Search Results (595)

T cell receptor (TCR) profiling using next-generation sequencing (NGS) enables high-throughput, in-depth analysis of repertoire diversity, offering numerous clinical applications. We developed a DNA-based strategy to analyse the TCRβ-chain using NGS and established reference values for T cell repertoire characteristics in 74 healthy donors, including 44 adults, 20 paediatrics, and 10 cord blood units (CBUs). Additionally, four paediatric patients with combined immunodeficiency (CID) or severe CID (SCID) due to deleterious mutations in recombination activating genes (RAG) were analysed. The developed strategy demonstrated high specificity, reproducibility, and sensitivity, and all functional variable and joining genes were detected with minimal PCR bias. All donors had a Gaussian-like distribution of complementary-determining region 3 length, with lower presence of non-templated nucleotides and higher proportion of non-functional clonotypes in CBUs. Both CBUs and paediatrics showed greater convergence and TCRβ diversity was significantly lower in adults and donors with cytomegalovirus-positive serostatus. Finally, an analysis of paediatric patients with RAG-SCID/CID showed significantly shorter CDR3 region length and lower repertoire diversity compared to healthy paediatrics. In summary, we developed a reliable and feasible TCRβ sequencing strategy for application in the clinical setting, and established reference values that could assist in the diagnosis and monitoring of pathological conditions affecting the T cell repertoire. Full article

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

High parasitic capacitance from poly-insulator-poly capacitors in complementary metal oxide semiconductor (CMOS) processes presents a major bottleneck to achieving high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs) in imaging systems. This study proposes a Phase-Adjustable SAR ADC that addresses this limitation through a reconfigurable architecture. The design utilizes a phase-adjustable logic unit to switch between a conventional SAR mode for high-speed operation and a noise-shaping (NS) SAR mode for high-resolution conversion, actively suppressing in-band quantization noise. An improved SAR logic unit facilitates the insertion of an adjustable phase while concurrently achieving an 86% area reduction in the core logic block. A prototype was fabricated and measured in a 0.35-µm CMOS process. In conventional mode, the ADC achieved a 7.69-bit effective number of bits at 2 MS/s. By activating the noise-shaping circuitry, performance was significantly enhanced to an 11.06-bit resolution, corresponding to a signal-to-noise-and-distortion ratio (SNDR) of 68.3 dB, at a 125 kS/s sampling rate. The results demonstrate that the proposed architecture effectively leverages the trade-off between speed and accuracy, providing a practical method for realizing high-performance ADCs despite the inherent limitations of non-ideal passive components. Full article

Inertial measurement unit (IMU)-based gait biometrics have attracted increasing attention for unobtrusive identity recognition. While recent studies often fuse signals from multiple sensor positions and time–frequency features, the actual contribution of each sensor location and signal modality remains insufficiently explored. In this work, we present a comprehensive quantitative analysis of the role of different IMU placements and feature domains in gait-based identity recognition. IMU data were collected from three body positions (shank, waist, and wrist) and processed to extract both time-domain and frequency-domain features. An attention-gated fusion network was employed to weight each signal branch adaptively, enabling interpretable assessment of their discriminative power. Experimental results show that shank IMU dominates recognition accuracy, while waist and wrist sensors primarily provide auxiliary information. Similarly, the contribution of time-domain features to classification performance is the greatest, while frequency-domain features offer complementary robustness. These findings illustrate the importance of sensor and feature selection in designing efficient, scalable IMU-based identity recognition systems for wearable applications. Full article

We design a phase rendering technique that exploits the link between the angular deviations of a beam and the optical implementation of cross-correlations. We employ two suitably coded gratings, which are incorporated as part of a new device here called a dark-field, Lau interferometer. To this end, we use a first grating whose unit cell is coded with the white and black versions of a Barker sequence. We employ a second grating that is coded as the Babinet’s complementary of the first grating. We describe the cross-correlation operation by using a compact matrix formulation, which is amenable to numerical evaluation. Full article

Although concern for animal welfare may have been linked to humans since the domestication of livestock, the term itself first appeared in the United Kingdom in the 1960s. The emergence of the concept of animal welfare occurred in a society undergoing a clear transition from patriarchal to emancipatory values based on the concept of freedom. However, coinciding with the recognition of animals as sentient beings in the EU and the emergence of concepts such as a “Life Worth Living”, the Five Freedoms were complemented. In fact, the values of a knowledge society—through autonomy, justice, and equality—create the conditions for a society more connected to its emotions. This entire movement culminated in an updated and complementary definition called “the Five Domains,” in which the mental states of animals and their emotions are essential. However, in the meantime, the market is dominated by several animal welfare certification schemes that focus on inputs (what humans provide) rather than outcomes (animal-based indicators), reflecting an anthropocentric perspective that does not consider the actual experiences of animals from farm to farm. In a knowledge society, where emotions are so important, this approach will be considered unacceptable someday. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

Background: As diabetes mellitus continues to escalate into a global health crisis, particularly in China, the limitations of conventional pharmacotherapy underscore the need for complementary interventions. This study systematically reviews two decades of research progress on acupuncture for diabetes management. Methods: A total of 391 publications met the inclusion criteria from the Web of Science Core Collection (2004–2024) using the search terms “acupuncture” AND “diabetes”. These comprised 294 original studies and 97 reviews. CiteSpace 6.3.R1 was used to perform multidimensional analyses, including co-occurrence networks, centrality algorithms, and silhouette metrics across countries/regions, institutions, authors, journals, references, and keywords. Results: The analysis shows a significant increase in publications on acupuncture for diabetes management after 2013. China and the United States lead in research output, yet collaboration between the two countries remains limited. Most researchers currently work within isolated clusters, underscoring the need for greater exchanges and cooperation. Furthermore, this study identified three key research hotspots: insulin resistance, complications, and interdisciplinary research. Conclusions: This bibliometric analysis reveals dynamic growth patterns and paradigm shifts in acupuncture and diabetes research. The findings provide valuable implications for integrating acupuncture into diabetes treatment. Full article

Objective: Suicide is a major public health problem with multiple biological and psychosocial determinants. Although the kynurenine/tryptophan (KYN/TRP) pathway has been implicated in the neurobiology of suicidal behavior, clinical findings remain inconsistent. This study aimed to evaluate serum tryptophan, kynurenine, and the KYN/TRP ratio in patients presenting to the emergency department after a suicide attempt and to examine their association with suicide risk. Methods: This prospective, cross-sectional, and comparative study was conducted between November 2024 and June 2025 in the Emergency Department of Konya City Hospital. A total of 120 participants were enrolled, including 60 suicide attempt cases and 60 healthy controls. Serum tryptophan and kynurenine levels were measured using the ELISA method, and the KYN/TRP ratio was calculated in molar units. The Beck Suicide Intent Scale (SIS) was administered to the case group. Group comparisons and correlation analyses were performed using appropriate statistical tests, and effect sizes with 95% confidence intervals were reported. Results: Compared with controls, patients showed significantly lower levels of tryptophan (median 35.4 vs. 54.4; p = 0.002), kynurenine (median 1534.5 vs. 2384.0; p < 0.001), and the KYN/TRP ratio (40.9 ± 16.2 vs. 48.8 ± 20.8; p = 0.02). No significant correlations were found between SIS scores and tryptophan (p = 0.180), kynurenine (p = 0.668), or the KYN/TRP ratio (p = 0.246). Subgroup analyses based on psychiatric history or psychiatric consultation recommendations also revealed no significant differences. Conclusions: Serum tryptophan, kynurenine, and the KYN/TRP ratio were significantly reduced in patients with suicide attempts compared to healthy controls. However, these biochemical parameters were not associated with SIS scores. Our findings suggest that tryptophan, kynurenine, and the KYN/TRP ratio may serve as complementary biomarkers but cannot replace clinical and psychometric assessments. Larger, multicenter, and longitudinal studies are needed to clarify their potential clinical value. Full article

Objectives: This study aimed to evaluate the relationship between the median nerve cross-sectional area (CSA, mm²) and clinical findings, blood test results, and electrodiagnostic (EDX) measurements in patients with carpal tunnel syndrome (CTS). Methods: This cross-sectional study included 62 patients (111 hands). The median nerve CSA was assessed using ultrasound (US). The clinical assessment included symptom duration, symptom severity, the Boston Carpal Tunnel Questionnaire (BCTQ), and physical examination. Patient-level analyses used the CSA of the most symptomatic hand for clinical and laboratory variables (n = 62 patients). Hand-level EDX analyses accounted for within-patient clustering by reporting right and left hands separately. Associations were summarized with Spearman’s ρ and 95% confidence intervals (CIs); multiplicity was addressed using Benjamini–Hochberg false discovery rate (FDR). EDX units: latency ms, amplitude mV/µV, and velocity m/s. Results: CSA was not associated with global symptom burden (Visual Analog Scale; BCTQ). No laboratory marker remained significant after FDR across the full panel. By contrast, CSA correlated with EDX impairment at the hand level with low-to-moderate effect sizes; for example, distal motor latency was positively associated with CSA on the right (ρ = 0.557, 95% CI 0.334–0.733) and left (ρ = 0.318, 95% CI 0.022–0.578). CSA also correlated positively with CTS EDX severity (right: ρ = 0.449, 95% CI 0.223–0.646; left: ρ = 0.354, 95% CI 0.071–0.609). Conclusions: Ultrasonographic CSA was associated with electrophysiologic impairment and was not associated with overall symptom burden; laboratory signals did not survive FDR control. Accordingly, CSA may serve as a complementary morphologic adjunct to clinical assessment and EDX, with limited utility as a stand-alone severity metric. Full article

Hydraulic turbine governor servomotors in wind solar hydro complementary energy systems face significant fatigue failure challenges due to high-frequency regulation. This study develops an intelligent fatigue monitoring and prediction system based on strain sensors, specifically designed for the frequent regulation requirements of complementary systems. A multi-point monitoring network was constructed using resistive strain sensors, integrated with temperature and vibration sensors for multimodal data fusion. Field validation was conducted at an 18.56 MW hydroelectric unit, covering guide vane opening ranges from 13% to 63%, with system response time <1 ms and a signal-to-noise ratio of 65 dB. A simulation model combining sensor measurements with finite element simulation was established through fine-mesh modeling to identify critical fatigue locations. The finite element analysis results show excellent agreement with experimental measurements (error < 8%), validating the simulation model approach. The fork head was identified as the critical component with a stress concentration factor of 3.4, maximum stress of 51.7 MPa, and predicted fatigue life of 1.2 × 10⁶ cycles (12–16 years). The cylindrical pin shows a maximum shear stress of 36.1 MPa, with fatigue life of 3.8 × 10⁶ cycles (16–20 years). Monte Carlo reliability analysis indicates a system reliability of 51.2% over 20 years. This work provides an effective technical solution for the predictive maintenance and digital operation of wind solar hydro complementary systems. Full article

Myricetin (MYR), a naturally occurring flavonoid widely distributed in fruits and vegetables, was investigated for its potential to reduce inflammation-induced hyperexcitability in the spinal trigeminal nucleus caudalis (SpVc), which is associated with hyperalgesia. The study also compared MYR’s impact with that of celecoxib (CEL), a non-steroidal anti-inflammatory drug (NSAID). To induce inflammation, Complete Freund’s adjuvant was injected into the whisker pads of rats. Subsequently, we measured the mechanical escape threshold by applying mechanical stimuli to the orofacial region. We found that inflamed rats exhibited a significantly lower threshold compared to naive rats (each group, n = 4). This reduced threshold returned to the naive level two days after the administration of MYR (16 mg/kg, i.p.), CEL (10 mg/kg, i.p.), and a combination of MYR (8 mg/kg, i.p.) + CEL (5 mg/kg, i.p.). To investigate the nociceptive neural response to orofacial mechanical stimulation, we performed extracellular single-unit recordings to measure the activity of SpVc wide-dynamic range (WDR) neurons in anesthetized subjects. In inflamed rats, administration of MYR, CEL, or 1/2MYR + 1/2CEL (each group, n = 4) significantly reduced both the average spontaneous activity and the evoked firing rate of SpVc neurons in response to non-painful and painful mechanical stimuli. The increased average receptive field size in inflamed rats was normalized to the naive level following treatment with MYR, CEL, or 1/2MYR + 1/2CEL. These findings suggest that MYR administration can mitigate inflammatory hyperalgesia by reducing the heightened excitability of SpVc WDR neurons. This supports the notion that MYR could be a viable therapeutic option in complementary and alternative medicine for preventing trigeminal inflammatory mechanical hyperalgesia, potentially serving as an alternative to selective cyclooxygenase-2 blockers. Full article

Accurate prediction of ship motion is essential for ensuring the safety and efficiency of maritime operations. However, the ship dynamics’ nonlinear, non-stationary, and environment-dependent nature presents significant challenges for reliable short-term forecasting. This study uses a simulated dataset designed to reflect realistic maritime variability to evaluate the performance of recurrent neural network (RNN)-based models—including RNN, LSTM, GRU, and Bi-LSTM—under both single and multi-environment conditions. The analysis examines the effects of input sequence length, downsampling intervals, model complexity, and input dimensionality. Results show that Bi-LSTM consistently outperforms unidirectional architectures, particularly in complex multi-environment scenarios. In single-environment settings, the prediction horizon exceeded 40 s, while it decreased to around 20 s under more variable conditions, reflecting generalization challenges. Multi-degree-of-freedom (DOF) inputs enhanced performance by capturing the coupled nature of ship dynamics, whereas incorporating wave height data yielded inconsistent results. A sequence length of 200 timesteps and a downsampling interval of 5 effectively balanced motion feature preservation with high-frequency noise reduction. Increasing model size improved accuracy up to 256 hidden units and 10 layers, beyond which performance gains diminished. Additionally, Peak Matching was introduced as a complementary metric to MSE, emphasizing the importance of accurately predicting motion extrema for practical maritime applications. Full article

Background and Objectives: Mechanical ventilation, while essential, can precipitate ventilator-induced lung injury (VILI) due to excessive mechanical stress. Among respiratory mechanics, driving pressure (ΔP) has emerged as the most robust predictor of mortality, with mechanical power (MP) and tidal volume (TV), respiratory rate (RR), positive end-expiratory pressure (PEEP), and peak inspiratory pressure (P_peak) also potentially influencing clinical outcomes. This study primarily evaluated whether the implementation of a standardized Lung and Diaphragm Protective Ventilation (LDPV) protocol, designed to minimize ΔP, reduced intensive care unit (ICU) mortality. Secondary objectives included assessing the prognostic impact of MP, P_peak, TV, RR, and PEEP on mortality in the pre- and post-LDPV implementation periods. Materials and Methods: In this retrospective cohort study, a total of 3468 adult ICU patients receiving invasive mechanical ventilation between 2012 and 2024 were analyzed. Patients were categorized into two groups: pre-LDPV (2012–2018) and post-LDPV (2019–2024). Ventilatory data were automatically collected using the Metavision system and evaluated through receiver operating characteristic (ROC) derived cutoffs, survival modeling, and Cox proportional hazards regression. Results: Implementation of the LDPV protocol was associated with a significant reduction in ICU mortality (47.7% vs. 41.1%, p < 0.0001) and a shorter ICU length of stay. Patients in the post-LDPV cohort (2019–2024) exhibited lower ΔP (median 12.9 vs. 14.3 cmH₂O), lower MP (median 15.0 vs. 17.0 J/min), improved respiratory system compliance, and reduced peak inspiratory pressure (P_peak) and tidal volume (TV_e) compared to the pre-LDPV cohort (2012–2018). Analysis revealed that the reduction in ΔP was the most significant determinant of improved survival; median ΔP decreased by approximately 2 cmH₂O (from 14.3 to 12.9 cmH₂O). Elevated MP and P_peak were also predictive of mortality, while compliance below 34 mL/cmH₂O consistently indicated a poor prognosis across both study periods. Conclusions: Implementation of an LDPV protocol significantly reduced ICU mortality, primarily through the systematic reduction in ΔP, while MP and its components provided complementary prognostic information. These findings underscore ΔP as the primary modifiable determinant of survival, with MP, P_peak, TV, and PEEP serving as secondary indicators of VILI. Full article

Multi-energy systems could utilize the complementary characteristics of heterogeneous energy to improve operational flexibility and energy efficiency. However, seasonal fluctuations and uncertainty of load would have a great influence on the effectiveness of the system planning scheme. Regarding this issue, this paper proposes a photovoltaic power (PV) station and thermal energy storage (TES) capacity planning model with considering the electrical load uncertainty based on a stochastic optimization method. And four-season load demand scenarios are built by Generative Adversarial Networks (GANs). At last, the proposed capacity configuration model is tested in a case study, and the results show the influence of seasonal fluctuations in load, scenario number, and TES capacity. Full article