AAV2-PDE6B restores retinal structure and function in the retinal degeneration 10 mouse model of retinitis pigmentosa by promoting phototransduction and inhibiting apoptosis

Retinitis pigmentosa is a group of inherited diseases that lead to retinal degeneration and photoreceptor cell death.However,there is no effective treatment for retinitis pigmentosa caused by PDE6B mutation.Adeno-associated virus(AAV)-mediated gene therapy is a promising strategy for treating retinitis pigmentosa.The aim of this study was to explore the molecular mechanisms by which AAV2-PDE6B rescues retinal function.To do this,we injected retinal degeneration 10(rd10)mice subretinally with AAV2-PDE6B and assessed the therapeutic effects on retinal function and structure using dark-and light-adapted electroretinogram,optical coherence tomography,and immunofluorescence.Data-independent acquisition-mass spectrometry-based proteomic analysis was conducted to investigate protein expression levels and pathway enrichment,and the results from this analysis were verified by real-time polymerase chain reaction and western blotting.AAV2-PDE6B injection significantly upregulated PDE6βexpression,preserved electroretinogram responses,and preserved outer nuclear layer thickness in rd10 mice.Differentially expressed proteins between wild-type and rd10 mice were closely related to visual perception,and treating rd10 mice with AAV2-PDE6B restored differentially expressed protein expression to levels similar to those seen in wild-type mice.Kyoto Encyclopedia of Genes and Genome analysis showed that the differentially expressed proteins whose expression was most significantly altered by AAV2-PDE6B injection were enriched in phototransduction pathways.Furthermore,the phototransductionrelated proteins Pde6α,Rom1,Rho,Aldh1a1,and Rbp1 exhibited opposite expression patterns in rd10 mice with or without AAV2-PDE6B treatment.Finally,Bax/Bcl-2,p-ERK/ERK,and p-c-Fos/c-Fos expression levels decreased in rd10 mice following AAV2-PDE6B treatment.Our data suggest that AAV2-PDE6B-mediated gene therapy promotes phototransduction and inhibits apoptosis by inhibiting the ERK signaling pathway and upregulating Bcl-2/Bax expression in retinitis pigmentosa.

Artificial Bear Bile:A Novel Approach to Balancing Medical Requirements and Animal Welfare

Bear bile has been a valuable and effective medicinal material in traditional Chinese medicine(TCM)for over 13 centuries.However,the current practice of obtaining it through bear farming is under scrutiny for its adverse impact on bear welfare.Here,we present a new approach for creating artificial bear bile(ABB)as a high-quality and sustainable alternative to natural bear bile.This study addresses the scientific challenges of creating bear bile alternatives through interdisciplinary collaborations across various fields,including resources,chemistry,biology,medicine,pharmacology,and TCM.A comprehensive efficacy assessment system that bridges the gap between TCM and modern medical terminology has been established,allowing for the systematic screening of therapeutic constituents.Through the utilization of chemical synthesis and enzyme engineering technologies,our research has achieved the environmentally friendly,large-scale production of bear bile therapeutic compounds,as well as the optimization and recomposition of ABB formulations.The resulting ABB not only closely resembles natural bear bile in its composition but also offers advantages such as consistent product quality,availability of raw materials,and independence from threatened or wild resources.Comprehensive preclinical efficacy evaluations have demonstrated the equivalence of the therapeutic effects from ABB and those from commercially available drained bear bile(DBB).Furthermore,preclinical toxicological assessment and phase I clinical trials show that the safety of ABB is on par with that of the currently used DBB.This innovative strategy can serve as a new research paradigm for developing alternatives for other endangered TCMs,thereby strengthening the integrity and sustainability of TCM.

废弃菌菇渣无害化处置工艺优化研究

为有效提高菇渣废弃物的无害化处置效率,进行了发酵菌剂的引选与无害化处置工艺优化研究,结果表明,菇渣中添加30%比例的牛粪或菇渣中添加10%以上的牛粪及3‰复合菌剂均能大大促进菇渣堆肥升温腐熟发酵过程,缩短物料无害化处置周期,随着处理时间的延长,物料pH表现出先上升后逐步下降的趋势。试验中香菇渣物料的发芽指数在无害化处理5天以后均达到了50%以上的安全范围。研究结论证明通过调节菇渣物料C/N及添加复合菌剂可以显著提高菇渣废弃物的无害化处置效率。

A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Model architecture-oriented combat system effectiveness simulation based on MDE

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

Radiation segmentectomy for hepatic malignancies: Indications, devices,dosimetry, procedure, clinical outcomes, and toxicity of yttrium-90 microspheres

Radiation segmentectomy(RS) is a new approach to90 Y radioembolization that has been designed to increase the safety and efficacy of radioembolization in patients with unresectable hepatic malignancies. With this technique,high doses(>190 Gy) of radiation are delivered to the tumor through radioembolization performed in a segmental fashion, potentially increasing the radiation dose to the tumor while minimizing injury to the liver parenchyma. The aim of this review is to provide a summary of the indications, device choice, dosimetry, procedure, clinical outcomes, and toxicity of RS based on the clinical series currently available.

Towards improving detection performance for malware with a correntropy-based deep learning method

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT paradigm.Currently,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so far.However,there are also some problems.For example,considering the noise and outliers in the existing datasets of malware,some methods are not robust enough.Therefore,the accuracy of malware classification still needs to be improved.Aiming at this issue,we propose a novel method that combines the correntropy and the deep learning model.In our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of outliers.We present the detailed design process of our method.Furthermore,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

Seasonal variation in the three-dimensional structures of coastal thermal front off western Guangdong

The seasonal structure and dynamic mechanism of oceanic surface thermal fronts(STFs)along the western Guangdong coast over the northern South China Sea shelf were analyzed using in situ observational data,remote sensing data,and numerical simulations.Both in situ and satellite observations show that the coastal thermal front exhibits substantial seasonal variability,being strongest in winter when it has the greatest extent and strongest sea surface temperature gradient.The winter coastal thermal front begins to appear in November and disappears after the following April.Although runoff water is more plentiful in summer,the front is weak in the western part of Guangdong.The frontal intensity has a significant positive correlation with the coastal wind speed,while the change of temperature gradient after September lags somewhat relative to the alongshore wind.The numerical simulation results accurately reflect the seasonal variation and annual cycle characteristics of the frontal structure in the simulated area.Based on vertical cross-section data,the different frontal lifecycles of the two sides of the Zhujiang(Pearl)River Estuary are analyzed.

Photocatalytic Degradation of Organic Dye Methyl Orange with Phosphotungstic Acid

Silicotungstic acid and phosphotungstic acid were prepared and characterized by Fourier Transform Infrared Spectroscopy (FTIR) and X-ray diffraction (XRD). The results showed that the prepared catalysts possess classical Keggin structure. The factors on the degradation of methyl orange, such as the kind of catalyst, the amount of catalyst, the original concentration of dye and illumination time were investigated under metal halide lamp. The degradation of methyl orange is up to 93.6% with phosphotungstic acid at the best reaction conditions at 8.89 g/L concentration of phosphotungstic acid, 5.56 mg/L concentration of methyl orange and 80 min illumination time.

A Novel Hidden Danger Prediction Method in CloudBased Intelligent Industrial Production Management Using Timeliness Managing Extreme Learning Machine

To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.

On-chip Spectrometer Formed by a Multi-stage Structure

With apparent size and weight advantages,on-chip spectrometer could be a good choice for the spectrum analysis application which has been widely used in numerous areas such as optical network performance monitoring,materials analysis and medical research.In order to realize the broadband and the high resolution simultaneously,we propose a new on-chip spectrometer structure,which is a two-stage structure.The coarse wavelength division is realized by the cascaded Mach-Zehnder interferometers,which is the first stage of the spectrometer.The output of the Mach-Zehnder interferometers are further dispersed by the second stage structure,which can be realized either by arrayed waveguide gratings or by digital Fourier transform spectrometer structure.We further implemented the thermo-optic modulation for the arrayed waveguide gratings to achieve a higher spectral resolution.The output channel wavelengths of the spectrometer are modulated by the embedded heater to obtain the first order derivative spectra of the input optical signal to obtain a 2nm resolution.With respect to the computer simulation and device characterization results,the 400nm spectral range and the nanoscale resolution have been demonstrated.

Research on the Novel Business English Education Mode based on Feedback Learning and Interactive Teaching Method

In this paper, we conduct research on the novel English education mode based on feedback learning and interactive teaching method. Business English, which is based on general English, from the angle of English language features of text represents connotation of business English vocabulary expressed more, word ambiguity phenomenon is relatively common, meaning and pragmatic choice depends on the context. Our research starts from the analysis of the novel education pattern with the feedback learning and interactive teaching integration. Our education mode will let the students and the teachers interact with others and encourage the better participation passion that is meaningful.

CEPC Technical Design Report

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international partners.The complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs factory.In its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs bosons.Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of magnitude.This Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering aspects.Additionally,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection procedures.Construction is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 years.The commencement of experiments and data collection could potentially be initiated in the mid-2030s.

Ce6 nanoassemblies:Molecular mechanism and strategies for combinational anticancer therapy Special Collection:Aggregation-Induced Processes and Functions

Nowadays,cancer has become the leading cause of death worldwide,driving the need for effective therapeutics to improve patient prognosis.Photodynamic therapy(PDT)has been widely applied as an antitumor modality,owing to its minimal invasiveness,localized tumor damage,and high safety profile.However,its efficacy is limited by poor stability of photosensitizers,inadequate tumor accumulation,and a complex tumor microenvironment.To overcome these challenges,extensive endeavors have been made to explore the co-assembly of the widely used photosensitizer chlorin e6(Ce6)with various functional small molecules to enhance pharmacodynamic activity.This review provides a comprehensive overview of current studies on Ce6-based nanoparticles for effective PDT and precise delivery of functional molecules.The self-assembly mechanism will be discussed in detail,with a focus on potential strategies for combinational therapy with PDT.

Central similarity consistency hashing for asymmetric image retrieval

Asymmetric image retrieval methods have drawn much attention due to their effectiveness in resource-constrained scenarios.They try to learn two models in an asymmetric paradigm,i.e.,a small model for the query side and a large model for the gallery.However,we empirically find that the mutual training scheme(learning with each other)will inevitably degrade the performance of the large gallery model,due to the negative effects exerted by the small query one.In this paper,we propose Central Similarity Consistency Hashing(CSCH),which simultaneously learns a small query model and a large gallery model in a mutually promoted manner,ensuring both high retrieval accuracy and efficiency on the query side.To achieve this,we first introduce heuristically generated hash centers as the common learning target for both two models.Instead of randomly assigning each hash center to its corresponding category,we introduce the Hungarian algorithm to optimally match each of them by aligning the Hamming similarity of hash centers to the semantic similarity of their classes.Furthermore,we introduce the instance-level consistency loss,which enables the explicit knowledge transfer from the gallery model to the query one,without the sacrifice of gallery performance.Guided by the unified learning of hash centers and the distilled knowledge from gallery model,the query model can be gradually aligned to the Hamming space of the gallery model in a decoupled manner.Extensive experiments demonstrate the superiority of our CSCH method compared with current state-of-the-art deep hashing methods.The open-source code is available at https://github.com/dubanx/CSCH.

Parishin C's prevention of Aβ_(1–42)-induced inhibition of long-term potentiation is related to NMDA receptors

The rhizome of Gastrodia elata(GE), a herb medicine, has been used for treatment of neuronal disorders in Eastern Asia for hundreds of years. Parishin C is a major ingredient of GE. In this study, the i.c.v. injection of soluble Aβ1–42oligomers model of LTP injury was used. We investigated the effects of parishin C on the improvement of LTP in soluble Aβ1–42oligomer–injected rats and the underlying electrophysiological mechanisms. Parishin C(i.p. or i.c.v.) significantly ameliorated LTP impairment induced by i.c.v. injection of soluble Aβ1–42oligomers. In cultured hippocampal neurons,soluble Aβ1–42oligomers significantly inhibited NMDAR currents while not affecting AMPAR currents and voltage-dependent currents. Pretreatment with parishin C protected NMDA receptor currents from the damage induced by Aβ. In summary, parishin C improved LTP deficits induced by soluble Aβ1–42oligomers. The protection by parishin C against Aβ-induced LTP damage might be related to NMDA receptors.

Anomaly Detection of Industrial Control Systems Based on Transfer Learning

Industrial Control Systems(ICSs)are the lifeline of a country.Therefore,the anomaly detection of ICS traffic is an important endeavor.This paper proposes a model based on a deep residual Convolution Neural Network(CNN)to prevent gradient explosion or gradient disappearance and guarantee accuracy.The developed methodology addresses two limitations:most traditional machine learning methods can only detect known network attacks and deep learning algorithms require a long time to train.The utilization of transfer learning under the modification of the existing residual CNN structure guarantees the detection of unknown attacks.One-dimensional ICS flow data are converted into two-dimensional grayscale images to take full advantage of the features of CNN.Results show that the proposed method achieves a high score and solves the time problem associated with deep learning model training.The model can give reliable predictions for unknown or differently distributed abnormal data through short-term training.Thus,the proposed model ensures the safety of ICSs and verifies the feasibility of transfer learning for ICS anomaly detection.

Effects of fluoxetine on protein expression of potassium ion channels in the brain of chronic mild stress rats

The purpose of this study is to investigate the expression of major potassium channel subtypes in the brain of chronical mild stress (CMS) rats and reveal the effects of fluoxetine on the expression of these channels. Rats were exposed to a variety of unpredictable stress for three weeks and induced anhedonia, lower sucrose preference, locomotor activity and lower body weight The protein expressions were determined by Western blot. CMS significantly increased the expression of Kv2.1 channel in frontal cortex but not in hippocampus, and the expression level was normalized after fluoxetine treatment. the expression of TREK-1 channel was also obviously increased in frontal cortex in CMS rats. Fluoxetine treatment might prevent this increase. However, the expression of Kv3.1 and Kv4.2 channels was considerably decreased in hippocampus after CMS, and was not affected by fluoxetine. These results suggest that different subtypes of potassium channels are associated with the pathophy-siology of depression and that the therapeutical effects of fluoxetine may relate to Kv2.1 and TREK-1 potassium channels. (C) 2015 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V. All rights reserved.

Phthalide-derived oxaspiroangelioic acids A–C with an unprecedented carbon skeleton from an aqueous extract of the Angelica sinensis root head

Three phthalide-derived analogues,oxaspiroangelioic acids A–C(1–3),were isolated as minor components of an aqueous extract of the Angelica sinensis root heads(guitou).Oxaspiroangelioic acids A and B were racemates separated into enantiomers by chiral HPLC.Their structures including absolute configurations were determined by spectroscopic data analysis,single crystal X-ray diffraction,exciton chirality method [7_(T)D$IF]and electronic circular dichroism(ECD) calculation.These compounds share an undescribed carbon skeleton,for which biosynthetic pathways are proposed.Compound 1 and its enantiomers showed almost identical activity inhibiting Tandem of P domains in a weak inwardly rectifying K^(+)channel 1(TREK-1).

Phototriggered targeting of nanocarriers for drug delivery

Stimuli-triggered targeting of drug delivery systems can both increase the therapeutic efficacy and lower toxicity by selectively delivering drugs at target sites with high specificity and efficiency. Light is a convenient and powerful stimulus for use in such drug delivery systems because it is readily available and noninvasive and offers excellent spatiotemporal control. The power and wavelength of light can be finely tuned for different photoresponsive systems to achieve efficient targeting at the tissue, cellular, or subcellular levels. Here, we have reviewed the various mechanisms for phototriggered targeting （phototargeting） of drug nanocarriers. We have discussed the three main phototargeting strategies： （1） targeting ligand activation; （2） particle size reduction; and （3） blood vessel disruption.