Structured Prediction for CRISP Inverse Kinematics Learning With Misspecified Robot Models

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function – even when this function is misspecified – to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks

Manifold structured prediction

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold-valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study

Manifold Structured Prediction

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our stud

Functional markers for gene mapping and genetic diversity studies in sugarcane

<p>Abstract</p> <p>Background</p> <p>The database of sugarcane expressed sequence tags (EST) offers a great opportunity for developing molecular markers that are directly associated with important agronomic traits. The development of new EST-SSR markers represents an important tool for genetic analysis. In sugarcane breeding programs, functional markers can be used to accelerate the process and select important agronomic traits, especially in the mapping of quantitative traits loci (QTL) and plant resistant pathogens or qualitative resistance loci (QRL). The aim of this work was to develop new simple sequence repeat (SSR) markers in sugarcane using the sugarcane expressed sequence tag (SUCEST database).</p> <p>Findings</p> <p>A total of 365 EST-SSR molecular markers with trinucleotide motifs were developed and evaluated in a collection of 18 genotypes of sugarcane (15 varieties and 3 species). In total, 287 of the EST-SSRs markers amplified fragments of the expected size and were polymorphic in the analyzed sugarcane varieties. The number of alleles ranged from 2-18, with an average of 6 alleles per locus, while polymorphism information content values ranged from 0.21-0.92, with an average of 0.69. The discrimination power was high for the majority of the EST-SSRs, with an average value of 0.80. Among the markers characterized in this study some have particular interest, those that are related to bacterial defense responses, generation of precursor metabolites and energy and those involved in carbohydrate metabolic process.</p> <p>Conclusions</p> <p>These EST-SSR markers presented in this work can be efficiently used for genetic mapping studies of segregating sugarcane populations. The high Polymorphism Information Content (PIC) and Discriminant Power (DP) presented facilitate the QTL identification and marker-assisted selection due the association with functional regions of the genome became an important tool for the sugarcane breeding program.</p

Hypoglycemia and the Origin of Hypoxia-Induced Reduction in Human Fetal Growth

The most well known reproductive consequence of residence at high altitude (HA >2700 m) is reduction in fetal growth. Reduced fetoplacental oxygenation is an underlying cause of pregnancy pathologies, including intrauterine growth restriction and preeclampsia, which are more common at HA. Therefore, altitude is a natural experimental model to study the etiology of pregnancy pathophysiologies. We have shown that the proximate cause of decreased fetal growth is not reduced oxygen availability, delivery, or consumption. We therefore asked whether glucose, the primary substrate for fetal growth, might be decreased and/or whether altered fetoplacental glucose metabolism might account for reduced fetal growth at HA.Doppler and ultrasound were used to measure maternal uterine and fetal umbilical blood flows in 69 and 58 residents of 400 vs 3600 m. Arterial and venous blood samples from mother and fetus were collected at elective cesarean delivery and analyzed for glucose, lactate and insulin. Maternal delivery and fetal uptakes for oxygen and glucose were calculated.The maternal arterial – venous glucose concentration difference was greater at HA. However, umbilical venous and arterial glucose concentrations were markedly decreased, resulting in lower glucose delivery at 3600 m. Fetal glucose consumption was reduced by >28%, but strongly correlated with glucose delivery, highlighting the relevance of glucose concentration to fetal uptake. At altitude, fetal lactate levels were increased, insulin concentrations decreased, and the expression of GLUT1 glucose transporter protein in the placental basal membrane was reduced.Our results support that preferential anaerobic consumption of glucose by the placenta at high altitude spares oxygen for fetal use, but limits glucose availability for fetal growth. Thus reduced fetal growth at high altitude is associated with fetal hypoglycemia, hypoinsulinemia and a trend towards lactacidemia. Our data support that placentally-mediated reduction in glucose transport is an initiating factor for reduced fetal growth under conditions of chronic hypoxemia

1H-NMR-Based Metabolic Profiling of Maternal and Umbilical Cord Blood Indicates Altered Materno-Foetal Nutrient Exchange in Preterm Infants

Background: Adequate foetal growth is primarily determined by nutrient availability, which is dependent on placental nutrient transport and foetal metabolism. We have used 1H nuclear magnetic resonance (NMR) spectroscopy to probe the metabolic adaptations associated with premature birth. Methodology: The metabolic profile in 1H NMR spectra of plasma taken immediately after birth from umbilical vein, umbilical artery and maternal blood were recorded for mothers delivering very-low-birth-weight (VLBW) or normo-ponderal full-term (FT) neonates. Principal Findings: Clear distinctions between maternal and cord plasma of all samples were observed by principal component analysis (PCA). Levels of amino acids, glucose, and albumin-lysyl in cord plasma exceeded those in maternal plasma, whereas lipoproteins (notably low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) and lipid levels were lower in cord plasma from both VLBW and FT neonates. The metabolic signature of mothers delivering VLBW infants included decreased levels of acetate and increased levels of lipids, pyruvate, glutamine, valine and threonine. Decreased levels of lipoproteins glucose, pyruvate and albumin-lysyl and increased levels of glutamine were characteristic of cord blood (both arterial and venous) from VLBW infants, along with a decrease in levels of several amino acids in arterial cord blood. Conclusion: These results show that, because of its characteristics and simple non-invasive mode of collection, cord plasma is particularly suited for metabolomic analysis even in VLBW infants and provides new insights into the materno-foetal nutrient exchange in preterm infants

Dwarf Galaxies of the Local Group

The Local Group (LG) dwarf galaxies offer a unique window to the detailed properties of the most common type of galaxy in the Universe. In this review, I update the census of LG dwarfs based on the most recent distance and radial velocity determinations. I then discuss the detailed properties of this sample, including (a) the integrated photometric parameters and optical structures of these galaxies, (b) the content, nature and distribution of their ISM, (c) their heavy-element abundances derived from both stars and nebulae, (d) the complex and varied star-formation histories of these dwarfs, (e) their internal kinematics, stressing the relevance of these galaxies to the dark-matter problem and to alternative interpretations, and (f) evidence for past, ongoing and future interactions of these dwarfs with other galaxies in the Local Group and beyond. To complement the discussion and to serve as a foundation for future work, I present an extensive set of basic observational data in tables that summarize much of what we know, and what we still do not know, about these nearby dwarfs. Our understanding of these galaxies has grown impressively in the past decade, but fundamental puzzles remain that will keep the Local Group at the forefront of galaxy evolution studies for some time.Comment: 66 pages; 9 figures; 8 table

Advances, challenges and future directions for stem cell therapy in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative condition where loss of motor neurons within the brain and spinal cord leads to muscle atrophy, weakness, paralysis and ultimately death within 3–5 years from onset of symptoms. The specific molecular mechanisms underlying the disease pathology are not fully understood and neuroprotective treatment options are minimally effective. In recent years, stem cell transplantation as a new therapy for ALS patients has been extensively investigated, becoming an intense and debated field of study. In several preclinical studies using the SOD1G93A mouse model of ALS, stem cells were demonstrated to be neuroprotective, effectively delayed disease onset and extended survival. Despite substantial improvements in stem cell technology and promising results in preclinical studies, several questions still remain unanswered, such as the identification of the most suitable and beneficial cell source, cell dose, route of delivery and therapeutic mechanisms. This review will cover publications in this field and comprehensively discuss advances, challenges and future direction regarding the therapeutic potential of stem cells in ALS, with a focus on mesenchymal stem cells. In summary, given their high proliferation activity, immunomodulation, multi-differentiation potential, and the capacity to secrete neuroprotective factors, adult mesenchymal stem cells represent a promising candidate for clinical translation. However, technical hurdles such as optimal dose, differentiation state, route of administration, and the underlying potential therapeutic mechanisms still need to be assessed

The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products

The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100,000 stars, including large representative samples of the stellar populations in the Galaxy, and a well-defined sample of 60 (plus 20 archive) open clusters. We provide internally consistent results calibrated on benchmark stars and star clusters, extending across a very wide range of abundances and ages. This provides a legacy data set of intrinsic value, and equally a large wide-ranging dataset that is of value for homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. This article provides an overview of the survey methodology, the scientific aims, and the implementation, including a description of the data processing for the GIRAFFE spectra. A companion paper (arXiv:2206.02901) introduces the survey results. Gaia-ESO aspires to quantify both random and systematic contributions to measurement uncertainties. Thus all available spectroscopic analysis techniques are utilised, each spectrum being analysed by up to several different analysis pipelines, with considerable effort being made to homogenise and calibrate the resulting parameters. We describe here the sequence of activities up to delivery of processed data products to the ESO Science Archive Facility for open use. The Gaia-ESO Survey obtained 202,000 spectra of 115,000 stars using 340 allocated VLT nights between December 2011 and January 2018 from GIRAFFE and UVES. The full consistently reduced final data set of spectra was released through the ESO Science Archive Facility in late 2020, with the full astrophysical parameters sets following in 2022