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BackgroundAssessment of pain is critical to its optimal treatment. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain is a multivalent, dynamic, and ambiguous phenomenon that is difficult to quantify, particularly when the patient’s ability to communicate is limited. The criterion standard of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as electrocardiography, electromyography, photoplethysmography, and electrodermal activity. However, pain assessment by using only these signals can be unreliable, as various other factors alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. ObjectiveOur objective is to further the development and research of a pain assessment tool for use with patients who are likely experiencing mild to moderate pain. We will collect observational data through wearable technologies, measuring facial electromyography, electrocardiography, photoplethysmography, and electrodermal activity. MethodsThis protocol focuses on the second phase of a larger study of multimodal signal acquisition through facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain and for building predictive models. We used state-of-the-art standard sensors to measure bioelectrical electromyographic signals and changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, we further developed the pain assessment tool and reconstituted it with modern wearable sensors, devices, and algorithms. In this second phase, we will test the smart pain assessment tool in communicative patients after elective surgery in the recovery room. ResultsOur human research protections application for institutional review board review was approved for this part of the study. We expect to have the pain assessment tool developed and available for further research in early 2021. Preliminary results will be ready for publication during fall 2020. ConclusionsThis study will help to further the development of and research on an objective pain assessment tool for monitoring patients likely experiencing mild to moderate pain. International Registered Report Identifier (IRRID)DERR1-10.2196/17783

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

In broadband terahertz waves generated by femtosecond lasers, spatial chirp will be simultaneously produced with the introduction of angular dispersion. The chirp characteristics of the terahertz wave will directly affect the frequency response, bandwidth response, and intensity response of the terahertz sensor. To enhance the capability of terahertz sensors, it is necessary to control and improve the chirped characteristics of broadband terahertz sources. We generate a chirped terahertz wave via optical rectification in a LiNbO3 prism using the technique of pulse front tilt. The effect of the pump-beam spot size on THz generation is systematically studied. The pump's spot size is manipulated using a telescope system. With a pump spot diameter of 1.8 mm, the scanning spectrum of the THz pulse is narrower and is divided into multiple distinct peaks. In contrast, using a pump spot diameter of 3.7 mm leads to increased efficiency in the generation of THz pulses. Also, we investigate the underlying properties governing the generation of chirped terahertz pulses using varying pump pulse spot diameters. [ABSTRACT FROM AUTHOR]

This study examines how different primary school campus layouts impact the wind environment and classroom ventilation in Xi'an, using simulations for winter and summer conditions. It evaluates four typical floorplans to find the best for outdoor wind quality and classroom ventilation. During winter, the outdoor wind speed at a height of 1.5 m remains below 5 m/s, adhering to the Green Building Evaluation Standard (GB/T50378-2019). Notably, Scenario 1 achieves higher wind speeds due to the canyon effect between buildings, facilitating effective air renewal. The wind speed amplification factors in all scenarios are within the permissible limit of 2, while Scenario 1 demonstrates superior outdoor wind performance. Wind pressure differences on building surfaces remain within the 5 Pa limit, with Scenario 3 exhibiting the lowest difference of 0.74 Pa, contributing to energy-efficient heating. In summer, Scenario 1 uniquely avoids vortex areas and windless zones, ensuring efficient airflow across the campus. Its open floor planning prevents the formation of stagnant air zones, in contrast to Scenarios 2, 3, and 4, which create enclosed or semi-enclosed spaces promoting vortex formation and windless areas. These findings underscore the benefits of Scenario 1's design in optimizing both winter and summer wind environments for energy efficiency and occupant comfort. This study recommends including adequately sized spaces in zigzag, branched, or enclosed floor plans to provide airflow and prevent high wind speeds. These results are crucial for shaping upcoming architectural plans to improve the environmental quality of school grounds, leading to improved health and comfort for students and teachers. [ABSTRACT FROM AUTHOR]

Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects. [ABSTRACT FROM AUTHOR]

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country's trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown. [ABSTRACT FROM AUTHOR]

Adoptive cell immunotherapy (ACT) is an innovative promising treatment for tumors. ACT is characterized by the infusion of active anti-tumor immune cells (specific and non-specific) into patients to kill tumor cells either directly or indirectly by stimulating the body's immune system. The patient's (autologous) or a donor's (allogeneic) immune cells are used to improve immune function. Chimeric antigen receptor (CAR) T cells (CAR-T) is a type of ACT that has gained attention. T cells from the peripheral blood are genetically engineered to express CARs that rapidly proliferate and specifically recognize target antigens to exert its anti-tumor effects. Clinical application of CAR-T therapy for hematological tumors has shown good results, but adverse reactions and recurrence limit its applicability. Tumor infiltrating lymphocyte (TIL) therapy is effective for solid tumors. TIL therapy exhibits T cell receptor (TCR) clonality, superior tumor homing ability, and low targeted toxicity, but its successful application is limited to a number of tumors. Regardless, TIL and CAR-T therapies are effective for treating cancer. Additionally, CAR-natural killer (NK), CAR-macrophages (M), and TCR-T therapies are currently being researched. In this review, we highlight the current developments and limitations of several types of ACT. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel double-quenching electrochemical luminescence (ECL) strategy; this strategy is based on the double-quenching effect of Ru-(bpy)32+-functionalized three-dimensional (3D) metal–organic framework (MOF) by MnO2/PDA. An ECL immunosensor for ultrasensitive detection of alpha-fetoprotein (AFP) has been designed. TMU-3 with a rich mesoporous structure can be used to encapsulate Ru-(bpy)32+, which can effectively reduce the leakage of Ru-(bpy)32+. Moreover, the ultraviolet–visible (UV–vis) absorption spectrum of the lamellar MnO2/PDA nanomaterials with a nanometer scale of about 1 μm overlaps with the ECL emission spectrum of the porous bamboo leaf-like Ru-(bpy)32+@TMU-3 with a nanometer scale of about 1 μm. Thus, an ECL resonance energy transfer (ECL-RET) mechanism between Ru-(bpy)32+@TMU-3 (donor) and MnO2/PDA (recipient) is formed. In this way, an ECL immunosensor with a dual-quenching strategy for ECL signal is developed. In order to test the practicability of this method in clinical analysis, the detection target marker AFP content ranged from 0.01 pg/mL to 5 ng/mL and the detection limit was set to 10.7 fg/mL. In summary, this work demonstrates the detection and application of a signal-quenched ECL immunosensor and ECL immunoassay based on a luminescence-functionalized MOF system for biomarker detection. It indicates a great prospect in the analysis of other biomarkers and clinical analysis. [ABSTRACT FROM AUTHOR]

The early detection of tumour markers is essential for the timely treatment of cancer, and thus realising the sensitive detection of carcinoembryonic antigen (CEA) is significant. Herein, we employed an electrochemical immunosensor as a simple analytical method. The loading of precious metals significantly amplifies the signal of electrochemical immunosensors. In this study, the successful loading of Au NPs was achieved via the galvanic displacement method. Moreover, CuS nanoflowers possessed intrinsic peroxidase-like activity, which could catalyse the oxidation of o-phenylenediamine (OPD) to generate a light-orange product in the presence of hydrogen peroxide (H2O2). The flower-shaped CuS not only provided active sites for the catalytic reaction, but also possessed a large specific surface area, which could provide abundant sites for the placement of Au nanoparticles. Thus, it was suitable for the labelling of Ab2 for the sensitive detection of CEA. The instant current response (i–t) method detected CEA quantitatively, and the immunosensor prepared under the optimized conditions exhibited an excellent analytical performance. The detection range of the sensor prepared in this study was 0.1–80 ng mL−1 and its detection limit was 5.5 pg mL−1 (S/N = 3). [ABSTRACT FROM AUTHOR]

In this article, an efficient high-throughput depth engine is proposed to generate high-quality 3-D depth maps for speckle-pattern structured-light depth cameras. A dynamic-binarization (DB) method is introduced with a significant reduction of computational complexity in contrast to the sum-of-absolute-distance (SAD) method. The depth map evaluation shows good robustness compared with other window-based correlation methods. Parallel architecture and reuse of intermediate results are employed for efficient hardware implementation. Our design is verified on a field-programmable gate array (FPGA) and implemented in the SMIC 55-nm CMOS technology, achieving a frame rate of 1731.77 fps ($640\times480$) with an area efficiency of 3.75 fps/KGE. The proposed engine shows a $2.71\times $ promotion of area efficiency in contrast to the SAD-based implementation. In addition, the subpixel estimation algorithm deployed in postprocessing is optimized for efficient hardware implementation, reducing the gate count by 69.2% without significant performance loss. [ABSTRACT FROM AUTHOR]

Sea ice mapping plays an integral role in ship navigation and meteorological modeling in the polar regions. Numerous published studies in sea ice classification using synthetic aperture radar (SAR) have reported high classification rates. However, many of these focus on numerical results based on sample points and ignore the quality of the inferred sea ice maps. We have designed and implemented a novel SAR sea ice classification algorithm where the spatial context, obtained by the unsupervised IRGS segmentation algorithm, is integrated with texture features extracted by a residual neural network (ResNet) and, using regional pooling, classifies ice and water. This algorithm is trained and tested on a published dataset and cross-validated using leave-one-out (LOO) strategy, obtaining an overall accuracy of 99.67% and outperforming several existing algorithms. In addition, visual results show that this new method produces sea ice maps with natural ice–water boundaries and fewer ice and water errors. [ABSTRACT FROM AUTHOR]

Kidney renal papillary cell carcinoma (KIRP), the second most common subtype of renal cell carcinoma, still lacks effective treatment regimens for individualized immunotherapy because of the heterogeneity of its elusive immune microenvironment. Therefore, we aimed to comprehensively evaluate the immune microenvironment of KIRP by using the computational biology strategy to analyze the expression profile data of 289 KIRP patients obtained from The Cancer Genome Atlas database. Based on multidimensional, multi-omics bioinformatics analysis, we found that the tumor of patients with KIRP exhibited "hot" tumor characteristics but the CD8+ T cells in the tumor tissues did not limit tumor progression. Thus, patients with KIRP may realize higher clinical benefits by receiving treatment that can reverse CD8+ T-cell exhaustion. Among them, C1 and C3 immune subtypes could realize the best efficacy of reversing CD8+ T-cell exhaustion. Moreover, CCL5 and FASLG expression may be related to the formation of the immunosuppressive microenvironment in the tumors of patients with KIRP. In conclusion, the immune microenvironment landscape presented in this study provides a novel insight for further experimental and clinical exploration of tailored immunotherapy for patients with KIRP. [ABSTRACT FROM AUTHOR]

An analog neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multiplier shows significant area and power reduction. The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog–digital interfaces. By implementing the sequential analog fabric, the engine’s mixed-signal interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 784 by 784 CTT computing engine is implemented using TSMC 28-nm CMOS technology and occupies 0.68 mm2. The simulated performance achieves 76.8 TOPS (8-bit) with 500 MHz clock frequency and consumes 14.8 mW. As an example, we utilize this computing engine to address a classic pattern recognition problem—classifying handwritten digits on MNIST database and obtained a performance comparable to state-of-the-art fully connected neural networks using 8-bit fixed-point resolution. [ABSTRACT FROM AUTHOR]

Automatic classification methods using satellite imagery are beneficial in the sea-ice-type mapping of the Arctic regions. In the near future, the RADARSAT Constellation Mission (RCM) will be launched, providing unique compact polarimetric (CP) synthetic aperture radar (SAR) data, expected to be an improvement over the current RADARSAT-2 dual-polarimetric SAR imagery. This motivates the implementation of a CP-dedicated automatic scene classification approach. First, an existing unsupervised segmentation algorithm called iterative region growing using semantics (IRGS) is used to segment ice-class homogeneous regions to reduce the impact of speckle noise. Second, a support vector machine (SVM) is used to classify the ice-type labels for each homogeneous region. Two complex quad-polarimetric RADARSAT-2 scenes are used to mathematically simulate the corresponding CP scenes for algorithm testing. Classification accuracy shows that using only the two CP intensity images leads to improved results compared with standard dual-polarimetric scenes. Using the CP data, the best classification results are obtained with the reconstructed QP data for the IRGS segmentation and all derived CP features for the SVM labeling. The results support the expected potential that CP scenes will provide improved sea-ice classification than the current operational dual-pol scenes. [ABSTRACT FROM AUTHOR]

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions. [ABSTRACT FROM AUTHOR]

GREM1 is a secreted protein that antagonizes bone morphogenetic proteins (BMPs) and participates in critical biological processes, including embryonic development, organogenesis and tissue differentiation. Gremlin 1 (GREM1) is also an inhibitor of TGF-β and a ligand for vascular endothelial growth factor receptor 2. In addition, GREM1 can induce cells, participate in the process of epithelial-mesenchymal transition, and then participate in tumor development. GREM1 has a variety of biological functions and can participate in the malignant progression of a variety of tumors through the BMP signaling pathway. GREM1 also can inhibit TGF-β in some tumors, thereby inhibiting tumors, and its involvement in tumor development varies in different types of cancer. The present review examines the role and function of GREM1 in tumors. GREM1 is expressed in a variety of tumor types. GREM1 expression can affect the epithelial-mesenchymal transformation of tumor cells. GREM1 has been studied in breast and colon cancer, and its potential role is to promote cancer. However, in pancreatic cancer, which was found to act differently from other cancer types, overexpression of GREM1 inhibits tumor metastasis. The present review suggests that GREM1 can be a diagnostic and prognostic indicator. In future studies, the study of GREM1 based on single-cell sequencing technology will further clarify its role and function in tumors. [ABSTRACT FROM AUTHOR]

This letter presents a novel method for ship classification that uses synthetic-aperture-radar images to distinguish ships based on superstructure scattering features. The ratio of dimensions, which combines the 2-D and 3-D properties of scattering, is explored as an effective and credible means to describe the scattering features of ships. The proposed method consists of three main stages: 1) ship isolation from the sea; 2) parametric vector $(F)$ estimation; and 3) categorization using a support vector machine (SVM) classifier. To depict ship features more accurately and reduce feature redundancy, we propose employing peak extraction to divide a ship into bow, middle, and stern instead of into three equal parts. The classification method is tested with RadarSat-2 images, and ground-truth information is supplied by an automatic identification system. The experimental results show that the proposed method can achieve satisfactory ship-classification performance compared with existing methods, with an overall accuracy exceeding 80%. [ABSTRACT FROM PUBLISHER]

Hexokinase (HK) plays a key role in various biological processes such as glycolysis of tumor cells. However, there is still a lack of systematic understanding of the contribution of HK family genes in different types of cancer. In the present study, we systematically analyzed the molecular changes and clinical correlations of HK family genes in 33 types of cancer extracted from more than 10,000 subjects. As a result, there were extensive genetic changes in HK family genes and the expression levels of HK family were significantly correlated with the activity of cancer marker-related pathways. In addition, HK family genes may be useful in predicting prognosis and therapeutic efficacy. Moreover, HK1,HK2 and HK3 may become potential oncogenes across a variety of cancer types. Furthermore, the oncogenic functions of HK1 in bladder cancer have been confirmed in vitro. Collectively, our results provide valuable resources to guide the mechanism and therapeutic analysis concerning the role of HK family genes in cancer. [ABSTRACT FROM AUTHOR]