscopy to determine structures of Escherichia coli RNAP, with or without TraR, and of an RNAP-promoter complex. TraR binding induced RNAP conformational changes not seen in previous crystallographic analyses, and a quantitative analysis revealed TraR-induced changes in RNAP conformational heterogeneity. These changes involve mobile regions of RNAP affecting promoter DNA interactions, including the ßlobe, the clamp, the bridge helix, and several lineage-specific insertions. Using mutational approaches, we show that these structural changes, as well as effects on σ70 region 1.1, are critical for transcription activation or inhibition, depending on the kinetic features of regulated promoters.

Transcriptional Responses to ppGpp and DksA.

The stringent response to nutrient deprivation is a stress response found throughout the bacterial domain of life. Although first described in proteobacteria for matching ribosome synthesis to the cell's translation status and for preventing formation of defective ribosomal particles, the response is actually much broader, regulating many hundreds of genes-some positively, some negatively. Utilization of the signaling molecules ppGpp and pppGpp for this purpose is ubiquitous in bacterial evolution, although the mechanisms employed vary. In proteobacteria, the signaling molecules typically bind to two sites on RNA polymerase, one at the interface of the ß' and ω subunits and one at the interface of the ß' secondary channel and the transcription factor DksA. The ß' secondary channel is targeted by other transcription regulators as well. Although studies on the transcriptional outputs of the stringent response date back at least 50 years, the mechanisms responsible are only now coming into focus.

Modulation of extracytoplasmic function (ECF) sigma factor promoter selectivity by spacer region sequence.

The ability of bacteria to adapt to stress depends on the conditional expression of specific sets of genes. Bacillus subtilis encodes seven extracytoplasmic function (ECF) sigma (σ) factors that regulate functions important for survival under conditions eliciting cell envelope stress. Of these, four have been studied in detail: σM, σW, σX and σV. These four σ factors recognize overlapping sets of promoters, although the sequences that determine this overlapping recognition are incompletely understood. A major role in promoter selectivity has been ascribed to the core -10 and -35 promoter elements. Here, we demonstrate that a homopolymeric T-tract motif, proximal to the -35 element, functions in combination with the core promoter sequences to determine selectivity for ECF sigma factors. This motif is most critical for promoter activation by σV, and contributes variably to activation by σM, σX and σW. We propose that this motif, which is a feature of the deduced promoter consensus for a subset of ECF σ factors from many species, imparts intrinsic DNA curvature to influence promoter activity. The differential effect of this region among ECF σ factors thereby provides a mechanism to modulate the nature and extent of regulon overlap.

The Effect of Successful Intubation on Patient Outcomes After Out-of-Hospital Cardiac Arrest in Taipei.

STUDY OBJECTIVE: The effect of out-of-hospital intubation in patients with out-of-hospital cardiac arrest remains controversial. The Taipei City paramedics are the earliest authorized to perform out-of-hospital intubation among Asian areas. This study evaluates the association between successful intubation and out-of-hospital cardiac arrest survival in Taipei. METHODS: We analyzed 6 years of Utstein-based registry data from nontrauma adult patients with out-of-hospital cardiac arrest who underwent out-of-hospital airway management including intubation, laryngeal mask airway, or bag-valve-mask ventilation. The primary analysis was intubation success on patient outcomes. The primary outcome was survival to discharge and the secondary outcomes included sustained return of spontaneous circulation and favorable neurologic survival. Sensitivity analysis was performed with intubation attempts rather than intubation success. Subgroup analysis of advanced life support-serviced districts was also performed. RESULTS: A total of 10,853 cases from 2008 to 2013 were analyzed. Among out-of-hospital cardiac arrest patients receiving airway management, successful intubation, laryngeal mask airway, and bag-valve-mask ventilation was reported in 1,541, 3,099, and 6,213 cases, respectively. Compared with bag-valve-mask device use, successful out-of-hospital intubation was associated with improved chances of sustained return of spontaneous circulation (adjusted odds ratio [aOR] 1.91; 95% confidence interval [CI] 1.66 to 2.19), survival to discharge (aOR 1.98; 95% CI 1.57 to 2.49), and favorable neurologic outcome (aOR 1.44; 95% CI 1.03 to 2.03). The results were comparable in sensitivity and subgroup analyses. CONCLUSION: In nontrauma adult out-of-hospital cardiac arrest in Taipei, successful out-of-hospital intubation was associated with improved odds of sustained return of spontaneous circulation, survival to discharge, and favorable neurologic outcome.

TraR directly regulates transcription initiation by mimicking the combined effects of the global regulators DksA and ppGpp.

The Escherichia coli F element-encoded protein TraR is a distant homolog of the chromosome-encoded transcription factor DksA. Here we address the mechanism by which TraR acts as a global regulator, inhibiting some promoters and activating others. We show that TraR regulates transcription directly in vitro by binding to the secondary channel of RNA polymerase (RNAP) using interactions similar, but not identical, to those of DksA. Even though it binds to RNAP with only slightly higher affinity than DksA and is only half the size of DksA, TraR by itself inhibits transcription as strongly as DksA and ppGpp combined and much more than DksA alone. Furthermore, unlike DksA, TraR activates transcription even in the absence of ppGpp. TraR lacks the residues that interact with ppGpp in DksA, and TraR binding to RNAP uses the residues in the ß' rim helices that contribute to the ppGpp binding site in the DksA-ppGpp-RNAP complex. Thus, unlike DksA, TraR does not bind ppGpp. We propose a model in which TraR mimics the effects of DksA and ppGpp together by binding directly to the region of the RNAP secondary channel that otherwise binds ppGpp, and its N-terminal region, like the coiled-coil tip of DksA, engages the active-site region of the enzyme and affects transcription allosterically. These data provide insights into the function not only of TraR but also of an evolutionarily widespread and diverse family of TraR-like proteins encoded by bacteria, as well as bacteriophages and other extrachromosomal elements.

ppGpp Binding to a Site at the RNAP-DksA Interface Accounts for Its Dramatic Effects on Transcription Initiation during the Stringent Response.

Throughout the bacterial domain, the alarmone ppGpp dramatically reprograms transcription following nutrient limitation. This "stringent response" is critical for survival and antibiotic tolerance and is a model for transcriptional regulation by small ligands. We report that ppGpp binds to two distinct sites 60 Å apart on E. coli RNA polymerase (RNAP), one characterized previously (site 1) and a second identified here at an interface of RNAP and the transcription factor DksA (site 2). The location and unusual tripartite nature of site 2 account for the DksA-ppGpp synergism and suggest mechanisms for ppGpp enhancement of DksA's effects on RNAP. Site 2 binding results in the majority of ppGpp's effects on transcription initiation in vitro and in vivo, and strains lacking site 2 are severely impaired for growth following nutritional shifts. Filling of the two sites at different ppGpp concentrations would expand the dynamic range of cellular responses to changes in ppGpp levels.

Demand Forecast Using Data Analytics for the Preallocation of Ambulances.

The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introduced in this study to manage and visualize the spatial distribution of demand data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user-desired sizes for the spatial grid and discretized temporal steps. We applied moving average, artificial neural network, sinusoidal regression, and support vector regression for the forecasting of prehospital emergency medical demand. The results from these approaches, as a reference, could be used for the preallocation of ambulances. A case study is conducted for the EMS in New Taipei City, where prehospital EMS data have been collected for three years. The model selection process has chosen different models with different input features for the forecast of different areas. The best daily mean absolute percentage error during testing of the EMS demand forecast is 23.01%, which is a reasonable forecast based on Lewis' definition. With the acceptable prediction performance, the proposed approach has its potential to be applied to the current practice.

Crosslink Mapping at Amino Acid-Base Resolution Reveals the Path of Scrunched DNA in Initial Transcribing Complexes.

RNA polymerase binds tightly to DNA to recognize promoters with high specificity but then releases these contacts during the initial stage of transcription. We report a site-specific crosslinking approach to map the DNA path in bacterial transcription intermediates at amino acid and nucleotide resolution. After validating the approach by showing that the DNA path in open complexes (RPO) is the same as in high-resolution X-ray structures, we define the path following substrate addition in "scrunched" complexes (RPITC). The DNA bulges that form within the transcription bubble in RPITC are positioned differently on the two strands. Our data suggest that the non-template strand bulge is extruded into solvent in complexes containing a 5-mer RNA, whereas the template strand bulge remains within the template strand tunnel, exerting stress on interactions between the ß flap, ß' clamp, and σ3.2. We propose that this stress contributes to σ3.2 displacement from the RNA exit channel, facilitating promoter escape.

Curation accuracy of model organism databases.

Manual extraction of information from the biomedical literature-or biocuration-is the central methodology used to construct many biological databases. For example, the UniProt protein database, the EcoCyc Escherichia coli database and the Candida Genome Database (CGD) are all based on biocuration. Biological databases are used extensively by life science researchers, as online encyclopedias, as aids in the interpretation of new experimental data and as golden standards for the development of new bioinformatics algorithms. Although manual curation has been assumed to be highly accurate, we are aware of only one previous study of biocuration accuracy. We assessed the accuracy of EcoCyc and CGD by manually selecting curated assertions within randomly chosen EcoCyc and CGD gene pages and by then validating that the data found in the referenced publications supported those assertions. A database assertion is considered to be in error if that assertion could not be found in the publication cited for that assertion. We identified 10 errors in the 633 facts that we validated across the two databases, for an overall error rate of 1.58%, and individual error rates of 1.82% for CGD and 1.40% for EcoCyc. These data suggest that manual curation of the experimental literature by Ph.D-level scientists is highly accurate. Database URL: http://ecocyc.org/, http://www.candidagenome.org//

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PURPOSE: Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. METHODS: Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. RESULTS: The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). CONCLUSION: A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Failure of mesenteric defect closure after Roux-en-Y gastric bypass.

BACKGROUND AND OBJECTIVES: Bowel obstructions following Roux-en-Y gastric bypass (RYGB) are a significant issue often caused by internal herniation. Controversy continues as to whether mesenteric defect closure is necessary to decrease the incidence of internal hernias after RYGB. Our purpose was to evaluate the effectiveness of closing the mesenteric defect at the jejunojejunostomy in patients who underwent RYGB by examining this potential space at reoperation for any reason. METHODS: We retrospectively reviewed medical records of patients undergoing surgery after RYGB from August 1999 to October 2008 to determine the status of the mesentery at the jejunojejunostomy. RESULTS: Eighteen patients underwent surgery 2 to 19 months after open (n=8) or laparoscopic (n=10) RYGB. All patients had documented suture closure of their jejunojejunostomy at the time of RYGB. Permanent (n=12) or absorbable (n=6) sutures were used for closures. Patients lost 23.6 kg to 62.1 kg before a reoperation was required for a ventral hernia (n=8), cholecystectomy (n=4), abdominal pain (n=4), or small bowel obstruction (n=2). Fifteen of the 18 patients had open mesenteric defects at the jejunojejunostomy despite previous closure; none were the cause for reoperation. CONCLUSION: Routine suture closure of mesenteric defects after RYGB may not be an effective permanent closure likely due to the extensive fat loss and weight loss within the mesentery.

Multi-level ground glass nodule detection and segmentation in CT lung images.

Early detection of Ground Glass Nodule (GGN) in lung Computed Tomography (CT) images is important for lung cancer prognosis. Due to its indistinct boundaries, manual detection and segmentation of GGN is labor-intensive and problematic. In this paper, we propose a novel multi-level learning-based framework for automatic detection and segmentation of GGN in lung CT images. Our main contributions are: firstly, a multi-level statistical learning-based approach that seamlessly integrates segmentation and detection to improve the overall accuracy for GGN detection (in a subvolume). The classification is done at two levels, both voxel-level and object-level. The algorithm starts with a three-phase voxel-level classification step, using volumetric features computed per voxel to generate a GGN class-conditional probability map. GGN candidates are then extracted from this probability map by integrating prior knowledge of shape and location, and the GGN object-level classifier is used to determine the occurrence of the GGN. Secondly, an extensive set of volumetric features are used to capture the GGN appearance. Finally, to our best knowledge, the GGN dataset used for experiments is an order of magnitude larger than previous work. The effectiveness of our method is demonstrated on a dataset of 1100 subvolumes (100 containing GGNs) extracted from about 200 subjects.