138 research outputs found

    Adaptive toolpath for 3-axis milling of thin walled parts

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    Meristematic connectome: A cellular coordinator of plant responses to environmental signals?

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    Mechanical stress in tree roots induces the production of reaction wood (RW) and the formation of new branch roots, both functioning to avoid anchorage failure and limb damage. The vascular cambium (VC) is the factor responsible for the onset of these responses as shown by their occurrence when all primary tissues and the root tips are removed. The data presented confirm that the VC is able to evaluate both the direction and magnitude of the mechanical forces experienced before coordinating the most fitting responses along the root axis whenever and wherever these are necessary. The coordination of these responses requires intense crosstalk between meristematic cells of the VC which may be very distant from the place where the mechanical stress is first detected. Signaling could be facilitated through plasmodesmata between meristematic cells. The mechanism of RW production also seems to be well conserved in the stem and this fact suggests that the VC could behave as a single structure spread along the plant body axis as a means to control the relationship between the plant and its environment. The observation that there are numerous morphological and functional similarities between different meristems and that some important regulatory mechanisms of meristem activity, such as homeostasis, are common to several meristems, supports the hypothesis that not only the VC but all apical, primary and secondary meristems present in the plant body behave as a single interconnected structure. We propose to name this structure \u201cmeristematic connectome\u201d given the possibility that the sequence of meristems from root apex to shoot apex could represent a pluricellular network that facilitates long-distance signaling in the plant body. The possibility that the \u201cmeristematic connectome\u201d could act as a single structure active in adjusting the plant body to its surrounding environment throughout the life of a plant is now proposed

    The Ariel Instrument Control Unit: its role within the Payload and B1 Phase design

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    Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey mission (Tinetti 2019; Puig et al. 2018; Pascale et al. 2018), has been selected in March 2018 by ESA for the fourth medium-class mission (M4) launch opportunity of the Cosmic Vision Program, with an expected lift off in late 2028. It is the first mission dedicated to measuring the chemical composition and thermal structures of the atmospheres of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of our own Solar System. Its Payload (P/L) (Eccleston and Tinetti 2018; Eccleston et al. 2017; Middleton et al. 2019), has been designed to perform transit spectroscopy from space during primary and secondary planetary eclipses in order to achieve a large unbiased survey concerning the nature of exoplanets atmospheres and their interiors, to determine the key factors affecting the formation and evolution of planetary systems (Tinetti et al. 2017, 2018). Ariel will observe hundreds of warm and hot transiting gas giants, Neptunes and super-Earths around a wide range of host star types, targeting planets hotter than ∼ 600 K to take advantage of their well-mixed atmospheres. It will exploit primary and secondary transit spectroscopy in the 1.10 to 7.80 μm spectral range and broad-band photometry in the optical (0.50 - 0.80 μm) and Near IR (0.80 - 1.10 μm). One of the two instruments of the Ariel Payload is the Fine Guidance System (FGS), including three photometric channels (two used for guiding as well as science) between 0.5-1.1 μm plus a low resolution NIR spectrometer for 1.1-1.95 μm range. Along with FGS an IR Spectrometer (AIRS) (Amiaux et al. 2017) is foreseen, providing low-resolution spectroscopy in two IR channels: Channel 0 (CH0) for the 1.95 − 3.90 μm band and Channel 1 (CH1) for the 3.90 − 7.80 μm range. Finally, an Active Cooler System (ACS) including a Ne Joule-Thomson cooler is adopted to provide active cooling capability to the AIRS detectors working at cryogenic temperatures. AIRS is located at the intermediate focal plane of the telescope and common optical system and it hosts two HgCdTe-based hybrid IR detectors and two cold front-end electronics (CFEE) for detectors control and readout. Each CFEE is driven by a Detector Control Unit (DCU) part of AIRS but hosted within and managed by the Instrument Control Unit (ICU) of the Payload (Focardi et al. 2018). ICU is a warm unit residing into the S/C Service Module (SVM) and it is based on a cold redundant configuration involving the Power Supply Unit (PSU) and the Commanding and Data Processing Unit (CDPU) boards; both DCUs are instead cross-strapped and can be managed by the nominal or the redundant (PSU+CDPU) chain. ICU is in charge of AIRS management, collecting scientific and housekeeping (HK) telemetries from the spectrometer and HK from the telescope (temperatures readings), the P/L Optical Bench (OB) and other Subsystems (SS), thanks to a warm slave unit (TCU, Telescope Control Unit) interfaced to the ICU. Science and HK telemetries are then forwarded to the S/C, for temporary storage, before sending them to Ground. Here we describe the status of the ICU design at the end of B1 Phase, prior to the Mission Adoption Review (MAR) by ESA, with some still open architectural choices to be addressed and finalised once selected the ICU industrial Prime contractor

    Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

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    PURPOSE: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database. METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups. CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research
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