Models for predicting vehicle emissions: A comprehensive review.

Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %-45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is an emerging approach for capturing spatial-temporal relationships based on remote sensing data. Moreover, limited data-driven studies have been performed to forecast particle matter emissions, the main contributors to urban pollution, calling for more attention for future research.

Quantifying uncertainty: Air quality forecasting based on dynamic spatial-temporal denoising diffusion probabilistic model.

Air pollution constitutes a substantial peril to human health, thereby catalyzing the evolution of an array of air quality prediction models. These models span from mechanistic and statistical strategies to machine learning methodologies. The burgeoning field of deep learning has given rise to a plethora of advanced models, which have demonstrated commendable performance. However, previous investigations have overlooked the salience of quantifying prediction uncertainties and potential future interconnections among air monitoring stations. Moreover, prior research typically utilized static predetermined spatial relationships, neglecting dynamic dependencies. To address these limitations, we propose a model named Dynamic Spatial-Temporal Denoising Diffusion Probabilistic Model (DST-DDPM) for air quality prediction. Our model is underpinned by the renowned denoising diffusion model, aiding us in discerning indeterminacy. In order to encapsulate dynamic patterns, we design a dynamic context encoder to generate dynamic adjacency matrices, whilst maintaining static spatial information. Furthermore, we incorporate a spatial-temporal denoising model to concurrently learn both spatial and temporal dependencies. Authenticating our model's performance using a real-world dataset collected in Beijing, the outcomes indicate that our model eclipses other baseline models in terms of both short-term and long-term predictions by 1.36% and 11.62% respectively. Finally, we conduct a case study to exhibit our model's capacity to quantify uncertainties.

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling.

Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes.

Protein phosphatase 2A activators reverse age-related behavioral changes by targeting neural cell senescence.

The contribution of cellular senescence to the behavioral changes observed in the elderly remains elusive. Here, we observed that aging is associated with a decline in protein phosphatase 2A (PP2A) activity in the brains of zebrafish and mice. Moreover, drugs activating PP2A reversed age-related behavioral changes. We developed a transgenic zebrafish model to decrease PP2A activity in the brain through knockout of the ppp2r2c gene encoding a regulatory subunit of PP2A. Mutant fish exhibited the behavioral phenotype observed in old animals and premature accumulation of neural cells positive for markers of cellular senescence, including senescence-associated ß-galactosidase, elevated levels cdkn2a/b, cdkn1a, senescence-associated secretory phenotype gene expression, and an increased level of DNA damage signaling. The behavioral and cell senescence phenotypes were reversed in mutant fish through treatment with the senolytic ABT263 or diverse PP2A activators as well as through cdkn1a or tp53 gene ablation. Senomorphic function of PP2A activators was demonstrated in mouse primary neural cells with downregulated Ppp2r2c. We conclude that PP2A reduction leads to neural cell senescence thereby contributing to age-related behavioral changes and that PP2A activators have senotherapeutic properties against deleterious behavioral effects of brain aging.

Higher cystatin C level increases the risk of delayed cerebral ischemia after endovascular treatment of aneurysmal subarachnoid hemorrhage: a case-control study.

Cystatin C (CysC) has been found to be associated with hemorrhagic and ischemic stroke in many studies. However, the association between CysC level and the risk of delayed cerebral ischemia after endovascular treatment of aneurysmal subarachnoid hemorrhage has been reported rarely. Our study was proposed to explore this association. Consecutive patients from June 2015 to February 2021 in this single-center retrospective study were selected. Univariate and multivariate analyses were used to identify potential prognostic risk factors for delayed cerebral ischemia, and the stability of the association was demonstrated by several statistical methods, such as subgroup analysis, interaction testing, generalized linear models, and propensity score matching. A total of 424 patients were included in the analysis. Cystatin C was independently associated with delayed cerebral ischemia. The independent effects of CysC on delayed cerebral ischemia were shown in generalized linear models with a logit link, and the results were relatively stable in crude, partial, and full models with ORs (95% CIs) for delayed cerebral ischemia. Subgroup analysis showed no significant subgroup differences in the effect of CysC on delayed cerebral ischemia. There was also no interaction effect between CysC and other confounders. Patients in the high CysC group had a higher risk of delayed cerebral ischemia than those in the low CysC group before and after propensity score matching. CysC level could be an independent predictor for the risk of delayed cerebral ischemia after endovascular treatment of aneurysmal subarachnoid hemorrhage.

Loss of atm in Zebrafish as a Model of Ataxia-Telangiectasia Syndrome.

Ataxia-telangiectasia mutated (ATM) is a key DNA damage signaling kinase that is mutated in humans with ataxia-telangiectasia (A-T) syndrome. This syndrome is characterized by neurodegeneration, immune abnormality, cancer predisposition, and premature aging. To better understand the function of ATM in vivo, we engineered a viable zebrafish model with a mutated atm gene. Zebrafish atm loss-of-function mutants show characteristic features of A-T-like motor disturbance, including coordination disorders, immunodeficiency, and tumorigenesis. The immunological disorder of atm homozygote fish is linked to the developmental blockade of hematopoiesis, which occurs at the adulthood stage and results in a decrease in infection defense but, with little effect on wound healing. Malignant neoplasms found in atm mutant fish were mainly nerve sheath tumors and myeloid leukemia, which rarely occur in A-T patients or Atm-/- mice. These results underscore the importance of atm during immune cell development. This zebrafish A-T model opens up a pathway to an improved understanding of the molecular basis of tumorigenesis in A-T and the cellular role of atm.

Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.

BACKGROUND: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. METHODS: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. FINDINGS: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886-0·936) in the internal dataset, 0·870 (95% CI 0·822-0·918) in external validation dataset 1, and 0·831 (95% CI 0·793-0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0-90·2] vs 78·3% [72·1-84·5], p<0·0001; 82·7% [78·5-86·9] vs 70·4% [59·1-81·7], p<0·0001). The average accuracy of DCNN-assisted evaluations for six radiologists reached 0·876 and were significantly augmented when they were DCNN-assisted (p<0·05). INTERPRETATION: The performance of DCNN-enabled ultrasound exceeded the average diagnostic level of radiologists matched the level of expert ultrasound image readers, and augmented radiologists' accuracy. However, these observations warrant further investigations in prospective studies or randomised clinical trials. FUNDING: National Key Basic Research Program of China, National Sci-Tech Support Projects, and National Natural Science Foundation of China.

The knockdown efficiency of telomere associated genes with specific methodology in a zebrafish cell line.

Zebrafish is broadly used as a model organism in gene loss-of-function studies in vivo, but its employment in vitro is greatly limited by the lack of efficient gene knockdown approaches in zebrafish cell lines such as ZF4. In this article, we attempted to induce silencing of telomere associated genes in ZF4 by applying the frequently-used siRNA transfection technology and a novel moiety-linked morpholino (vivo-MO). By proceeding with integrated optimization of siRNAs transfection and vivo-MOs treatment, we compared five transfection reagents and vivo-MOs simultaneously to evaluate the efficiency of terfa silencing in ZF4. 48 h after siRNAs transfection, Lipofectamine™ 3000 and X-tremeGENE™ HP leaded to knockdown in 35% and 43% of terfa transcription, respectively, while vivo-MO-terfa modulated 58% down-expression of zfTRF2 in contrast to vivo-MO-ctrl 72 h after treatment. Further siRNAs transfection targeting telomere associated genes by X-tremeGENE™ HP showed silencing in 40-68% of these genes without significant cytotoxicity and off-target effect. Our results confirmed the feasibility of gene loss-of-function studies in a zebrafish cell line, offered a systematic optimizing strategy to employ gene silencing experiments, and presented Lipofectamine™ 3000, X-tremeGENE™ HP and vivo-morpholinos as candidate gene silencing approaches for zebrafish in vitro gene loss-of-function studies. Successfully knockdown of shelterin genes further opened a new field for telomeric study in zebrafish.

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning.

Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate environmental pollution resulting from human activities. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high non-linearity and variation. This study used a novel technique, multi-agent deep reinforcement learning (MADRL), to simultaneously optimize dissolved oxygen (DO) and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA-driven strategy since it sacrifices environmental benefits but has lower cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA-SW scenario decreases 10 kg PO4-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the authors discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions. In a nutshell, there are still limitations of this work and future studies are required.

Relationship of Serum Uric Acid to Hematoma Volume and Prognosis in Patients with Acute Supratentorial Intracerebral Hemorrhage.

BACKGROUND: Oxidative stress and inflammation play important roles in the neuronal injury caused by intracerebral hemorrhage (ICH). Uric acid (UA), an important natural antioxidant, might reduce the neuronal injury caused by ICH. Delineating the relationship between UA and ICH will enhance our understanding of antioxidative mechanisms in recovery from ICH. METHODS: We conducted a retrospective study of 325 patients with acute supratentorial ICH to investigate the relationship between serum UA levels and hematoma volumes and prognosis. A hematoma volume of ≥30 mL was defined as a large hematoma. An unfavorable outcome was defined as a modified Rankin scale score of 4-6 on day 30. RESULTS: The serum UA level was significantly lower in the patients with a large hematoma volume (median, 306 µmol/L; 25th to 75th percentile, 243-411 µmol/L) than in those with a small hematoma volume (median, 357 µmol/L; 25th to 75th percentile, 271-442 µmol/L; P = 0.012). Similarly, the unfavorable outcome group had had lower serum UA levels (median, 309 vs. 363 µmol/L; P = 0.009) compared with the favorable outcome group. The results of the multivariate logistic analysis indicated that a lower serum UA level was associated with a larger hematoma volume (odds ratio, 0.996; P = 0.006) and an unfavorable outcome (odds ratio, 0.997; P = 0.030). CONCLUSIONS: The results from the present study have indicated that in patients with acute supratentorial ICH, a low serum UA level might indicate that the patient has a large hematoma volume and might be a risk factor for a poor day 30 functional prognosis.

Are they necessary? Preventive therapies for post-stroke depression: A meta-analysis of RCTs.

To explore the necessity and feasibility of early anti-depressive therapies in acute stroke patients, we conducted a meta-analysis of currently available randomized control studies (RCTs). Literature search in six databases was done with keywords of cerebrovascular accident, depression and prevention. Only RCTs that met the inclusion criteria were enrolled for further analysis. Twelve eligible studies were included in this meta-analysis. Prophylactic anti-depressive therapies following acute stroke were shown to reduce the incidence of depression in the patients (RR = =0.33, 95% CI: 0.25 to 0.43, p < 0.001), improve symptoms of depression (WMD: 5.73, 95% CI: 4.18 to 7.29, p < 0.001), improve motor function (WMD: 12.56, 95%CI: 9.07 to 16.04, p < 0.001) and neurological function (WMD: 1.13, 95%CI: 0.57 to 1.69, p < 0.001). However, anti-depressive therapies showed no effects on mortality (RR = 1.63, 95%CI: 0.55 to 4.85, p = 0.377) and adverse events incidence (RR = 0.93, 95%CI: 0.53 to 1.64, p = 0.806). Anti-depressive therapies following acute stroke is effective thus deserves to be advocated.