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Abstract Bridging the gap between genotype and phenotype in GWAS studies is challenging. A multitude of genetic variants have been associated with immune-related diseases, including cancer, yet the interpretability of most variants remains low. Here, we investigate the quantitative components in the T cell receptor (TCR) repertoire, the frequency of clusters of TCR sequences predicted to have common antigen specificity, to interpret the genetic associations of diverse human diseases. We first developed a statistical model to predict the TCR components using variants in the TRB and HLA loci. Applying this model to over 300,000 individuals in the UK Biobank data, we identified 2309 associations between TCR abundances and various immune diseases. TCR clusters predicted to be pathogenic for autoimmune diseases were significantly enriched for predicted autoantigen-specificity. Moreover, four TCR clusters were associated with better outcomes in distinct cancers, where conventional GWAS cannot identify any significant locus. Collectively, our results highlight the integral role of adaptive immune responses in explaining the associations between genotype and phenotype.

Abstract For superconducting quantum processors, microwave signals are delivered to each qubit from room-temperature electronics to the cryogenic environment through coaxial cables. Limited by the heat load of cabling and the massive cost of electronics, such an architecture is not viable for millions of qubits required for fault-tolerant quantum computing. Monolithic integration of the control electronics and the qubits provides a promising solution, which, however, requires a coherent cryogenic microwave pulse generator that is compatible with superconducting quantum circuits. Here, we report such a signal source driven by digital-like signals, generating pulsed microwave emission with well-controlled phase, intensity, and frequency directly at millikelvin temperatures. We showcase high-fidelity readout of superconducting qubits with the microwave pulse generator. The device demonstrated here has a small footprint, negligible heat load, great flexibility to operate, and is fully compatible with today’s superconducting quantum circuits, thus providing an enabling technology for large-scale superconducting quantum computers.

Abstract The proliferation of computation-intensive technologies has led to a significant rise in the number of datacenters, posing challenges for high-speed and power-efficient datacenter interconnects (DCIs). Although inter-DCIs based on intensity modulation and direct detection (IM-DD) along with wavelength-division multiplexing technologies exhibit power-efficient and large-capacity properties, the requirement of multiple laser sources leads to high costs and limited scalability, and the chromatic dispersion (CD) restricts the transmission length of optical signals. Here we propose a scalable on-chip parallel IM-DD data transmission system enabled by a single-soliton Kerr microcomb and a reconfigurable microring resonator-based CD compensator. We experimentally demonstrate an aggregate line rate of 1.68 Tbit/s over a 20-km-long SMF. The extrapolated energy consumption for CD compensation of 40-km-SMFs is ~0.3 pJ/bit, which is calculated as being around 6 times less than that of the commercial 400G-ZR coherent transceivers. Our approach holds significant promise for achieving data rates exceeding 10 terabits.

ABSTRACTPeripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.

Breast cancer is the leading cancer diagnosed in women globally, with brain metastasis emerging as a major cause of death, particularly in human epidermal growth factor receptor 2 positive and triple-negative breast cancer subtypes. Comprehensive understanding of the molecular foundations of central nervous system metastases is imperative for the evolution of efficacious treatment strategies. Lipocalin-2 (LCN2), a secreted iron transport protein with multiple functions, has been linked to the progression of breast cancer brain metastasis (BCBM). In primary tumors, LCN2 promotes the proliferation and angiogenesis of breast cancer cells, triggers the epithelial-mesenchymal transition, interacts with matrix metalloproteinase-9, thereby facilitating the reorganization of the extracellular matrix and enhancing cancer cell invasion and migration. In brain microenvironment, LCN2 undermines the blood-brain barrier and facilitates tumor seeding in the brain by modulating the behavior of key cellular components. In summary, this review meticulously examines the fuel role of LCN2 in BCBM cascade, and investigates the potential mechanisms involved. It highlights the potential of LCN2 as both a therapeutic target and biomarker, indicating that interventions targeting LCN2 may offer improved outcomes for patients afflicted with BCBM.

Few-shot human action recognition, a prominent area in computer vision, has garnered increasing attention and broader use in real-life scenarios. Extracting spatio-temporal skeletal information from human movement videos offers interpretable and data-efficient features. However, existing spatio-temporal feature encoders face challenges such as handling sequence boundaries and coping with noise. In order to solve the above problems, this paper proposes a temporal complement method to optimize the Dynamic Time Warping (DTW) algorithm based on the feature representation of the human skeleton sequence. DTW helps to find optimal alignment between sequences by warping them in the time domain. This is quite useful specially in scenarios where training data is limited. However, DTW has the drawback that the optimal alignment path is highly sensitive to errors in the time series distance matrix. Therefore, we apply a Virtual Adversarial Training method to improve the anti-noise capability of the algorithm. Here, We introduce adversarial perturbations in the training phase to the time series distance matrix, thus incentivizing the model to be resilient to such noise. Our method achieves highest accuracy among protonet, DTW and DASTM methods for the 5-way-1-shot setting for the NTU-S (77.7%), and Kinetics (41.2%) datasets. For the 5-way-5-shot setting, our method achieves highest accuracy of 51.8% for Kinetics dataset when compared with the other approaches.

Electropolishing is a widely used technique for polishing additive manufactured (AM) components, while complex internal surface polishing remains a challenge. In this study, we explore the use of ethaline as an electrolyte and investigate the effects of temperature, time, stirring speed, and voltage on the electropolishing effectiveness for AM tubes without pre-treatment through orthogonal experiments. The optimal combination of these factors is then applied in further electropolishing experiments on straight tubes with large length-to-diameter ratios and an angled tube. Our results indicate that temperature has the most significant impact on internal surface electropolishing performance, and other factors’ effects are also analyzed. Ethaline can be a promising electrolyte for internal surface electropolishing of AM components because of its high viscosity, which is validated by flow field simulation of the hydrodynamic conditions inside the tubes.

Machining-induced residual stress (MIRS) in thin-walled components affects their machining accuracy, especially for large-size thin-walled components. This study focuses on the bodies of marine diesel engines, exploring the distribution of MIRS and distortion caused by the gravity-coupled machining residual stress during the boring process of the main bearing hole. The research obtained the distribution of MIRS and the machining distortion based on the finite element method and the mapping method. It examined the influence rules of various parameters, such as the cutting speed, feed, and depth of cut, on MIRS and machining distortion. The study shows that cutting speed, feed, and depth of cut are vital factors affecting MIRS and machining distortion. For the machining distortion of large-size and thin-walled components, their own weight is an essential factor that cannot be ignored. By optimizing the wall thickness, the distortion range can be effectively controlled, supporting the lightweight design of the structure.

Methanol is a promising alternative fuel due to its wide availability of raw materials, mature production processes, and low production cost. However, because of the low cetane number, methanol must include a more reactive fuel to assist with combustion when used in compression ignition (CI) engines. In this study, based on the optical CI engine platform, methanol is injected into the intake port, and diesel is directly injected into the cylinder to achieve dual-fuel combustion. The effects of the methanol energy ratios and diesel split injection strategies on combustion are investigated. The results show that the premixed blue flame was mainly concentrated in the near wall region, whereas the yellow flame produced by diesel combustion tended to concentrate in the central region as the methanol energy ratio increased. When the methanol energy ratio exceeded 50%, the ignition delay was significantly prolonged, while the flame area was greatly reduced. Meanwhile, the peak values for the cylinder pressure and heat release rate decreased significantly, indicating a significant deterioration in combustion. At the earlier diesel pre-injection timing at −58°, the overall dual-fuel combustion at each main injection timing exhibited low-temperature premixed combustion characteristics, with a lower peak exothermic rate and flame brightness. At the later pre-injection timing at −33°, the spray flame at all main injection timings could be observed, with higher peak heat release rates and indications of thermal efficiency. Combustion at later main injection timings was characterized by diffusion combustion, and the main injection timing could effectively regulate the combustion process through phase adjustment.

As a crucial task in natural language processing, table question answering has garnered significant attention from both the academic and industrial communities. It enables intelligent querying and question answering over structured data by translating natural language into corresponding SQL statements. Recently, there have been notable advancements in the general domain table question answering task, achieved through prompt learning with large language models. However, in specific domains, where tables often have a higher number of columns and questions tend to be more complex, large language models are prone to generating invalid SQL or NoSQL statements. To address the above issue, this paper proposes a novel few-shot table prompt question answering approach. Specifically, we design a prompt template construction strategy for structured SQL generation. It utilizes prompt templates to restructure the input for each test data and standardizes the model output, which can enhance the integrity and validity of generated SQL. Furthermore, this paper introduces a contrastive exemplar selection approach based on the question patterns and formats in domain-specific contexts. This enables the model to quickly retrieve the relevant exemplars and learn characteristics about given question. Experimental results on the two datasets in the domains of electric energy and structural inspection show that the proposed approach outperforms the baseline models across all comparison settings.

Abstract Stable cathodes with multiple redox-active centres affording a high energy density, fast redox kinetics and a long life are continuous pursuits for aqueous zinc-organic batteries. Here, we achieve a high-performance zinc-organic battery by tuning the electron delocalization within a designed fully conjugated two-dimensional hydrogen-bonded organic framework as a cathode material. Notably, the intermolecular hydrogen bonds endow this framework with a transverse two-dimensional extended stacking network and structural stability, whereas the multiple C = O and C = N electroactive centres cooperatively trigger multielectron redox chemistry with super delocalization, thereby sharply boosting the redox potential, intrinsic electronic conductivity and redox kinetics. Further mechanistic investigations reveal that the fully conjugated molecular configuration enables reversible Zn2+/H+ synergistic storage accompanied by 10-electron transfer. Benefitting from the above synergistic effects, the elaborately tailored organic cathode delivers a reversible capacity of 498.6 mAh g−1 at 0.2 A g−1, good cyclability and a high energy density (355 Wh kg−1).

Abstract Recent studies revealed a new biological process that malignant cancer cells hijack mitochondria from nearby T cells, providing another potential mechanism for immune evasion. We further confirmed this process at the single‐cell genomic level through MERCI, a novel algorithm for tracking mitochondrial (MT) transfer. Applied to human cancer samples, MERCI identified a new cancer phenotype linked to MT hijacking, correlating with rapid tumour proliferation and poor patient survival. This discovery offers insights into the limitations of current cancer immunotherapies and suggests new therapeutic avenues targeting MT transfer to enhance cancer treatment efficacy.

Li-N2 batteries present a relatively novel approach to N2 immobilization, and an advanced N2/Li3N cycling method is introduced in this study. The low operating overpotential of metal–air batteries is quite favorable to their stable cycling performance, providing a prospect for the development of a new type of battery with extreme voltage. The battery system of Li-N2 uses N2 as the positive electrode, lithium metal as the negative electrode, and a conductive medium containing soluble lithium salts as the electrolyte. In accordance with its voltage-distribution trend, a variety of lithium-nitrogen molecule intermediates are produced during the discharge process. There is a lack of theoretical description of material changes at the microscopic level during the discharge process. In this paper, the first-principles approach is used to simulate and analyze possible material changes during the discharge process of Li-N2 batteries. The discharge process is simulated on a 4N-graphene anode substrate model, and simulations of its electrostatic potential, Density of States (DOS), HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) aspects confirm that the experimentally found Li3N becomes the final stabilized product of the Li-N2 battery. It can also be seen in the density of states that graphene with adsorption of 4N transforms from semiconducting to metallic properties. In addition, the differential charge also indicates that the Li-N2 material has a strong adsorption effect on the substrate, which can play the dual role of electricity storage and nitrogen fixation.

Land exhibits distinct carbon source/sink states under varying utilization conditions. Under the climate change environment, developing low-carbon land use optimization strategies has become an urgent problem. Thus, this study examines the characteristics of land use carbon emissions (LUCE) resulting from land use and land cover change (LUCC) in Chongqing under five shared socioeconomic pathways (SSPs). The results show: (1) From 2000 to 2020, the LUCE in Chongqing increased significantly and then decreased slightly. The improvement of energy technology can limit carbon emissions from expanding construction. (2) In 2030, Chongqing will have the highest LUCE in SSP5 scenario, followed by SSP4 scenario, and the lowest in SSP2 scenario. However, some districts and counties in Chongqing City have the opposite situation to the whole research area. (3) In the study area, LUCE exhibits a significant positive spatial correlation with Moran's indices of 0.48, 0.27, and 0.39 in 2000, 2010, and 2020, respectively. Carbon emissions in Chongqing primarily concentrate in the main urban areas, with high-emission areas spreading outward from specific centers in the districts and counties. In 2030, spatial LUCE aggregation will decrease significantly, especially in the SSP3 scenario, with a Moran's index of 0.25. When adjusting the land use structure, policymakers need to implement differentiated land use management strategies, considering the characteristics of LUCE and their development directions. This study confirms that socioeconomic development has a direct impact on LUCE. The results of this study provide a reference for the government’s land-use assessment, achieving low-carbon development goals, and identifying regional development patterns.

A non-classical light source is essential for implementing a wide range of quantum information processing protocols, including quantum computing, networking, communication and metrology. In the microwave regime, propagating photonic qubits, which transfer quantum information between multiple superconducting quantum chips, serve as building blocks for large-scale quantum computers. In this context, spectral control of propagating single photons is crucial for interfacing different quantum nodes with varied frequencies and bandwidths. Here a deterministic microwave quantum light source was demonstrated based on superconducting quantum circuits that can generate propagating single photons, time-bin encoded photonic qubits and qudits. In particular, the frequency of the emitted photons can be tuned in situ as large as 200 MHz. Even though the internal quantum efficiency of the light source is sensitive to the working frequency, it is shown that the fidelity of the propagating photonic qubit can be well preserved with the time-bin encoding scheme. This work thus demonstrates a versatile approach to realizing a practical quantum light source for future distributed quantum computing.

Abstract The developing advances of microresonator-based Kerr cavity solitons have enabled versatile applications ranging from communication, signal processing to high-precision measurements. Resonator dispersion is the key factor determining the Kerr comb dynamics. Near the zero group-velocity-dispersion (GVD) regime, low-noise and broadband microcomb sources are achievable, which is crucial to the application of the Kerr soliton. When the GVD is almost vanished, higher-order dispersion can significantly affect the Kerr comb dynamics. Although many studies have investigated the Kerr comb dynamics near the zero-dispersion regime in microresonator or fiber ring system, limited by dispersion profiles and dispersion perturbations, the near-zero-dispersion soliton structure pumped in the anomalous dispersion side is still elusive so far. Here, we theoretically and experimentally investigate the microcomb dynamics in fiber-based Fabry-Perot microresonator with ultra-small anomalous GVD. We obtain 2/3-octave-spaning microcombs with ~10 GHz spacing, >84 THz span, and >8400 comb lines in the modulational instability (MI) state, without any external nonlinear spectral broadening. Such widely-spanned MI combs are also able to enter the soliton state. Moreover, we report the first observation of anomalous-dispersion based near-zero-dispersion solitons, which exhibits a local repetition rate up to 8.6 THz, an individual pulse duration 32 THz and >3200 comb lines. These two distinct comb states have their own advantages. The broadband MI combs possess high conversion efficiency and wide existing range, while the near-zero-dispersion soliton exhibits relatively low phase noise and ultra-high local repetition rate. This work complements the dynamics of Kerr cavity soliton near the zero-dispersion regime, and may stimulate cross-disciplinary inspirations ranging from dispersion-controlled microresonators to broadband coherent comb devices.

Abstract The ongoing COVID-19 pandemic has demonstrated that viral diseases represent an enormous public health and economic threat to mankind and that individuals with compromised immune systems are at greater risk of complications and death from viral diseases. The development of broad-spectrum antivirals is an important part of pandemic preparedness. Here, we have engineer a series of designer cells which we term autonomous, intelligent, virus-inducible immune-like (ALICE) cells as sense-and-destroy antiviral system. After developing a destabilized STING-based sensor to detect viruses from seven different genera, we have used a synthetic signal transduction system to link viral detection to the expression of multiple antiviral effector molecules, including antiviral cytokines, a CRISPR-Cas9 module for viral degradation and the secretion of a neutralizing antibody. We perform a proof-of-concept study using multiple iterations of our ALICE system in vitro, followed by in vivo functionality testing in mice. We show that dual output ALICESaCas9+Ab system delivered by an AAV-vector inhibited viral infection in herpetic simplex keratitis (HSK) mouse model. Our work demonstrates that viral detection and antiviral countermeasures can be paired for intelligent sense-and-destroy applications as a flexible and innovative method against virus infection.

Successfully controlling an optical signal by a single gate photon would have great applicability for quantum networks and all-optical computing. Here, the authors realise a single-photon transistor in the microwave regime based on superconducting quantum circuits.

Abstract Background Transurethral resection of the bladder tumor (TURBT) is one of the most established urological procedures for the treatment of the primary non-muscle-invasive bladder cancer (NMIBC). The aim of the study is to evaluate the efficacy and safety of 980 nm diode laser as a treatment for primary NMIBC. Methods Eighty-eight patients with NMIBC were treated by en bloc transurethral resection with 980 nm diode laser, and 76 patients were treated by plasmakinetic transurethral resection from May 2016 to July 2019 at the Department of Urology, Tangdu Hospital, Air Force Medical University. The clinical data were collected and compared between the two groups. Results The bladder irrigation time was shortened in 980 nm diode laser group compared to that of plasmakinetic transurethral resection group (4.1 ± 0.6 vs 13.1 ± 3.1 h, p < 0.001). A total of 13.2% (10/76) patients experienced obturator nerve reflex, and 5.3% (4/76) experienced delayed bleeding in plasmakinetic transurethral resection group, while no obturator nerve reflex and delayed bleeding cases were observed in 980 nm diode laser group (p < 0.05). The postoperative catheterization and hospitalization time showed no significant difference between the two groups. The median follow-up time was 27 months (13–38 months). No significant difference in the recurrence rate was observed between the two groups. Conclusions The 980 nm diode laser is an effective and safe tool in transurethral resection of NMIBC using en bloc technique. It has less perioperative complications and shortened bladder irrigation time.

In this study, an algal–bacterial symbiotic consortium was integrated with the sediment microbial fuel cell (SMFC) to construct an algal–bacterial cathode SMFC (AC-SMFC) for excess sewage sludge treatment and electricity generation. A bacterial cathode SMFC (BC-SMFC) and a static settling system (SS-system) were used as controls. Electrochemical analysis confirmed that the algal–bacterial biofilm on the cathode improved electricity production. The maximum power density of AC-SMFC was 75.21 mW/m2, which was 65.70% higher than that of the BC-SMFC (45.39 mW/m2). After 60 days of treatment, AC-SMFC achieved much higher removal efficiencies of the total chemical oxygen demand (TCOD) (59.60%), suspended solids (SS) (62.42%), and volatile suspended solids (VSS) (71.44%) in the sediment, compared to BC-SMFC and the SS-system, exhibiting an effective degradation of the organic matter in the sediment sludge. Moreover, the lower concentration of total nitrogen (TN) and total phosphorus (TP) in the overlying water of AC-SMFC demonstrated that the algae on the cathode could inhibit the accumulation of nitrogen and phosphorus released from the sediments. The three-dimensional excitation–emission matrix (EEM) fluorescence spectroscopy revealed that the tryptophan protein and aromatic protein in the loosely bound extracellular polymeric substances (LB-EPS) of the sediment sludge in the AC-SMFC were significantly decreased. Additionally, the abundance of functional microbiota in the AC-SMFC increased, such as Trichococcus, Alphaproteobacteria, and Clostridia, which contributed to electricity generation and sludge degradation. The combined application of microalgae and the SMFC provided a promising approach for excess sludge reduction and energy recovery.

BackgroundMelanoma is a malignant tumor that originates from the canceration of melanocytes with a high rate of invasiveness and lethality. Immune escape has been regarded as an important mechanism for tumor development, while the treatment of immune checkpoint inhibitors (ICIs) is beneficial in restoring and enhancing the body’s anti-tumor immune response to kill tumor cells. To date, ICIs therapy has achieved remarkable efficacy in treating melanoma patients. Despite the significant clinical benefits of ICIs, multiple complications such as rashes, thyroiditis, and colitis occur in melanoma patients. In this study, we aim to explore the development process and trends in the field of ICIs-related complications in melanoma, analyze current hot topics, and predict future research directions.MethodsWe screened the most relevant literatures on ICIs-related complications in melanoma from 2011 to 2021 in the Web of Science Core Collection (WoSCC). Using VOSviewer, CiteSpace and R language packages, we analyzed the research trends in this field.ResultsA total of 1,087 articles were screened, and the USA had the highest number of publications (publications = 454, citations = 60,483), followed by Germany (publications = 155, citations = 27,743) and Italy (publications = 139, citations = 27,837). The Memorial Sloan Kettering Cancer Center had the most publications, but the Angeles Clinic and Research Institute had the highest average citation rate. Lancet oncology (IF, 2021 = 54.43) was the most prominent of all journals in terms of average citation rate. Reference and keyword cluster analysis revealed that anti-tumor efficacy, adjuvant treatment, clinical response, clinical outcome, etc. were the hotspots and trends of research in recent years.ConclusionsThis study offers a comprehensive summary and analysis of global research trends on ICIs-related complications in melanoma. Over the past decade, there has been a significant increase in the number of publications on this topic. However, the safety and benefits of retreatment after the recovery of ICIs-related complications remain unknown. Therefore,the establishment of related prediction models, as well as the immunotherapy of melanoma with ICIs in combination with other adjuvant therapies, are future research hotspots.

Improved treatment of glioblastoma (GBM) can be achieved by inducing differentiation of glioblastoma stem cells (GSCs). Here, the authors show that zinc finger protein 117 (ZNF117) is a regulator of GSC differentiation via Notch signaling through interaction with JAG2, and can be targeted for therapy.

In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process Regression (GPR), and Particle Swarm Optimization (PSO) to provide an effective solution for super-high arch dam stress prediction. This research focuses on three strategically selected measurement points within the dam, characterized by complex stress conditions. The predicted results from the EPR are compared with those from GPR, Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), using actual stress data measured at research points within a super-high arch dam in Southwest China. The findings reveal that the proposed EPR model attains a maximum mean absolute error (MAE) of 0.02916 and a maximum root mean square error (RMSE) of 0.03055, surpassing the compared models. As a result, the EPR model introduces an innovative computational framework for stress prediction in super-high arch dams, excelling in handling stress data characterized by high vibration frequencies and providing more accurate predictions.