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Gamma/delta T (γδ T)cells possess a unique mechanism for killing tumors, making them highly promising and distinguished among various cell therapies for tumor treatment. This review focuses on the major histocompatibility complex (MHC)-independent recognition of antigens and the interaction between γδ T cells and solid tumor cells. A comprehensive review is provided regarding the classification of human gamma-delta T cell subtypes, the characteristics and mechanisms underlying their functions, as well as their r545egulatory effects on tumor cells. The involvement of γδ T cells in tumorigenesis and migration was also investigated, encompassing potential therapeutic targets such as apoptosis-related molecules, the TNF receptor superfamily member 6(FAS)/FAS Ligand (FASL) pathways, butyrophilin 3A-butyrophilin 2A1 (BTN3A-BTN2A1) complexes, and interactions with CD4, CD8, and natural killer (NK) cells. Additionally, immune checkpoint inhibitors such as programmed cell death protein 1/Programmed cell death 1 ligand 1 (PD-1/PD-L1) have the potential to augment the cytotoxicity of γδ T cells. Moreover, a review on gamma-delta T cell therapy products and their corresponding clinical trials reveals that chimeric antigen receptor (CAR) gamma-delta T therapy holds promise as an approach with encouraging preclinical outcomes. However, practical issues pertaining to manufacturing and clinical aspects need resolution, and further research is required to investigate the long-term clinical side effects of CAR T cells. In conclusion, more comprehensive studies are necessary to establish standardized treatment protocols aimed at enhancing the quality of life and survival rates among tumor patients utilizing γδ T cell immunotherapy.

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.

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.

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.

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.

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.

The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Constellation Mission imagery on a daily basis. Inspired by the demand for a computer-based mapping system, we have developed an automatic sea-ice classification method that integrates spatial contexture (unsupervised segmentation) with textural features (supervised pixel-level labeling). First, the full-scene image is oversegmented, and the segments are merged into homogeneous regions across the entire scene. Second, pixel-based classifiers (support vector machine and random forest) are compared for their ability to label the generated homogeneous regions. Finally, the segmentation and labeling are combined using a proposed energy function. The proposed method was tested on 18 dual-polarization RADARSAT-2 scenes acquired over the Beaufort Sea. This dataset contains water, young ice, first-year ice, and multiyear ice covering melt, summer, and freeze-up seasons. The proposed method obtains an average classification accuracy of 86.33% based on the leave-one-out validation. The experimental results show that the proposed method achieves promising classification results in both the quantity and quality measurements compared with benchmark methods. The robustness against incidence angle variance indicates that the proposed method is well qualified for operational sea-ice mapping.

In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by ChatGPT, equipped with GPT-3.5 and GPT-4, and those written by humans. In this work, we performed multi-dimensional scaling (MDS) to confirm the distributions of 216 texts of three classes (72 academic papers written by 36 single authors, 72 texts generated by GPT-3.5, and 72 texts generated by GPT-4 on the basis of the titles of the aforementioned papers) focusing on the following stylometric features: (1) bigrams of parts-of-speech, (2) bigram of postpositional particle words, (3) positioning of commas, and (4) rate of function words. MDS revealed distinct distributions at each stylometric feature of GPT (3.5 and 4) and human. Although GPT-4 is more powerful than GPT-3.5 because it has more parameters, both GPT (3.5 and 4) distributions are overlapping. These results indicate that although the number of parameters may increase in the future, GPT-generated texts may not be close to that written by humans in terms of stylometric features. Second, we verified the classification performance of random forest (RF) classifier for two classes (GPT and human) focusing on Japanese stylometric features. This study revealed the high performance of RF in each stylometric feature: The RF classifier focusing on the rate of function words achieved 98.1% accuracy. Furthermore the RF classifier focusing on all stylometric features reached 100% in terms of all performance indexes (accuracy, recall, precision, and F1 score). This study concluded that at this stage we human discriminate ChatGPT from human limited to Japanese language.

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.

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.

Background Kidney renal clear cell carcinoma (KIRC) affects the genitourinary system. Although treatment of KIRC in early stages can be highly successful, this type of cancer is difficult to detect until later stages of disease that are less easily treatable. Previous studies have focused on tumor cells, but have ignored the contributions of the tumor microenvironment. Methods We analyzed KIRC gene expression data from The Cancer Genome Atlas with the ESTIMATE algorithm to identify differentially expressed genes. Through protein–protein interaction network analysis, we identified clusters and picked genes from the clusters for further analysis. Differential expression, Kaplan–Meier, and univariate Cox analyses were used to select prognostic biomarkers. Gene Set Enrichment Analysis (GSEA) and Tumor Immune Estimation Resource (TIMER) analysis were used to explore the immune characteristics of these genes as biomarkers. Results Through the ESTIMATE algorithm and other system biology tools, TNFSF13B was identified as a prognostic biomarker. TNFSF13B is highly expressed in tumors, and high expression of TNFSF13B leads to poor prognosis. Further GSEA and TIMER analysis revealed that the expression of TNFSF13B was related to the immune signaling pathway and lymphocyte infiltration. Our findings strongly suggest that TNFSF13B may be a potential biomarker or target related to the tumor microenvironment for KIRC.

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

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.

In this paper, a heat pump air conditioning system (HPACS) with refrigerant R134a based on the functional requirements of battery electric vehicle is designed and tested. Experiments were conducted to evaluate the effects of different ambient temperature, air flow rate of internal condenser, expansion valve (EXV) opening and compressor speed. The results demonstrate that air flow rate of internal condenser, EXV opening and compressor speed have important impact on heating capacity, compressor power consumption and coefficient of performance (COP) under several ambient temperatures. To verify the HPACS can also provide the heating capacity required by the battery electric vehicle cabin in cold climate, the system was also tested under a −5 °C ambient temperature, it was found that the heating capacity is 3.6 kW and the COP is 3.2, demonstrating that the system has high energy efficiency. In addition, heating process analysis of the HPACS under lower temperature is studied by exergy principle. The results indicate that compressor is the highest exergy destruction in all components, accounting for 55%. The percentage of exergy destruction in other components is about 28%, 12% and 5% for the expansive valve, condenser, and evaporator. Furthermore, air flow rate of internal condenser, ambient temperature and expansion valve opening have important impact on exergy destruction and exergy efficiency of the HPACS.

Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently, the Canadian Ice Service (CIS) monitors lakes using synthetic aperture radar (SAR) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, we present an automatic ice-mapping approach which integrates unsupervised segmentation from the Iterative Region Growing using Semantics (IRGS) algorithm with supervised random forest (RF) labeling. IRGS first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. Recently, these output regions were manually labeled by the user to generate ice maps, or were labeled using a Support Vector Machine (SVM) classifier. Here, three labeling methods (Manual, SVM, and RF) are applied after IRGS segmentation to perform ice-water classification on 36 RADARSAT-2 scenes of Great Bear Lake (Canada). SVM and RF classifiers are also tested without integration with IRGS. An accuracy assessment has been performed on the results, comparing outcomes with author-generated reference data, as well as the reported ice fraction from CIS. The IRGS-RF average classification accuracy for this dataset is 95.8%, demonstrating the potential of this automated method to provide detailed and reliable lake ice cover information operationally.

The use of cellulose nanocrystals (CNC) in high performance coatings is attractive for micro-scale structures or device fabrication due to the anisotropic geometry, however CNC are insulating materials. Carbon nanotubes (CNT) are also rod-shaped nanomaterials that display high mechanical strength and electrical conductivity. The hydrophobic regions of surface-modified CNC can interact with hydrophobic CNT and aid in association between the two anisotropic nanomaterials. The long-range electrostatic repulsion of CNC plays a role in forming a stable CNT and CNC mixture dispersion in water, which is integral to forming a uniform hybrid film. At concentrations favorable for film formation, the multiwalled nanotubes + CNC mixture dispersion shows cellular network formation, indicating local phase separation, while the single-walled nanotube + CNC mixture dispersion shows schlieren texture, indicating liquid crystal mixture formation. Conductive CNT + CNC hybrid films (5−20 μm thick) were cast on glass microscope slides with and without shear by blade coating. The CNT + CNC hybrid films electrical conductivity increased with increasing CNT loadings and some anisotropy was observed with the sheared hybrid films, although to a lesser extent than what was anticipated. Percolation models were applied to model the hybrid film conductivity and correlate with the hybrid film microstructure.