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Autonomous Emergency Braking (AEB) potentially brings significant improvements in automotive safety due to its ability to autonomously prevent collisions in situations where the driver may not be able to do so. Driven by the poor performance of the state of the art in recent testing, this work provides an online solution to identify critical parameters such as the current and maximum friction coefficients. The method introduced here, namely Torque Vectoring for Active Learning (TVAL), can perform state and parameter estimation whilst following the driver's input. Importantly with less power requirements than normal driving. Our method is designed with a crucial focus on ensuring minimal disruption to the driver, allowing them to maintain full control of the vehicle. Additionally, we exploit a rain/light sensor to drive the observer resampling to maintain estimation certainty across prolonged operation. Then a scheme to modulate TVAL is introduced that considers powertrain efficiency, safety, and availability in an online fashion. Using a high-fidelity vehicle model and drive cycle we demonstrate the functionality of TVAL controller across changing road surfaces where we successfully identify the road surface whenever possible.

This paper proposes a comprehensive hierarchical control framework for autonomous decision-making arising in robotics and autonomous systems. In a typical hierarchical control architecture, high-level decision making is often characterised by discrete state and decision/control sets. However, a rational decision is usually affected by not only the discrete states of the autonomous system, but also the underlying continuous dynamics even the evolution of its operational environment. This paper proposes a holistic and comprehensive design process and framework for this type of challenging problems, from new modelling and design problem formulation to control design and stability analysis. It addresses the intricate interplay between traditional continuous systems dynamics utilized at the low levels for control design and discrete Markov decision processes (MDP) for facilitating high-level decision making. We model the decision making system in complex environments as a hybrid system consisting of a controlled MDP and autonomous (i.e. uncontrolled) continuous dynamics. Consequently, the new formulation is called as hybrid Markov decision process (HMDP). The design problem is formulated with a focus on ensuring both safety and optimality while taking into account the influence of both the discrete and continuous state variables of different levels. With the help of the model predictive control (MPC) concept, a decision maker design scheme is proposed for the proposed hybrid decision making model. By carefully designing key ingredients involved in this scheme, it is shown that the recursive feasibility and stability of the proposed autonomous decision making scheme are guaranteed. The proposed framework is applied to develop an autonomous lane changing system for intelligent vehicles., Comment: 11 pages, 14 figures, submitted to Automatica

The analysis of full temporal data in time-domain near-infrared optical tomography (TD NIROT) measurements enables valuable information to be obtained about tissue properties with good temporal and spatial resolution. However, the large amount of data obtained is not easy to handle in the image reconstruction. The goal of the project is to employ full-temporal data from a TD NIROT modality. We improved TD data-based 3D image reconstruction and compared the performance with other methods using frequency domain (FD) and temporal moments. The iterative reconstruction algorithm was evaluated in simulations with both noiseless and noisy in-silico data. In the noiseless cases, a superior image quality was achieved by the reconstruction using full temporal data, especially when dealing with inclusions at 20 mm and deeper in the tissue. When noise similar to measured data was present, the quality of the recovered image from full temporal data was no longer superior to the one obtained from the analysis of FD data and temporal moments. This indicates that denoising methods for TD data should be developed. In conclusion, TD data contain richer information and yield better image quality.

Cerebral veins have received increasing attention due to their importance in preoperational planning and the brain oxygenation measurement. There are different modalities to image those vessels, such as magnetic resonance angiography (MRA) and recently, contrast-enhanced (CE) 3D gradient-echo sequences. However, the current techniques have certain disadvantages, i.e., the long examination time, the requirement of contrast agents or inability to measure oxygenation. Near-infrared optical tomography (NIROT) is emerging as a viable new biomedical imaging modality that employs near infrared light (650-950 nm) to image biological tissue. It was proven to easily penetrate the skull and therefore enables the brain vessels to be assessed. NIROT utilizes safe non-ionizing radiation and can be applied in e.g., early detection of neonatal brain injury and ischemic strokes. The aim is to develop non-invasive label-free dynamic time domain (TD) NIROT to image the brain vessels. A simulation study was performed with the software (NIRFAST) which models light propagation in tissue with the finite element method (FEM). Both a simple shape mesh and a real head mesh including all the segmented vessels from MRI images were simulated using both FEM and a hybrid FEM-U-Net network, we were able to visualize the superficial vessels with NIROT with a Root Mean Square Error (RMSE) lower than 0.079.

Time is one of the most critical factors in preventing brain lesions due to hypoxic ischemia in preterm infants. Since early detection of low oxygenation is vital and the time window for therapy is narrow, near-infrared optical tomography (NIROT) must be able to process the high-dimensional data provided by today's advanced systems in the shortest possible time. Deep learning approaches are attractive because they can exploit such high information density while reducing inference time. The aim of this study was to evaluate the performance of a hybrid convolutional neural network, designed for NIROT image reconstruction and trained on synthetic data. Generalization capability was assessed using measurements on phantoms of a surface topology more divergent than the range of variation in the geometries of the in-silico data, with unseen, non-spherical inclusion shapes, and with source and detector arrangements different from those used for data generation. Substantial gains in speed, localization accuracy, and high image quality were achieved even under the highly varied measurement conditions.

Near-infrared spectroscopy (NIRS) is a non-invasive optical method for monitoring cerebral oxygenation. Changes in regional blood flow and oxygenation due to neurovascular coupling are important biomarkers of neuronal activation. So far, there has been little research on multilayer tissue phantoms with tuneable blood flow, blood volume, and optical properties to simulate local changes in oxygenation at different depths. The aim of this study is to design, fabricate and characterize a complex dynamic phantom based on multilayer microfluidics with controllable blood flow, blood volume, and optical properties for testing NIRS instruments. We developed a phantom prototype with two microfluidic chips embedded at two depths inside a solid silicone phantom to mimic the vessels in the scalp and in the cortex. To simulate the oxygenation and perfusion of tissue, a solution with blood-like optical properties was sent into the microchannels by a pump with a programmable pressure controller. The pressure adjusted the volume of the microfluidic chips representing a distension of blood vessels. The optical changes in the superficial and deep layers were measured by a commercially available frequency domain NIRS instrument. The NIRS successfully detected the changes in light intensity elicited by the changes in the pressure input to the two layers. In conclusion, the microfluidics-based imaging phantom was successfully designed and fabricated and mimics brain functional activity. This technique has great potential for testing other optical devices, e.g., diffuse correlation spectroscopy, pulse oximetry, and optical coherence tomography.

Recently, finetuning pretrained Vision-Language Models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in Visual Question Answering (VQA). However, as VLMs scale, finetuning full model parameters for a given task in low-resource settings becomes computationally expensive, storage inefficient, and prone to overfitting. Current parameter-efficient tuning methods dramatically reduce the number of tunable parameters, but there still exists a significant performance gap with full finetuning. In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. Specifically, MixPHM is a lightweight module implemented by multiple PHM-experts in a mixture-of-experts manner. To reduce parameter redundancy, MixPHM reparameterizes expert weights in a low-rank subspace and shares part of the weights inside and across experts. Moreover, based on a quantitative redundancy analysis for adapters, we propose Redundancy Regularization to reduce task-irrelevant redundancy while promoting task-relevant correlation in MixPHM representations. Experiments conducted on VQA v2, GQA, and OK-VQA demonstrate that MixPHM outperforms state-of-the-art parameter-efficient methods and is the only one consistently surpassing full finetuning., Comment: 13 pages, 6 figures, 9 tables. Accepted to CVPR 2023

At the Dialogue Robot Competition 2023 (DRC2023), which was held to improve the capability of dialogue robots, our team developed a system that could build common ground and take more natural turns based on user utterance texts. Our system generated queries for sightseeing spot searches using the common ground and engaged in dialogue while waiting for user comprehension., Comment: This paper is part of the proceedings of the Dialogue Robot Competition 2023

The ability to understand spatial-temporal patterns for crowds of people is crucial for achieving long-term autonomy of mobile robots deployed in human environments. However, traditional historical data-driven memory models are inadequate for handling anomalies, resulting in poor reasoning by robot in estimating the crowd spatial distribution. In this article, a Receding Horizon Optimization (RHO) formulation is proposed that incorporates a Probability-related Partially Updated Memory (PPUM) for robot path planning in crowded environments with uncertainties. The PPUM acts as a memory layer that combines real-time sensor observations with historical knowledge using a weighted evidence fusion theory to improve robot's adaptivity to the dynamic environments. RHO then utilizes the PPUM as a informed knowledge to generate a path that minimizes the likelihood of encountering dense crowds while reducing the cost of local motion planning. The proposed approach provides an innovative solution to the problem of robot's long-term safe interaction with human in uncertain crowded environments. In simulation, the results demonstrate the superior performance of our approach compared to benchmark methods in terms of crowd distribution estimation accuracy, adaptability to anomalies and path planning efficiency.

Motion forecasting plays a crucial role in autonomous driving, with the aim of predicting the future reasonable motions of traffic agents. Most existing methods mainly model the historical interactions between agents and the environment, and predict multi-modal trajectories in a feedforward process, ignoring potential trajectory changes caused by future interactions between agents. In this paper, we propose a novel Future Feedback Interaction Network (FFINet) to aggregate features the current observations and potential future interactions for trajectory prediction. Firstly, we employ different spatial-temporal encoders to embed the decomposed position vectors and the current position of each scene, providing rich features for the subsequent cross-temporal aggregation. Secondly, the relative interaction and cross-temporal aggregation strategies are sequentially adopted to integrate features in the current fusion module, observation interaction module, future feedback module and global fusion module, in which the future feedback module can enable the understanding of pre-action by feeding the influence of preview information to feedforward prediction. Thirdly, the comprehensive interaction features are further fed into final predictor to generate the joint predicted trajectories of multiple agents. Extensive experimental results show that our FFINet achieves the state-of-the-art performance on Argoverse 1 and Argoverse 2 motion forecasting benchmarks., Comment: 11 pages, 8 figures, 12 tables

Anti-lock Brake System (ABS) is a mandatory active safety feature on road vehicles with analogous systems for aircraft and locomotives. This feature aims to prevent locking of the wheels when braking and to improve the handling performance, as well as reduce stopping distance of the vehicle. Estimation uncertainties in the vehicle state and environment (road surface) are often neglected or handled separately from the ABS controller, leading to sub-optimal braking. In this paper, a Dual Control for Exploration-Exploitation (DCEE) approach is taken toward the ABS problem which achieves both accurate state (and environment) estimation and superior braking performance. Compared with popular Extremum Seeking methods, improvements of up to $15\%$ and $8.5\%$ are shown in stopping time and stopping distance, respectively. A Regularized Particle Filter with Markov Chain Monte Carlo step is used to estimate vehicle states and parameters of the Magic Formula tyre model that includes the peak friction coefficient for the environment. The effectiveness of the DCEE approach is demonstrated across a range of driving scenarios such as low and high speeds; snow, wet and dry roads and changing road surfaces.

ContextMethyl mercaptan (CH3SH) is a colorless, toxic gas with potential for occupational exposure and used as a weapon of mass destruction. Inhalation at high concentrations can result in dyspnea, hypoventilation, seizures, and death. No specific methyl mercaptan antidote exists, highlighting a critical need for such an agent. Here, we investigated the mechanism of CH3SH toxicity, and rescue from CH3SH poisoning by the vitamin B12 analog cobinamide, in mammalian cells. We also developed lethal CH3SH inhalation models in mice and rabbits, and tested the efficacy of intramuscular injection of cobinamide as a CH3SH antidote.ResultsWe found that cobinamide binds to CH3SH (Kd = 84 µM), and improved growth of cells exposed to CH3SH. CH3SH reduced cellular oxygen consumption and intracellular ATP content and activated the stress protein c-Jun N-terminal kinase (JNK); cobinamide reversed these changes. A single intramuscular injection of cobinamide (20 mg/kg) rescued 6 of 6 mice exposed to a lethal dose of CH3SH gas, while all six saline-treated mice died (p = 0.0013). In rabbits exposed to CH3SH gas, 11 of 12 animals (92%) treated with two intramuscular injections of cobinamide (50 mg/kg each) survived, while only 2 of 12 animals (17%) treated with saline survived (p = 0.001).ConclusionWe conclude that cobinamide could potentially serve as a CH3SH antidote.

Near-infrared optical tomography (NIROT), a promising imaging modality for early detection of oxygenation in the brain of preterm infants, requires data acquisition at the tissue surface and thus an image reconstruction adaptable to cephalometric variations and surface topologies. Widely used model-based reconstruction methods come with the drawback of huge computational cost. Neural networks move this computational load to an offline training phase, allowing much faster reconstruction. Our aim is a data-driven volumetric image reconstruction that generalises well to different surfaces, increases reconstruction speed, localisation accuracy and image quality. We propose a hybrid convolutional neural network (hCNN) based on the well-known V-net architecture to learn inclusion localisation and absorption coefficients of heterogenous arbitrary shapes via a joint cost function. We achieved an average reconstruction time of 30.45 s, a time reduction of 89% and inclusion detection with an average Dice score of 0.61. The CNN is flexible to surface topologies and compares well in quantitative metrics with the traditional model-based (MB) approach and state-of-the-art neuronal networks for NIROT. The proposed hCNN was successfully trained, validated and tested on in-silico data, excels MB methods in localisation accuracy and provides a remarkable increase in reconstruction speed.

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.

The problem of robustness in adverse weather conditions is considered a significant challenge for computer vision algorithms in the applicants of autonomous driving. Image rain removal algorithms are a general solution to this problem. They find a deep connection between raindrops/rain-streaks and images by mining the hidden features and restoring information about the rain-free environment based on the powerful representation capabilities of neural networks. However, previous research has focused on architecture innovations and has yet to consider the vulnerability issues that already exist in neural networks. This research gap hints at a potential security threat geared toward the intelligent perception of autonomous driving in the rain. In this paper, we propose a universal rain-removal attack (URA) on the vulnerability of image rain-removal algorithms by generating a non-additive spatial perturbation that significantly reduces the similarity and image quality of scene restoration. Notably, this perturbation is difficult to recognise by humans and is also the same for different target images. Thus, URA could be considered a critical tool for the vulnerability detection of image rain-removal algorithms. It also could be developed as a real-world artificial intelligence attack method. Experimental results show that URA can reduce the scene repair capability by 39.5% and the image generation quality by 26.4%, targeting the state-of-the-art (SOTA) single-image rain-removal algorithms currently available.

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.

Benefiting from large-scale pretrained vision language models (VLMs), the performance of visual question answering (VQA) has approached human oracles. However, finetuning such models on limited data often suffers from overfitting and poor generalization issues, leading to a lack of model robustness. In this paper, we aim to improve input robustness from an information bottleneck perspective when adapting pretrained VLMs to the downstream VQA task. Input robustness refers to the ability of models to defend against visual and linguistic input variations, as well as shortcut learning involved in inputs. Generally, the representations obtained by pretrained VLMs inevitably contain irrelevant and redundant information for a specific downstream task, resulting in statistically spurious correlations and insensitivity to input variations. To encourage representations to converge to a minimal sufficient statistic in multimodal learning, we propose Correlation Information Bottleneck (CIB), which seeks a tradeoff between compression and redundancy in representations by minimizing the mutual information (MI) between inputs and representations while maximizing the MI between outputs and representations. Moreover, we derive a tight theoretical upper bound for the mutual information between multimodal inputs and representations, incorporating different internal correlations that guide models to learn more robust representations and facilitate modality alignment. Extensive experiments consistently demonstrate the effectiveness and superiority of the proposed CIB in terms of input robustness and accuracy., Comment: 20 pages, 6 figures, 13 tables

The multi-source electromechanical coupling makes the energy management of fuel cell electric vehicles (FCEVs) relatively nonlinear and complex especially in the types of 4-wheel-drive (4WD) FCEVs. Accurate state observing for complicated nonlinear system is the basis for fantastic energy managing in FCEVs. Aiming at releasing the energy-saving potential of FCEVs, a novel learning-based robust model predictive control (LRMPC) strategy is proposed for a 4WD FCEV, contributing to suitable power distribution among multiple energy sources. The well-designed strategy based on machine learning (ML) translates the knowledge of the nonlinear system to the explicit controlling scheme with superior robust performance. To start with, ML methods with high regression accuracy and superior generalization ability are trained offline to establish the precise state observer for SOC. Then, explicit data tables for SOC generated by state observer are used for grabbing accurate state changing, whose input features include the vehicle status and the states of vehicle components. To be specific, the vehicle velocity estimation for providing future speed reference is constructed by deep forest. Next, the components including explicit data tables and vehicle velocity estimation are combined with model predictive control (MPC) to release the state-of-the-art energy-saving ability for the multi-freedom system in FCEVs, whose name is LRMPC. At last, the detailed assessment is performed in simulation test to validate the advancing performance of LRMPC. The corresponding results highlight the optimal control effect in energy-saving potential and strong real-time application ability of LRMPC., Comment: 16 pages, 16 figures

Ying Liu, Jia He, Jingjing Jiang, Yujuan Wang, Ting Liu Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, Nanchong, People’s Republic of ChinaCorrespondence: Ting Liu, Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuan South Road, Shunqing District, Nanchong, Sichuan Province, 637000, People’s Republic of China, Tel +8615082780013, Email 332656467@qq.comAbstract: Reactive arthritis (ReA) is uncommon. The present case is a Chinese man who has been treated with adalimumab and leflunomide to control ankylosing spondylitis (AS). During the treatment, the patient developed a range of symptoms, including fever, fatigue, pustular rash, suppurative urethritis, genital ulcers, oral ulcers, bilateral uveitis, heel pain and swelling and pain of the knee and ankle joints. The laboratory studies revealed the presence of HLA-B27, and urethral secretions were positive for Ureaplasma urealyticum. The patient was eventually diagnosed with ReA. The development of ReA may be related to the combination of adalimumab and leflunomide, which reduces immune function and triggers activation of potential U. urealyticum. The patient received 3 weeks of antibiotics, corticosteroids and non-steroidal anti-inflammatory drugs (NSAIDs), resulting in a significant improvement. The dose of corticosteroids was gradually reduced, and adalimumab was reintroduced. The patient was followed up for 3 months without recurrence.Keywords: reactive arthritis, Reiter syndrome, ankylosing spondylitis, urethritis, arthritis, uveitis, HLA-B27

Jia He, Xi Duan, Ting Liu, Hao Yang, Jingjing Jiang, Yunzhu Mu Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, Nanchong, People’s Republic of ChinaCorrespondence: Yunzhu Mu, Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuan South Road, Shunqing District, Nanchong City, Sichuan Province, 637000, People’s Republic of China, Tel +8615983785601, Email hymyz@126.comAbstract: Immune-related adverse events have been reported in relation to programmed cell death protein-1 (PD-1). However, there are few reports on PD-1 inhibitor-induced bullous pemphigoid. We report the case of a patient who developed bullous pemphigoid following long-term administration of sintilimab for renal cell carcinoma. He developed scattered erythema, blisters, and generalized pruritus for 1 week before admission. Histopathology showed subepidermal blisters and direct immunofluorescence showed linear deposition of immunoglobulin G and complement 3 on the basement membrane; the level of BP180 was 146.93U/mL. A regimen containing methylprednisolone, minocycline, and niacinamide was administered and the patient was discharged following resolution of symptoms.Keywords: bullous pemphigoid, PD-1, PD-L1, sintilimab

Jingjing Jiang *, Yujuan Chen *, Qi He, Jiao Yang, Zhengzhong Zhang, Hao Yang, Huan Zhang, Chuan Yang Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, Nanchong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhengzhong Zhang, Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuan South Road, Shunqing District, Nanchong City, Sichuan Province, 637000, People’s Republic of China, Tel +8618080339898, Email laowaiaeo@163.comAbstract: Nevus sebaceous (NS) is a benign congenital hamartoma with the potential to develop into a secondary benign or malignant skin tumors. Two or more skin tumors rarely arise simultaneously from a single NS lesion. Herein, we report a case of syringocystadenoma papilliferum and basal cell carcinoma arising in an NS, based on dermoscopic, reflectance confocal microscopic, and histopathological findings.Keywords: nevus sebaceous, syringocystadenoma papilliferum, basal cell carcinoma, dermoscopy, reflectance confocal microscopy

Lei Zhang,1 Yixiong Li,2 Hao Yang,1 Yunzhu Mu,1 Jingjing Jiang,1 Xi Duan1 1Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, People’s Republic of China; 2Department of Anorectal, The First Affiliated Hospital of Chengdu Medical College, Chengdu, People’s Republic of ChinaCorrespondence: Xi Duan, Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan South Road, Shunqing District, Nanchong City, Sichuan Province, 637000, People’s Republic of China, Tel +8618783983097, Email dancing@126.comAbstract: We describe a 62-year-old woman with a 1-month-old unhealed cat bite wound caused by Mycobacterium chelonae presenting with persistent infection despite treatment with cefazolin and amoxicillin. This is the first reported case of M. chelonae infection caused by a cat bite. Cat bites are affected by the cat’s food microbiome, so clinicians should consider the cat’s diet if wounds are not responding as expected to first-line therapy.Keywords: cat bite, Mycobacterium chelonae, wound infection

Jingjing Jiang,1 Junjie Zeng,2 Qi He,1 Jiao Yang,1 Shenglan Wang,1 Zhengzhong Zhang1 1Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, People’s Republic of China; 2Department of Dermatology, Taikang Sichuan Southwest Hospital Company Limited, Chengdu, Sichuan Province, People’s Republic of ChinaCorrespondence: Zhengzhong Zhang, Department of Dermatology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan South Road, Shunqing District, Nanchong, Sichuan Province, 637000, People’s Republic of China, Tel +8618080339898, Email laowaiaeo@163.comPurpose: To identify the mutations of the NEMO gene in two Chinese females with incontinentia pigmenti.Patients and Methods: Patients were both from Nanchong, Sichuan Province. Genomic DNA was extracted from the peripheral blood of patients and patient 1ʹs father. The mutations of the NEMO gene in patient 1 by GAP polymerase chain reaction and Sanger sequencing and her father were detected. NEMO-specific polymerase chain reaction and Sanger sequencing were used to identify the NEMO gene mutation in patient 2.Results: DNA analysis identified a rare frameshift mutation, c.723_c.724insCAGG(p.A242QfsX15) in exon 5 of the NEMO gene in patient 1 with a family history but not in her healthy father. The common deletion of exons 4– 10 of the NEMO gene was found in sporadic patient 2.Conclusion: Our data revealed that the rare frameshift mutation, c.723_c.724insCAGG(p.A242QfsX15) in exon 5 of the NEMO gene in patient 1 and the deletion of exons 4– 10 of the NEMO gene in patient 2 could cause the occurrence of IP. Genetic testing is helpful for early diagnosis and genetic counseling for families.Keywords: Chinese, incontinentia pigmenti, mutation, NEMO gene, NF-κB pathway

In a turbid medium such as biological tissue, near-infrared optical tomography (NIROT) can image the oxygenation, a highly relevant clinical parameter. To be an efficient diagnostic tool, NIROT has to have high spatial resolution and depth sensitivity, fast acquisition time, and be easy to use. Since many tissues cannot be penetrated by near-infrared light, such tissue needs to be measured in reflection mode, i.e., where light emission and detection components are placed on the same side. Thanks to the recent advance in single-photon avalanche diode (SPAD) array technology, we have developed a compact reflection-mode time-domain (TD) NIROT system with a large number of channels, which is expected to substantially increase the resolution and depth sensitivity of the oxygenation images. The aim was to test this experimentally for our SPAD camera-empowered TD NIROT system. Experiments with one and two inclusions, i.e., optically dense spheres of 5mm radius, immersed in turbid liquid were conducted. The inclusions were placed at depths from 10mm to 30mm and moved across the field-of-view. In the two-inclusion experiment, two identical spheres were placed at a lateral distance of 8mm. We also compared short exposure times of 1s, suitable for dynamic processes, with a long exposure of 100s. Additionally, we imaged complex geometries inside the turbid medium, which represented structural elements of a biological object. The quality of the reconstructed images was quantified by the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and dice similarity. The two small spheres were successfully resolved up to a depth of 30mm. We demonstrated robust image reconstruction even at 1s exposure. Furthermore, the complex geometries were also successfully reconstructed. The results demonstrated a groundbreaking level of enhanced performance of the NIROT system based on a SPAD camera.

Time-domain near-infrared optical tomography (TD NIROT) techniques based on diffuse light were gaining performance over the last years. They are capable of imaging tissue at several centimeters depth and reveal clinically relevant information, such as tissue oxygen saturation. In this work, we present the very first in vivo results of our SPAD camera-based TD NIROT reflectance system with a temporal resolution of ∼116 ps. It provides 2800 time of flight source-detector pairs in a compact probe of only 6 cm in diameter. Additionally, we describe a 3-step reconstruction procedure that enables accurate recovery of structural information and of the optical properties. We demonstrate the system’s performance firstly in reconstructing the 3D-structure of a heterogeneous tissue phantom with tissue-like scattering and absorption properties within a volume of 9 cm diameter and 5 cm thickness. Furthermore, we performed in vivo tomography of an index finger located within a homogeneous scattering medium. We employed a fast sampling rate of 2.5 Hz to detect changes in tissue oxygenation. Tomographic reconstructions were performed in true 3D, and without prior structural information, demonstrating the powerful capabilities of the system. This shows its potential for clinical applications.

Thyroid hormone (TH) and thyroid hormone receptor (THR) regulate stem cell proliferation and differentiation during development, as well as during tissue renewal and repair in the adult. THR undergoes posttranslational modification by small ubiquitin-like modifier (SUMO). We generated the THRA (K283Q/K288R)-/- mouse model for in vivo studies and used human primary preadipocytes expressing the THRA sumoylation mutant (K283R/K288R) and isolated preadipocytes from mutant mice for in vitro studies. THRA mutant mice had reduced white adipose stores and reduced adipocyte cell diameter on a chow diet, compared to wild-type, and these differences were further enhanced after a high fat diet. Reduced preadipocyte proliferation in mutant mice, compared to wt, was shown after in vivo labeling of preadipocytes with EdU and in preadipocytes isolated from mice fat stores and studied in vitro. Mice with the desumoylated THRA had disruptions in cell cycle G1/S transition and this was associated with a reduction in the availability of cyclin D2 and cyclin-dependent kinase 2. The genes coding for cyclin D1, cyclin D2, cyclin-dependent kinase 2 and Culin3 are stimulated by cAMP Response Element Binding Protein (CREB) and contain CREB Response Elements (CREs) in their regulatory regions. We demonstrate, by Chromatin Immunoprecipitation (ChIP) assay, that in mice with the THRA K283Q/K288R mutant there was reduced CREB binding to the CRE. Mice with a THRA sumoylation mutant had reduced fat stores on chow and high fat diets and reduced adipocyte diameter.

Video Question Answering (VideoQA), aiming to correctly answer the given question based on understanding multi-modal video content, is challenging due to the rich video content. From the perspective of video understanding, a good VideoQA framework needs to understand the video content at different semantic levels and flexibly integrate the diverse video content to distill question-related content. To this end, we propose a Lightweight Visual-Linguistic Reasoning framework named LiVLR. Specifically, LiVLR first utilizes the graph-based Visual and Linguistic Encoders to obtain multi-grained visual and linguistic representations. Subsequently, the obtained representations are integrated with the devised Diversity-aware Visual-Linguistic Reasoning module (DaVL). The DaVL considers the difference between the different types of representations and can flexibly adjust the importance of different types of representations when generating the question-related joint representation, which is an effective and general representation integration method. The proposed LiVLR is lightweight and shows its performance advantage on two VideoQA benchmarks, MRSVTT-QA and KnowIT VQA. Extensive ablation studies demonstrate the effectiveness of LiVLR key components., Comment: 11 pages, 5 figures, Code: https://github.com/jingjing12110/LiVLR-VideoQA

Encouraging progress has been made towards Visual Question Answering (VQA) in recent years, but it is still challenging to enable VQA models to adaptively generalize to out-of-distribution (OOD) samples. Intuitively, recompositions of existing visual concepts (\ie, attributes and objects) can generate unseen compositions in the training set, which will promote VQA models to generalize to OOD samples. In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to implicitly model the problem. X-GGM leverages graph generative modeling to iteratively generate a relation matrix and node representations for the predefined graph that utilizes attribute-object pairs as nodes. Furthermore, to alleviate the unstable training issue in graph generative modeling, we propose a gradient distribution consistency loss to constrain the data distribution with adversarial perturbations and the generated distribution. The baseline VQA model (LXMERT) trained with the X-GGM scheme achieves state-of-the-art OOD performance on two standard VQA OOD benchmarks, \ie, VQA-CP v2 and GQA-OOD. Extensive ablation studies demonstrate the effectiveness of X-GGM components. Code is available at \url{https://github.com/jingjing12110/x-ggm}., Comment: Accepted by ACM MM2021

Photoelectrochemical (PEC) water splitting is a promising way to generate clean hydrogen fuel from water and sunlight. The ideal photocathodes for hydrogen evolution reaction (HER) should have good electrical contact and mechanical adhesion on the interface between the semiconductor and the catalyst, and be stable during operation. However, the interfacial properties and the stability have not been intensively studied. We investigated the electrical and mechanical properties on the nanoscale of the interface of commonly used Si/Pt nanoparticles (Pt-NPs) electrodes with Pt-NPs as a catalyst, and showed that the Pt-NPs have a weaker adhesion in electrolyte than in air, and less than half of the Pt-NPs carry high currents, limiting the performance of the common Si/Pt-NPs electrodes. Furthermore, we explored the interfacial engineering of using TiO2 deposited by atomic layer deposition (ALD), and showed that annealed TiO2 led to higher open circuit voltages than the as grown ones by the possible formation of an interfacial Si-O-Ti mixture layer. Besides, the stability and corrosion behavior of CdTe electrodes for HER in the dark was studied in 1.0 M H2SO4(aq) and 1.0 M KOH(aq). The conditions studied herein include the electrochemical corrosion when biased at -100 mV vs. the reversible hydrogen electrode (RHE), the chemical corrosion when left at open circuit voltage (OCV), and the electrochemical corrosion with an active HER Pt catalyst overlayer when biased at -100 mV vs. RHE. The corrosion comes mostly from chemical corrosion and is reduced at negative bias in electrochemical condition. With a Pt catalyst overlayer at -100 mV vs. RHE, the corrosion rate is further reduced, indicating the promising utilization of CdTe for HER in PEC cells.

Context: Pheochromocytomas and paragangliomas (PPGLs) are characterized by distinct genotype-phenotype relationships according to studies largely restricted to Caucasian populations. Objective: To assess for possible differences in genetic landscapes and genotype-phenotype relationships of PPGLs in Chinese versus European populations. Design: Cross-sectional study. Setting: 2 tertiary-care centers in China and 9 in Europe. Participants: Patients with pathologically confirmed diagnosis of PPGL, including 719 Chinese and 919 Europeans. Main Outcome Measures: Next-generation sequencing performed in tumor specimens with mutations confirmed by Sanger sequencing and tested in peripheral blood if available. Frequencies of mutations were examined according to tumor location and catecholamine biochemical phenotypes. Results: Among all patients, higher frequencies of HRAS, FGFR1, and EPAS1 mutations were observed in Chinese than Europeans, whereas the reverse was observed for NF1, VHL, RET, and SDHx. Among patients with apparently sporadic PPGLs, the most frequently mutated genes in Chinese were HRAS (16.5% [13.6-19.3] vs 9.8% [7.6-12.1]) and FGFR1 (9.8% [7.6-12.11 vs 2.2% [1.1-3.3]), whereas among Europeans the most frequently mutated genes were NF1 (15.9% [13.2-18.6) vs 6.6% [4.7-8.5)) and SDHx (10.7% [8.4-13.0] vs 4.2% [2.6-5.7]). Among Europeans, almost all paragangliomas lacked appreciable production of epinephrine and identified gene mutations were largely restricted to those leading to stabilization of hypoxia inducible factors. In contrast, among Chinese there was a larger proportion of epinephrine-producing paragangliomas, mostly due to HRAS and FGFR1 mutations. Conclusions: This study establishes Sino-European differences in the genetic landscape and presentation of PPGLs, including ethnic differences in genotype-phenotype relationships indicating a paradigm shift in our understanding of the biology of these tumors.

A novel and potent anti-tuberculosis drug combination pyrazinamide (PZA), ethambutol (EMB), protionamide (PTO), and clofazimine (CFZ) that rapidly kills Mycobacterium tuberculosis (Mtb) in the lungs has been identified using the artificial-intelligence-enabled parabolic response surface approach. A universal and highly sensitive two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) method for the simultaneous determination of PZA, EMB, PTO, and CFZ in various biological samples in different states (liquid samples: plasma, bile, and urine; solid samples: tissue and feces) using simple pretreatment was established and validated. For the first dimension of this column-switching arrangement, the automated purification and enrichment of the drugs were achieved on a Polar-RP column. The subsequent analytical separation was performed on an Agilent Zorbax SB-Aq column, and the total loop time was 7.5 min. The positive-ionization mode with multiple reaction monitoring was used for detection. The sensitivity was good with no carry-over detected, and the lower limit of quantification ranged from 100 to 500 pg/mL. This quantification method was fully validated and proved to be robust in accordance with US Food and Drug Administration guidelines. High recoveries (85.3-111.4%) and accuracies (92.1-109.3%), together with high precision values (0.5-13.8%), were verified in all matrices. All standard curves showed favorable linearities with r2 > 0.995. This validated method was applied to study plasma pharmacokinetics, tissue distribution, and excretion in Sprague-Dawley rats after oral administration of the drug combination.

Fluorescence molecular tomography (FMT) emerges as a powerful non-invasive imaging tool with the ability to resolve fluorescence signals from sources located deep in living tissues. Yet, the accuracy of FMT reconstruction depends on the deviation of the assumed optical properties from the actual values. In this work, we improved the accuracy of the initial optical properties required for FMT using a new-generation time-domain (TD) near-infrared optical tomography (NIROT) system, which effectively decouples scattering and absorption coefficients. We proposed a multimodal paradigm combining TD-NIROT and continuous-wave (CW) FMT. Both numerical simulation and experiments were performed on a heterogeneous phantom containing a fluorescent inclusion. The results demonstrate significant improvement in the FMT reconstruction by taking the NIROT-derived optical properties as prior information. The multimodal method is attractive for preclinical studies and tumor diagnostics since both functional and molecular information can be obtained., (OLD)Applied Quantum Architectures, OLD QCD/Charbon Lab, QN/High Resolution Electron Microscopy

Near infrared optical tomography (NIROT) is a non-invasive imaging technique to provide physiological information e.g. the oxygenation of tissue. For image reconstruction in clinical and preclinical scenarios, models to accurately describe light propagation are needed. This work aims to assess the accuracy and efficiency of different models, which paves the way for an optimal design of model-based image reconstruction algorithms in NIROT for realistic tissue geometries and heterogeneities. Two popular simulators were evaluated: the Monte Carlo (MC) method based MCX and the finite element method (FEM) based Toast++. We compared simulated results with experimental data measured on a homogeneous silicone phantom with well-calibrated parameters. The laser light was focused on the center of the phantom surface and images were captured by a CCD camera in both reflection and transmission modes. For transmittance measurements, the two models showed good agreement. Both achieve a cosine similarity of ~99%. In contrast, for reflectance measurements, FEM results deviated more from the measured values than MC, yielding similarity values of 86% and 94%, respectively. This study recommends the use of MC for NIROT in reflection mode and both MC and FEM yield excellent results for transmission mode.

The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people. Most of the existing methods are limited to the composition recognition of single-attribute-object, and can hardly distinguish the compositions with similar appearances. In this paper, a graph-based model is proposed that can flexibly recognize both single- and multi-attribute-object compositions. The model maps the visual features of images and the attribute-object category labels represented by word embedding vectors into a latent space. Then, according to the constraints of the attribute-object semantic association, distances are calculated between visual features and the corresponding label semantic features in the latent space. During the inference, the composition that is closest to the given image feature among all compositions is used as the reasoning result. In addition, we build a large-scale Multi-Attribute Dataset (MAD) with 116,099 images and 8,030 composition categories. Experiments on MAD and two other single-attribute-object benchmark datasets demonstrate the effectiveness of our approach., Comment: Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), currently under review

In many clinical applications it is relevant to observe dynamic changes in oxygenation. Therefore the ability of dynamic imaging with time domain (TD) near-infrared optical tomography (NIROT) will be important. But fast imaging is a challenge. The data acquisition of our handheld TD NIROT system based on single photon avalanche diode (SPAD) camera and 11 light sources was consequently accelerated. We tested the system on a diffusive medium simulating tissue with a moving object embedded. With 3D image reconstruction, the moving object was correctly located using only 0.2 s exposure time per source.

Fluorescence molecular tomography (FMT) emerges as a powerful non-invasive imaging tool with the ability to resolve fluorescence signals from sources located deep in living tissues. Yet, the accuracy of FMT reconstruction depends on the deviation of the assumed optical properties from the actual values. In this work, we improved the accuracy of the initial optical properties required for FMT using a new-generation time-domain (TD) near-infrared optical tomography (NIROT) system, which effectively decouples scattering and absorption coefficients. We proposed a multimodal paradigm combining TD-NIROT and continuous-wave (CW) FMT. Both numerical simulation and experiments were performed on a heterogeneous phantom containing a fluorescent inclusion. The results demonstrate significant improvement in the FMT reconstruction by taking the NIROT-derived optical properties as prior information. The multimodal method is attractive for preclinical studies and tumor diagnostics since both functional and molecular information can be obtained.

Near infrared optical tomography (NIROT) is an emerging modality that enables imaging the oxygenation of tissue, which is a biomarker of tremendous clinical relevance. Measuring in reflectance is usually required when NIROT is applied in clinical scenarios. Single photon avalanche diode (SPAD) array technology provides a compact solution for time domain (TD) NIROT to gain huge temporal and spatial information. This makes it possible to image complex structures in tissue. The main aim of this paper is to validate the wavelength normalization method for our new TD NIROT experimentally by exposing it to a particularly difficult challenge: the recovery of two inclusions at different depths. The proposed reconstruction algorithm aims to tackle systematic errors and other artifacts with known wavelength-dependent relation. We validated the device and reconstruction method experimentally on a silicone phantom with two inclusions: one at depth of 10 mm and the other at 15 mm. Despite this tough challenge for reflectance NIROT, the system was able to localize both inclusions accurately.

Hydrogen sulfide is a highly toxic gas-second only to carbon monoxide as a cause of inhalational deaths. Its mechanism of toxicity is only partially known, and no specific therapy exists for sulfide poisoning. We show in several cell types, including human inducible pluripotent stem cell (hiPSC)-derived neurons, that sulfide inhibited complex IV of the mitochondrial respiratory chain and induced apoptosis. Sulfide increased hydroxyl radical production in isolated mouse heart mitochondria and F2-isoprostanes in brains and hearts of mice. The vitamin B12 analog cobinamide reversed the cellular toxicity of sulfide, and rescued Drosophila melanogaster and mice from lethal exposures of hydrogen sulfide gas. Cobinamide worked through two distinct mechanisms: direct reversal of complex IV inhibition and neutralization of sulfide-generated reactive oxygen species. We conclude that sulfide produces a high degree of oxidative stress in cells and tissues, and that cobinamide has promise as a first specific treatment for sulfide poisoning.

Today's teens are growing up in a world where social media has existed throughout their entire lives, and there have been concerns about the influence it has on their overall wellbeing. In November 2018, the Pew Research Center published a study examining the social media habits and experiences of teens in the United States. Among the study's major findings, large majorities of surveyed teens said that their social media use was responsible for "strengthening friendships, exposing them to different viewpoints and helping people their age support causes they care about." However, roughly 40 percent of teens also noted that "they at times feel overwhelmed by the drama on social media and pressure to construct only positive images of themselves." Readers interested in more details may read the full 34-page report online or download it as a PDF, along with the survey's top-line questionnaire results. This analysis was based on Pew surveys conducted in March and April of 2018 of 743 US teens aged 13-17 and 1,058 parents of teens, and it was written by Pew researchers Monica Anderson and Jingjing Jiang.

A minimal setup for optoacoustic (OA) imaging requires an ultrasound probe and a pulsed laser. Such a system is capable of imaging small blood vessels and is sensitive to variations in their oxygen saturation. However, absolute oxygenation values cannot be obtained without a proper correction for the varying light fluence resulting from the optical attenuation in the surrounding tissue. Other techniques, such as near-infrared optical tomography (NIROT) can be employed to assist OA imaging for fluence compensation. In this paper, we propose using blood vessels as virtual fluence detectors (VD), which serve as light detectors for NIROT image reconstructions. By avoiding the use of real photon detectors, a simpler system could be implemented in a hand-held device comparable in size with conventional ultrasound probes. Even for a low number of VDs it provides increased informational value which, in combination with a large number of light sources, results in precise reconstructions. We define a tomographic inverse problem based on ratios of OA signals measured at several wavelengths where optical properties of VDs, tumor and normal tissue can be reconstructed simultaneously. The use of ratio data effectively removes light source skin coupling errors for the case of emission in a single point, which is required for clinical applications. We have defined the mathematical structure of an inverse problem where chromophore concentrations for normal, tumor and embedded VDs are obtained simultaneously from this ratio data. To test the performance of our approach we show an image reconstruction on a virtual phantom with an embedded tumor in the vicinity of eight blood vessels. We conclude that this limited number of VDs, located in areas of maximum sensitivity result in high quality reconstructions. For the simplest case of a single blood vessel located in a homogeneous tissue, we present a graphical user interface based toolbox for conducting virtual experiments. The toolbox c

Near-infrared optical tomography (NIROT) has great promise for many clinical problems. Here we focus on the study of brain function. During NIROT image reconstruction of brain activity, an inverse problem has to be solved that is sensitive to small superficial perturbations on the head such as e.g. birthmarks on the skin and hair. To consider these perturbations, standard physical modeling is unpractical, since it requires the implementation of detailed information that is generally unavailable. The aim here was to test whether artificial neural networks (ANN) are able to handle such perturbations and thus detect brain activity correctly. For simplicity, we created a virtual test model, where we simulated a pattern of activated and resting brain regions, which was covered by skin features like hair or melanin. We compared the performance of this ANN approach with that of an inverse problem based on a Monte Carlo (MC) model for light propagation. We conclude that ANNs tolerate substantially higher levels of skin perturbations than MC models and consequently are more suitable for detecting brain activity.

Imaging brain oxygenation is crucial for preventing brain lesions in preterm infants. Our aim is to build and validate a near-infrared optical tomography (NIROT) sensor for the head of neonates. This sensor, combined with an optoacoustic device, will enable quantitative monitoring of the structural and functional information of the brain. Since the head of preterm infants is small and fragile great care must be taken to produce a comfortable and compact device in which a sufficient number of light sources and detectors can be implemented. Here we demonstrate our first prototype. Heterogeneous silicone phantoms were produced to validate the prototype's data acquisition, data processing, and image reconstruction. Reconstructed optical properties agree well with the target values. The mechanical performance of the new NIROT sensor prototype confirms its suitability for the clinical application.

The accuracy of images obtained by Diffuse Optical Tomography (DOT) could be substantially increased by the newly developed time resolved (TR) cameras. These devices result in unprecedented data volumes, which present a challenge to conventional image reconstruction techniques. In addition, many clinical applications require taking photons in air regions like the trachea into account, where the diffusion model fails. Image reconstruction techniques based on photon tracking are mandatory in those cases but have not been implemented so far due to computing demands. We aimed at designing an inversion algorithm which could be implemented on commercial graphics processing units (GPUs) by making use of information obtained with other imaging modalities. The method requires a segmented volume and an approximately uniform value for the reduced scattering coefficient in the volume under study. The complex photon path is reduced to a small number of partial path lengths within each segment resulting in drastically reduced memory usage and computation time. Our approach takes advantage of wavelength normalized data which renders it robust against instrumental biases and skin irregularities which is critical for realistic clinical applications. The accuracy of this method has been assessed with both simulated and experimental inhomogeneous phantoms showing good agreement with target values. The simulation study analyzed a phantom containing a tumor next to an air region. For the experimental test, a segmented cuboid phantom was illuminated by a supercontinuum laser and data were gathered by a state of the art TR camera. Reconstructions were obtained on a GPU-installed computer in less than 2 h. To our knowledge, it is the first time Monte Carlo methods have been successfully used for DOT based on TR cameras. This opens the door to applications such as accurate measurements of oxygenation in neck tumors where the presence of air regions is a problem for conventional approaches.

Background: Asthma is a chronic airway inflammatory disease characterized by an imbalance in both Th1 and Th2 cytokines. Exhaled nitric oxide (NO) is elevated in asthma, and is a potentially useful non-invasive marker of airway inflammation. However, the origin and underlying mechanisms of intersubject variability of exhaled NO are not yet fully understood. We have previously described NO gas phase release from normal human bronchial epithelial cells (NHBEs, tracheal origin). However, smaller airways are the major site of morbidity in asthma. We hypothesized that IL-13 or cytomix (IL-1 beta, TNF-alpha, and IFN-gamma) stimulation of differentiated small airway epithelial cells (SAECs, generation 10-12) and A549 cells (model cell line of alveolar type II cells) in culture would enhance NO gas phase release. Methods: Confluent monolayers of SAECs and A549 cells were cultured in Transwell plates and SAECs were allowed to differentiate into ciliated and mucus producing cells at an air-liquid interface. The cells were then stimulated with IL-13 (10 ng/mL) or cytomix (10 ng/mL for each cytokine). Gas phase NO release in the headspace air over the cells was measured for 48 hours using a chemiluminescence analyzer. Results: In contrast to our previous result in NHBE, baseline NO release from SAECs and A549 is negligible. However, NO release is significantly increased by cytomix (0.51 +/- 0.18 and 0.29 +/- 0.20 pl.s(-1).cm(-2), respectively) reaching a peak at approximately 10 hours. iNOS protein expression increases in a consistent pattern both temporally and in magnitude. In contrast, IL-13 only modestly increases NO release in SAECs reaching a peak (0.06 +/- 0.03 pl.s-1.cm(-2)) more slowly (30 to 48 hours), and does not alter NO release in A549 cells. Conclusion: We conclude that the airway epithelium is a probable source of NO in the exhaled breath, and intersubject variability may be due, in part, to variability in the type (Th1 vs Th2) and location (large vs small airwa

Currently available cyanide antidotes must be given by intravenous injection over 5-10 min, making them ill-suited for treating many people in the field, as could occur in a major fire, an industrial accident, or a terrorist attack. These scenarios call for a drug that can be given quickly, e.g., by intramuscular injection. We have shown that aquohydroxocobinamide is a potent cyanide antidote in animal models of cyanide poisoning, but it is unstable in solution and poorly absorbed after intramuscular injection. Here we show that adding sodium nitrite to cobinamide yields a stable derivative (referred to as nitrocobinamide) that rescues cyanide-poisoned mice and rabbits when given by intramuscular injection. We also show that the efficacy of nitrocobinamide is markedly enhanced by coadministering sodium thiosulfate (reducing the total injected volume), and we calculate that ∼1.4 mL each of nitrocobinamide and sodium thiosulfate should rescue a human from a lethal cyanide exposure.

Low level light therapy (LLLT) has numerous therapeutic benefits, including improving wound healing, but the precise mechanisms involved are not well established; in particular, the underlying role of cytochrome C oxidase (C-ox) as the primary photoacceptor and the associated biochemical mechanisms still require further investigation. We previously showed the nitric oxide (NO) donating drug nitrosyl-cobinamide (NO-Cbi) enhances wound healing through a cGMP/cGMP-dependent protein kinase/ERK1/2 mechanism. Here, we show that the combination of LLLT and NO-Cbi markedly improves wound healing compared to either treatment alone. LLLT-enhanced wound healing proceeded through an electron transport chain-C-ox-dependent mechanism with a reduction of reactive oxygen species and increased adenosine triphosphate production. C-ox was validated as the primary photoacceptor by three observations: increased oxygen consumption, reduced wound healing in the presence of sodium azide, and disassociation of cyanide, a known C-ox ligand, following LLLT. We conclude that LLLT and NO-Cbi accelerate wound healing through two independent mechanisms, the electron transport chain-C-ox pathway and cGMP signaling, respectively, with both resulting in ERK1/2 activation.

No direct stimulation of human peripheral blood mononuclear cells proliferation could be detected with incubation of various AB extracts including JAB80E70 in vitro. However, ex vivo assay performed using BALB/c mice orally fed with either water (control) or JAB80E70 for 28 days indicated for the first time that extract-treated groups significantly lowered the ex vivo mitogen-stimulated lymphocyte proliferation., Our study started with the preparations of different AB extracts using various solvent systems with or without heating. Among all AB extracts tested, the extract JAB80E70 (extracted with 70% v/v ethanol at 80°C) showed the strongest selective suppression on the growth of human leukemia NB-4 and K-562 cells. The anti-leukemia effect of JAB80E70 was also confirmed in vivo using xenografted NB-4 bearing athymic nude mice model., Phytochemical studies suggested that the selective anti-leukemia activity of JAB80E70-W-B-1 was retained in a relatively polar sub-fraction. One compound was isolated and identified as adenosine from JAB80E70-W-B-1. To the best of our knowledge, this is the first report of the presence of adenosine in AB, even though it exhibited no anti-leukemia activity in vitro., The mushroom Agaricus blazei (AB) is traditionally used as a remedy against various cancers, infections and immune-related diseases. In the present study, the underlying mechanisms of the anti-tumor and immunomodulatory effects of AB extracts on human leukemia cells were systematically investigated., With bioassay-guided fractionation, a sub-fraction (JAB80E70-W-B-1) with almost 5-fold improved selective cytotoxicity towards NB-4 cells was obtained. Both JAB80E70 and JAB80E70-W-B-1 could induce apoptosis characteristic DNA fragmentation and nucleosomes enrichment in NB-4 cells. An increase in the pro-apoptotic and a decrease in the anti-apoptotic Bcl-2 family proteins expression were observed in NB-4 cells treated with JAB80E70-W-B-1. JAB80E70-W-B-1 was also found to enhance activities of caspases 3, 8 and 9 in NB-4 cells. However, the activation of caspases 3 and 9 was not affected by the inhibition of caspase 8. Furthermore, JAB80E70-W-B-1 induced apoptosis in NB-4 cells was accompanied by a significant reduction in mitochondria membrane potential and telomerase activity. These results demonstrated that JAB80E70-W-B-1 induced apoptosis in NB-4 cells was dependent on caspase activity, and involved multiple molecular pathways., Jiang, Jingjing., Adviser: Lau Bik San Clara., Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3454., Thesis (Ph.D.)--Chinese University of Hong Kong, 2008., Includes bibliographical references (leaves 224-250)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web., s in English and Chinese., School code: 1307., isbn: 9781109226621, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

In the last few decades, “ecotourism” has emerged as a much talked about topic that is frequently linked to the term “sustainable development”. Despite the fact that the definition of ecotourism has been expanded from primarily pristine nature-based areas to modified areas, relatively few studies have been devoted to evaluating the relationship between “ecotourism” and “sustainable development” in those destinations. To address this research need, this thesis attempts to analyse the potential of ecotourism as a strategy for sustainable development, where it does not depend only on nature-based opportunities. Relevant literature on the topics of ecotourism and sustainable development was examined to develop a framework to assess ecotourism in a case study area. The case study employed was Tengtou village, China, which is one of the earliest national eco-villages. Tengtou hosted 76,200 tourists in 2006. Data was collected using qualitative methods, which included semi-structured interviews, informal discussions, focus groups, participant observation and questionnaire surveys. The research revealed that ecotourism has brought a variety of favourable impacts, and the local people hold optimistic attitudes about ecotourism, which indicate the feasibility of the ecotourism industry and the comparative success it has achieved. On the other hand, the research also found several potentially adverse effects, which suggest that ecotourism does not yet make a full contribution to local sustainable development. This thesis concludes that effective regulation and sound planning play a significant role in enabling the sustainability of ecotourism. Meanwhile, the importance of local people’s active involvement at different levels of ecotourism development in ensuring ecotourism’s success, in the long term, was also revealed. This issue of local participation, strongly pushed in the international ecotourism literature, is not something which has characterised most ecotourism initiat