On Locally Dyadic Stationary Processes

We introduce the concept of local dyadic stationarity, to account for non-stationary time series, within the framework of Walsh-Fourier analysis. We define and study the time varying dyadic ARMA models (tvDARMA). It is proven that the general tvDARMA process can be approximated locally by either a tvDMA and a tvDAR process.Comment: 27 pages, 2 figure

Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p

Whole genome scanning of a Mediterranean basin hotspot collection provide new insights into olive tree biodiversity and biology

Olive tree (Olea europaea L. subsp. europaea var. europaea) is one of the most important species of the Mediterranean region and one of the most ancient species domesticated. The availability of whole genome assemblies and annotations of olive tree cultivars and oleaster (O. europaea subsp. europaea var. sylvestris) has contributed to a better understanding of genetic and genomic differences between olive tree cultivars. However, compared to other plant species there is still a lack of genomic resources for olive tree popula-tions that span the entire Mediterranean region. In the present study we developed the most complete genomic variation map and the most comprehensive catalog/resource of molecular variation to date for 89 olive tree genotypes originating from the entire Mediterranean basin, revealing the genetic diversity of this commercially significant crop tree and explaining the divergence/similarity among different variants. Addi-tionally, the monumental ancient tree ‘Throuba Naxos’ was studied to characterize the potential origin or routes of olive tree domestication. Several candidate genes known to be associated with key agronomic traits, including olive oil quality and fruit yield, were uncovered by a selective sweep scan to be under selection pressure on all olive tree chromosomes. To further exploit the genomic and phenotypic resources obtained from the current work, genome-wide association analyses were performed for 23 morphological and two agronomic traits. Significant associations were detected for eight traits that provide valuable candidates for fruit tree breeding and for deeper understanding of olive tree biology.info:eu-repo/semantics/publishedVersio

Stem cell factor is implicated in microenvironmental interactions and cellular dynamics of chronic lymphocytic leukemia

The inflammatory cytokine Stem Cell Factor (SCF, ligand of c-kit receptor) has been implicated as a pro-oncogenic driver and an adverse prognosticator in several human cancers. Increased SCF levels have recently been reported in a small series of patients with chronic lymphocytic leukemia (CLL), however its precise role in CLL pathophysiology remains elusive. In this study, CLL cells were found to predominantly express the membrane isoform of SCF that is known to elicit a more robust activation of the c-kit receptor. SCF was significantly overexpressed in CLL cells compared to healthy tonsillar B cells whilst it correlated with adverse-prognostic biomarkers, shorter time-to-first treatment and shorter overall survival. Activation of immune receptors and long-term cell-cell interactions with the mesenchymal stroma led to an elevation of SCF primarily in adverse-prognostic CLL cases. On the contrary, suppression of oxidative stress and the BTK inhibitor Ibrutinib negated SCF levels. Interestingly, SCF significantly correlated with mitochondrial dynamics and HIF-1α which have previously been linked with clinical aggressiveness in CLL. SCF was able to elicit direct biological effects in CLL cells affecting redox homeostasis and cell proliferation. Overall, the aberrantly expressed SCF in CLL cells emerges as a key response regulator to microenvironmental stimuli whilst correlating with poor prognosis. On these grounds, specific targeting of this inflammatory molecule could serve as a novel therapeutic approach in CLL

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy-efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes that provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements

Downregulation of sphingosine 1-phosphate (S1P) receptor 1 by dexamethasone inhibits S1P-induced mesangial cell migration

Sphingosine 1-phosphate (S1P) is generated by sphingosine kinase (SK)-1 and -2 and acts mainly as an extracellular ligand at five specific receptors, denoted S1P1-5. After activation, S1P receptors regulate important processes in the progression of renal diseases, such as mesangial cell migration and survival. Previously, we showed that dexamethasone enhances SK-1 activity and S1P formation, which protected mesangial cells from stress-induced apoptosis. Here we demonstrate that dexamethasone treatment lowered S1P1 mRNA and protein expression levels in rat mesangial cells. This effect was abolished in the presence of the glucocorticoid receptor antagonist RU-486. In addition, in vivo studies showed that dexamethasone downregulated S1P1 expression in glomeruli isolated from mice treated with dexamethasone (10 mg/kg body weight). Functionally, we identified S1P1 as a key player mediating S1P-induced mesangial cell migration. We show that dexamethasone treatment significantly lowered S1P-induced migration of mesangial cells, which was again reversed in the presence of RU-486. In summary, we suggest that dexamethasone inhibits S1P-induced mesangial cell migration via downregulation of S1P1. Overall, these results demonstrate that dexamethasone has functional important effects on sphingolipid metabolism and action in renal mesangial cells