Fish-on-Chips:unveiling neural processing of chemicals in small animals through precise fluidic control

Precise chemical cue presentation alongside advanced brainwide imaging techniques is important to the study of chemosensory processing in animals.Nevertheless,the dynamic nature of chemical-carrying media,such as water or air,poses a significant challenge for delivering highly-controlled chemical flow to an animal subject.Moreover,contact-based cue manipulation and delivery easily shift the position of the animal subject,which is often undesirable for high-quality brain imaging.Additionally,more advanced interfacing tools that align with the diverse range of body part sizes of an animal,ranging from micrometer-scale neurons to meter-long limbs,are much needed.This is particularly crucial when dealing with dimensions that are beyond the reach of conventional experimental tools.

Self-consistent and precise measurement of time-dependent radiative albedo of gold based on specially symmetrical triple-cavity Hohlraum

A self-consistent and precise method to determine the time-dependent radiative albedo,i.e.,the ratio of the reemission flux to the incident flux,for an indirect-drive inertial confinement fusion Hohlraum wall material is proposed.A specially designed symmetrical triple-cavity gold Hohlraum is used to create approximately constant and near-equilibrium uniform radiation with a peak temperature of 160 eV.The incident flux at the secondary cavity waist is obtained from flux balance analysis and from the shock velocity of a standard sample.The results agree well owing to the symmetrical radiation in the secondary cavity.A self-consistent and precise time-dependent radiative albedo is deduced from the reliable reemission flux and the incident flux,and the result from the shock velocity is found to have a smaller uncertainty than that from the multi-angle flux balance analysis,and also to agree well with the result of a simulation using the HYADES opacity.

Clinical value of precise rehabilitation nursing in management of cerebral infarction

BACKGROUND Cerebral infarction,previously referred to as cerebral infarction or ischemic stroke,refers to the localized brain tissue experiencing ischemic necrosis or softening due to disorders in brain blood supply,ischemia,and hypoxia.The precision rehabilitation nursing model for chronic disease management is a continuous,fixed,orderly,and efficient nursing model aimed at standardizing the clinical nursing process,reducing the wastage of medical resources,and improving the quality of medical services.AIM To analyze the value of a precise rehabilitation nursing model for chronic disease management in patients with cerebral infarction.METHODS Patients(n=124)admitted to our hospital with cerebral infarction between November 2019 and November 2021 were enrolled as the study subjects.The random number table method was used to divide them into a conventional nursing intervention group(n=61)and a model nursing intervention group(n=63).Changes in the nursing index for the two groups were compared after conventional nursing intervention and precise rehabilitation intervention nursing for chronic disease management.RESULTS Compared with the conventional intervention group,the model intervention group had a shorter time to clinical symptom relief(P<0.05),lower Hamilton Anxiety Scale and Hamilton Depression Scale scores,a lower incidence of total complications(P<0.05),a higher disease knowledge mastery rate,higher safety and quality,and a higher overall nursing satisfaction rate(P<0.05).CONCLUSION The precision rehabilitation nursing model for chronic disease management improves the clinical symptoms of patients with cerebral infarction,reducing the incidence of total complications and improving the clinical outcome of patients,and is worthy of application in clinical practice.

Empowerment of Precise Ideological and Political Education in Higher Education with Educational Digitalization

The study explores how educational digitalization enables the precise development of ideological and political education in colleges and universities.Digital transformation enables colleges and universities to accurately define educational objectives,content strategies,effect evaluation,and process management,and realize the precision and intelligence of ideological and political education.The application of big data technology enhances the data-oriented thinking of teachers and students,promotes the accurate application of data,and improves the efficiency of ideological and political education.The research also prospected a new vision of the digital construction of ideological and political courses and clarified the theoretical and practical path of the implementation and evaluation mode of ideological and political courses under digital empowerment.Education digitalization enables precise ideological and political education,which is a key way to promote the innovative development of ideological and political education in colleges and universities and will strongly support the improvement of the overall quality of higher education and the training of excellent talents.

医科达Precise直线加速器常见故障及维修三例

医科达Precise医用直线加速器为精准治疗设备,为保证放射治疗工作,快速解决故障问题,本研究主要以Precise直线加速器的常见故障类型为对象,运用科学方法结合实际工作经验,分析总结其维修步骤方法,为相关维修工作者提供参考。在分析医用直线加速器的结构和工作原理的基础上,总结我科加速器频繁出现的“Dose Rate Mon”连锁、“Touch Guard”连锁和“Finepot offset Y2”连锁的故障原因,通过科学的方法逐一排查,最终有效地顺利解决故障问题。在充分掌握加速器结构和原理的基础上,通过故障原因分析与故障维修,有效排除故障问题并总结分析,确保机器的正常运转和临床治疗的工作正常开展,为治疗实施的稳定提供有力保障。

Efficient Improvement of Nutritional Content in Rice Grains by Precise Base Editing of OsROS1

Aleurone forms the outermost layer of the rice endosperm and plays a critical role in apoplastic nutrient uptake during endosperm development.Thickening the aleurone layer has been proposed to significantly increase the nutrient content of rice grains.In this study,we used a CRISPR/Cas9-mediated precise base editing method to target OsROS1,a gene associated with aleurone thickness,in the background of the japonica glutinous rice cultivar Zhennuo 19。

An ERA5 tropospheric parameters-augmented approach for improving GNSS precise point positioning

Precise Point Positioning(PPP) technology has developed into a potent instrument for geodetic positioning, ionospheric modeling, tropospheric atmospheric parameter detection, and seismic monitoring.As atmospheric reanalysis data products’ accuracy and spatiotemporal resolution have improved recently, it has become important to apply these products to obtain high-accuracy tropospheric delay parameters, like zenith tropospheric delay(ZTD) and tropospheric horizontal gradient. These tropospheric delay parameters can be applied to PPP to reduce the convergence time and to increase the accuracy in the vertical direction of the position. The European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5) atmospheric reanalysis data is the latest product with a high spatiotemporal resolution released by the European Center for Medium-Range Weather Forecasts(ECMWF). Only a few researches have evaluated the application of ERA5 data to Global Navigation Satellite System(GNSS)PPP. Therefore, this study compared and validated the ZTD products derived from ERA5 data using ZTD values provided by 290 global International GNSS Service(IGS) stations for 2016-2017. The results indicated a stable performance for ZTD, with annual average bias and RMS values of 0.23 cm and 1.09 cm,respectively. Further, GNSS observations for one week in each of the four seasons(spring: DOY 92-98;summer: DOY 199-205;autumn: DOY 275-281;and winter: DOY 22-28) from 34 multi-GNSS experiments(MGEX) stations distributed globally in 2016 were considered to evaluate the performance of ERA5-derived tropospheric delay products in GNSS PPP. The performance of ERA5-enhanced PPP was compared with that of the two standard GNSS PPP schemes(without estimated tropospheric horizontal gradient and with estimated tropospheric horizontal gradient). The results demonstrated that ERA5-enhanced GNSS PPP showed no significant improvement in the convergence times in both the Eastern(E) and Northern(N) directions, while the average convergence time over four weeks in the vertical(U)direction improved by 53.3% and 52.7%, respectively(in the case of pngm station). The average convergence times for each week in the U direction of the northern and southern hemisphere stations indicated a decrease of 16.3%, 12.6%, 9.6%, and 9.1%, and 16.9%, 9.6%, 8.9%, and 14.5%, respectively.Regarding positioning accuracy, ERA5-enhanced PPP showed an improvement of 13.3% and 16.2% over the two standard PPP schemes in the U direction, respectively. No significant improvement in the positioning performance was observed in both the E and N directions. Thus, this study demonstrated the potential application of the ERA5 tropospheric parameters-augmented approach to Beidou navigation and positioning.

Precise Rehabilitation Strategies for Functional Impairment in Children with Cerebral Palsy

This paper explores the effect of precise rehabilitation strategies under the international classification of functioning,disability and health for children and youth(ICF-CY)on the motor function of children with cerebral palsy.Under the framework of ICF-CY,the observation team is designed and evaluated from physical functions,activities and participation,environmental factors,and devel-ops individualized rehabilitation strategies that are tailored to individual character-istics.The control group was assessed by traditional methods and treatment plans and measures were formulated and guided.The course of treatment was 12 months.The scores of GMFM-88,Peabody Motor Development Scale-2con-cluding fine motor quotient(PDMS-FM),WeeFIM and the ability of daily living(ADL)scale(Barthel index,BI)score were compared before and after treatment.The research shows that precise rehabilitation strategy mode for children with cer-ebral palsy under the ICF-CY framework can effectively improve the motor func-tion and the ability of daily living(ADL)in children with cerebral palsy.Rehabilitation evaluation and treatment mode of children with cerebral palsy under ICF-CY framework can effectively improve the gross motor function of children with cerebral palsy.Individualized evaluation and analysis and guidance of comprehensive rehabilitation treatment have certain advantages.The overall treatment effect is better than that of traditional rehabilitation evaluation.

Researchers Develop New Tools for Precise Large DNA Insertions

GAO Caixia’s group from the Institute of Genetics and Developmental Biology(IGDB)of the Chinese Academy of Sciences(CAS)has developed a new genome editing technology that achieves efficient and precise targeted insertion of large DNA segments in plants.The new technology,called prime editing-mediated recombination of opportune targets(PrimeRoot),combines an optimized dual-ePPE editor protein previously published by the group with a highly efficient tyrosine site-specific recombinase,Cre.It can achieve one-step,precise targeted insertion of large DNA segments in rice and maize with an efficiency up to 6%and has been used to successfully insert DNA segments up to 11.1 kb.

Innovative Techniques for Precise Site-Specific Sample Preparation and In situ Structural Study of Cells

Nanomachines and ultrastructures inside cells are the basic units involved in life activities.They perform specific physiological functions through close cooperation with each other.Seeing is believing.Studying the in situ assembly and function of these complicated and precise nanostructures has been a hot topic in life science.Cryo-electron tomography(cryo-ET)is currently the main technique for in situ structural analysis.However,due to the limitation of electron beam penetration,cell and tissue samples should be milled to lamella of~200 nanometers using focused ion beams(FIB)for imaging.However,the random milling technique brings great challenges to the study of specific targets with relatively low abundance in cells.The prepared cryo-lamella sample often fails to retain the target of interest.

Atomically precise gold nanoclusters for healthcare applications

The potential application of gold nanoparticles(GNPs)in biomedicine has been extensively reported.However,there is still too much puzzle about their real face and potential health risks in comparison with the commercial drug molecules.The emergence of atomically precise gold nanoclusters(APGNCs)provides the opportunity to address the puzzle due to their ultrasmall size,defined molecular formula,editable surface engineering,available structures and unique physicochemical properties including excellent biocompatibility,strong luminescence,enzyme-like activity and efficient renal clearance,et al.Recently,these advantages of APGNCs also endow them promising performances in healthcare such as bioimaging,drug delivery,antibacterial and cancer therapy.Especially,their clear composition and structures like the commercial drug molecules facilitate the study of their functions and the structure-activity relationship in healthcare,which is essential for the guided design of APGNC nanomedicine.Therefore,this review will focus the advantages and recent progress of APGNCs in health care and envision their prospects for the future.

医科达Precise直线加速器常见故障及维修分析

医科达Precise直线加速器广泛应用于肿瘤疾病的放射治疗,对肿瘤患者疾病的治疗与后续身体恢复具有重要意义。应注重并对医科大Precise直线加速器的管理,及时发现该设备出现的故障,并采取科学、合理的方式进行维修,保证该设备可以安全、稳定的运行。结合4例常见故障,分析医科达Precise直线加速器的维修方法,为设备维修提供参考。

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis(TB),especially multidrug-resistant TB and extensively drug-resistant TB,has strongly motivated the research and development of new anti-TB drugs.New strategies to facilitate drug combinations,including pharmacokinetics-guided dose optimization and toxicology studies of first-and second-line anti-TB drugs have also been introduced and recommended.Liquid chromatography-mass spectrometry(LC-MS)has arguably become the gold standard in the analysis of both endo-and exo-genous compounds.This technique has been applied successfully not only for therapeutic drug monitoring(TDM)but also for pharmacometabolomics analysis.TDM improves the effectiveness of treatment,reduces adverse drug reactions,and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window.Based on TDM,the dose would be optimized individually to achieve favorable outcomes.Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs,aiding in the discovery of potential biomarkers for TB diagnostics,treatment monitoring,and outcome evaluation.This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades.Besides,we discussed the advantages and disadvantages of this technique in practical use.The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted.Lastly,we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies(pharmacometrics,drug and vaccine developments,machine learning/artificial intelligence,among others)to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were previously proposed and analyzed.These specially designed methods use reduced precision for the implicit computations and full precision for the explicit computations.In this work,we analyze the stability properties of these methods and their sensitivity to the low-precision rounding errors,and demonstrate their performance in terms of accuracy and efficiency.We develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed-precision additive Runge-Kutta(MP-ARK)methods.The convergence,accuracy,and runtime of these methods are explored.We show that for a given level of accuracy,suitably chosen MP-ARK methods may provide significant reductions in runtime.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background:Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs(ARLncRNAs)on the prognosis of hepatocellular carcinoma(HCC).Methods:We analyzed 371 HCC samples from TCGA,identifying expression networks of ARLncRNAs using autophagy-related genes.Screening for prognostically relevant ARLncRNAs involved univariate Cox regression,Lasso regression,and multivariate Cox regression.A Nomogram was further employed to assess the reliability of Riskscore,calculated from the signatures of screened ARLncRNAs,in predicting outcomes.Additionally,we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis,using consensus clustering to identify subgroups related to ARLncRNAs.Results:The screening process identified 27 ARLncRNAs,with 13 being associated with HCC prognosis.Consequently,a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC.Patients in the high-risk group showed very poor prognoses in most clinical categories.The Riskscore was closely related to immune cell scores,such as macrophages,and the DEGs between different groups were implicated in metabolism,cell cycle,and mitotic processes.Notably,high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel,suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC.We further identified C2 as the Paclitaxel subtype,where patients exhibited higher Riskscores,reduced survival rates,and more severe clinical progression.Conclusion:The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC.The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression.With the identification of ARLncRNAsrelated isoforms,these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

Bronchoalveolar lavage fluid assessment facilitates precision medicine for lung cancer

Lung cancer is the most common and fatal malignant disease worldwide and has the highest mortality rate among tumor-related causes of death.Early diagnosis and precision medicine can significantly improve the survival rate and prognosis of lung cancer patients.At present,the clinical diagnosis of lung cancer is challenging due to a lack of effective non-invasive detection methods and biomarkers,and treatment is primarily hindered by drug resistance and high tumor heterogeneity.Liquid biopsy is a method for detecting circulating biomarkers in the blood and other body fluids containing genetic information from primary tumor tissues.Bronchoalveolar lavage fluid(BALF)is a potential liquid biopsy medium that is rich in a variety of bioactive substances and cell components.BALF contains information on the key characteristics of tumors,including the tumor subtype,gene mutation type,and tumor environment,thus BALF may be used as a diagnostic supplement to lung biopsy.In this review,the current research on BALF in the diagnosis,treatment,and prognosis of lung cancer is summarized.The advantages and disadvantages of different components of BALF,including cells,cell-free DNA,extracellular vesicles,and micro RNA are introduced.In particular,the great potential of extracellular vesicles in precision diagnosis and detection of drug-resistant for lung cancer is highlighted.In addition,the performance of liquid biopsies with different body fluid sources in lung cancer detection are compared to facilitate more selective studies involving BALF,thereby promoting the application of BALF for precision medicine in lung cancer patients in the future.

Hepatitis B virus genotypes in precision medicine of hepatitis Brelated hepatocellular carcinoma:Where we are now

Hepatitis B virus(HBV)infection is a major player in chronic hepatitis B that may lead to the development of hepatocellular carcinoma(HCC).HBV genetics are diverse where it is classified into at least 9 genotypes(A to I)and 1 putative genotype(J),each with specific geographical distribution and possible different clinical outcomes in the patient.This diversity may be associated with the precision medicine for HBV-related HCC and the success of therapeutical approaches against HCC,related to different pathogenicity of the virus and host response.This Editorial discusses recent updates on whether the classification of HBV genetic diversity is still valid in terms of viral oncogenicity to the HCC and its precision medicine,in addition to the recent advances in cellular and molecular biology technologies.

A new method for evaluating the firing precision of multiple launch rocket system based on Bayesian theory

How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS considering the credibility of simulation system based on Bayesian theory is proposed in this paper.First of all,a comprehensive index system for the credibility of the simulation system of the firing precision of the MLRS is constructed combined with the group analytic hierarchy process.A modified method for determining the comprehensive weight of the index is established to improve the rationality of the index weight coefficients.The Bayesian posterior estimation formula of firing precision considering prior information is derived in the form of mixed prior distribution,and the rationality of prior information used in estimation model is discussed quantitatively.With the simulation tests,the different evaluation methods are compared to validate the effectiveness of the proposed method.Finally,the experimental results show that the effectiveness of estimation method for firing precision is improved by more than 25%.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.