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Abstract Background This study explored similarities and differences among Chinese patients and rheumatologists in their attitudes towards and perceptions of traditional Chinese medicine (TCM) for Sjögren’s syndrome (SS), including analyzing factors that influenced their decision making. Methods An anonymous questionnaire was used to conduct a multicenter survey among patients with SS at three tertiary care medical centers in Beijing and among rheumatology clinicians at several hospitals across China. Results were analyzed using descriptive statistics. Results There were 942 valid questionnaires from patients from 31 provinces and cities in China, with a male-to-female ratio of approximately 1:14, a mean age of 48.81 years, and a median disease duration of 7 (4, 10) years. There were 320 valid questionnaires from rheumatologists, covering 30 provinces and cities in China, with a male-to-female ratio of approximately 0.87:1, a mean age of 48 years, and a median work duration of 10.5 (6, 15) years. The rheumatologists treated a median of 15 (11, 50) SS cases per month, and the median proportion of SS to all rheumatic diseases was 6.66% (6–10%). Many patients believed TCM could cure the root of the disease, and the most expected TCM therapies were TCM patent prescriptions and medicinal teas. Conversely, rheumatologists placed high value on the efficacy of TCM, and most commonly prescribed Chinese herbal decoctions. Most doctor-patient groups were positive about TCM treatment, citing the low side effects as the major advantage. Regression analysis showed that for patients over 40 years old with a course of disease > 4 years, the probability of using TCM has increased by 1–6 times; the probability of recommending TCM in clinical work of doctors who have worked for more than 15 years, TCM and integrated traditional Chinese and western medicine has increased 1–2 times. Conclusions TCM has become widely accepted and earned attention from doctor-patient groups, especially among older patients and experienced rheumatologists. However, negative prejudices and absence of accurate information about TCM treatments and SS itself require improvement. The contradiction between TCM dosage form and efficacy is a major problem, and patient demand for convenient and efficient TCM patent preparations suggests future work should focus on developing TCM patent preparations with clear compositions and mechanisms.

Grain boundaries (GBs) play a crucial role on the structural stability and mechanical properties of Cu and its alloys. In this work, molecular dynamics (MD) simulations are employed to study the effects of Fe solutes on the formation energy, excess volume, dislocations and melting behaviors of GBs in CuFe alloys. It is illustrated that Fe solute affects the structural stability of Cu GBs substantially, the formation energy of GBs is reduced, but the thickness and melting point of GBs are increased, that is, the structural stability of Cu GBs is significantly improved owing to the Fe solutes. A strong scaling law exists between the formation energy, excess volume, thickness and melting point of GBs. Therefore, Fe solid solute plays an important role in the characteristics of GBs in bi-crystal Cu.

The potential microclimatic effects of street trees are influenced by their ecological characteristics, planting patterns, and street orientations, especially in subtropical hot and humid areas. To investigate these effects, four typical street tree species in Guangzhou were selected for study during the transition seasons: Khaya senegalensis, Terminalia neotaliala, Ficus microcarpa, and Mangifera indica. Air temperature (AT), relative humidity (RH), solar radiation (SR), surface temperature (ST), wind speed (WS), and the leaf area index (LAI) were monitored. The cooling effects of these four species and the resulting improvements in human thermal comfort (HTC) were assessed. The influences of tree planting patterns and street orientations on cooling benefits were systematically analyzed. The results indicate that, during transition seasons, the four street trees, on average, can block 96.68% of SR, reduce AT by 1.45 °C and ST by 10.25 °C, increase RH by 5.26%, and lower the physiologically equivalent temperature (PET) by 8.34 °C. Terminalia neotaliala, reducing AT and PET by 1.76 °C and 12.4 °C, respectively, offers the greatest potential for microclimate improvement. Among the four tree species, the variations in ST (ΔST) and PET (ΔPET) were minimal, at only 0.76 °C and 0.25 °C, respectively. The average differences in AT and PET between inter-tree and under-tree environments were 0.06 °C and 0.98 °C, respectively. The AT reduction rate was 1.7 times higher in the double-row planting pattern compared to the single-row planting pattern. Street trees planted in the northwest–southeast (NW-SE) orientation exhibited a 16.96% lower WS reduction than those in other orientations. The northeast–southwest (NE-SW) orientation showed the least potential to enhance human thermal comfort. Compared to NE-SW, the northwest–southeast (NW-SE) orientation achieved twice the rate of AT reduction, while the north–south (N-S) orientation improved it by 1.3 times. This data analysis aids in assessing the impact of green infrastructure on urban climates and demonstrates the year-round microclimatic benefits of street trees.

In view of the problem that vibration of superstructures under vibration loads of metro trains causes, this research used a metro depot and superstructure project as its background and proposed a numerical simulation method based on the impedance analytical model and finite element model to simulate and predict the vibration and secondary noise response of subway trains affecting multi-story buildings at different locations on the ground and in the superstructure. The method’s accuracy was verified using real measurement data. The research shows that vibrations generated by subway operations vertically at lower floors remain relatively unchanged, then slowly attenuate before increasing near the top floors. Mitigation measures should primarily address four aspects: rails, fasteners, sleepers, and roadbed. The adverse effects of vibration can be controlled by reducing the excitation intensity of the vibration source, attenuating vibrations along the propagation path, and isolating vibrations in the foundation and interior of the building. This research method can quickly and accurately predict the vibration and noise conditions of superstructure properties and provide support for vibration and noise reduction in practical engineering.

Recent advancements in immunotherapy have substantially improved overall survival and quality of life among patients with cancer. Notably, immune checkpoint inhibitors (ICIs) have emerged as a revolutionary strategy, particularly in the management of advanced cancers. However, the success of ICIs is accompanied by the challenge of immune-related adverse events. Although rare, cardiovascular adverse events associated with ICIs are associated with high fatality rates and rapid clinical progression, thereby necessitating timely intervention. This review explores the histopathologic characteristics of ICI-induced myocarditis, shedding light on the complexities of diagnosis and management. Several studies examining the histopathologic features of ICI-induced myocarditis have emphasized the roles of macrophages and the potential utility of ancillary tests such as immunohistochemistry. Quantifying CD68+ macrophage abundance may enhance diagnostic sensitivity, thereby providing valuable insights into clinical outcomes. In conclusion, this review underscores the need for a nuanced approach to diagnosing ICI-induced myocarditis. The comprehensive exploration of histopathologic characteristics, ancillary tests, and emerging diagnostic markers provides valuable guidance for practicing pathologists. As the population of ICI-treated patients with cancer continues to grow, optimizing immunohistochemistry panels and refining diagnostic criteria will be crucial to address the unique challenges posed by ICI-induced myocarditis.

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R2 value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R2 (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings’ cooling load.

Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.

Modeling, validating, and simulating are three essential parts in investigating the heat mitigation benefits of urban trees (BUT). Therefore, 81 relevant studies from the last ten years are reviewed, analyzed, and summarized in this study. Three main ways for urban trees to adjust the environment are summarized, including shade creation and radiation modification, cooling effects of transpiration, and airflow blocking and modification effects. Research works are analyzed with regard to four categories: (1) heat and moisture exchange mechanisms and their mathematical modeling; (2) verification of modeling predictions based on measurements; (3) thermal performance simulation and prediction; and (4) environmental assessment and human thermal comfort analyses. Future research opportunities are discussed: (1) conduct real-time and in-depth measurements to analyze the mechanisms of heat and moisture transfer of trees in different areas; (2) develop tree radiation attenuation, airflow resistance, and transpiration models to accurately describe heat and moisture transfer processes in the urban environment; and (3) establish a three-dimensional numerical simulation method that can accurately simulate the urban thermal environment with trees. This review provides researchers with an overview and potential research opportunities on the thermal effects of urban trees.

Flame retardant modification of leaf fibers was carried out to solve the technical problem of poor fire resistance of plant fibers and improve the utilization rate of urban fallen leaves in building materials. The modification scheme adopts three flame retardants, i.e., ammonium polyphosphate (APP), magnesium hydroxide (MH), and aluminum hydroxide (ATH), and two covering layers, i.e., pure acrylic polymer lotion and water glass (Na2O · nSiO2) solution. The modified leaf fiber's combustion behavior and pyrolysis properties were tested and analyzed. The physical and mechanical characteristics, as well as the thermal insulation qualities, of leaf fiber cement-based composites (LFCC) were studied at high temperatures. The findings revealed that the three flame retardants had an effect on the chemical structure of leaf fibers. In comparison to leaf fibers without flame-retardant modification, flame-retardant-modified leaf fibers have a much greater thermal stability. and its LOI is greater than 27.0%, which is a fire-retardant material. Except for the sample with water glass as the modified cover layer, at high temperatures, the composite flame-retardant fiber LFCC's mass-loss rate is lower compared with fibers without flame-retardant modification or fibers modified with only one kind of flame-retardant. In the composite flame-retardant modified fiber LFCC, the samples with better strength at high temperature are those with ATH replacing 30% and 50% MH. The thermal conductivity of LFCC is negatively correlated with the range of temperature change.

AimWe sought to evaluate the efficacy of acupuncture in treating the main symptoms of primary Sjögren’s syndrome, specifically dryness, pain, and fatigue.MethodsA total of 120 patients with primary Sjögren’s syndrome were randomized in a parallel-group, controlled trial. Participants received acupuncture or sham acupuncture for the first 8 weeks, then were followed for 16 weeks thereafter. The primary outcome was the proportion of participants with a ≥ 30% reduction in ≥ 2 of 3 numeric analog scale scores for dryness, pain, and fatigue. The secondary outcomes included the European League Against Rheumatism (EULAR) Sjögren’s Syndrome Patient-reported Index (ESSPRI); the EULAR Sjögren’s Syndrome Disease Activity Index; the Schirmer test score; unstimulated saliva flow; serum immunoglobulin G, A, and M concentrations; the Medical Outcome Study Short Form 36 score; salivary gland ultrasound imaging; and the Hospital Anxiety and Depression Scale score.ResultsThe proportions of patients meeting the primary endpoint were 28.33% (17/60) in the acupuncture group and 31.66% (19/60) in the sham group, without a statistically significant difference (P = 0.705). The IgG concentration at week 16 and the homogeneity in ultrasonography of the salivary glands at week 8 showed significant differences between the 2 groups (P = 0.0490 and P = 0.0334, respectively). No other differences were observed between the 2 groups. ESSPRI and unstimulated saliva flow were improved in both groups compared to baseline, albeit with a significant difference between them.ConclusionIn patients with primary Sjögren’s syndrome, acupuncture did not satisfactorily improve symptoms compared to placebo. However, interesting discoveries and possible underlying reasons were demonstrated and discussed, which may be useful to studies in the future.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT02691377].

The concentrations of small microplastics (diameter

The received signal of passive radar based on mobile communication signals contains direct and multipath interference (DMI) signals from multiple co-channel base stations (CCBS). The direct signal spatial spectrum of each CCBS should be obtained first to eliminate the co-channel interference. The performance of the traditional spatial spectrum estimation algorithms based on uniform linear array (ULA) is related to the number of array elements. In the complex co-channel interference environment, the array requires an ultra-large number of array elements and the spatial spectrum estimation resolution is poor. This paper proposes a method for estimating the direct signal spatial spectrum of the CCBS by fusing coprime array and compressive sensing. Firstly, an augmented virtual array signal is constructed by using the second-order statistics of the received signals of the coprime array and then the compressive sensing algorithm is used to estimate the spatial spectrum of the direct signal of the CCBS. The proposed method can achieve higher resolution and higher-degrees-of-freedom (DOFs) performance than traditional ULA by using the same number of array elements. The effectiveness of the proposed method is verified by numerical simulation analysis and experimental data.

Glaciers and glacial lakes in the Bosula Mountain Range need special attention, because their instability may cause disastrous consequences to the downstream settlements and the Sichuan-Tibet Road. The latter is a pivotal traffic line in the Southeast Tibetan Plateau. In order to investigate the state of glaciers and glacial lakes in the Bosula Mountain Range, we estimated the changes in glacier/glacial lake boundaries, glacier surface elevation, and glacier flow velocity between 2000 and 2021 based on multisource remote sensing data. Our results showed that, from the period 2000–2013 to the period 2013–2021, the average shrinking rate of glacier area increased from 0.99 km2/a to 1.74 km2/a, and the average expanding rate of glacial lake area increased from 0.04 km2/a to 0.06 km2/a. From the period 1990–2011 to the period 2015–2019, the average thinning rate of glaciers increased from 0.83 m/a to 1.58 m/a. These results indicate the Bosula Mountain Range is one of the fastest melting glacierized regions in the High Mountain Asia, and the factors that account for this may include quick temperature rise, abundant summer rainfall, and thin debris cover. In spite of strong ice melting, the observed changes in glacier boundaries, surface elevation, and flow velocity show no sign of surge activity, and the frequency of glacier lake outburst has not increased since 1989. Currently, three proglacial lakes that expanded quickly during 2000–2021 are now prominent hazards. They are directly threatened by accidental ice calving and ice avalanche, and their outburst could cause considerable damage to the downstream settlements and the Sichuan-Tibet Road.

A compact spoof surface plasmon polariton (SSPP) low-pass filter is proposed. By adopting a novel fishbone structure, the effective depth of the groove is increased, reducing the filter width by 24.84%. The length of the filter is reduced by 22.23% with a new transmission structure. To intuitively display this structure, the filter is designed and fabricated. The area of the filter is 47.44 mm×8 mm. The results demonstrate that the insertion and return losses are less than 3 dB and greater than 13 dB, respectively, in a wideband range of 0-10.00 GHz. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]