基于“3414”试验模型的四川盆地中部水稻氮磷钾肥效应研究

本研究采用“3414”肥料效应田间试验,旨在确定四川盆地中部资中县水稻的最佳施肥量,并构建高产施肥模型。通过设置氮、磷、钾3因素,每个因素4个水平,共14个处理,每个处理重复3次。结果表明:建立的三元二次肥料效应回归方程的相关系数(R^(2))为0.976。纯氮施用量为215.28 kg/hm^(2),五氧化二磷施用量为101.40 kg/hm^(2),氧化钾施用量为104.14 kg/hm^(2),水稻最大产量为7923.56 kg/hm^(2);纯氮最佳施用量为196.83 kg/hm^(2),五氧化二磷施用量为74.81 kg/hm^(2),氧化钾施用量为102.90 kg/hm^(2),水稻最佳产量为7867.96 kg/hm^(2)。建立的肥料效应模型可在四川盆地中部类似水稻产区推广,用于水稻生产上对施肥量的预测。

基于3414试验设计的云南雪茄烟叶生产施肥技术研究

为明确云南省瑞丽雪茄烟区土壤合理施肥方案,采用“3414”肥料试验设计,通过熵权法和回归分析,计算了各处理对应的烟株农艺性状综合得分,探究了N、P、K对雪茄烟株长势和烟叶产量的影响,筛选出最佳施肥方案。结果表明:(1)株高对雪茄烟株农艺性状的得分所占权重最大,处理6(N_(2)P_(2)K_(2))对应的得分最高。(2)在一定范围内增加N、P、K的施用量,对雪茄烟的产量有积极作用,然而随着施肥量的增加产量又有所下降。(3)构建了N、P、K施用量对雪茄烟产量的二元二次和三元二次回归模型,最佳施肥方案为N施用量在118.7~126.9 kg/hm^(2)之间,P_(2)O_(5)施用量在75.7~81.4 kg/hm^(2)之间,K_(2)O施用量在342.1~366.5 kg/hm^(2)之间,目标产量在1816.3~1812.5 kg/hm^(2)之间。

基于“3414”试验的切花芍药氮磷钾推荐施肥量研究

针对切花芍药生长过程中盲目施肥的现状,以二年生芍药‘向天歌’为研究对象,采用“3414”试验施肥方案,结合隶属函数法,研究氮、磷、钾肥不同用量对切花芍药生长的影响,探讨切花芍药生产适宜的肥料用量。结果表明:不同施肥处理对芍药株高、茎粗、蕾径、分株数和切花数影响显著(P<0.05);氮、磷缺素区的综合隶属函数值小于无肥区,缺钾区大于无肥区,对芍药生长的影响为氮>磷>钾;N2P2K1处理的综合隶属函数值最大,为0.61,芍药植株生长最好;对各处理的综合隶属函数值和施肥量进行肥料效应函数拟合分析,一元二次、二元二次肥料效应函数方程拟合相关性均达显著水平,三元二次肥料效应方程为非典型模型,采用频率分析法计算肥料推荐量。综合分析各肥料效应函数方程的推荐用量及相应的综合隶属函数值,本试验切花芍药推荐施肥量为N 212.7 kg/hm^(2)、P_(2)O_(5)46.2 kg/hm^(2)、K_(2)O 49.9 kg/hm^(2),施肥配比为1∶0.21∶0.23,可用于本试验区切花芍药生产的施肥参考。

甘南高寒阴湿区宽幅匀播青稞“3414”肥效试验

为了将宽幅匀播技术与“3414”肥效试验相结合,应用于甘南高寒阴湿区青稞种植与示范推广中,推动该地区青稞种植产业发展。采用“3414”最优回归设计,以青稞甘青9号为指示品种,在宽幅匀播条件下研究了“3414”肥效试验对青稞生长及产量的影响,并建立了青稞产量与氮、磷、钾施量之间的肥效回归方程,以提高肥效,促进甘南高寒阴湿区青稞生产优质高产。结果表明,宽幅匀播条件下,甘青9号最佳产量为6640.32kg/hm^(2)时,施肥量为N36.34kg/hm^(2)、P_(2)O_(5)55.86kg/hm^(2)、K_(2)O7.09kg/hm^(2);最大产量为6834.09kg/hm^(2)时,施肥量为N80.66 kg/hm^(2)P_(2)O_(5)62.31kg/hm^(2)、K_(2)O16.58kg/hm。肥效回归方程中施肥量与青稞产量拟合度显著,可应用于大田生产。

Efficacy and predictive factors of transarterial chemoembolization combined with lenvatinib plus programmed cell death protein-1 inhibition for unresectable hepatocellular carcinoma

BACKGROUND The efficacy and safety of transarterial chemoembolization(TACE)combined with lenvatinib plus programmed cell death protein-1(PD-1)for unresectable hepato-cellular carcinoma(HCC)have rarely been evaluated and it is unknown which factors are related to efficacy.AIM To evaluate the efficacy and independent predictive factors of TACE combined with lenvatinib plus PD-1 inhibitors for unresectable HCC.METHODS This study retrospectively enrolled patients with unresectable HCC who received TACE/lenvatinib/PD-1 treatment between March 2019 and April 2022.Overall survival(OS)and progression-free survival(PFS)were determined.The objective response rate(ORR)and disease control rate(DCR)were evaluated in accordance with the modified Response Evaluation Criteria in Solid Tumors.Additionally,the prognostic factors affecting the clinical outcome were assessed.RESULTS One hundred and two patients were enrolled with a median follow-up duration of 12.63 months.The median OS was 26.43 months(95%CI:17.00-35.87),and the median PFS was 10.07 months(95%CI:8.50-11.65).The ORR and DCR were 61.76%and 81.37%,respectively.The patients with Barcelona Clinic Liver Cancer Classification(BCLC)B stage,early neutrophil-to-lymphocyte ratio(NLR)response(decrease),or early alpha-fetoprotein(AFP)response(decrease>20%)had superior OS and PFS than their counterparts.CONCLUSION This study showed that TACE/lenvatinib/PD-1 treatment was well tolerated with encouraging efficacy in patients with unresectable HCC.The patients with BCLC B-stage disease with early NLR response(decrease)and early AFP response(decrease>20%)may achieve better clinical outcomes with this triple therapy.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

Cytokine release syndrome triggered by programmed death 1 blockade(sintilimab)therapy in a psoriasis patient:A case report

BACKGROUND In recent years,immune checkpoint inhibitors(ICIs)have demonstrated remarkable efficacy across diverse malignancies.Notably,in patients with advanced gastric cancer,the use of programmed death 1(PD-1)blockade has significantly prolonged overall survival,marking a pivotal advancement comparable to the impact of Herceptin over the past two decades.While the therapeutic benefits of ICIs are evident,the increasing use of immunotherapy has led to an increase in immune-related adverse events.CASE SUMMARY This article presents the case of a patient with advanced gastric cancer and chronic plaque psoriasis.Following sintilimab therapy,the patient developed severe rashes accompanied by cytokine release syndrome(CRS).Fortunately,effective management was achieved through the administration of glucocorticoid,tocilizumab,and acitretin,which resulted in favorable outcomes.CONCLUSION Glucocorticoid and tocilizumab therapy was effective in managing CRS after PD-1 blockade therapy for gastric cancer in a patient with chronic plaque psoriasis.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Efficacy of chemotherapy containing bevacizumab in patients with metastatic colorectal cancer according to programmed cell death ligand 1

BACKGROUND Bevacizumab,an anti-vascular endothelial growth factor(VEGF)monoclonal antibody,inhibits angiogenesis and reduces tumor growth.Serum VEGF-C,lactate dehydrogenase,and inflammatory markers have been reported as predictive markers related to bevacizumab treatment.Programmed cell death ligand 1(PD-L1)could act upon VEGF receptor 2 to induce cancer cell angiogenesis and metastasis.AIM To investigate the efficacy of bevacizumab-containing chemotherapy in patients with metastatic colorectal cancer(CRC)according to the expression of PD-L1.METHODS This analysis included CRC patients who received bevacizumab plus FOLFOX or FOLFIRI as first-line therapy between June 24,2014 and February 28,2022,at Samsung Medical Center(Seoul,South Korea).Analysis of patient data included evaluation of PD-L1 expression by the combined positive score(CPS).We analyzed the efficacy of bevacizumab according to PD-L1 expression status in patients with CRC.RESULTS A total of 124 patients was included in this analysis.Almost all patients were treated with bevacizumab plus FOLFIRI or FOLFOX as the first-line chemotherapy.While 77%of patients received FOLFOX,23%received FOLFIRI as backbone first-line chemotherapy.The numbers of patients with a PD-L1 CPS of 1 or more,5 or more,or 10 or more were 105(85%),64(52%),and 32(26%),respectively.The results showed no significant difference in progression-free survival(PFS)and overall survival(OS)with bevacizumab treatment between patients with PDL1 CPS less than 1 and those with PD-L1 CPS of 1 or more(PD-L1<1%vs PD-L1≥1%;PFS:P=0.93,OS:P=0.33),between patients with PD-L1 CPS less than 5 and of 5 or more(PD-L1<5%vs PD-L1≥5%;PFS:P=0.409,OS:P=0.746),and between patients with PD-L1 CPS less than 10 and of 10 or more(PD-L1<10%vs PD-L1≥10%;PFS:P=0.529,OS:P=0.568).CONCLUSION Chemotherapy containing bevacizumab can be considered as first-line therapy in metastatic CRC irrespective of PD-L1 expression.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

In vivo astrocyte reprogramming following spinal cord injury

Harmful and helpful roles of astrocytes in spinal cord injury(SCI):SCI induce gradable sensory,motor and autonomic impairments that correlate with the lesion severity and the rostro-caudal location of the injury site.The absence of spontaneous axonal regeneration after injury results from neuron-intrinsic and neuron-extrinsic parameters.Indeed,not only adult neurons display limited capability to regrow axons but also the injury environment contains inhibitors to axonal regeneration and a lack of growth-promoting factors.Amongst other cell populations that respond to the lesion,reactive astrocytes were first considered as only detrimental to spontaneous axonal regeneration.Indeed,astrocytes.

Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity

Enhanced osteoclastogenesis and osteoclast activity contribute to the development of osteoporosis,which is characterized by increased bone resorption and inadequate bone formation.As novel antiosteoporotic therapeutics are needed,understanding the genetic regulation of human osteoclastogenesis could help identify potential treatment targets.This study aimed to provide an overview of transcriptional reprogramming during human osteoclast differentiation.Osteoclasts were differentiated from CD14+monocytes from eight female donors.RNA sequencing during differentiation revealed 8980 differentially expressed genes grouped into eight temporal patterns conserved across donors.These patterns revealed distinct molecular functions associated with postmenopausal osteoporosis susceptibility genes based on RNA from iliac crest biopsies and bone mineral density SNPs.Network analyses revealed mutual dependencies between temporal expression patterns and provided insight into subtype-specific transcriptional networks.The donor-specific expression patterns revealed genes at the monocyte stage,such as filamin B(FLNB)and oxidized low-density lipoprotein receptor 1(OLR1,encoding LOX-1),that are predictive of the resorptive activity of mature osteoclasts.The expression of differentially expressed G-protein coupled receptors was strong during osteoclast differentiation,and these receptors are associated with bone mineral density SNPs,suggesting that they play a pivotal role in osteoclast differentiation and activity.The regulatory effects of three differentially expressed G-protein coupled receptors were exemplified by in vitro pharmacological modulation of complement 5 A receptor 1(C5AR1),somatostatin receptor 2(SSTR2),and free fatty acid receptor 4(FFAR4/GPR120).Activating C5AR1 enhanced osteoclast formation,while activating SSTR2 decreased the resorptive activity of mature osteoclasts,and activating FFAR4 decreased both the number and resorptive activity of mature osteoclasts.In conclusion,we report the occurrence of transcriptional reprogramming during human osteoclast differentiation and identified SSTR2 and FFAR4 as antiresorptive G-protein coupled receptors and FLNB and LOX-1 as potential molecular markers of osteoclast activity.These data can help future investigations identify molecular regulators of osteoclast differentiation and activity and provide the basis for novel antiosteoporotic targets.

Metabolic reprogramming in skeletal cell differentiation

The human skeleton is a multifunctional organ made up of multiple cell types working in concert to maintain bone and mineral homeostasis and to perform critical mechanical and endocrine functions.From the beginning steps of chondrogenesis that prefigures most of the skeleton,to the rapid bone accrual during skeletal growth,followed by bone remodeling of the mature skeleton,cell differentiation is integral to skeletal health.

Glia-to-neuron reprogramming to the rescue?

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

Neurocircuit regeneration by extracellular matrix reprogramming

The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Key Factors to Consider When Introducing a New Vaccine in Low-Income Settings: Lessons from Malawi Expanded Program on Immunization

Introduction: As new vaccines become available, countries must assess the relevance to introduce them into their vaccination schedules. Malawi has recently introduced several new vaccines and plans to introduce more. This study was conducted to identify key factors that need to be considered when deciding to introduce a new vaccine and current challenges faced by low and middle income countries using Malawi as an example. Methodology: The study employed a desk review approach, examining published literature from various sources such as PubMed, Medline, and Google Scholar. Policy documents from organizations like the World Health Organization, GAVI the Alliance, and the Ministry of Health for Malawi were also included. A total of 99 articles and documents on new vaccine introduction, challenges of immunization, policy documents in immunization and health systems strengthening were included. The review focused on addressing five key areas critical to new vaccine introduction namely: the need for a vaccine, availability of the vaccine, safety and effectiveness of the vaccine, demand for the vaccine, and the prudent use of public or private funds. Results: Malawi considered the burden of cervical cancer and the significance of malaria in the country when introducing the HPV and malaria vaccines. The country opted for vaccines that can be handled by the cold chain capacity and available human resources. Despite that malaria vaccine and Typhoid Conjugate Vaccine trials were done in country, there are limited vaccine safety and efficacy trials conducted in Malawi, leading to a reliance on WHO-prequalified vaccines. Demand for newly introduced vaccines varied, with high demand for Oral Cholera Vaccine during a cholera outbreak, while demand for COVID-19 vaccines decreased over time. Although cost-effectiveness studies were limited in the country, 2 studies indicated that Typhoid Conjugate Vaccine and malaria vaccine would be cost effective. All these have been implemented despite having challenges like lack of accurate surveillance data, inadequate cold chain capacity, limited safety and efficacy vaccine clinical trials, political influence, and limited funding. Conclusion: Despite several challenges Malawi set a good example of the careful considerations required before introducing a new vaccine. The process involves data review, priority setting, precise planning, and consultation with stakeholders. Low-income countries should invest in vaccine safety, efficacy, and cost-effectiveness trials.

贞丰县下等肥力地小麦“3414”肥料效应试验

[目的]探索贞丰县下等肥力地小麦氮、磷、钾肥最佳施肥配比。[方法]通过小麦“3414”肥料效应田间试验,对不同施肥处理的小麦产量进行分析比较,以确定最佳施肥配比。[结果]施用不同配比的氮、磷、钾肥对小麦的农艺性状和产量有不同的影响,当施肥量为N 390 kg/hm^(2),P_(2)O_(5)750 kg/hm^(2),K_(2)O 124.5 kg/hm^(2)时,小麦的产量最高,为4 971.0 kg/hm^(2),较未施肥区增产2 131.5 kg/hm^(2)。[结论]在与该试验相同肥力条件的下等肥力地种植小麦,推荐最佳施肥量为:N 390 kg/hm^(2),P_(2)O_(5)750 kg/hm^(2),K_(2)O 124.5 kg/hm^(2)。

Research and Practice of Cardiopulmonary Rehabilitation Training Programs

In today’s society, the incidence of cardiopulmonary diseases is increasing annually, seriously affecting patients’ quality of life. Therefore, developing a scientific and effective rehabilitation training program is of great significance. This study first analyzes the theoretical basis of cardiopulmonary rehabilitation training, including the effects of aerobic exercise, interval training, and strength training on cardiopulmonary function. Based on this, a comprehensive rehabilitation training program is designed, which includes personalized training plans, comprehensive interventions, multidisciplinary collaboration, patient education, and regular follow-up visits. The cardiopulmonary rehabilitation training plan developed in this study has certain scientific practicability, which provides a theoretical basis for cardiopulmonary rehabilitation training, and also provides a reference for medical institutions, rehabilitation centers and communities, which is helpful for promotion and application to a wider range of patients with cardiopulmonary diseases.

基于“3414”肥效试验建立BP神经网络寻优的新模型初探

针对上海市崇明滧东地区传统施肥方式不能满足水稻需肥规律的问题,以水稻“3414”肥效试验结果为数据来源,以N、P_(2)O_(5)、K_(2)O施用量为优化目标,建立3-7-1拓扑结构BP神经网络模型,通过遗传算法得到最优产量下的最优施肥配比。预测结果表明:当地较优的施肥配比是N、P_(2)O_(5)、K_(2)O的施用量分别为24.94、0.87、4.27 kg/亩(1亩=667 m^(2)),预计最高产量为531.5 kg/亩。通过验证试验,在较优的施肥配比条件下,水稻的实际产量为548.7 kg/亩,验证了BP神经网络模型预测结果的准确性。