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Dynamic movement primitives (DMP) are motion building blocks suitable for real-world tasks. We suggest a methodology for learning the manifold of task and DMP parameters, which facilitates runtime adaptation to changes in task requirements while ensuring predictable and robust performance. For efficient learning, the parameter space is analyzed using principal component analysis and locally linear embedding. Two manifold learning methods: kernel estimation and deep neural networks, are investigated for a ball throwing task in simulation and in a physical environment. Low runtime estimation errors are obtained for both learning methods, with an advantage to kernel estimation when data sets are small.

In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques. To provide context, we start by tracing the origin of algorithm unrolling and providing a comprehensive tutorial on how to unroll iterative algorithms into deep networks. We then extensively cover algorithm unrolling in a wide variety of biomedical imaging modalities and delve into several representative recent works in detail. Indeed, there is a rich history of iterative algorithms for biomedical image synthesis, which makes the field ripe for unrolling techniques. In addition, we put algorithm unrolling into a broad perspective, in order to understand why it is particularly effective and discuss recent trends. Finally, we conclude the chapter by discussing open challenges, and suggesting future research directions.

Breast cancer is the most common malignancy in women. Mammographic findings such as microcalcifications and masses, as well as morphologic features of masses in sonographic scans, are the main diagnostic targets for tumor detection. However, improved specificity of these imaging modalities is required. A leading alternative target is neoangiogenesis. When pathological, it contributes to the development of numerous types of tumors, and the formation of metastases. Hence, demonstrating neoangiogenesis by visualization of the microvasculature may be of great importance. Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. Yet, challenges such as long reconstruction time, dependency on prior knowledge of the system Point Spread Function (PSF), and separability of the Ultrasound Contrast Agents (UCAs), need to be addressed for translation of super-resolution US into the clinic. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. We present in vivo human results of three different breast lesions acquired with a clinical US scanner. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs. Each of the recoveries exhibits a different structure that corresponds with the known histological structure. This study demonstrates the feasibility of in vivo human super resolution, based on a clinical scanner, to increase US specificity for different breast lesions and promotes the use of US in the diagnosis of breast pathologies.

Intestinal epithelial cells are multi-tasked cells that participate in digestion and absorption as well as in protection of the digestive tract. While information on the physiology and immune functions of intestinal epithelial cells in mammals is abundant, little is known of their immune function in birds and other species. Our main objectives were to study the development of anti-bacterial innate immune functions in the rapidly developing gut of the pre- and post-hatch chick and to determine the functional diversity of epithelial cells. After establishing primary intestinal epithelial cell cultures, we demonstrated their capacity to uptake and process bacteria. The response to bacterial products, LPS and LTA, induced expression of pro-inflammatory cytokine genes (IL-6, IL-18) as well as the expression of the acute phase proteins avidin, lysozyme and the secretory component derived from the polymeric immunoglobulin receptor. These proteins were then localized in gut sections, and the goblet cell was shown to store avidin, lysozyme as well as secretory component. Lysozyme staining was also located in a novel rod-shaped intestinal cell, situated at different loci along the villus, thus deviating from the classical Paneth cell in the mammal, that is restricted to crypts. Thus, in the chicken, the intestinal epithelium, and particularly goblet cells, are committed to innate immune protection. The unique role of the goblet cell in chicken intestinal immunity, as well as the unique distribution of lysozyme-positive cells highlight alternative solutions of gut protection in the bird.

Synaptic receptors in the human brain consist of multiple protein subunits, many of which have multiple variants, coded by different genes, and are differentially expressed across brain regions and developmental stages. The brain can tune the electrophysiological properties of synapses to regulate plasticity and information processing by switching from one protein variant to another. Such condition-dependent variant switch during development has been demonstrated in several neurotransmitter systems including NMDA and GABA. Here we systematically detect pairs of receptor-subunit variants that switch during the lifetime of the human brain by analyzing postmortem expression data collected in a population of donors at various ages and brain regions measured using microarray and RNA-seq. To further detect variant pairs that co-vary across subjects, we present a method to quantify age-corrected expression correlation in face of strong temporal trends. This is achieved by computing the correlations in the residual expression beyond a cubic-spline model of the population temporal trend, and can be seen as a nonlinear version of partial correlations. Using these methods, we detect multiple new pairs of context dependent variants. For instance, we find a switch from GLRA2 to GLRA3 that differs from the known switch in the rat. We also detect an early switch from HTR1A to HTR5A whose trends are negatively correlated and find that their age-corrected expression is strongly positively correlated. Finally, we observe that GRIN2B switch to GRIN2A occurs mostly during embryonic development, presumably earlier than observed in rodents. These results provide a systematic map of developmental switching in the neurotransmitter systems of the human brain.

In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the relative impact of control measures and the long-term causal contribution of each NPI are still a topic of debate. We present a method to rigorously study the effectiveness of interventions on the rate of the time-varying reproduction number R t and on human mobility, considered here as a proxy measure of policy adherence and social distancing. We frame our model using a causal inference approach to quantify the impact of five governmental interventions introduced until June 2020 to control the outbreak in 113 countries: confinement, school closure, mask wearing, cultural closure, and work restrictions. Our results indicate that mobility changes are more accurately predicted when compared to reproduction number. All NPIs, except for mask wearing, significantly affected human mobility trends. From these, schools and cultural closure mandates showed the largest effect on social distancing. We also found that closing schools, issuing face mask usage, and work-from-home mandates also caused a persistent reduction on R t after their initiation, which was not observed with the other social distancing measures. Our results are robust and consistent across different model specifications and can shed more light on the impact of individual NPIs.

Genetic alterations in COL4A2 are less common than those of COL4A1 and their fetal phenotype has not been described to date. We describe a three-generation family with an intragenic deletion in COL4A2 associated with a prenatal diagnosis of recurrent fetal intracerebral hemorrhage (ICH), and a myriad of cerebrovascular manifestations. Exome sequencing, co-segregation analysis, and imaging studies were conducted on eight family members including two fetuses with antenatal ICH. Histopathological evaluation was performed on the terminated fetuses. An intragenic heterozygous pathogenic in-frame deletion; COL4A2, c.4151_4168del, (p.Thr1384_Gly1389del) was identified in both fetuses, their father with hemiplegic cerebral palsy (CP), as well as other family members. Postmortem histopathological examination identified microscopic foci of heterotopias and polymicrogyria. The variant segregated in affected individuals demonstrating varying degrees of penetrance and a wide phenotypic spectrum including periventricular venous hemorrhagic infarction causing hemiplegic CP, polymicrogyria, leukoencephalopathy, and lacunar stroke. We present radiographic, pathological, and genetic evidence of prenatal ICH and show, for what we believe to be the first time, a human pathological proof of polymicrogyria and heterotopias in association with a COL4A2 disease-causing variant, while illustrating the variable phenotype and partial penetrance of this disease. We highlight the importance of genetic analysis in fetal ICH and hemiplegic CP.

BackgroundThe phenotype of Parkinson’s disease (PD) is variable with mutations in genes such as LRRK2 and GBA explaining part of this heterogeneity. Additional genetic and environmental factors contribute to disease variability.ObjectiveTo assess the association between biochemical markers, PD severity and probability score for prodromal PD, among GBA and LRRK2 mutation carriers.MethodsLevels of uric acid, vitamin D, C-reactive protein, microalbumin/creatinine ratio (ACR), white blood count (WBC), hemoglobin, platelets, neutrophil/lymphocyte ratio and estimated glomerular filtration rate (eGFR) were assessed from patients with PD and non-manifesting carriers (NMC) of mutations in GBA and LRRK2, together with disease related questionnaires enabling the construction of the MDS prodromal probability score.ResultA total of 241 patients with PD: 105 idiopathic PD (iPD), 49 LRRK2-PD and 87 GBA-PD and 412 non-manifesting subjects; 74 LRRK2-NMC, 118 GBA-NMC and 220 non-manifesting non-carriers (NMNC), participated in this study. No significant differences in biochemical measures were detected among patients with PD or non-manifesting carriers. Among GBA-PD patients, worse motor performance was associated with ACR (B = 4.68, 95% CI (1.779–7.559); p = 0.002). The probability score for prodromal PD among all non-manifesting participants was associated with eGFR; NMNC (B = − 0.531 95% CI (− 0.879 to − 0.182); p < 0.001, LRRK2-NMC (B = − 1.014 95% CI (− 1.663 to − 0.366); p < 0.001) and GBA-NMC (B = − 0.686 95% CI (1.300 to − 0.071); p = 0.029).ConclusionSub-clinical renal impairment is associated with increased likelihood for prodromal PD regardless of genetic status. While the mechanism behind this finding needs further elucidation, it suggests that kidney function might play a role in PD pathogenesis.

In order toevaluate the influence of the metabolic syndrome (MS) (obesity, hypertension, elevated triglycerides, reduced levels of HDL cholesterol and glucose impairment) on the phenotype of LRRK2 and GBA Parkinson’s disease (PD), and on the prevalence of prodromal features among individuals at risk, we collected, laboratory test results, blood pressure, demographic, cognitive, motor, olfactory and affective information enabling the assessment of each component of MS and the construction of the MDS prodromal probability score. The number of metabolic components and their levels were compared between participants who were separated based on disease state and genetic status. One hundred and four idiopathic PD, 40 LRRK2 -PD, 70 GBA -PD, 196 healthy non-carriers, 55 LRRK2 -NMC and 97 GBA -NMC participated in this study. PD groups and non manifesting carriers (NMC) did not differ in the number of metabolic components (p = 0.101, p = 0.685, respectively). LRRK2 -PD had higher levels of triglycerides (p = 0.015) and higher rates of prediabetes (p = 0.004), while LRRK2 -NMC had higher triglyceride levels (p = 0.014). NMC with probability rates for prodromal PD above 50% had higher frequencies of hypertriglyceridemia and prediabetes (p < 0.005, p = 0.023 respectively). While elevated triglycerides and prediabetes were more frequent among LRRK2 carriers, MS does not seem to influence GBA and LRRK2 -PD phenotype.

Homeostasis preservation is essential for animal survival, and any event that causes a disturbance in homeostasis is defined as a stressor. Here, we aimed to evaluate the effect of scratch brushes and stages as an environmental enrichment to alleviate stress in dairy goats. Twenty-four mixed-breed goats were divided into two groups according to common physiological conditions in breeding farms: milking and dry (milk-producing and non-milk-producing, respectively). Ten days after exposure to environmental enrichment treatment or not (control), blood was sampled. Following the enrichment, we observed a reduction in reactive oxidative stress metabolites, advanced glycation end products (AGEs), and their binding protein (transferrin) in the dry goats, as determined by an ELISA. In contrast, no change in AGEs, along with an increase in transferrin levels, was observed in the milking goats. Moreover, oxytocin levels decreased in the dry and increased in the milking goats, while serotonin levels increased in the dry and remained unchanged in the milking goats. Additionally, gene expression of the cytokines, IL-6 and IL-1ß, and anti-oxidative proteins, lysozyme and transferrin (in peripheral blood leukocytes), as determined by qPCR, presented the same pattern: down-regulation in the dry or up-regulation in the milking goats. In conclusion, a reliable methodology was developed for measuring husbandry stress in goats and to improve dairy goats’ husbandry practice. Current environmental enrichment produced different responsiveness in goats correlated to their physiological status: beneficial effect in dry goats, detrimental effect in milking goats.