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Management of diabetic chronic wound exudate is a serious challenge in healthcare worldwide since it is related to the speed of diabetic wound healing. However, current foam dressings not only absorb fluid to generate swelling and compress the wound to hinder wound healing but also are very thick and less comfortable to use. Herein, a superabsorbent self-pumping ultrathin dressing is reported to accelerate diabetic wound healing by achieving superior exudate absorption and management in an ultrathin state. The self-pumping dressing is composed of a drainage layer loaded with anthocyanidin and a thermoplastic polyurethane absorbent layer embedded with superabsorbent particles. The dressing realizes the self-pumping process of unidirectional exudate draining to the absorption layer through the drainage layer without significant dressing swelling to compress the diabetic wound. The dressing is experimentally proven to unidirectionally drain excessive exudate with inflammatory factors and modulate the conversion of macrophages from M1 to M2 in diabetic wounds, thereby promoting the healing of diabetic skin ulcers faster than commercial foam dressings. Therefore, the dressing provides a new idea and novel method for accelerating diabetic skin ulcer healing.

•Overall survival and most safety outcomes with axi-cel were consistent across race/ethnicity in relapsed/refractory large B-cell lymphoma.•Real-world outcomes with CAR T-cell therapy are different in non-Hispanic Black patients, possibly related to barriers to access.

As a second-order nonlinear optical phenomenon, the bulk photovoltaic (BPV) effect is expected to break through the Shockley–Queisser limit of thermodynamic photoelectron conversion and improve the energy conversion efficiency of photovoltaic cells. Here, we have successfully induced a strong flexo-photovoltaic (FPV) effect, a form of BPV effect, in strained violet phosphorene nanosheets (VPNS) by utilizing strain engineering at the h-BN nanoedge, which was first observed in nontransition metal dichalcogenide (TMD) systems. This BPV effect was found to originate from the disruption of inversion symmetry induced by uniaxial strain applied to VPNS at the h-BN nanoedge. We have revealed the intricate relationship between the bulk photovoltaic effect and strain gradients in VPNS through thickness-dependent photovoltaic response experiments. A bulk photovoltaic coefficient of up to 1.3 × 10–3V–1and a polarization extinction ratio of 21.6 have been achieved by systematically optimizing the height of the h-BN nanoedge and the thickness of VPNS, surpassing those of reported TMD materials (typically less than 3). Our results have revealed the fundamental relationship between the FPV effect and the strain gradients in low-dimensional materials and inspired further exploration of optoelectronic phenomena in strain-gradient engineered materials.

The sensitivity of triboelectric nanogenerators (TENGs) to the surface charge density highlights the significance of triboelectric materials and their modifications. Efforts have been directed toward developing effective strategies for increasing the surface charge density, expanding the potential applications of TENGs. This study proposes the use of irradiation technology for grafting to modify the electron-donating capability of poly(ether sulfone) (PES), thereby affording a dual benefit of enhancing the surface charge density and inducing a shift in the position of PES from negative to positive within the triboelectric series. The TENG based on grafted PES has resulted in a significant 3-fold increase in surface charge density compared to that of pristine PES, reaching 263 µC m–2. The surface charge density can be further increased to 502 µC m–2through charge pumping. Notably, irradiation technology presents advantages over chemical grafting methods, particularly in terms of sustainability and environmental friendliness. This innovative approach shows great potential in advancing the domain of TENGs.

Solid acid will be an ecofriendly and efficient catalyst for the hydrolysis of polyethylene terephthalate (PET). Very highly Zr-doped MFI-structured zeolites as catalysts for PET hydrolysis were successfully synthesized for the first time. Comprehensive characterizations confirmed the successful incorporation of Zr into the framework. To comprehend the role of Zr within the framework, density functional calculations were employed to investigate its impact on the surface properties and hydrolysis processes. The results demonstrated that Zr d-orbitals enhance Lewis acidity of the catalyst by contributing to the formation of zeolite’s lowest unoccupied molecular orbitals. The interaction between the model molecule and the catalyst surface was enhanced by doping Zr, leading to electron redistribution toward the surface direction on the carbonyl oxygen of model molecule. Consequently, the carbonyl carbon developed a partial positive charge, and the hydrolysis reaction was promoted. The experimental results validated that the Zr-doped catalyst exhibited a 64% enhancement in the reaction rate.

Mineral adsorption-induced molecular fractionation of dissolved organic matter (DOM) affects the composition of both DOM and OM adsorbed and thus stabilized by minerals. However, it remains unclear what mineral properties control the magnitude of DOM fractionation. Using a combined technique approach that leverages the molecular composition identified by ultrahigh resolution 21 T Fourier transform ion cyclotron resonance mass spectrometry and adsorption isotherms, we catalogue the compositional differences that occur at the molecular level that results in fractionation due to adsorption of Suwannee River fulvic acid on aluminum (Al) and iron (Fe) oxides and a phyllosilicate (allophane) species of contrasting properties. The minerals of high solubility (i.e., amorphous Al oxide, boehmite, and allophane) exhibited much stronger DOM fractionation capabilities than the minerals of low solubility (i.e., gibbsite and Fe oxides). Specifically, the former released Al3+to solution (0.05–0.35 mM) that formed complexes with OM and likely reduced the surface hydrophobicity of the mineral-OM assemblage, thus increasing the preference for adsorbing polar DOM molecules. The impacts of mineral solubility are exacerbated by the fact that interactions with DOM also enhance metal release from minerals. For sparsely soluble minerals, the mineral surface hydrophobicity, instead of solubility, appeared to be the primary control of their DOM fractionation power. Other chemical properties seemed less directly relevant than surface hydrophobicity and solubility in fractionating DOM.

The aim of this study is to investigate the performance and reliability of urethral valve driven by ultrasonic-vaporized steam. The performance model of urethral valve is established to analyze the driving and opening/closing performances of urethral valve. The reliability model of urethral valve is obtained, and the reliability simulation algorithm is proposed to calculate the reliability index of urethral valve. The numerical simulation and experimental results show that urethral valve has a good opening/closing performance, the driving performance can be improved by increasing ultrasonic intensity, radiation area and ultrasonic frequency, and the corrosion and aging of driving bags are the weak links of urethral valve.

Physics-based multi-scale corrosion simulation plays a vital role in predicting the evolution of pitting corrosion on large civil infrastructure such as miter gates, contributing to a model-informed structural health monitoring (SHM) strategy for risk-based asset health management. The physics-based analysis, however, may not accurately reflect the underlying true physics due to various uncertainty sources and needs to be updated using Bayesian inference methods based on observations to make the prediction closer to field observations. However, traditional Bayesian inference requires the evaluation of a likelihood function, which is often unavailable due to the complex model architecture and various surrogate models used in the analysis. Therefore, likelihood-free inference approaches are required for the updating of the multi-scale corrosion simulation models. This paper meets this need by proposing a conditional invertible neural network (cINN)-based Bayesian model updating method for an existing corrosion simulation model. We first train an cINN model based on simulated observations generated from a high-fidelity forward corrosion analysis model. A convolutional neural network (CNN)-based feature extraction algorithm is then employed to extract key features from corrosion images. After that, the extracted corrosion features from CNN is used as inputs of the cINN model to directly obtain posterior distributions of uncertain corrosion model parameters without evaluating the likelihood function. The results show that the proposed cINN-based model updating approach can provide more accurate inference results with a reduced computational cost in comparison to the classical approximate Bayesian computation (ABC) approach.

Path planning plays a vital role in ensuring the efficient and safe operation of off-road autonomous ground vehicles (AGVs). Current methods mostly focus on minimizing the travel time or path length of AGVs and often overlook the fact that the AGVs could fail in many ways during the operation due to stochastic and rough terrain conditions. The objective of this article is to not only generate a proper path for the vehicle but also ensure that the planned path is overall reliable and safe for the vehicle with complex terrain conditions. To achieve this goal, this article develops a reliability-based mission planning method for off-road AGVs subject to two failure modes in terms of mobility (the maximum attainable speed and vehicle vertical acceleration) induced by the uncertain ground properties of the terrain. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility for any given terrain conditions of a path. Mobility reliability of an AGV is then analyzed using surrogate modeling methods considering uncertainty sources in the off-road terrain conditions. After that, the reliability constraints for the two failure modes are integrated with the Rapidly-exploring Random Tree Star (RRT*) algorithm to identify an optimal path, which is the shortest path while satisfying the reliability requirements of the two considered failure modes. Results of a case study demonstrated the effectiveness of the proposed methods for path planning with the consideration of uncertainty in the deformable terrain.

The design of a bending angle sensor consisting of plastic optical fibers (POFs) embedded in transparent tubing is proposed for the detection of the angle of human joints. Highly sensitive sensing is achieved by the bending of the transparent tubing between the two fiber end faces which is functioning as a hollow-core optical fiber with the air inside the tubing as the core and the tubing as the cladding. Simulation results based on the ray tracing method show that when the transparent tubing was bent, both end-face coupling and internal reflection mechanisms coexisted in the beam transmission process, with reflection in the tubing being the main mechanism at a large bending angle. A motor-driven angle measurement system is constructed to achieve accurate control of the bending angle. Experimental results show that the sensor can achieve ultra-wide-angle detection ranging from 0° to 180° with a high sensitivity of 0.136 dB/° and a linear regression coefficient ( $\text{R}^{{2}}{)}$ of 0.997. Finally, the dynamic response characteristics of the sensor are tested on both motor-controlled bending and joint bending, and the results show fast response, good repeatability, and stability of the proposed sensor.

A 15-bit quadrature digital power amplifier (DPA) is presented for wide efficiency enhancement coverage on the IQ complex plane and high dynamic power range. Complex-domain Doherty is proposed with IQ-cell-sharing and single-supply Class-G techniques to provide 32 power-added efficiency (PAE) peaks, good linearity, and simplified power management design. The efficiency enhancement and average efficiency of modulation signal can be verified and evaluated by a theoretical analysis using a switched-capacitor power amplifier (SCPA) model. The special control logic scheme enabling the combination of IQ-cell-sharing and Class-G techniques is implemented at a low cost. Fabricated in a 28-nm CMOS technology, the proposed DPA occupies a 0.74-mm2 compact die size with a single-transformer footprint. The DPA achieves a peak output power of 29.9 dBm with a PAE of 38.0% at 2.3 GHz. For long-term evolution (LTE) 20-MHz 64-QAM signal, the DPA obtains 24.5-dBm average output power ( $P_{\mathbf {avg}}$ ), 25.3% average PAE with 70.4-dB dynamic power range, and −47.3-dB error vector magnitude (EVM) floor at 2.3 GHz. For the 802.11ax 40-MHz 256-QAM signal, it achieves 19.3-dBm Pavg, 19.2% average PAE with 50.4-dB dynamic power range, and −43.0-dB EVM floor at 2.4 GHz.

This article presents an 8-way serial power combined transformer-based quadrature digital power amplifier (DPA) with in-phase/quadrature (IQ)-reuse and Doherty techniques for high output power and high efficiency. An 8-way serial-combining transformer (SCT) power combiner is employed to achieve 4.1 W (36.1 dBm) peak output power. IQ-reuse and transformer-based Doherty techniques are adopted to enhance the efficiency at 3 and 6 dB power back-offs (PBOs). Implemented in 28 nm CMOS and powered by 1.1 and 2.2 V supply voltages, this quadrature DPA achieves >30% peak power-added efficiency (PAE) across 2.7–3.2 GHz and its 1 dB radio frequency (RF) bandwidth ranges from 2.6 to 3.2 GHz. Besides, it obtains the PAE of 25.3% and 22.6% at 6 and 3 dB PBOs, respectively. For long term evolution (LTE) 20 MHz 64 quadratic amplitude modulation (QAM) signal, this DPA achieves 29.4 dBm average output power, 18.5% average PAE at −25 dB error vector magnitude (EVM) limit, and it realizes almost 60 dB dynamic power range from −30 to 29.4 dBm with EVM below −25 dB at 2.9 GHz.

Reliability-based global path planning incorporates reliability constraints into path planning to ensure that off-road autonomous ground vehicles can operate reliably in uncertain off-road environments. Current two-stage reliability-based path planning methods involve separate stages for surrogate modeling of mobility prediction and global path planning, resulting in a large number of unnecessary mobility simulations that makes the approaches computationally expensive. To tackle this challenge, this work proposes a novel efficient reliability-based global path planning approach, named ER-RRT*, which couples adaptive surrogate modeling with the rapidly-exploring random tree star (RRT*) algorithm. Firstly, a surrogate model for vehicle mobility prediction is used to guide the exploration of random trees subject to a mobility reliability constraint. Subsequently, the exploration trees and reliability assessment are employed to inform mobility simulations for the surrogate model refinement. These steps are implemented iteratively and thereby drastically reducing the required mobility simulations for path planning through the integration of adaptive surrogate modeling with global path planning. With a focus on the uncertainty in the slope map and soil properties of deformable terrain, we demonstrate ER-RRT* using a case study and compare it with the current two-stage approach. The results show that ER-RRT* is much more efficient than the current method in both computational time and the required number of mobility simulations for surrogate model construction. In addition, the path identified by ER-RRT* exhibits a comparable cost in distance to its counterpart obtained using the two-stage method.

AE-CuNCs with excellent fluorescence were prepared by a templated method.

Objective: To investigate non-anticoagulant factors that affect blood coagulation in the extracorporeal circulation (ECC) circuit of regional citrate anticoagulation (RCA) protocol for hemodialysis (HD).Method: The clinical characteristics of patients undergoing an individualized RCA protocol for HD between February 2021 and March 2022 were collected; Coagulation scores, pressures in various parts of the ECC circuit, the incidence of coagulation, and citrate concentrations in the ECC circuit during treatment were determined, and non-anticoagulant factors affecting coagulation in the ECC circuit were analyzed.Result: The lowest clotting rate was 2.8% in patients with arteriovenous fistula in various vascular access. Patients on Fresenius dialysis had a lower rate of clotting in the cardiopulmonary bypass line than patients on other brands of dialyzer. Low-throughput dialyzers are less likely to clot than high-throughput dialyzers. There are significant differences in the incidence of coagulation among different nurses during citrate anticoagulant hemodialysis.Conclusion: In the process of citrate anticoagulant hemodialysis, non-anticoagulant factors such as coagulation status, vascular access, dialyzer selection, and operator quality will affect the anticoagulant effect.

In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem.

With wide impedance bandwidth (IBW), wide axial-ratio bandwidth (ARBW), stable AR beamwidth, and stable half-power beamwidth (HPBW), a dual-circularly polarized (DCP) crossed-dipole antenna is proposed. Each dipole consisting of two patches is fed by a pair of parallel transmission lines, leading to a wide IBW. To enhance the ARBW and AR beamwidth, a quadrature wideband coupler is used, and the overlapping bandwidth of IBW and ARBW is up to 100%. The quadrature coupler is integrated with two pairs of parallel transmission line, and are printed on two substrates, which are squeezed into the space between the radiators and reflector, resulting into a compact antenna structure. With a total height of 0.016λL, a prototype is fabricated and measured, showing 1.5-VSWR IBW of larger than 104% for both left-handed CP (LHCP) and right-handed CP (RHCP), 3 dB ARBW of up to 100% both for LHCP and RHCP, and port-to-port (polarization) isolation of lager than 30 dB in 1.70–2.82 GHz, so the effective bandwidth is about 50% (1.70–2.82 GHz). 3 dB AR beamwidth of 76° ± 5°, stable DCP characteristics with average gain of 7.2 dBi and HPBWs of 67° ± 4° are achieved.

Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10−7V, and achieve an ultra-low energy consumption of 2.14 × 10−18J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.

•The prevalence of tet(X4)in humans is higher than in previous studies in China.•Age was associated with the colonization of tet(X4)-positive isolates.•The tet(X4)-positive Escherichia coliwere highly diverse.•tet(X4)was adjacent to ISVsa3 on the plasmids.

Selective laser melting (SLM) is a commonly used technique in additive manufacturing to produce metal components with complex geometries and high precision. However, the poor process reproducibility and unstable product reliability has hindered its wide adoption in practice. Hence, there is a pressing demand for in-situ quality monitoring and real-time process control. In this paper, a feature-level multi-sensor fusion approach is proposed to combine acoustic emission signals with photodiode signals to realize in-situ quality monitoring for intelligence-driven production of SLM. An off-axial in-situ monitoring system featuring a microphone and a photodiode is developed to capture the process signatures during the building process. According to the 2D porosity and 3D density measurements, the collected acoustic and optical signals are grouped into three categories to indicate the quality of the produced parts. In consideration of the laser scanning information, an approach to transform the 1D signal to 2D image is developed. The converted images are then used to train a convolutional neural network so as to extract and fuse the features derived from the two individual sensors. In comparison with several baseline models, the proposed multi-sensor fusion approach achieves the best performance in quality monitoring.

Surrogate models have become increasingly essential for replacing simulation models in additive manufacturing (AM) process analysis and design, particularly for assessing the impact of microstructural variations and process imperfections (aleatory uncertainty). However, these surrogate models can introduce predictive errors, introducing epistemic uncertainty. The challenge arises when dealing with image input data, which is inherently high-dimensional, making it challenging to apply existing uncertainty quantification (UQ) techniques effectively. To address this challenge, this study develops a new UQ methodology based on an existing concept of combining convolutional neural network (CNN) and Gaussian process (GP) regression (GPR). This CNN-GP method converts both numerical and image inputs into a unified, larger-sized image dataset, enabling direct dimension reduction with CNN. Subsequently, GPR constructs the surrogate model, not only providing predictions but also quantifying the associated model uncertainty. This approach ensures that the surrogate model considers both input-related aleatory uncertainty and model-related epistemic uncertainty when it is used for prediction, enhancing confidence in image-based AM simulations and informed decision-making. Three examples validate the high accuracy and effectiveness of the proposed method.

•The present study has identified TMSB10 as a cancer-promoting protein and a serum tumor marker in CRC diagnosis.•We proposes here a potential regulating strategy of TMSB10 which works via regulating the phosphorylation of p38 on the cell cycle and apoptosis of human CRC cells.•The multiple roles of TMSB10 in cell cycle progression and apoptosis suggest that TMSB10 could be used as a valuable biomarker for as well as a target to improve the clinical post-surgery efficacy of radio- as well as chemotherapy of CRC chemotherapy.

A solid electrolyte interphase (SEI) with a robust mechanical property and a high ionic conductivity is imperative for high-performance zinc metal batteries. However, it is difficult to form such a SEI directly from an electrolyte. In this work, a molecular crowding effect is based on the introduction of Zn(OTF)2and Zn(ClO4)2to 2 mol/L ZnSO4electrolytes. Simulations and experiments indicate that the Zn(OTF)2and Zn(ClO4)2not only create a molecularly crowded electrolyte environment to promote the interaction of Zn2+and OTF−, but also participate in the reduction to construct a robust and high ionic-conductive SEI, thus promoting metal zinc deposition to the (002) crystal surface. With this molecular crowding electrolyte, a high current density of 1 mA/cm2can be obtained by assembling symmetric batteries with Zn as the anode for over 1000 h. And in a temperature environment of −10 °C, a current density of 1 mA/cm2can be obtained by assembling symmetric batteries with Zn for over 200 h. Zn//Bi2S3/VS4@C cells achieve a CE rate of up to 99.81% over 1000 cycles. Hence, the utilization of a molecular crowding electrolyte is deemed a highly effective approach to fabricating a sophisticated SEI for a zinc anode.

• CeO x can regulate electronic structure and increase oxygen vacancies concentration. • MoO 2 and CeO x heterojunction can enhance the intrinsic activities and stability. • MoO 2 CeO x /NF shows both better HER activity than Pt/C and better OER activity than RuO 2 in alkaline solution, and at the same time can achieve superior HER activity in acidic condition. • DFT calculations confirm that hetero-interfaces can increase the conductivity. Development of a low-cost and durable bifunctional electrocatalyst with high catalytic activity by hybridizing rare earth metal and non-noble metal is of utmost significance. Herein, a bifunctional electrocatalyst consisting of MoO 2 and CeO x heterostructure is synthesized via interfacial engineering. Particularly, due to its unique hetero-interface with abundant oxygen vacancies, the MoO 2 -CeO x /NF is able to exhibit outstanding hydrogen evolution reaction (HER) performance with an overpotential of 26 mV at 10 mA cm−2, and a preferable oxygen evolution reaction (OER) overpotential of 272 mV at 100 mA cm−2 in 1 M KOH. The recorded HER and OER overpotentials of MoO 2 -CeO x /NF are clearly smaller than those of Pt/C (HER overpotential of 27 mV) and RuO 2 (OER overpotential of 393 mV) at the same current density. More importantly, MoO 2 -CeO x /NF can also exhibit good HER activity with an overpotential of 50 mV at 10 mA cm−2 in acidic media. Thus, it is expected that this work can offer a new approach in the realization of high-catalytic alkaline overall water splitting and acidic HER performance through the rational design of rare-earth metal and non-noble metal heterostructure. [ABSTRACT FROM AUTHOR]

Soybean is one of the most important food crops worldwide. Like other legumes, soybean can form symbiotic relationships with Rhizobiumspecies. Nitrogen fixation of soybean viaits symbiosis with Rhizobiumis pivotal for sustainable agriculture. Type III effectors (T3Es) are essential regulators of the establishment of the symbiosis, and nodule number is a feature of nitrogen-affected nodulation. However, genes encoding T3Es at quantitative trait loci (QTLs) related to nodulation have rarely been identified. Chromosome segment substitution lines (CSSLs) have a common genetic background but only a few loci with heterogeneous genetic information; thus, they are suitable materials for identifying candidate genes at a target locus. In this study, a CSSL population was used to identify the QTLs related to nodule number in soybean. Single nucleotide polymorphism (SNP) markers and candidate genes within the QTLs interval were detected, and it was determined which genes showed differential expression between isolines. Four candidate genes (GmCDPK28, GmNAC1, GmbHLH, and GmERF5) linked to the SNPs were identified as being related to nodule traits and pivotal processes and pathways involved in symbiosis establishment. A candidate gene (GmERF5) encoding a transcription factor that may interact directly with the T3E NopAA was identified. The confirmed CSSLs with important segments and candidate genes identified in this study are valuable resources for further studies on the genetic network and T3Es involved in the signaling pathway that is essential for symbiosis establishment.

Soluble sugar content in seeds is an important quality trait of soybean. In this study, 57 quantitative trait loci (QTLs) related to soluble sugar contents in soybean seeds were collected from databases and published papers. After meta-overview-collinearity integrated analysis to refine QTL intervals, eight consensus QTLs were identified. To further verify the consensus QTLs, a population of chromosome segment substitution lines (CSSLs) was analyzed. Two lines containing fragments covering the regions of consensus QTLs and the recurrent parent were selected: one line showed high soluble sugar contents associated with a consensus QTL fragment, and the other line showed low soluble sugar contents. Transcriptome sequencing was conducted for these two lines at the early, middle, and late stages of seed development, which identified 158, 109 and 329 differentially expressed genes, respectively. Based on the analyses of re-sequencing data of the CSSLs and the consensus QTL region, three candidate genes (Glyma.19G146800, Glyma.19G122500, and Glyma.19G128500) were identified in the genetic fragments introduced from wild soybean. Sequence comparisons between the two CSSL parents SN14 and ZYD00006 revealed a single nucleotide polymorphism (SNP) mutation in the coding sequence of Glyma.19G122500, causing a non-synonymous mutation in the amino acid sequence that affected the predicted protein structure. A Kompetitive allele-specific PCR (KASP) marker was developed based on this SNP and used to evaluate the CSSLs. These results lay the foundation for further research to identify genes related to soluble sugar contents in soybean seeds and for future soybean breeding.