Dendritic cells derived from pluripotent stem cells:Potential of large scale production

Human pluripotent stem cells(hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are promising sources for hematopoietic cells due to their unlimited growth capacity and the pluripotency. Dendritic cells(DCs), the unique immune cells in the hematopoietic system, can be loaded with tumor specific antigen and used as vaccine for cancer immunotherapy. While autologous DCs from peripheral blood are limited in cell number, hPSC-derived DCs provide a novel alternative cell source which has the potential for large scale production. This review summarizes recent advances in differentiating hPSCs to DCs through the intermediate stage of hematopoietic stem cells. Step-wise growth factor induction has been used to derive DCs from hPSCs either in suspension cultureof embryoid bodies(EBs) or in co-culture with stromal cells. To fulfill the clinical potential of the DCs derived from hPSCs, the bioprocess needs to be scaled up to produce a large number of cells economically under tight quality control. This requires the development of novel bioreactor systems combining guided EB-based differentiation with engineered culture environment. Hence, recent progress in using bioreactors for hPSC lineage-specific differentiation is reviewed. In particular, the potential scale up strategies for the multistage DC differentiation and the effect of shear stress on hPSC differentiation in bioreactors are discussed in detail.

An intelligent system for calculating the scale of rational,enlarged production of an underground non-ferrous metal mine

The enlarged production scale of underground non-ferrous metal mines is affected by many uncertain factors difficult to describe mathematically with any level of accuracy.The problem can be solved by a synthesis of artificial intelligence.Based on the analysis of the major factors affecting the scale of enlarged production,we first interpreted in detail the design principles and structure of the intelligent system.Secondly,we introduced an ANN subsystem.In order to ensure technological and scale efficien- cies of the training samples for ANN,we filtrated the samples with a DEA method.Finally,we trained the intelligent system,which was proved to be very efficient.

Large-Scale Production of Microalgal Lipids Containing High Levels of Docosahexaenoic Acid upon Fermentation of <i>Aurantiochytrium</i>sp. KRS101

In this study, large-scale production of microalgal lipid containing high levels of docosahexaenoic acid (DHA) by fermentation of Aurantiochytrium sp. KRS101 was performed. The microalgal strain yielded productivity of docosahex-aenoic acid (DHA) productivity of 1.08 and 1.6 g/L/d by fermentation at 300-L and 5000-L scale stirrer-type bioreactor. The productivity was significantly enhanced upto 5.6 g/L/d by fermentation at 6000-L scale airlift-type bioreactor, probably due to the reduced shearing force. The microalgal lipid could be efficiently recovered by safe extraction methods such as ethanol extraction, hot water extraction or supercritical fluid extraction, promising commercial potential of the microalgal DHA-rich lipid in the food and feed industry.

Study on the Production Benefit of Large-scale Tobacco Growers

The tobacco growers with the growing area of greater than or equal to 100 mu,have become an important subject of tobacco production in Weng'an County of Guizhou Province. Regulating and developing the production and business activities of large-scale tobacco growers, plays an important role in stabilizing tobacco production,reducing costs and increasing efficiency in Weng'an County. Through the field survey of large-scale growers' production activities,this article analyzes the input and output levels,and explores the key factors influencing benefit, in order to provide a basis for further regulating the tobacco growing practices and improving the flue-cured tobacco production benefit.

Characterization of Small-Scale Farmers and Assessment of Their Access to Crop Production Information in Selected Counties of Kenya

Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.

Association of polymorphisms of Nramp1 gene with immune function and production performance of large white pig

The present research was designed to study the association of polymorphism of natural resistance-associated macrophage proteinl （Nrampl） with some immune function and the production performance in Large White pig. The PCR-RFLP technique was applied to analyze the correlation between the polymorphisms of Nrampl gene and immune function [value of Polymorphonuclear Leukocytes （PMN） obtained by Nitroblue Tetrazolium （NBT） Reduction and effect of Cytotoxin in Monocyte] and production performance in 165 Large White pigs. The results showed that there was one Nde I restriction locus in Large White pig, and both values of PMN by NBT Reduction and effect of Cytotoxin in Monocyte in genotype BB were higher than those in genotype AB （P〈0.05）. Simultaneously, the weight of 180-day-old pigs with genotype BB was higher than that with genotype AB （P〈0.05）. The results indicated that there was a significant correlation between different genotypes of Nrampl gene and Immune function and production performance, and it can be regarded as a candidate gene of disease resistance. All these results provide valuable reference to further studies of pig disease resistance.

Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data

The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for(sub-)surface data segmentation.Recently developed fully reversible or fully invertible networks can mostly avoid memory limitations by recomputing the states during the backward pass through the network.This results in a low and fixed memory requirement for storing network states,as opposed to the typical linear memory growth with network depth.This work focuses on a fully invertible network based on the telegraph equation.While reversibility saves the major amount of memory used in deep networks by the data,the convolutional kernels can take up most memory if fully invertible networks contain multiple invertible pooling/coarsening layers.We address the explosion of the number of convolutional kernels by combining fully invertible networks with layers that contain the convolutional kernels in a compressed form directly.A second challenge is that invertible networks output a tensor the same size as its input.This property prevents the straightforward application of invertible networks to applications that map between different input-output dimensions,need to map to outputs with more channels than present in the input data,or desire outputs that decrease/increase the resolution compared to the input data.However,we show that by employing invertible networks in a non-standard fashion,we can still use them for these tasks.Examples in hyperspectral land-use classification,airborne geophysical surveying,and seismic imaging illustrate that we can input large data volumes in one chunk and do not need to work on small patches,use dimensionality reduction,or employ methods that classify a patch to a single central pixel.

Strong electronic coupling of CoNi and N-doped-carbon for efficient urea-assisted H2 production at a large current density

Exploiting efficient urea oxidation reaction(UOR)and hydrogen evolution reaction(HER)catalysts are significant for energy-saving H2 production through urea-assisted water electrolysis,but it is still challenging.Herein,carbon-encapsulated CoNi coupled with CoNiMoO(CoNi@CN-CoNiMoO)is prepared by solvothermal method and calcination to enhance the activity/stability of urea-assisted water electrolysis at large current density.It exhibits good activity for UOR(E10/1,000=1.29/1.40 V)and HER(E-10/-1000=-45/-245 mV)in 1.0 M KOH+0.5 M urea solution.For the UOR||HER system,CoNi@CN-CoNiMoO only needs 1.58 V at 500 mA cm-2 and shows good stability.Density functional theory calculation suggests that the strong electronic interaction at the interface between NiCo alloy and N-doping-carbon layers can optimize the adsorption/desorption energy of UOR/HER intermediates and accelerate the water dissociation,which can expedite urea decomposition and Volmer step,thus increasing the UOR and HER activity,respectively.This work provides a new solution to design UOR/HER catalysts for H2 production through urea-assisted water electrolysis.

MATHEMATICAL BASIS FOR LARGE SCALE GIS AND TERRESTRIAL DIGITAL PRODUCTS

The problem of spatial mathematical basis has been encountered by both large scale GIS and spatial digital products theoretically and practically.It is also a basic problem in the development of the whole geo_information science.After analyzing the status quo and the limitations of the space mathematical base of GIS,this paper points out definitely that the geodetic coordinate system is uniform,which can show the location of any point of the global exactly and uniquely in form of ( B,L,H ) and is the most proper reference system of large_scale GIS and Digital Earth.Moreover,this paper also puts forward a set of practical model of the standard "map projection".Finally,this paper introduces a DRG system based on this model.

Novel process for large scale synthesis of N-(4-hydroxyphenyl) retinamide

A method to synthesize anticancer drug N-（ 4- hydroxyphenyl） retinamide （4-HPR）on a large scale is described. It consists of the preferred steps of reacting all-trans retinoic acid with thionyl chloride to form retinoyl chloride and then reacting with triethylamine to generate retinoyl ammonium salt which in turn is reacted with p-aminophenol to eventually produce 4-HPR. This process can overcome many scale-up challenges that exist in the methods reported in the literature and provide an easy, mild and high yield route for large scale synthesis of 4-HPR. Moreover, the effects of the molar ratios of the reagents on the yield are examined. The best molar ratios are a 2.0 molar equivalence of thionyl chloride and a 3.0 molar equivalence of paminophenol to retinoic acid, and the total yield is 80. 7%.

NEW LIMITED MEMORY SYMMETRIC RANK ONE ALGORITHM FOR LARGE-SCALE UNCONSTRAINED OPTIMIZATION

A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one （SSR1） update with the limited memory and nonmonotone line search technique. In this algorithm, the descent search direction is generated by inverse limited memory SSR1 update, thus simplifying the computation. Numerical comparison of the algorithm and the famous limited memory BFGS algorithm is given. Comparison results indicate that the new algorithm can process a kind of large-scale unconstrained optimization problems.

Large Scale VLSI Module Placement Using LFF Heuristics by Stages

We present a deterministic algorithm for large-scale VLSI module placement. Following the less flexibility first （LFF） principle,we simulate a manual packing process in which the concept of placement by stages is introduced to reduce the overall evaluation complexity. The complexity of the proposed algorithm is （N1 ＋ N2 ） × O（ n^2 ） ＋ N3× O（n^4lgn） ,where N1, N2 ,and N3 denote the number of modules in each stage, N1 ＋ N2 ＋ N3 = n, and N3〈〈 n. This complexity is much less than the original time complexity of O（n^5lgn）. Experimental results indicate that this approach is quite promising.

Compact Scanning Tunneling Microscope with Large Scale Searching Capability for Mmultidisciplinary Applications

We present the design and performance of a home-built scanning tunneling microscope （STM）, which is compact （66 mm tall and 25 mm in diameter）, yet equipped with a 3D atomic precision piezoelectric motor in which the Z coarse approach relies on a high simplic-ity friction-type walker （of our own invention） driven by an axially cut piezoelectric tube. The walker is vertically inserted in a piezoelectric scanner tube （PST） with its brim laying at on the PST end as the inertial slider （driven by the PST） for the XZ （sample plane） motion. The STM is designed to be capable of searching rare microscopic targets （defects, dopants, boundaries, nano-devices, etc.） in a macroscopic sample area （square millimeters） under extreme conditions （low temperatures, strong magnetic elds, etc.） in which it ts. It gives good atomic resolution images after scanning a highly oriented pyrolytic graphite sample in air at room temperature.

Large Scale Aspects of India-China Summer Monsoon Rainfall

This study investigates the dominant modes of variability in monthly and seasonal rainfall over the India-China region mainly through Empirical Orthogonal Function (EOF) analysis. The EOFs have shown that whereas the rainfall over India varies as one coherent zone, that over China varies in east-west oriented bands. The influence of this banded structure extends well into India.Relationship of rainfall with large scale parameters such as the subtropical ridge over the Indian and the western Pacific regions, Southern Oscillation, the Northern Hemispheric surface air temperature and stratospheric winds have also been investigated. These results show that the rainfall over the area around 40°N, 110°E over China is highly related with rainfall over India. The subtropical ridge over the Indian region is an important predictor over India as well an over the northern China region. '

Large scale classification with local diversity AdaBoost SVM algorithm

Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.

Local and global approaches of affinity propagation clustering for large scale data

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.

A LIMITED MEMORY QUASI-NEWTON METHOD FOR LARGE SCALE PROBLEM

We study how to use the SR1 update to realize minimization methods for problems where the storage is critical. We give an update formula which generates matrices using information from the last m iterations. The numerical tests show that the method is efficent.

Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform:Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.

Multidisciplinary design optimization on production scale of underground metal mine

In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of production, safety and environmental impact in the underground metal mine was established by using multidisciplinary design optimization method. The coupling effects from various disciplines were fully considered, and adaptive mutative scale chaos immunization optimization algorithm was adopted to solve multidisciplinary design optimization model of underground metal mine production scale. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine reflect the actual operating conditions more realistically, the production scale is about 1.25 Mt/a (Lead and zinc metal content of 160 000 t/a), the economic life is approximately 14 a, corresponding coefficient of production profits can be increased to 15.13%, safety factor can be increased to 5.4% and environmental impact coefficient can be reduced by 9.52%.