High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

Impact of oxygen incorporation on interface optimization and defect suppression for efficiency enhancement in Cu_(2)ZnSn(S,Se)_(4)solar cells

Kesterite Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)solar cells suffer from severe carrier recombination,limiting the photovoltaic performance.Unfavorable energy band alignment at the p-n junction and defective front interface are two main causes.Herein,oxygen incorporation in CZTSSe via absorber air-annealing was developed as a strategy to optimize its surface photoelectric property and reduce the defects.With optimized oxygen incorporation conditions,the carrier separation and collection behavior at the front interface of the device is improved.In particular,it is found that oxygen incorporated absorber exhibits increased band bending,larger depletion region width,and suppressed absorber defects.These indicate the dynamic factors for carrier separation become stronger.Meanwhile,the increased potential difference between grain boundaries and intra grains combined with the decreased concentration of interface deep level defect in the absorber provide a better path for carrier transport.As a consequence,the champion efficiency of CZTSSe solar cells has been improved from 9.74%to 12.04%with significantly improved open-circuit voltage after optimized air-annealing condition.This work provides a new insight for interface engineering to improve the photoelectric conversion efficiency of CZTSSe devices.

Enhancing Defect-Induced Dipole Polarization Strategy of SiC@MoO_(3)Nanocomposite Towards Electromagnetic Wave Absorption

Defect engineering in transition metal oxides semiconductors(TMOs)is attracting considerable interest due to its potential to enhance conductivity by intentionally introducing defects that modulate the electronic structures of the materials.However,achieving a comprehensive understanding of the relationship between micro-structures and electromagnetic wave absorption capabilities remains elusive,posing a substantial challenge to the advancement of TMOs absorbers.The current research describes a process for the deposition of a MoO_(3)layer onto SiC nanowires,achieved via electro-deposition followed by high-temperature calcination.Subsequently,intentional creation of oxygen vacancies within the MoO_(3)layer was carried out,facilitating the precise adjustment of electromagnetic properties to enhance the microwave absorption performance of the material.Remarkably,the SiC@MO-t4 sample exhibited an excellent minimum reflection loss of-50.49 dB at a matching thickness of 1.27 mm.Furthermore,the SiC@MO-t6 sample exhibited an effective absorption bandwidth of 8.72 GHz with a thickness of 2.81 mm,comprehensively covering the entire Ku band.These results not only highlight the pivotal role of defect engineering in the nuanced adjustment of electromagnetic properties but also provide valuable insight for the application of defect engineering methods in broadening the spectrum of electromagnetic wave absor ption effectiveness.SiC@MO-t samples with varying concentrations of oxygen vacancies were prepared through in-situ etching of the SiC@MoO_(3)nanocomposite.The presence of oxygen vacancies plays a crucial role in adjusting the band gap and local electron distribution,which in turn enhances conductivity loss and induced polarization loss capacity.This finding reveals a novel strategy for improving the absorption properties of electromagnetic waves through defect engineering.

Defect Detection in Manufacturing: An Integrated Deep Learning Approach

This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and DeepLabV3 for segmentation. DenseNet121 achieved high accuracy with defect classification as it achieved 92.34% accuracy during testing. This model significantly outperformed benchmark models like VGG16 and ResNet50, which achieved 72.59% and 92.01% accuracy, respectively. Similarly, for segmentation, DeepLabV3 showed high performance in localizing and categorizing defects, achieving a Dice coefficient of 84.21% during training and 69.77% during validation. The dataset includes steels which have four different types of defects and the DeepLab model was particularly effective with detection of Defect 4, with a Dice coefficient of 87.69% in testing. The model performs suboptimally in segmentation of Defect 1, achieving an accuracy of 64.81%. The overall system’s integration of classification and segmentation, alongside thresholding techniques, resulted in improved precision (92.31%) and reduced false positives. Overall, the proposed deep learning system achieved superior defect detection accuracy and reliability compared to existing models in the literature.

Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep- Learning Techniques

As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.

Perioperative management of postoperative sigmoid colon cancer complicated by a large abdominal wall defect:A case report

BACKGROUND Large abdominal wall defect(LAWD)measures>20 cm in width.LAWD can easily lead to intestinal necrosis,peritonitis,other complications,and even mul-tiple organ dysfunction syndrome.Multiple intestinal fistulas are high-flow fis-tulas that can cause severe water–electrolyte imbalance and malnutrition,as well as inflammation,high metabolic status,and chronic intestinal failure caused by intestinal fluid corrosion in tissues around the orifice fistulas.CASE SUMMARY This article summarizes the nursing experience of a patient with sigmoid carci-noma who has LAWD with multiple intestinal fistula due to repeated operations for postoperative complications.The key points of care:Scientific assessment of nutritional status,dynamic adjustment of nutritional support programmes,com-prehensive adoption of enteral nutrition,parenteral nutrition and combined nut-rition of enteral and parenteral;taking good care of abdominal wall defects and intestinal fistulas;continuous flushing of the abdominal drainage tube and low negative pressure drainage;prevention of venous thrombosis;strengthening of physical exercise;implementation of positive psychological interventions.CONCLUSION After more than 7 months of careful care,the patient's physical fitness has been well recovered,local inflammation is well controlled,which wins the opportunity for the operation,and the postoperative recovery is good.

In-doping collaboratively controlling back interface and bulk defects to achieve efficient flexible CZTSSe solar cells

Focusing on the low open circuit voltage(V_(OC))and fill factor(FF)in flexible Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)solar cells,indium(In)ions are introduced into the CZTSSe absorbers near Mo foils to modify the back interface and passivate deep level defects in CZTSSe bulk concurrently for improving the performance of flexible device.The results show that In doping effectively inhibits the formation of secondary phase(Cu(S,Se)_(2))and VSndefects.Further studies demonstrate that the barrier height at the back interface is decreased and the deep level defects(Cu_(Sn)defects)in CZTSSe bulk are passivated.Moreover,the carrier concentration is increased and the V_(OC) deficit(V_(OC,def))is decreased significantly due to In doping.Finally,the flexible CZTSSe solar cell with 10.01%power conversion efficiency(PCE)has been obtained.The synergistic strategy of interface modification and bulk defects passivation through In incorporation provides a new thought for the fabrication of efficient flexible kesterite-based solar cells.

Defect engineering of ternary Cu-In-Se quantum dots for boosting photoelectrochemical hydrogen generation

Heavy-metal-free ternary Cu–In–Se quantum dots(CISe QDs)are promising for solar fuel production because of their low toxicity,tunable band gap,and high light absorption coefficient.Although defects significantly affect the photophysical properties of QDs,the influence on photoelectrochemical hydrogen production is not well understood.Herein,we present the defect engineering of CISe QDs for efficient solar-energy conversion.Lewis acid–base reactions between metal halide–oleylamine complexes and oleylammonium selenocarbamate are modulated to achieve CISe QDs with the controlled amount of Cu vacancies without changing their morphology.Among them,CISe QDs with In/Cu=1.55 show the most outstanding photoelectrochemical hydrogen generation with excellent photocurrent density of up to 10.7 mA cm-2(at 0.6 VRHE),attributed to the suitable electronic band structures and enhanced carrier concentrations/lifetimes of the QDs.The proposed method,which can effectively control the defects in heavy-metal-free ternary QDs,offers a deeper understanding of the effects of the defects and provides a practical approach to enhance photoelectrochemical hydrogen generation.

Defect Engineering of Disordered Carbon Anodes with Ultra-High Heteroatom Doping Through a Supermolecule-Mediated Strategy for Potassium-Ion Hybrid Capacitors

Amorphous carbons are promising anodes for high-rate potassium-ion batteries.Most low-temperature annealed amorphous carbons display unsatisfactory capacities.Heteroatom-induced defect engineering of amorphous carbons could enhance their reversible capacities.Nevertheless,most lignocellulose biomasses lack heteroatoms,making it a challenge to design highly heteroatom-doped carbons(>10 at%).Herein,we report a new preparation strategy for amorphous carbon anodes.Nitrogen/sulfur co-doped lignin-derived porous carbons(NSLPC)with ultra-high nitrogen doping levels(21.6 at%of N and 0.8 at%of S)from renewable lignin biomacromolecule precursors were prepared through a supramolecule-mediated pyrolysis strategy.This supermolecule/lignin composite decomposes forming a covalently bonded graphitic carbon/amorphous carbon intermediate product,which induces the formation of high heteroatom doping in the obtained NSLPC.This unique pyrolysis chemistry and high heteroatom doping of NSLPC enable abundant defective active sites for the adsorption of K+and improved kinetics.The NSLPC anode delivered a high reversible capacity of 419 mAh g^(-1)and superior cycling stability(capacity retention of 96.6%at 1 A g^(-1)for 1000 cycles).Potassiumion hybrid capacitors assembled by NSLPC anode exhibited excellent cycling stability(91%capacity retention for 2000 cycles)and a high energy density of 71 Wh kg^(-1)at a power density of 92 W kg^(-1).

Engineer Nanoscale Defects into Selective Channels:MOF-Enhanced Li^(+) Separation by Porous Layered Double Hydroxide Membrane

Two-dimensional(2D)membrane-based ion separation technology has been increasingly explored to address the problem of lithium resource shortage,yet it remains a sound challenge to design 2D membranes of high selectivity and permeability for ion separation applications.Zeolitic imidazolate framework functionalized modified layered double hydroxide(ZIF-8@MLDH)composite membranes with high lithium-ion(Li^(+)) permeability and excellent operational stability were obtained in this work by in situ depositing functional ZIF-8 nanoparticles into the nanopores acting as framework defects in MLDH membranes.The defect-rich framework amplified the permeability of Li^(+),and the site-selective growth of ZIF-8 in the framework defects bettered its selectivity.Specifically speaking,the ZIF-8@MLDH membranes featured a high permeation rate of Li^(+) up to 1.73 mol m^(−2) h^(−1) and a desirable selectiv-ity of Li^(+)/Mg^(2+) up to 31.9.Simulations supported that the simultaneously enhanced selectivity and permeability of Li+are attributed to changes in the type of mass transfer channels and the difference in the dehydration capacity of hydrated metal cations when they pass through nanochannels of ZIF-8.This study will inspire the ongoing research of high-performance 2D membranes through the engineering of defects.

Decade Milestone Advancement of Defect-Engineered g-C_(3)N_(4) for Solar Catalytic Applications

Over the past decade, graphitic carbon nitride(g-C_(3)N_(4)) has emerged as a universal photocatalyst toward various sustainable carbo-neutral technologies. Despite solar applications discrepancy, g-C_(3)N_(4) is still confronted with a general fatal issue of insufficient supply of thermodynamically active photocarriers due to its inferior solar harvesting ability and sluggish charge transfer dynamics. Fortunately, this could be significantly alleviated by the “all-in-one” defect engineering strategy, which enables a simultaneous amelioration of both textural uniqueness and intrinsic electronic band structures. To this end, we have summarized an unprecedently comprehensive discussion on defect controls including the vacancy/non-metallic dopant creation with optimized electronic band structure and electronic density, metallic doping with ultraactive coordinated environment(M–N_(x), M–C_(2)N_(2), M–O bonding), functional group grafting with optimized band structure, and promoted crystallinity with extended conjugation π system with weakened interlayered van der Waals interaction. Among them, the defect states induced by various defect types such as N vacancy, P/S/halogen dopants, and cyano group in boosting solar harvesting and accelerating photocarrier transfer have also been emphasized. More importantly, the shallow defect traps identified by femtosecond transient absorption spectra(fs-TAS) have also been highlighted. It is believed that this review would pave the way for future readers with a unique insight into a more precise defective g-C_(3)N_(4) “customization”, motivating more profound thinking and flourishing research outputs on g-C_(3)N_(4)-based photocatalysis.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Icariin accelerates bone regeneration by inducing osteogenesisangiogenesis coupling in rats with type 1 diabetes mellitus

BACKGROUND Icariin(ICA),a natural flavonoid compound monomer,has multiple pharmacological activities.However,its effect on bone defect in the context of type 1 diabetes mellitus(T1DM)has not yet been examined.AIM To explore the role and potential mechanism of ICA on bone defect in the context of T1DM.METHODS The effects of ICA on osteogenesis and angiogenesis were evaluated by alkaline phosphatase staining,alizarin red S staining,quantitative real-time polymerase chain reaction,Western blot,and immunofluorescence.Angiogenesis-related assays were conducted to investigate the relationship between osteogenesis and angiogenesis.A bone defect model was established in T1DM rats.The model rats were then treated with ICA or placebo and micron-scale computed tomography,histomorphometry,histology,and sequential fluorescent labeling were used to evaluate the effect of ICA on bone formation in the defect area.RESULTS ICA promoted bone marrow mesenchymal stem cell(BMSC)proliferation and osteogenic differentiation.The ICA treated-BMSCs showed higher expression levels of osteogenesis-related markers(alkaline phosphatase and osteocalcin)and angiogenesis-related markers(vascular endothelial growth factor A and platelet endothelial cell adhesion molecule 1)compared to the untreated group.ICA was also found to induce osteogenesis-angiogenesis coupling of BMSCs.In the bone defect model T1DM rats,ICA facilitated bone formation and CD31hiEMCNhi type H-positive capillary formation.Lastly,ICA effectively accelerated the rate of bone formation in the defect area.CONCLUSION ICA was able to accelerate bone regeneration in a T1DM rat model by inducing osteogenesis-angiogenesis coupling of BMSCs.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Transcatheter Ventricular Septal Defect Closure with Nit-Occlud LêVSD Device—Five Years’Experience and Literature Review

Introduction:Transcatheter closure is an alternative to ventricular septal defect(VSD)occlusion surgery.Nit-Occlud LêVSD coil is a new device yet to be evaluated.The study aimed to evaluate immediate and midterm results after transcatheter closure with the Nit-Occlud LêVSD device.Methods:The retrospective analysis included 30 patients with VSD referred for closure during the period from October 2015 to December 2020.Results:At the time of intervention,the patients’mean age and body weights were 7.5±5.6 years and 29.3±19.1 kg.The majority of the defects had perimembranous location(24/30),four defects had muscular and two outlet subaortic position.The mean effective right-side diameter of the VSDs was 3.6±1.3 mm.Single ventricular fibrillation,device embolization,and hemolysis developed in different patients and were successfully treated.None of the patients had a complete atrioventricular block.The coil was successfully placed in 25/30(83.3%)patients.The majority of the devices were 10 mm×6 mm(11/25)and 12 mm×8 mm(8/25)in size.Two patients required the implantation of a second device.The follow-up period was 2.1±1.4 years.Complete VSD closure was achieved in 48%of cases immediately after the intervention,74%during 2.1±1.6 months after the procedure,and 81%over follow-up.The remaining patients had a trivial residual defect.During the follow-up,approximately one-third of patients developed trivial aortic and mitral valve regurgitation,and half of the patients acquired trace/mild tricuspid regurgitation.Standardized(z-score)left ventricular end-diastolic diameter(0.15±0.37 vs.0.92±0.82,p=0.005)and left atrium dimension(0.47±0.58 vs.1.89±1.11,p=0.005),as well as the left atrium to aortic root ratio(1.2±0.1 vs.1.4±0.2,p=0.005)showed a significant decrease over follow-up related to the period before intervention.Conclusion:Intervention with Nit-Occlud
LêVSD coil showed appropriate results regarding VSD closure rate,complications,and chamber remodeling.The introduction of this device into clinical practice is a significant step forward in transcatheter perimembranous VSD occlusion.

Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Emerging role of liquid biopsy in rat sarcoma virus mutated metastatic colorectal cancer:A case report

BACKGROUND In patients with metastatic colorectal cancer(mCRC),the treatment options are limited and have been proved to be affected by rat sarcoma virus(RAS)mutational status.In RAS wild-type(wt)patients,the combination of antiepidermal growth factor receptor(EGFR)monoclonal antibodies with chemotherapy(CT)is more effective than CT alone.On the other hand,RAS-mutated patients are not eligible for treatment with anti-EGFR antibodies.CASE SUMMARY Eleven patients with initially RAS-mutated mCRC were followed from diagnosis to May 2022.At the time of cell-free DNA determination,five patients had undergone one CT line,five patients had undergone two CT lines,and one patient had undergone three CT lines(all in combination with bevacizumab).At the second and third treatment lines[second line(2L),third line(3L)],patients with neo-RAS wt received a combination of CT and cetuximab.In neo-RAS wt patients treated with anti-EGFR,our findings indicated an increase in progression-free survival for both 2L and 3L(14.5 mo,P=0.119 and 3.9 mo,P=0.882,respectively).Regarding 2L overall survival,we registered a slight increase in neo-RAS wt patients treated with anti-EGFR(33.6 mo vs 32.4 mo,P=0.385).At data cut-off,two patients were still alive:A RAS-mutated patient undergoing 3L treatment and a neo-RAS wt patient who received 2L treatment with anti-EGFR(ongoing).CONCLUSION Our case series demonstrated that monitoring RAS mutations in mCRC by liquid biopsy may provide an additional treatment line for neo-RAS wt patients.

Defect-induced electron rich nanodomains in CoSe_(0.5)S_(1.5)/GA realize fast ion migration kinetics as sodium-ion capacitor anode

Optimizing charge migration and alleviating volume expansion in anode materials are the key to improve the electrochemical performance for sodium-ion storage devices.Herein,a hierarchical porous conducting matrix confining defect-rich selenium doped cobalt dichalcogenide(CoSe_(0.5)S_(1.5)/GA)is constructed as a promising SICs anode based on the guidance of theoretical calculation analysis.The increased defect concentration significantly enhanced the disorder degree of the compound and presented electron aggregation around the S atoms,which effectively modulated the electronic structure,further enabling high rate and ultra-capacity sodium storage.Moreover,strong interfacial coupling could construct spatial constraint to alleviate volume expansion as well as maintain electrode integrity and stability.The CoSe_(0.5)S_(1.5)/GA electrode can deliver a high capacity of 310.1 mA h g^(-1)after 2000 cycles at 1 A g^(-1),and the CoSe_(0.5)S_(1.5)/GA//AC sodium ion capacitor can exhibit an outstanding energy density of 237.5 W h kg^(-1).A series of characterization and theoretical calculation convincingly reveal that the defect moieties can regulate the Na^(+)storage and diffusion kinetics,which prove that our defect manufacture coupling with space-confined strategy can provide deep insights into the development of high-performance Na^(+)storage devices.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.