Four-year overall survival update from the phase III HIMALAYA study of tremelimumab plus durvalumab in unresectable hepatocellular carcinoma.

BACKGROUND: In the phase III HIMALAYA study (NCT03298451) in unresectable hepatocellular carcinoma (uHCC), STRIDE (Single Tremelimumab Regular Interval Durvalumab) significantly improved overall survival (OS) versus sorafenib; durvalumab monotherapy was noninferior to sorafenib for OS. Results reported herein are from a 4-year updated OS analysis of HIMALAYA. PATIENTS AND METHODS: Participants with uHCC and no previous systemic treatment were randomized to STRIDE (n = 393), durvalumab (n = 389), or sorafenib (n = 389). The updated data cut-off was 23 January 2023. OS and serious adverse events (AEs) were assessed. Additionally, baseline characteristics and subsequent therapies were analyzed in long-term survivors (≥36 months beyond randomization). RESULTS: For STRIDE, durvalumab, and sorafenib, median [95% confidence interval (CI)] follow-up was 49.12 months (46.95-50.17 months), 48.46 months (46.82-49.81 months), and 47.31 months (45.08-49.15 months), respectively. OS hazard ratio (95% CI) for STRIDE versus sorafenib was 0.78 (0.67-0.92). The 36-month OS rate for STRIDE was 30.7% versus 19.8% for sorafenib. The 48-month OS rate remained higher for STRIDE at 25.2%, versus 15.1% for sorafenib. The long-term OS benefit of STRIDE was observed across clinically relevant subgroups and was further improved in participants who achieved disease control. Long-term survivors with STRIDE (n = 103) included participants across clinically relevant subgroups, and 57.3% (59/103) had no reported subsequent anticancer therapy. No new serious treatment-related AEs occurred with STRIDE from the primary analysis (17.5%; 68/388). Durvalumab maintained OS noninferiority to sorafenib and no late-onset safety signals were identified. CONCLUSIONS: These data represent the longest follow-up to date in phase III studies in uHCC. The unprecedented 3- and 4-year OS rates reinforce the sustained long-term OS benefit of STRIDE versus sorafenib. STRIDE maintained a tolerable yet differentiated safety profile from other current uHCC therapies. Results continue to support the long-term benefits of STRIDE in a diverse population, reflective of uHCC globally.

Ab initio kinetics of the CH3NH + NO2 reaction: formation of nitramines and N-alkyl nitroxides.

This study provides a detailed understanding of how the reaction between CH3NH, one of the primary products of the CH3NH2 + OH/Cl reactions, and NOx occurs in the atmosphere since the reaction is expected to be a dominant sink for the tropospheric CH3NH radical. First, we focus on the reaction of the aminyl radical CH3NH with NO2, complementing the known reaction between CH3NH and NO, to provide the overall picture of the CH3NH + NOx system. The reaction was meticulously examined across the extended range of temperature (298-2000 K) and pressure (0.76-76 000 torr) using quantum chemistry calculations and kinetic modeling based on the framework of the Rice-Ramsperger-Kassel-Marcus (RRKM)-based master equation. Highly correlated electronic structure calculations unveil that the intricate reaction mechanism of the CH3NH + NO2 reaction, which can proceed through O-addition or N-addition to form NO2, encompasses numerous steps, channels, and various intermediates and products. The temperature-/pressure-dependent kinetic behaviors and product distribution of the CH3NH + NO2 reaction are revealed under atmospheric and combustion conditions. The main products under atmospheric conditions are found to be CH3NHO and NO, as well as CH3NHNO2, while under combustion conditions, the primary products are only CH3NHO and NO. Given its stability under ambient conditions, CH3NHNO2, a nitramine, is believed to have the potential to induce DNA damage, which can ultimately result in severe cancers. Secondly, by building upon prior research on the CH3NH + NO system, this study shows that the reaction of CH3NH with NOx holds greater importance in urban areas with elevated NOx emissions than other oxidants like O2. Furthermore, this reaction occurs swiftly and results in the creation of various compounds, such as the carcinogenic nitrosamine (CH3NHNO), carcinogenic nitramine (CH3NHNO2), CH3NNOH, (CH3NN + H2O) and (CH3NHO + NO).

Mechanistic and Kinetic Insights into OH-Initiated Atmospheric Oxidation of Hymexazol: A Computational Study.

Hymexazol is a volatile fungicide widely used in agriculture, causing its abundance in the atmosphere; thus, its atmospheric fate and conversion are of great importance when assessing its environmental impacts. Herein, we report a theoretical kinetic mechanism for the oxidation of hymexazol by OH radicals, as well as the subsequent reactions of its main products with O2 and then with NO by using the Rice-Ramsperger-Kassel-Marcus-based Master equation kinetic model on the potential energy surface explored at the ROCBS-QB3//M06-2X/aug-cc-pVTZ level. The predicted total rate constants ktotal(T, P) for the reaction between hymexazol and OH radicals show excellent agreement with scarcely available experimental values (e.g., 3.6 × 10-12 vs (4.4 ± 0.8) × 10-12 cm3/molecule/s at T = 300 K and P = 760 Torr); thus, the calculated kinetic parameters can be confidently used for modeling/simulation of N-heterocycle-related applications under atmospheric and even combustion conditions. The model shows that 3,4-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-5-yl (IM2), 3,5-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-4-yl (IM3), and (3-hydroxy-1,2-oxazol-5-yl)methyl (P8) are the main primary intermediates, which form the main secondary species of (3,4-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-5-yl)dioxidanyl (IM4), (3,5-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-4-yl)dioxidanyl (IM7), and ([(3-hydroxy-1,2-oxazol-5-yl)methyl]dioxidanyl (IM11), respectively, through the reactions with O2. The main secondary species then can react with NO to form the main tertiary species, namely, (3,4-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-5-yl)oxidanyl (P19), (3,5-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-4-yl)oxidanyl (P21), and [(3-hydroxy-1,2-oxazol-5-yl)methyl]oxidanyl (P23), respectively, together with NO2. Besides, hymexazol could be a persistent organic pollutant in the troposphere due to its calculated half-life τ1/2 of 13.7-68.1 h, depending on the altitude.

AI vs. AI: Can AI Detect AI-Generated Images?

The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework for reliably detecting AI-generated images from real ones through Convolutional Neural Networks (CNNs). First, GAN-generated images were collected based on different tasks and different architectures to help with the generalization. Then, transfer learning was applied. Finally, several Class Activation Maps (CAM) were integrated to determine the discriminative regions that guided the classification model in its decision. Our approach achieved 100% on our dataset, i.e., Real or Synthetic Images (RSI), and a superior performance on other datasets and configurations in terms of its accuracy. Hence, it can be used as an evaluation tool in image generation. Our best detector was a pre-trained EfficientNetB4 fine-tuned on our dataset with a batch size of 64 and an initial learning rate of 0.001 for 20 epochs. Adam was used as an optimizer, and learning rate reduction along with data augmentation were incorporated.

MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation.

BACKGROUND AND OBJECTIVE: Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules. METHODS: We propose a new UNet-based backbone with multi-branch attention auxiliary learning mechanism, which contains three novel modules, namely, Projection module, Fast Cascading Context module, and Boundary Enhancement module, to further enhance the nodule feature representation. Based on that, we build MANet, a lung nodule localization network that simultaneously detects and segments precise nodule positions. Furthermore, our MANet contains a Proposal Refinement step which refines initially generated proposals to effectively reduce false positives and thereby produce the segmentation quality. RESULTS: Comprehensive experiments on the combination of two benchmarks LUNA16 and LIDC-IDRI show that our proposed model outperforms state-of-the-art methods in the tasks of nodule detection and segmentation tasks in terms of FROC, IoU, and DSC metrics. Our method reports an average FROC score of 88.11% in lung nodule detection. For the lung nodule segmentation, the results reach an average IoU score of 71.29% and a DSC score of 82.74%. The ablation study also shows the effectiveness of the new modules which can be integrated into other UNet-based models. CONCLUSIONS: The experiments demonstrated our method with multi-branch attention auxiliary learning ability are a promising approach for detecting and segmenting the pulmonary nodule instances compared to the original UNet design.

OH-initiated oxidation of vinyl butyrate: ab initio insights.

The widespread use of vinyl butyrate (CH2CHOC(O)CH2CH2CH3 or VB) in the polymer industry and daily-life materials inevitably results in its emission into the atmosphere. Therefore, understanding the mechanism and kinetics of the VB conversion is critical for evaluating its fate and environmental impacts. Herein, we theoretically investigate the chemical transformation of VB initiated by OH radicals in the atmosphere using the stochastic Rice-Ramsperger-Kassel-Marcus (RRKM)-based master equation kinetic model on the potential energy surface explored at the M06-2X/aug-cc-pVTZ level of theory. Showing excellent agreement with limited experimental kinetic data, the VB + OH kinetic model reveals that H-abstraction from Cß (i.e., -CßH2CH3) prevails over the OH-addition to the double bond (CC), even at low temperatures. The detailed analyses, including those of the time-resolved species profiles, reaction rate, and reaction flux, reveal the reaction mechanism shift with temperature (causing the U-shaped temperature dependence of k(T, P)) and the noticeable pressure dependence of k(T, P) at low temperatures. The secondary chemistry under atmospheric conditions (namely, the reaction of the main product with O2 and its subsequent reactions with NO) was then characterized within the same framework to reveal the detailed kinetic mechanism (e.g., [4-(ethenyloxy)-4-oxobutan-2-yl]oxidanyl (IM12) + NO2 is the dominant channel under atmospheric conditions), suggesting VB is not a persistent organic pollutant and a new environmental concern regarding the formed NO2. Also, the kinetic behaviors of vinyl butyrate and its oxidation products were extended from atmospheric to combustion conditions for further applications. Moreover, through TD-DFT calculations, it is shown that several related important species (i.e., 1-(ethenyloxy)-1-oxobutan-2-yl (P4), [4-(ethenyloxy)-4-oxobutan-2-yl]dioxidanyl (IM7), and IM12) can potentially undergo photolysis in the atmosphere.

Instance-Level Few-Shot Learning With Class Hierarchy Mining.

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to effectively classify novel objects. These features are extracted from abundant data of base classes, which could be utilized to reasonably describe classes with scarce data. Specifically, we propose a novel superclass approach that automatically creates a hierarchy considering base and novel classes as fine-grained classes for few-shot instance segmentation (FSIS). Based on the hierarchical information, we design a novel framework called Soft Multiple Superclass (SMS) to extract relevant features or characteristics of classes in the same superclass. A new class assigned to the superclass is easier to classify by leveraging these relevant features. Besides, in order to effectively train the hierarchy-based-detector in FSIS, we apply the label refinement to further describe the associations between fine-grained classes. The extensive experiments demonstrate the effectiveness of our method on FSIS benchmarks. The source code is available here: https://github.com/nvakhoa/superclass-FSIS.

Revisiting natural user interaction in virtual world.

Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers. The virtual environment is operated on both Google Cardboard and HoloLens, the two popular wireless head-mounted displays. Evaluation results reveal the preferences of users over different natural user interaction methods.

Multimodal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing.

In this article, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed cross-modal info-max hashing (CMIMH). First, to learn informative representations that can preserve both intramodal and intermodal similarities, we leverage the recent advances in estimating variational lower bound of MI to maximizing the MI between the binary representations and input features and between binary representations of different modalities. By jointly maximizing these MIs under the assumption that the binary representations are modeled by multivariate Bernoulli distributions, we can learn binary representations, which can preserve both intramodal and intermodal similarities, effectively in a mini-batch manner with gradient descent. Furthermore, we find out that trying to minimize the modality gap by learning similar binary representations for the same instance from different modalities could result in less informative representations. Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks. Quantitative evaluations on standard benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.

Metal complexes of benzimidazole-derived as potential anti-cancer agents: synthesis, characterization, combined experimental and computational studies.

In this study, a series of 14 Cu (II), Zn (II), Ni (II) and Ag (I) complexes containing bis-benzimidazole derivatives were successfully designed and synthesized from 2-(1H-benzimidazole-2-yl)-phenol derivatives and corresponding metal salt solutions. The compound structures were identified by FT-IR, 1H-NMR, powder X-ray diffraction and ESI-MS analyses, and the presence of the metal in the complexes was confirmed by ultraviolet-visible spectroscopy and ICP optical emission spectrometry. Electronic structure calculations were also carried out to describe the detailed structures in addition to the electronic absorption spectra of the ligands. The cytotoxic activity of the complexes was evaluated against three human cancer cell lines: lung (A549), breast (MDA-MB-231) and prostate (PC3) cancer cells. All complexes inhibited anti-proliferative cancer cells better than free ligands, especially Zn (II) and Ag (I) complexes, which are most sensitive to MDA-MB-231 cells. In addition, showing the growth inhibition of three cancer cell lines with IC50 < 10.4 µM, complexes C1 , C3 and C14 could be considered potential multi-targeted anti-cancer agents.

Investigation on monosodium urate deposition in the first metatarsophalangeal joint and ankle of primary gout patients using dual-energy computed tomography.

BACKGROUND: Gout is caused by deposition of monosodium urate (MSU) crystals. One of the tools of choice to identify MSU crystals is the Dual-Energy Computed Tomography (DECT). This study aims to determine MSU crystal deposition using DECT by comparing its detection in the first metatarsophalangeal joints (MTPJ) with that in the ankles, as well as to analyse the association between the crystal deposition and anthropometrics, clinical characteristics, and serum biochemical levels of a primary gout patient. MATERIALS AND METHODS: This cross-sectional study included patients (n = 94) from the Clinic Hoa Hao Medic Medical Centre in Vietnam, who were diagnosed with primary gout with pain/swelling of at least one ankle or first MTPJ. DECT of both joints was used to identify MSU. Statistical analyses were performed using the Student's t-test, Wilcoxon ranksum, Pearson's chi-square, and Spearman's tests. RESULTS: Approximately 80% had MSU crystal deposition in the ankle and/or first MTPJ with no significant difference in deposition between the two joints. MSU deposition was significantly associated with disease duration (p = 0.003), flare-ups (p = 0.006), and cut-off of 6 weeks' duration (p = 0.006), bone erosion (p = 0.006), and palpable tophi (p = 0.003). There was no association between MSU deposition with age, body mass index (BMI), hypertension, serum levels of uric acid (UA), creatinine, high-sensitive C-reactive protein (hsCRP), total cholesterol (C-total), and triglyceride (TG). CONCLUSIONS: MSU deposition occurred in both ankle and first MTP at the same rate. The deposition was associated with disease duration and flare-ups. Prevention of flare-ups seems helpful to limit MSU crystal deposition.

On The Radical Scavenging and DNA Repairing Activities by Natural Oxygenated Diterpenoids: Theoretical Insights.

Diterpenoids are abundant and important compounds in Euphorbia species owing to their structural diversity; therefore, in this study, we investigate the modern-concept antioxidant activities, including free-radical scavenging and oxidative DNA damage repairing, of highly oxygenated diterpenoids originating from the aerial part of Euphorbia helioscopia. Four compounds with structural types of ent-abietane, containing a fused furan ring in their structures, including euphelionolide A (1), euphelionolide D (2), euphelionolide I (3), and euphelionolide L (4) are selected. First, the radical-scavenging activity of these compounds was evaluated with two typical radicals HOO• and HO• in water and pentyl ethanoate (PEA, to mimic lipid environment) via three main mechanisms, namely, hydrogen atom transfer (HAT), radical adduct formation (RAF), and single electron transfer. It is found that the studied compounds are able to scavenge free radicals at multiple reactive sites favorably via HAT and RAF mechanisms, in which the former dominates in the case with HOO• while both mechanisms are competitive in the reaction with HO•. Second, chemical repairing of DNA damage is modeled with the H-atom and single electron being transferred from the studied molecules to damaged 2'-deoxyguanosine (2dG) (i.e., 2dG• radicals and 2dG•+ radical cation). Among the four compounds, euphelionolide A is shown as the most effective radical scavenger and also the highest potential species for chemical repairing of radical-damaged DNA in both water and PEA.

Ab Initio Kinetics of Initial Thermal Pyrolysis of Isopropyl Propionate: A Revisited Study.

This work reports a detailed mechanism of the initial thermal pyrolysis of isopropyl propionate, (C2H5C(=O)OCH(CH3)2), an important biodiesel additive/surrogate, for a wide range of T = 500-2000 K and P = 7.6-76 000 Torr. The detailed kinetic behaviors of the title reaction on the potential energy surface constructed at the CBS-QB3 level were investigated using the RRKM-based master equation (RRKM-ME) rate model, including hindered internal rotation (HIR) and tunneling corrections. It is revealed that the C3H6 elimination occurring via a six-centered retro-ene transition state is dominant at low temperatures, while the homolytic fission of the C-C bonds becomes more competitive at higher temperatures. The tunneling treatment is found to slightly increase the rate constant at low temperatures (e.g., ∼1.59 times at 563 K), while the HIR treatment, being important at high temperatures, decreases the rate (e.g., by 5.9 times at 2000 K). Showing a good agreement with experiments in low-temperature kinetics, the kinetic model reveals that the pressure effect should be taken into account at high temperatures. Finally, the temperature- and pressure-dependent kinetic mechanism, consisting of the calculated thermodynamic and kinetic data, is provided for further modeling and simulation of any related systems.

Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite.

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page.

Intensified salinity intrusion in coastal aquifers due to groundwater overextraction: a case study in the Mekong Delta, Vietnam.

Groundwater salinization is one of the most severe environmental problems in coastal aquifers worldwide, causing exceeding salinity in groundwater supply systems for many purposes. High salinity concentration in groundwater can be detected several kilometers inland and may result in an increased risk for coastal water supply systems and human health problems. This study investigates the impacts of groundwater pumping practices and regional groundwater flow dynamics on groundwater flow and salinity intrusion in the coastal aquifers of the Vietnamese Mekong Delta using the SEAWAT model-a variable-density groundwater flow and solute transport model. The model was constructed in three dimensions (3D) and accounted for multi-aquifers, variation of groundwater levels in neighboring areas, pumping, and paleo-salinity. Model calibration was carried for 13 years (2000 to 2012), and validation was conducted for 4 years (2013 to 2016). The best-calibrated model was used to develop prediction models for the next 14 years (2017 to 2030). Six future scenarios were introduced based on pumping rates and regional groundwater levels. Modeling results revealed that groundwater pumping activities and variation of regional groundwater flow systems strongly influence groundwater level depletion and saline movement from upper layers to lower layers. High salinity (>2.0 g/L) was expected to expand downward up to 150 m in depth and 2000 m toward surrounding areas in the next 14 years under increasing groundwater pumping capacity. A slight recovery in water level was also observed with decreasing groundwater exploitation. The reduction in the pumping rate from both local and regional scales will be necessary to recover groundwater levels and protect fresh aquifers from expanding paleo-saline in groundwater.

Masked Face Analysis via Multi-Task Deep Learning.

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation.

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

New Mechanistic Insights into Atmospheric Oxidation of Aniline Initiated by OH Radicals.

This study theoretically reports the comprehensive kinetic mechanism of the aniline + OH reaction in the range of 200-2000 K and 0.76-7600 Torr. The temperature- and pressure-dependent behaviors, including time-resolved species profiles and rate coefficients, were studied within the stochastic RRKM-based master equation framework with the reaction energy profile, together with molecular properties of the species involved, characterized at the M06-2X/aug-cc-pVTZ level. Hindered internal rotation and Eckart tunneling treatments were included. The H-abstraction from the -NH2 moiety (to form C6H5NH (P1)) is found to prevail over the OH-addition on the C atom at the ortho site of aniline (to form 6-hydroxy-1-methylcyclohexa-2,4-dien-1-yl (I2)) with the atmospheric rate expressions (in cm3/molecule/s) as kabstraction(P1) = 3.41 × 101 × T-4.56 × exp (-255.2 K/T) for 200-2000 K and kaddition(I2) = 3.68 × 109 × T-7.39 × exp (-1163.9 K/T) for 200-800 K. The U-shaped temperature-dependent characteristics and weakly positive pressure dependence at low temperatures (e.g., T ≤ 800 K and P = 760 Torr) of ktotal(T) are also observed. The disagreement in ktotal(T) between the previous calculations and experimental studies is also resolved, and atmospheric aniline is found to be primarily removed by OH radicals (τOH ∼ 1.1 h) in the daytime. Also, via TD-DFT simulations, it is recommended to include P1 and I2 in any atmospheric photolysis-related model.

Multiple dehydrogenation of fluorene cation and neutral fluorene using the statistical molecular fragmentation model.

The statistical molecular fragmentation (SMF) model was used to analyze the 306 fragmentation channels (containing 611 different species) that result from the fluorene (C13H10+) cation losing up to three hydrogen atoms (neutral radicals and/or a proton). Breakdown curves from such analysis permit one to extract experimentally inaccessible information about the fragmentation of the fluorene cation, such as the locations of the lost hydrogen atoms (or proton), yields of the neutral fragments, electronic states of the residues, and quantification of very low probability channels that would be difficult to detect. Charge localization during the fragmentation pathways was studied to provide a qualitative understanding of the fragmentation process. Breakdown curves for both the fluorene cation and neutral fluorene were compared. The SMF results match the rise and fall of the one hydrogen loss yield experimentally measured by imaging photoelectron-photoion coincidence spectroscopy using a VUV synchrotron.

Behavioural insights and attitudes on community masking during the initial spread of COVID-19 in Hong Kong.

INTRODUCTION: Community face mask use during the coronavirus disease 2019 (COVID-19) pandemic has considerably differed worldwide. Generally, Asians are more inclined to wear face masks during disease outbreaks. Hong Kong has emerged relatively unscathed during the initial outbreak of COVID-19, despite its dense population. Previous infectious disease outbreaks influenced the local masking behaviour and response to public health measures. Thus, local behavioural insights are important for the successful implementation of infection control measures. This study explored the behaviour and attitudes of wearing face masks in the community during the initial spread of COVID-19 in Hong Kong. METHODS: We observed the masking behaviour of 10 211 pedestrians in several regions across Hong Kong from 1 to 29 February 2020. We supplemented the data with an online survey of 3199 respondents' views on face mask use. RESULTS: Among pedestrians, the masking rate was 94.8%; 83.7% wore disposable surgical masks. However, 13.0% wore surgical masks incorrectly with 42.5% worn too low, exposing the nostrils or mouth; 35.5% worn 'inside-out' or 'upside-down'. Most online respondents believed in the efficacy of wearing face mask for protection (94.6%) and prevention of community spread (96.6%). Surprisingly, 78.9% reused their mask; more respondents obtained information from social media (65.9%) than from government websites (23.2%). CONCLUSIONS: In Hong Kong, members of the population are motivated to wear masks and believe in the effectiveness of face masks against disease spread. However, a high mask reuse rate and errors in masking techniques were observed. Information on government websites should be enhanced and their accessibility should be improved.