Multivariate Principal Component Analysis Research Articles

Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.

The random survival forest (RSF) is a non-parametric alternative to the Cox proportional hazards model in modeling time-to-event data. In this article, we developed a modeling framework to incorporate multivariate longitudinal data in the model building process to enhance the predictive performance of RSF. To extract the essential features of the multivariate longitudinal outcomes, two methods were adopted and compared: multivariate functional principal component analysis and multivariate fast covariance estimation for sparse functional data. These resulting features, which capture the trajectories of the multiple longitudinal outcomes, are then included as time-independent predictors in the subsequent RSF model. This non-parametric modeling framework, denoted as functional survival forests, is better at capturing the various trends in both the longitudinal outcomes and the survival model which may be difficult to model using only parametric approaches. These advantages are demonstrated through simulations and applications to the Alzheimer's Disease Neuroimaging Initiative.

Physical and Chemical Properties of Soil in the Sahelian Region: Case Study in Nioro du Rip, Senegal

The locality of Nioro du Rip is facing intense erosion, loss of agricultural land, soil pollution and soil degradation. Today, there is limited information about the soil physical and chemical properties in the locality. In this work, we describe the main essential factors or mechanism that control the evolution of the soil in the study area.
 The physical and chemical properties of soils encountered along a NE-SW transect in are analyzed in this paper. The statistical analysis results revealed low structural stability of soils in general, due to their low organic matter content and exchangeable bases and their predominantly silty texture. A net trend towards acidification, which is more pronounced in the lower-bottom and terrace soils, provides information on the conditions that are increasingly unfavorable to agricultural development.
 The multivariate principal component analysis (PCA) identified the preponderance of two factors among the four primarily involved in soil geochemical composition. These include a mineralization process (expressed through the first principal component (PC1), which causes soils to be rich in elements (Ca2+, K+,C, N, MO) controlling their structure and fertility level; The PC2 axis expresses the spatial differentiation phenomenon of the soil granulometric composition: soils forming cluster poles according to their textural affinity in the projection of the plane formed by these two components , with on one side the sandy-dominated soils of the shallows and terraces and on the other the clay-dominated soils of the plateau and the slope. A clear reversal of textural polarity in the studied topo sequence that must be blamed on the strong water erosion in this area.

Comparative study of toxic heavy metal residues and other properties of honey from different environmental production systems.

Honeybees forage a large spatial area around the hives. In addition, honey production takes place in various environments, and polluted environment is often hard to detect. It impacts both human and beehive health, especially through honey which is used for human consumption. Pollen analysis was conducted by a novel approach through a multivariate principal component analysis where it was possible to obtain grouping patterns related to foraging plant species. Samples of honey were acquired from three different environmental production systems: (i) honey from the apiaries in the vicinity of thermal power plant, (ii) apiary of certified organic production and (iii) the conventional production with semi-controlled production. Significantly higher contents of the Pb, Cd and Zn are found in the analysed honeys taken near the thermal power plant compared with those of the other analysed honeys. The origin of Zn, Pb and Cd in the honey is the contaminated forage plants and foraging honeybees. Honey from certified organic production differentiated significantly from other two types of production by the water content, electrical conductivity and total soluble solids and notably it contained significantly less ash and lead. There is a clear advantage of certified organic honey in terms of heavy metal residues as the most prominent pollution factor in honey. Therefore, honey can be used as the broad range environmental pollution indicator, as bees will forage on polluted plants and bring the pollutant from a wide spatial range inside the hive, where it can be traced in the honey. Graphical abstract.

Comprehensive evaluation of robotic global performance based on modified principal component analysis

The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly used. Because of the diversity and correlation of robotic global performance indexes, the two multivariate statistical methods principal component analysis and kernel principal component analysis methods can be used, respectively, to comprehensively evaluate the global performance of PUMA560 robot with different dimensions. When using the kernel principal component analysis method, the kernel function and parameters directly have an effect on the result of comprehensive performance evaluation. Because kernel principal component analysis with polynomial kernel function is time-consuming and inefficient, a new kernel function based on similarity degree is proposed for the big sample data. The new kernel function is proved according to Mercer’s theorem. By comparing different dimension reduction effects of principal component analysis method, the kernel principal component analysis method with polynomial kernel function, and the kernel principal component analysis method with the new kernel function, the kernel principal component analysis method with the new kernel function could deal more effectively with the nonlinear relationship among indexes, and its calculation result is more reasonable for containing more comprehensive information. The simulation shows that the kernel principal component analysis method with the new kernel function has the advantage of low time consuming, good real-time performance, and good ability of generalization.

Soil moisture estimation using Bayesian Maximum Entropy algorithm from FY3-B, MODIS and ASTER GDEM remote-sensing data in a maize region of HeBei province, China

ABSTRACT The Bayesian Maximum Entropy (BME) algorithm that can model larger-scale spatial heterogeneity and integrate multiple types of data is a better spatial estimation algorithm in avoiding the circular problem and improving the estimation accuracy of parameters than original empirical geostatistic and spatial statistic methods. To obtain higher-resolution and higher-accuracy soil moisture (SM) data, this study used the BME algorithm to integrate the multiple environmental factor variables related to SM as auxiliary data (11 data sets in total) such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Albedo products with 1 kilometre (km) grid resolution from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) satellite data, and the Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, Relief amplitude products generated from ASTER GDEM data. First, the FengYun 3-B satellite (FY3-B) SM product with 25 km grid resolution was downscaled to 1 km grid resolution using the visible, infrared (IR), and microwave fusion method. The downscaled FY3-B SM data and auxiliary data were used to generate the weighted probability soft data (SD) under four cases, case 1: 500 sample points of SD, case 2: 450 sample points of SD, case 3: 400 sample points of SD, and case 4: 350 sample points of SD. Particularly, we used two methods, the multivariable correlation analysis method and principal component analysis (PCA) method to get the weight values of environmental factor variables, i.e. NDVI, LST, Albedo, the Digital Elevation Model (DEM), Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, and Relief amplitude with SM. Then, the in-situ SM measurements as hard data (HD) were used to calibrate the weighted probability SD in the procedure of BME SM estimation. The SM under four cases were estimated from the weighted probability SD and HD using BME algorithm. Finally, comparisons of the estimated SM using BME algorithm with in-situ SM measurements at maize study area were carried out in this study. Our results indicated that the accuracy of the estimated SM using BME algorithm, the root-mean-square error (RMSE) = 0.049 cm3 cm−3, the correlation coefficient (r) = 0.639, the unbiased RMSE (RMSEu) = 0.047 cm3 cm−3, and Bias = 0.002 cm3 cm−3, under case 1 based on PCA method was obviously better than the downscaled FY3-B SM (RMSE = 0.079 cm3 cm−3, r = 0.096, RMSEu = 0.069 cm3 cm−3, and Bias = 0.039 cm3 cm−3) that was generated by the visible, IR, and microwave fusion method. We concluded that integrating auxiliary data into SM estimation using BME algorithm could further improve the downscaled FY3-B SM that was generated by visible, IR, and microwave fusion method.

English

In this research, nine fruits cultivated in Northern Amazon were studied: Abiu, acerola, araca, bacupari, biriba, camu-camu, fruta-do-conde, graviola and tapereba, with the objective of carrying out a proximate and nutritional study of the pulps of the Northern Amazon fruits. They were graviola (76.83 ± 0.02 Kcal 100 g-1), bacupari (53.15 ± 0.02 Kcal 100 g-1) and fruta-do-conde (46.66 ± 0.02 Kcal 100 g-1). Among the macronutrients, potassium with high concentration stood out, especially in graviola (541.16 ± 0.24 mg.100 g-1) and biriba (468.21 ± 0.13 mg 100 g-1). Among the micronutrients, iron concentrations was high in araca pulp (3.04 ± 0.02 mg 100 g-1), and abiu was abundant in zinc (3.71 ± 0.02 mg 100 g-1) and manganese (6.61 ± 0.11 mg 100 g-1). The presence of cobalt at the level of traces in some of the pulps studied stood out. The Pearson correlation coefficient was evaluated, as well as the statistical treatment by multivariate analysis Principal Component Analysis (PCA) to establish the correlation between the variables studied. Key words: Amazonian fruit, functional food, nutrients.

Geo-multivariate analysis of crop yield and soil properties of students industrial training farm in Ado Ekiti, Southwest Nigeria

ABSTRACT Relating crop yield to soil properties at the field scale is a tool in site-specific management planning for sustainable crop production. This study investigated the relationship between some soil properties and the yield of three crops (cowpea, maize and cassava + maize) grown on Students Industrial Work Experience Scheme Training Farm in Ado Ekiti, Nigeria using descriptive, multivariate and geostatistical techniques. Soil samples were collected from 0 - 20 cm depth at the centre of 184 grids from a 3.0 ha field to determine some soil physico-chemical properties. Crop yield was collected from demarcated areas in each grid. The descriptive statistics indicated that the soil properties and yield varied widely under the different crops. The cross-variograms showed moderate to strong spatial dependence for the soil properties. The multivariate principal component and hierarchal cluster analyses showed sand, clay, organic matter, bulk density, pH, electrical conductivity, and available phosphorus as the minimum dataset for upscaling soil quality assessment program. The multivariate and geostatistical analyses revealed significant relationships between soil properties and crop yield. These findings will enable optimization of the farmer’s field through specific soil and nutrient management, reduce crop yield variability, and promote a safe environment.

Aspartate β-hydroxylase targeting in castration-resistant prostate cancer modulates the NOTCH/HIF1α/GSK3β crosstalk.

Castration-resistant prostate cancer (CRPC) is an incurable stage of the disease. A multivariate principal component analysis on CRPC in vitro models identified aspartyl (asparaginyl) β hydrolase (ASPH) as the most relevant molecule associated with the CRPC phenotype. ASPH is overexpressed in various malignant neoplasms and catalyzes the hydroxylation of aspartyl and asparaginyl residues in the epidermal growth factor (EGF)-like domains of proteins like NOTCH receptors and ligands, enhancing cell motility, invasion and metastatic spread. Bioinformatics analyses of ASPH in prostate cancer (PCa) and CRPC datasets indicate that ASPH gene alterations have prognostic value both in PCa and CRPC patients. In CRPC cells, inhibition of ASPH expression obtained through specific small interfering RNA or culturing cells in hypoxic conditions, reduced cell proliferation, invasion and cyclin D1 expression through modulation of the NOTCH signaling. ASPH and HIF1α crosstalk, within a hydroxylation-regulated signaling pathway, might be transiently driven by the oxidative stress evidenced inside CRPC cells. In addition, increased phosphorylation of GSK3β by ASPH silencing demonstrates that ASPH regulates GSK3β activity inhibiting its interactions with upstream kinases. These findings demonstrate the critical involvement of ASPH in CRPC development and may represent an attractive molecular target for therapy.

APLICACIÓN DE TÉCNICAS MULTIVARIANTES EN LA APTITUD DE LAS TIERRAS AGRÍCOLAS EN CARABOBO, VENEZUELA

<p><strong>Background.</strong> The selection of the best agroclimatic zones is of significant importance for the entity's agricultural, rural and forestry development, mainly because the inhabitants and local actors of the Carabobo state can already count on a study that indicates the places to produce their food. , use them to the maximum and in the best way. <strong>Objective.</strong> Analyze and interpret the suitability of agricultural land in Carabobo through multivariate techniques. <strong>Methodology.</strong> The surface data (Km<sup>2</sup>) were used for the dates of May-August of four strategic crops: corn, onion, tomato and potato. Principal Component Analysis (PCA), using Partial Least Squares Regression Discriminant Analysis (PLSDA) and Hierarchical Cluster Analysis were applied. <strong>Results.</strong> Using the PCA, the two components that accounted for 67.6% of the total variation were selected. This allowed a classification of the municipalities with the largest suitable area of crops according to the sowing date, discriminating with greater incidence the area suitable for maize in the Libertador, Carlos Arvelo, Valencia and Naguanagua municipalities, whose ideal sowing date would be established from May 11-20. <strong>Conclusion.</strong> The multivariate PCA method used represented a tool to describe and characterize the diversity of the areas studied in agriculture in Carabobo, the results of which could be the basis for agricultural development strategies.</p>

Exploration of Mathematical Model of Listed Companies Financial Early-warning

Financial early-warning is the important section for financial risk analysis and financial management; its purpose is to accurately discover and recognize financial risks in advance. The financial early-warning system lies in the whole process of production and operating activities of enterprises. Each enterprise should set up the applicable early-warning system, help enterprises foresee and prevent the financial risk and financial crisis. How to conduct accurate early-warning may reflect whether the risk management of enterprises is good or bad. This paper sets up the early-warning mathematical model of financial risk by selecting the samples of financial database of listed companies through the multivariable principal component analysis. Meanwhile, it reflects different risk degrees, so as to predict the financial risk of enterprises.

Neuro-Clinical Signatures of Language Impairments after Acute Stroke: A VBQ Analysis of Quantitative Native CT Scans

Ischemic stroke affects language production and/or comprehension and leads to devastating long-term consequences for patients and their families. Previous studies have shown that neuroimaging can increase our knowledge of the basic mechanisms of language recovery. Currently, models for predicting patients' outcomes have limited use in the clinic for the evaluation and optimization of rehabilitative strategies mostly because that are often based on high-resolution magnetic resonance imaging (MRI) data, which are not always possible to carry out in the clinical routine. Here, we investigate the use of Voxel-Based Morphometry (VBM), multivariate modelling and native Computed Tomography (nCT) scans routinely acquired in the acute stage of stroke for identifying biological signatures that explicate the relationships between brain anatomy and types of impairments. 80 stroke patients and 30 controls were included. nCT-scans were acquired in the acute ischemia stage and bedside clinical assessment from board-certified neurologist based on the NIH stroke scale. We use a multivariate Principal Component Analyses (PCA) to identify the brain signatures group the patients according to the presence or absence of impairment and identify the association between local Grey Matter (GM) and White Matter (WM) nCT values with the presence or absence of the impairment. Individual patient's nCT scans were compared to a group of controls' with no radiological signs of stroke to provide an automated delineation of the lesion. Consistently across the whole group the regions that presented significant difference GM and WM values overlap with known areas that support language processing. In summary, the method applied to nCT scans performed in the acute stage of stroke provided robust and accurate information about brain lesions' location and size, as well as quantitative values. We found that nCT and VBQ analyses are effective for identifying neural signatures of concomitant language impairments at the individual level, and neuroanatomical maps of aphasia at the population level. The signatures explicate the neurophysiological mechanisms underlying aetiology of the stroke. Ultimately, similar analyses with larger cohorts could lead to a more integrated multimodal model of behaviour and brain anatomy in the early stage of ischemic stroke.

A generalization of functional clustering for discrete multivariate longitudinal data.

This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.

Coastal Phytoplankton Pigments Composition in Three Tropical Estuaries of Indonesia

In this study, the composition and distribution of phytoplankton pigments and its relation to nutrients and light was investigated, and an elaboration of using it as a proxy for phytoplankton group composition followed, in different nutrient-level tropical bays of Indonesia. Phytoplankton pigment analysis by using High Performance Liquid Chromatographer (HPLC) resulted in a set of pigments of chlorophyll-a (Chl-a), chlorophyll-b (Chl-b), chlorophyll-c (Chl-c), lutein, zeaxanthin, fucoxanthin, peridinin, diadinoxanthin, and ß-carotene. Linear multi regression and multivariate principal component analysis (PCA) showed that algae pigments correlate positively with nutrients and are not significantly correlated with underwater light and water transparency, suggesting important roles of nutrients for phytoplankton development in tropical estuaries. There were differences in total algae pigment concentration between bays (p < 0.005), showing that the eutrophic system of Jakarta Bay was the highest (mean of 10.55 μg L−1), Lampung Bay was the second highest (mean of 3.37 μg L−1), and the lowest were the oligotrophic waters of Semangka Bay (mean of 0.80 μg L−1). At all bays studied, high nutrient sites, which were located in the river mouths and inner part of the bay, were always characterized by high phytoplankton pigment concentration. Pigment composition had a high correlation with phytoplankton composition: diatoms with fucoxanthin, dinoflagellates with peridinin and Chl-c, and Chlorophyceae with Chl-b and lutein. This conformity suggests that algae pigments can be used as a biomarker for phytoplankton group determination along with microscopic species identification.

Qualitative analysis of particulate matter emission from diesel engine fueled with Jet A-1 under multivariate combustion boundaries by principal component analysis

The impact of Jet A-1 fuel on diesel engine performance and particulate matter emission was experimentally and analytically investigated in this study. Differential mobility spectrometer 500 and soot particle aerosol mass spectrometer were employed to analyze the size distributions and chemical compositions of soot particles from engine. Since the underlying potential of applying Jet fuel on diesel engine relies on the synergistic play of fuel properties and multivariate engine combustion boundaries, qualitative analysis and principal component analysis were conducted to identify the effect of multivariate engine combustion boundaries on soot particle distributions based on the tested results for both Jet A-1 and diesel fuels. Meanwhile, data ellipse method using eigenvalues and eigenvectors was used to display the correlation relationship of soot particle distributions between varied fuels. It was found nucleation mode particles were prevalence in premixed combustion while accumulated model particles were dominated in diffusion combustion. With the development of diffusion combustion, the particles size distribution became stronger linear dependence for diesel and Jet A-1. Furthermore, Jet A-1 favours producing more nucleated particles due to intensified fuel atomization and premixed combustion, which results in increased organics composition. At high engine load, although accumulation mode particles show dominance in both tested fuels, the number density of the accumulation mode particles and the mass fraction of black carbon of Jet A-1 are lower than those of diesel. In summary, Jet A-1 generally has positive particles reduction capability on diesel engines as compared to commercial diesel fuel.

Hierarchical sparse functional principal component analysis for multistage multivariate profile data

Modern manufacturing systems typically involve multiple production stages, the real-time status of which can be tracked continuously using sensor networks that generate a large number of profiles associated with all process variables at all stages. The analysis of the collective behavior of the multistage multivariate profile data is essential for understanding the variance patterns of the entire manufacturing process. For this purpose, two major challenges regarding the high data dimensionality and low model interpretability have to be well addressed. This article proposes integrating Multivariate Functional Principal Component Analysis (MFPCA) with a three-level structured sparsity idea to develop a novel Hierarchical Sparse MFPCA (HSMFPCA), in which the stage-wise, profile-wise and element-wise sparsity are jointly investigated to clearly identify the informative stages and variables in each eigenvector. In this way, the derived principal components would be more interpretable. The proposed HSMFPCA employs the regression-type reformulation of the PCA and the reparameterization of the entries of eigenvectors, and enjoys an efficient optimization algorithm in high-dimensional settings. The extensive simulations and a real example study verify the superiority of the proposed HSMFPCA with respect to the estimation accuracy and interpretation clarity of the derived eigenvectors.

Multivariate principal component analysis to evaluate growth performances in Malabari goats of India.

Evaluation of growth performances in Malabari goats was done with body weight and major morphometric traits, viz. body height, body length and chest girth at 6, 9 and 12months, respectively. Data pertaining on 1082 Malabari goats spread over a period of 5years (from 2014 to 2018) were used in the study. Least squares analysis of traits was done to adjust the effect of major significant non-genetic factors. Traits were analysed by using Varimax rotated principal component analysis (PCA) with Kaiser normalization to explain growth performances. Out of twelve principal components, PCA revealed four components explained about 67.78% of total variation. The first component (PC1) explained 28.02% of total variation. It was represented by significantly positive high loading of BH9, BH12 and BH6. The second component explained 15.090% of total variance with high loading of distance between BL9, BL6 and BL12. The third component explained 12.643% of variance and showed high component loadings for CG9, CG6 and CG12. The fourth factor accounted for 12.020% of total variability with comparatively higher loading WT12, WT9 and WT6. The communality ranged from 0.562 for BL12 to 0.848 for BH9. The body weight of adult Malabari goats was predicted using stepwise multiple regression of different interdependent morphometric traits and principal components. The multiple regression model with PC1 and PC2 was most precise with coefficient of determination (R2) value 74%. Therefore, the study revealed that extracted components revealed maximum variability of growth performances in Malabari goats which could be effectively used for selection and breeding programmes.

Isolation, characterization and selection of potential probiotic lactic acid bacteria from feces of wild boar, native pig and commercial pig

In livestock production, lactic acid bacteria (LAB) play an important role in improving performance, mitigating diseases due to their ability to maintain the balance of gastrointestinal microbes and resistance to pathogenic bacteria. As resident adapted microorganisms, LAB should be isolated and characterized from the homologous host to improve their efficiency as additives in pig feeding. In present study, cultivable LAB numbers from commercial pig (CP), native pig (NP) and wild boar (WB) samples ranged from 4.64 to 7.39 (log10 CFU/g), and the LAB counts in commercial pig and native pig feces were much higher than wild boar. Ninety-six LAB strains were detected in commercial pig, native pig and wild boar feces, being represented by thirteen different species of Enterococcus, Lactobacillus, Pediococcus and Weissella genera. Tests in vitro were conducted including survival in simulated gastrointestinal tract conditions, antimicrobial activity, organic acid production, hydrophobicity, auto-aggregation, Caco-2 surface adhesion, haemolytic activity and antibiotic resistance. According to the results of acid and bile salt resistant and bacteriostatic test, twelve LAB strains were selected as potential probiotics and their cell surface properties were further evaluated. Based on multivariate principal component analysis, nine probiotic LAB strains (Lactobacillus plantarum NCUA001005 and NCUA001014, Pediococcus pentosaceus NCUA006009, Lactobacillus reuteri NCUA064001 and NCUA064006, Lactobacillus johnsonii NCUA063001, NCUA063006 and NCUA063008, Lactobacillus casei NCUA011001) showed better characteristics and might be selected to explore their potential use as probiotics. These strains were good candidates for further investigation in vivo studies to assess their potential health benefits as feed supplements.

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence.

Background and AimAbout 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options.MethodsData of 263 patients (mean age 37.0 ± 14.0‐years, 176, 77% male) with acute severe UC admitted during a 12‐year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN).ResultsOf 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C‐reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower.ConclusionIt is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.

A Reinforcement Learning Method Using Multifunctional Principal Component Analysis for Human-Like Grasping

Postural synergies allow a rich set of hand configurations to be represented in lower dimension space compared to the original joint space. In our previous works, we have shown that this can be extended to trajectories thanks to the multivariate functional principal component analysis, obtaining a set of basis functions able to represent grasping movements learned from human demonstration. In this article, we introduce a human cognition-inspired approach for generalizing and improving robot grasping skills in this motion synergies subspace. The use of a reinforcement learning (RL) algorithm allows the robot to explore the surrounding space and improve its capability to reach and grasp objects. The learning method is the policy improvement with path integrals, running in the policy space. Bootstrapped with synergy coefficients obtained from neural networks, the policy reward is based on a force closure grasp quality index computed at the end of the task, measuring how a firm is the grip. We finally show that combining neural networks and RL allows the robot manipulator to have a good initial estimate of the grasping configuration and faster convergence to an optimal grasp with respect to a database approach, the latter a less general solution in presence of new objects.

Craniofacial growth and development in a mouse model of Down syndrome

Down syndrome (DS) is a developmental disorder caused by trisomy of human chromosome 21. Ts65Dn, a widely used mouse model of DS, has facilitated the study of mechanisms underlying the genotypic and phenotypic anomalies associated with DS. Previous studies have shown the adult cranium and brain to be markedly smaller in size in Ts65Dn mice relative to their euploid littermates. To determine how postnatal developmental patterns contribute to cranial variation in trisomic mice, we quantitatively compared growth trajectories from birth (postnatal day 0, P0) to postnatal day 6 (P6) in Ts65Dn and euploid mice. We hypothesized that trisomy affects cranial growth rate in Ts65Dn mice, causing size and shape differences between Ts65Dn and euploid mice. Multivariate regression and principal components analyses of craniofacial shape and size show some subtle differences between the trisomic and euploid mice at P0, but by P6 the trisomic mice are considerably smaller than the euploids. Interestingly, Ts65Dn at P6 are closer in both size and shape to P0 euploid mice than to euploid mice at P6 or newborn Ts65Dn. This could occur through changes in growth rate of Ts65Dn mice, resulting in the trisomic P6 mice retaining features common to younger, typically developing mice. Further research focusing on prenatal ages will provide a better understanding of how differences in timing of developmental events, such as delays in migration and mitosis of cranial neural crest cells in mouse model of DS, contribute to trisomic craniofacial growth and development.Support or Funding InformationR01‐HD038384 (RHR)