Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

ULK1-dependent phosphorylation of PKM2 antagonizes O-GlcNAcylation and regulates the Warburg effect in breast cancer.

Pyruvate kinase M2 (PKM2) is a central metabolic enzyme driving the Warburg effect in tumor growth. Previous investigations have demonstrated that PKM2 is subject to O-linked ß-N-acetylglucosamine (O-GlcNAc) modification, which is a nutrient-sensitive post-translational modification. Here we found that unc-51 like autophagy activating kinase 1 (ULK1), a glucose-sensitive kinase, interacts with PKM2 and phosphorylates PKM2 at Ser333. Ser333 phosphorylation antagonizes PKM2 O-GlcNAcylation, promotes its tetramer formation and enzymatic activity, and decreases its nuclear localization. As PKM2 is known to have a nuclear role in regulating c-Myc, we also show that PKM2-S333 phosphorylation inhibits c-Myc expression. By downregulating glucose consumption and lactate production, PKM2 pS333 attenuates the Warburg effect. Through mouse xenograft assays, we demonstrate that the phospho-deficient PKM2-S333A mutant promotes tumor growth in vivo. In conclusion, we identified a ULK1-PKM2-c-Myc axis in inhibiting breast cancer, and a glucose-sensitive phosphorylation of PKM2 in modulating the Warburg effect.

H2O2 self-supplying and GSH-depleting nanosystem for amplified NIR mediated-chemodynamic therapy of MRSA biofilm-associated infections.

Reactive oxygen species (ROS) has emerged as potent therapeutic agents for biofilm-associated bacterial infections. Chemodynamic therapy (CDT), involving the generation of high-energy ROS, displays great potential in the therapy of bacterial infections. However, challenges such as insufficient hydrogen peroxide (H2O2) and over-expressed glutathione (GSH) levels within the microenvironment of bacterial biofilms severely limit the antibacterial efficacy of CDT. Herein, we have developed a multifunctional nanoplatform (CuS@CaO2@Dex) by integrating copper sulfide (CuS) and calcium peroxide (CaO2) into dextran (Dex)-coated nanoparticles. This innovative platform enhanced ROS generation for highly efficient biofilm elimination by simultaneously supplying H2O2 and depleting GSH. The Dex-coating facilitated the penetrability of CuS@CaO2@Dex into biofilms, while CaO2 generated a substantial amount of H2O2 in the acidic biofilm microenvironment. CuS, through a Fenton-like reaction, catalyzed the conversion of self-supplied H2O2 into hydroxyl radicals (•OH) and consumed the overexpressed GSH. Additionally, the incorporation of near-infrared II (NIR II) laser irradiation enhanced the photothermal properties of CuS, improving the catalytic efficiency of the Fenton-like reaction for enhanced antibacterial effects. In vivo experiments have demonstrated that CuS@CaO2@Dex exhibited remarkable antibacterial and antibiofilm efficacy, exceptional wound healing capabilities, and notable biosafety. In summary, the Dex-coated nanoplatform proposed in this study, with its self-sterilization capability through ROS, holds significant potential for future biomedical applications.

Measuring and decomposing natural capital use in Xinjiang from a regional-industry perspective.

Accurately portraying the mechanism of the flow of natural resource consumption between regions and its impact on ecology is of crucial value in deepening the understanding of the coordinated relationship between population, resources, environment and development. Consequently, this promotes the sustainable development of the natural economy and society. Based on a regional-industrial perspective, this study used a localized three-dimensional ecological footprint model to measure and decompose natural resources in Xinjiang from 2005 to 2020. In doing so, the study clarified the supply, demand, and flow utilization of natural capital in Xinjiang, the balance of spatial and temporal allocation of resources, the coupling between economic growth and resource consumption, and the coordination between industrial structure and ecological environment. The results showed that (1) Xinjiang's per capita ecological deficit grew from 2.096 to 11.667 in 2005-2020. Moreover, the energy footprint was a decisive part of the ecological deficit throughout the study period. Furthermore, the trend of increased ecological pressure was higher in northern and eastern Xinjiang than in southern Xinjiang. (2) The overall Gini coefficient of Xinjiang's ecological carrying capacity was at the critical value of spatial equilibrium (0.4), with differences between the groups: Northern & Southern Xinjiang > Northern & Eastern Xinjiang > Eastern & Southern Xinjiang. The reasons for this inter-regional economic disparity are related to fiscal expenditure/GDP, level of urbanization, and regional industrial output. Overall, the decoupling relationship between environmental pressures and economic growth was optimistic. (3) From an industrial perspective, the levels of industrial structural efficiency and the industrial ecological harmony index were still relatively low, but the overall trend was on the rise. (4) Resource endowment, economic development, consumption structure, and population had significant driving effects on the ecological footprint, whereas environmental protection, science, and technology could inhibit its growth to a certain extent. This study aimed to provide an in-depth analysis of the current situation and problems of natural resource use in Xinjiang and provide theoretical and practical references for sustainable development in the region.

Machine learning-enabled performance prediction and optimization for iron-chromium redox flow batteries.

Iron-chromium flow batteries (ICRFBs) are regarded as one of the most promising large-scale energy storage devices with broad application prospects in recent years. However, transitioning from laboratory-scale development to industrial-scale deployment can be a time-consuming process due to the multitude of complex factors that impact ICRFB stack performance. Herein, a data-driven optimization methodology applying active learning, informed by an extensive survey of the literature encompassing diverse experimental conditions, is proposed to enable exceptional precision in predicting ICRFB system performance considering both operation conditions and key materials selection. Specifically, multitask ML models are trained on experimental data with a high prediction accuracy (R2 > 0.92) to link ICRFB properties to energy efficiency, coulombic efficiency, and capacity. We also interpret the ML models based on Shapley additive explanations and extract valuable insights into the importance of descriptors. It is noted that the operation conditions (current density and cycle number) and the electrode type are the most critical descriptors affecting the voltage efficiency and coulombic efficiency while the electrode size strongly affects the capacity. Moreover, active learning is used to explore the most optimized cases considering the highest energy efficiency and capacity. The versatility and robustness of the approach are demonstrated by the successful validation between ML prediction and our experiments of energy efficiency (±0.15%) and capacity (±0.8%). This work not only affords fruitful data-driven insight into the property-performance relationship, but also unveils the explainability of critical properties on the performance of ICRFBs, which accelerates the rational design of next-generation ICRFBs.

[Clinical Efficacy of Hypomethylating Agent Therapy in Patients with Chronic Myelomonocytic Leukemia].

OBJECTIVE: To observe the clinical efficacy and safety of hypomethylating agent therapy in chronic myelomonocytic leukemia (CMML). METHODS: From February 2014 to June 2021, the clinical data, efficacy, survival time and safety of CMML patients diagnosed in the Second Hospital of Hebei Medical University and treated with hypomethylating agent therapy were retrospectively analyzed. RESULTS: A total of 25 CMML patients received hypomethylating agent therapy, including 18 cases treated with decitabine (DEC) and 7 cases treated with azacytidine (AZA) as the basic treatment. Among them, 20 patients responded, and 7 patients got complete remission (CR). All patients with CR were treated with DEC as the basic treatment. Five cases of CR occurred in the first 4 courses of treatment. After a median follow-up of 16.4 (9.4-20.5) months, 4 patients with CR progressed to acute myeloid leukemia (AML). The median overall survival (OS) time of 25 CMML patients was 17.4 months (95%CI: 12.437-22.363). According to MD Anderson prognostic scoring system (MDAPS), CMML-specific prognostic scoring system (CPSS), CPSS molecular (CPSS-mol), Mayo molecular model (MMM), risk stratification of patients was performed, and the difference only between different risk stratification of MDAPS and survival time was statistically significant. Common adverse reactions of hypomethylating agent therapy in CMML patients included infection, gastrointestinal reaction, hematological toxicity, skin allergy and liver function damage. All patients' symptoms were improved after corresponding treatment. CONCLUSION: Hypomethylating agent therapy is effective and safe for CMML patients. CR mostly occurs in the first 4 courses of treatment, and hypomethylating agent therapy combined with low-dose chemotherapy can be used for patients who do not respond. Hypomethylating agent therapy can delay the disease, but can't prevent progression.

Irisin attenuates vascular remodeling in hypertensive mice induced by Ang II by suppressing Ca2+-dependent endoplasmic reticulum stress in VSMCs.

Vascular remodeling plays a vital role in hypertensive diseases and is an important target for hypertension treatment. Irisin, a newly discovered myokine and adipokine, has been found to have beneficial effects on various cardiovascular diseases. However, the pharmacological effect of irisin in antagonizing hypertension-induced vascular remodeling is not well understood. In the present study, we investigated the protection and mechanisms of irisin against hypertension and vascular remodeling induced by angiotensin II (Ang II). Adult male mice of wild-type, FNDC5 (irisin-precursor) knockout, and FNDC5 overexpression were used to develop hypertension by challenging them with Ang II subcutaneously in the back using a microosmotic pump for 4 weeks. Similar to the attenuation of irisin on Ang II-induced VSMCs remodeling, endogenous FNDC5 ablation exacerbated, and exogenous FNDC5 overexpression alleviated Ang II-induced hypertension and vascular remodeling. Aortic RNA sequencing showed that irisin deficiency exacerbated intracellular calcium imbalance and increased vasoconstriction, which was parallel to the deterioration in both ER calcium dysmetabolism and ER stress. FNDC5 overexpression/exogenous irisin supplementation protected VSMCs from Ang II-induced remodeling by improving endoplasmic reticulum (ER) homeostasis. This improvement includes inhibiting Ca2+ release from the ER and promoting the re-absorption of Ca2+ into the ER, thus relieving Ca2+-dependent ER stress. Furthermore, irisin was confirmed to bind to its receptors, αV/ß5 integrins, to further activate the AMPK pathway and inhibit the p38 pathway, leading to vasoprotection in Ang II-insulted VSMCs. These results indicate that irisin protects against hypertension and vascular remodeling in Ang II-challenged mice by restoring calcium homeostasis and attenuating ER stress in VSMCs via activating AMPK and suppressing p38 signaling.

Gustation-Inspired Dual-Responsive Hydrogels for Taste Sensing Enabled by Machine Learning.

Human gustatory system recognizes salty/sour or sweet tastants based on their different ionic or nonionic natures using two different signaling pathways. This suggests that evolution has selected this detection dualism favorably. Analogically, this work constructs herein bioinspired stimulus-responsive hydrogels to recognize model salty/sour or sweet tastes based on two different responses, that is, electrical and volumetric responsivities. Different compositions of zwitter-ionic sulfobetainic N-(3-sulfopropyl)-N-(methacryloxyethyl)-N,N-dimethylammonium betaine (DMAPS) and nonionic 2-hydroxyethyl methacrylate (HEMA) are co-polymerized to explore conditions for gelation. The hydrogel responses upon adding model tastant molecules are explored using electrical and visual de-swelling observations. Beyond challenging electrochemical impedance spectroscopy measurements, naive multimeter electrical characterizations are performed, toward facile applicability. Ionic model molecules, for example, sodium chloride and acetic acid, interact electrostatically with DMAPS groups, whereas nonionic molecules, for example, D(-)fructose, interact by hydrogen bonding with HEMA. The model tastants induce complex combinations of electrical and volumetric responses, which are then introduced as inputs for machine learning algorithms. The fidelity of such a trained dual response approach is tested for a more general taste identification. This work envisages that the facile dual electric/volumetric hydrogel responses combined with machine learning proposes a generic bioinspired avenue for future bionic designs of artificial taste recognition, amply needed in applications.

Natural bioactive lysosomes extracted from multiple cells for tumor therapy.

Nanoparticles of biological origin exhibit many unique properties in biological applications due to their exquisite structure, specific composition, and natural biological functionality. In this study, we obtained lysosomes from three distinct cell types (one normal cell and two activated immune cells) and demonstrated their potential as natural therapeutic nanoparticles for tumor therapy. In vitro experiments revealed that these lysosomes maintained their structural integrity, were well-distributed, and exhibited significant biological activity, which effectively induced cancer cell death by generating ROS and disrupting biological substrates. Additionally, in vivo investigations showed that these lysosomes could accumulate in tumor tissues after intravenous administration and exhibited exceptional therapeutic effects through the destruction of tumor blood vessels and the degradation of immunosuppressive proteins, with complete tumor disappearance in a single treatment. This research on the utilization of bioactive lysosomes for tumor treatment provides valuable insights into drug development and tumor treatment, particularly when conventional approaches have proven ineffective.

Characterization of a Novel Polysaccharide Derived from Rhizospheric Paecilomyces vaniformisi and Its Mechanism for Enhancing Salinity Resistance in Rice Seedlings.

Soil salinity is an important limiting factor in agricultural production. Rhizospheric fungi can potentially enhance crop salinity tolerance, but the precise role of signaling substances is still to be systematically elucidated. A rhizospheric fungus identified as Paecilomyces vaniformisi was found to enhance the salinity tolerance of rice seedlings. In this study, a novel polysaccharide (PPL2b) was isolated from P. vaniformisi and identified as consisting of Manp, Glcp, GalpA, and Galp. In a further study, PPL2b showed significant activity in alleviating salinity stress-induced growth inhibition in rice seedlings. The results indicated that under salinity stress, PPL2b enhances seed germination, plant growth (height and biomass), and biochemical parameters (soluble sugar and protein contents). Additionally, PPL2b regulates genes such as SOS1 and SKOR to decrease K+ efflux and increase Na+ efflux. PPL2b increased the expression and activity of genes related to antioxidant enzymes and nonenzyme substances in salinity-induced oxidative stress. Further study indicated that PPL2b plays a crucial role in regulating osmotic substances, such as proline and betaine, in maintaining the osmotic balance. It also modulates plant hormones to promote rice seedling growth and enhance their tolerance to soil salinity. The variables interacted and were divided into two groups (PC1 77.39% and PC2 18.77%) based on their relative values. Therefore, these findings indicate that PPL2b from P. vaniformisi can alleviate the inhibitory effects of salinity stress on root development, osmotic adjustment, ion balance, oxidative stress balance, and growth of rice seedlings. Furthermore, it suggests that polysaccharides produced by rhizospheric fungi could be utilized to enhance crop tolerance to salinity.

Lesion-specific cortical activation following sensory stimulation in patients with subacute stroke.

BACKGROUND: Sensory stimulation can play a fundamental role in the activation of the primary sensorimotor cortex (S1-M1), which can promote motor learning and M1 plasticity in stroke patients. However, studies have focused mainly on investigating the influence of brain lesion profiles on the activation patterns of S1-M1 during motor tasks instead of sensory tasks. Therefore, the objective of this study is to explore the lesion-specific activation patterns due to different brain lesion profiles and types during focal vibration (FV). METHODS: In total 52 subacute stroke patients were recruited in this clinical experiment, including patients with basal ganglia hemorrhage/ischemia, brainstem ischemia, other subcortical ischemia, cortical ischemia, and mixed cortical-subcortical ischemia. Electroencephalograms (EEG) were recorded following a resting state lasting for 4 min and three sessions of FV. FV was applied over the muscle belly of the affected limb's biceps for 3 min each session. Beta motor-related EEG power desynchronization overlying S1-M1 was used to indicate the activation of S1-M1, while the laterality coefficient (LC) of the activation of S1-M1 was used to assess the interhemispheric asymmetry of brain activation. RESULTS: (1) Regarding brain lesion profiles, FV could lead to the significant activation of bilateral S1-M1 in patients with basal ganglia ischemia and other subcortical ischemia. The activation of ipsilesional S1-M1 in patients with brainstem ischemia was higher than that in patients with cortical ischemia. No activation of S1-M1 was observed in patients with lesions involving cortical regions. (2) Regarding brain lesion types, FV could induce the activation of bilateral S1-M1 in patients with basal ganglia hemorrhage, which was significantly higher than that in patients with basal ganglia ischemia. Additionally, LC showed no significant correlation with the modified Barthel index (MBI) in all patients, but a positive correlation with MBI in patients with basal ganglia lesions. CONCLUSIONS: These results reveal that sensory stimulation can induce lesion-specific activation patterns of S1-M1. This indicates FV could be applied in a personalized manner based on the lesion-specific activation of S1-M1 in stroke patients with different lesion profiles and types. Our study may contribute to a better understanding of the underlying mechanisms of cortical reorganization.

Tailoring chemical composition of solid electrolyte interphase by selective dissolution for long-life micron-sized silicon anode.

Micron-sized Si anode promises a much higher theoretical capacity than the traditional graphite anode and more attractive application prospect compared to its nanoscale counterpart. However, its severe volume expansion during lithiation requires solid electrolyte interphase (SEI) with reinforced mechanical stability. Here, we propose a solvent-induced selective dissolution strategy to in situ regulate the mechanical properties of SEI. By introducing a high-donor-number solvent, gamma-butyrolactone, into conventional electrolytes, low-modulus components of the SEI, such as Li alkyl carbonates, can be selectively dissolved upon cycling, leaving a robust SEI mainly consisting of lithium fluoride and polycarbonates. With this strategy, raw micron-sized Si anode retains 87.5% capacity after 100 cycles at 0.5 C (1500 mA g-1, 25°C), which can be improved to >300 cycles with carbon-coated micron-sized Si anode. Furthermore, the Si||LiNi0.8Co0.1Mn0.1O2 battery using the raw micron-sized Si anode with the selectively dissolved SEI retains 83.7% capacity after 150 cycles at 0.5 C (90 mA g-1). The selective dissolution effect for tailoring the SEI, as well as the corresponding cycling life of the Si anodes, is positively related to the donor number of the solvents, which highlights designing high-donor-number electrolytes as a guideline to tailor the SEI for stabilizing volume-changing alloying-type anodes in high-energy rechargeable batteries.

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL  https://oncoctminer.chosenmedinfo.com.

The transformative cognition of English as a Foreign Language student teachers' personal practical knowledge.

This study investigates the process by which English as a Foreign Language (EFL) student teachers transmute their Pedagogical Content Knowledge (PCK) into Personal Practical Knowledge (PPK) within a blended learning community. Data sources, including conversation transcripts, reflective journals, and field notes, were meticulously examined utilizing the commonplaces of temporality, sociality, and place. Several key findings were unveiled: (1) the volume and focal points of transformed PPK varied across participants; (2) the metamorphosis of PCK into PPK was found to be selectively partial, filtered by factors such as previous learning experiences, course expectations, and levels of engagement; and (3) the selection process was molded within the dynamic interplay of the internal components of the blended learning community and the external socio-cultural conditions. The study concludes that the cognition mechanism of EFL student teachers' PPK is characterized by elements of variation, selection, and dynamism.

Periodic Intermittent Adaptive Control with Saturation for Pinning Quasi-Consensus of Heterogeneous Multi-Agent Systems with External Disturbances.

A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated. Furthermore, we propose three saturated adaptive pinning control protocols. The quasi-consensus is achieved through its own pinning as long as the agents remain connected to each other. Using the Lyapunov function method and inequality technique, the convergence range of the quasi-consensus error of a heterogeneous multi-agent system is obtained. Finally, the rationality of the proposed control protocol is verified through numerical simulation. Theoretical derivation and simulation results show that the novel proposed periodic intermittent adaptive control method with saturation can successfully be used to achieve the pinning of quasi-consensus of nonlinear heterogeneous multi-agent systems.

A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation.

It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in [Formula: see text], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.

Modified stomach-partitioning gastrojejunostomy for initially unresectable advanced gastric cancer with outlet obstruction: A case report.

BACKGROUND: Chemotherapy followed by gastrojejunostomy remains the main treatment for unresectable gastric cancer (GC) in the middle- or lower-third regions with gastric outlet obstruction (GOO). Radical surgery is performed as part of a multimodal treatment strategy for selected patients who respond well to chemotherapy. This study describes a case of successful radical resection with completely laparoscopic subtotal gastrectomy after a modified stomach-partitioning gastrojejunostomy (SPGJ) for obstruction relief, in a patient with GOO. CASE SUMMARY: During the initial esophagogastroduodenoscopy, an advanced growth was detected in the lower part of the stomach, which caused an obstruction in the pyloric ring. Following this, a computed tomography (CT) scan revealed the presence of lymph node metastases and tumor invasion in the duodenum, but no evidence of distant metastasis was found. Consequently, we performed a modified SPGJ, a complete laparoscopic SPGJ combined with No. 4sb lymph node dissection, for obstruction relief. Seven courses of adjuvant capecitabine plus oxaliplatin combined with Toripalimab (programmed death ligand-1 inhibitor) were administered thereafter. A preoperative CT showed partial response; therefore, completely laparoscopic radical subtotal gastrectomy with D2 lymphadenectomy was performed after conversion therapy, and pathological complete remission was achieved. CONCLUSION: Laparoscopic SPGJ combined with No. 4sb lymph node dissection was an effective surgical technique for initially unresectable GC with GOO.

Three-dimensional memristive Morris-Lecar model with magnetic induction effects and its FPGA implementation.

To characterize the magnetic induction flow induced by neuron membrane potential, a three-dimensional (3D) memristive Morris-Lecar (ML) neuron model is proposed in this paper. It is achieved using a memristor induction current to replace the slow modulation current in the existing 3D ML neuron model with fast-slow structure. The magnetic induction effects on firing activities are explained by the spiking/bursting firings with period-adding bifurcation and periodic/chaotic spiking-bursting patterns, and the bifurcation mechanisms of the bursting patterns are elaborated using the fast-slow analysis method to create two bifurcation sets. In particular, the 3D memristive ML model can also exhibit the homogeneous coexisting bursting patterns when switching the memristor initial states, which are effectively illustrated by the theoretical analysis and numerical simulations. Finally, a digitally FPGA-based hardware platform is developed for the 3D memristive ML model and the experimentally measured results well verify the numerical ones.

The Fragile culm19 (FC19) mutation largely improves plant lodging resistance, biomass saccharification, and cadmium resistance by remodeling cell walls in rice.

Cell wall is essential for plant upright growth, biomass saccharification, and stress resistance. Although cell wall modification is suggested as an effective means to increase biomass saccharification, it is a challenge to maintain normal plant growth with improved mechanical strength and stress resistance. Here, we reported two independent fragile culm mutants, fc19-1 and fc19-2, resulting from novel mutations of OsIRX10, produced by the CRISPR/Cas9 system. Compared to wild-type, the two mutants exhibited reduced contents of xylose, hemicellulose, and cellulose, and increased arabinose and lignin without significant alteration in levels of pectin and uronic acids. Despite brittleness, the mutants displayed increased breaking force, leading to improved lodging resistance. Furthermore, the altered cell wall and increased biomass porosity in fc19 largely increased biomass saccharification. Notably, the mutants showed enhanced cadmium (Cd) resistance with lower Cd accumulation in roots and shoots. The FC19 mutation impacts transcriptional levels of key genes contributing to Cd uptake, sequestration, and translocation. Moreover, transcriptome analysis revealed that the FC19 mutation resulted in alterations of genes mainly involved in carbohydrate and phenylpropanoid metabolism. Therefore, a hypothetic model was proposed to elucidate that the FC19 mutation-mediated cell wall remodeling leads to improvements in lodging resistance, biomass saccharification, and Cd resistance.

Extreme multistability and phase synchronization in a heterogeneous bi-neuron Rulkov network with memristive electromagnetic induction.

Memristive electromagnetic induction effect has been widely explored in bi-neuron network with homogeneous neurons, but rarely in bi-neuron network with heterogeneous ones. This paper builds a bi-neuron network by coupling heterogeneous Rulkov neurons with memristor and investigates the memristive electromagnetic induction effect. Theoretical analysis discloses that the bi-neuron network possesses a line equilibrium state and its stability depends on the memristor coupling strength and initial condition. That is, the stability of the line equilibrium state has a transition between unstable saddle-focus and stable node-focus via Hopf bifurcation. By employing parameters located in the stable node-focus region, dynamical behaviors related to the memristor coupling strength and initial conditions are revealed by Julia- and MATLAB-based multiple numerical tools. Numerical results demonstrate that the proposed heterogeneous bi-neuron Rulkov network can generate point attractor, period, chaos, chaos crisis, and period-doubling bifurcation. Note that extreme multistability are disclosed with respect to initial conditions of memristor and gated ion concentration. Coexisting infinitely multiple firing patterns of periodic firing patterns with different periodicities and chaotic firing patterns for different memristor initial conditions are demonstrated by phase portrait and time-domain waveform. Besides, the phase synchronization related to the memristor coupling strength and its initial condition is explored, which suggests that the two heterogeneous neurons become phase synchronization with large memristor coupling strength and initial condition. This also reflects that the plasticity of memristor synapse enables adaptive regulation in keeping energy balance between the neurons. What's more, MCU-based hardware experiments are executed to further confirm the numerical simulations.