Analysis of STEP AP242 standard and test of compliancy with modern CAD modellers

Obravnavani problem te naloge zajema pregled nevtralnega orodja za izmenjavo podatkov STEP AP242 in modelirnikov na področju metode MBD. Z izdelavo enakega modela v različnih modelirnikih, dodajanjem PMI na modele in pretvorbo modelov v STEP AP242 se naredi pregled ustreznosti pretvorbe in modelirnikov. Datoteke se pregleda tudi v tekstovni obliki, kar omogoči preverjanje ohranjanja prej podanih PMI. Rezultati kažejo, da so pretvorbe ustrezne, modelirniki pa kompatibilni s standardom ISO 10303-242:2014 (STEP AP242) in omogočajo metodo MBD.The problem that this work deals with involves an overview of neutral tool for exchange of information STEP AP242 and modellers in the field of method MBD. With creation of same model in different modellers and adding PMI on those models and transforming them into STEP AP242 format, we can make an overview of suitability of transformation and modellers. Files are also overviewed in text form and that enables us to test the retention of pre-added PMI. Results show us that the transformation is correctly made and modellers are compatible with standard ISO 10303-242:2014 (STEP AP242) and that they are enabling the MBD method

Swelling and eicosanoid metabolites differentially gate TRPV4 channels in retinal neurons and glia.

Activity-dependent shifts in ionic concentrations and water that accompany neuronal and glial activity can generate osmotic forces with biological consequences for brain physiology. Active regulation of osmotic gradients and cellular volume requires volume-sensitive ion channels. In the vertebrate retina, critical support to volume regulation is provided by Müller astroglia, but the identity of their osmosensor is unknown. Here, we identify TRPV4 channels as transducers of mouse Müller cell volume increases into physiological responses. Hypotonic stimuli induced sustained [Ca(2+)]i elevations that were inhibited by TRPV4 antagonists and absent in TRPV4(-/-) Müller cells. Glial TRPV4 signals were phospholipase A2- and cytochrome P450-dependent, characterized by slow-onset and Ca(2+) waves, and, in excess, were sufficient to induce reactive gliosis. In contrast, neurons responded to TRPV4 agonists and swelling with fast, inactivating Ca(2+) signals that were independent of phospholipase A2. Our results support a model whereby swelling and proinflammatory signals associated with arachidonic acid metabolites differentially gate TRPV4 in retinal neurons and glia, with potentially significant consequences for normal and pathological retinal function

Records export, transfer and ingest recommendations and SIP Creation Tools

This report describes a software deliverable as it delivers a number of E-ARK tools: • ERMS Export Module (a tool for exporting records and their metadata from ERMS in a controlled manner); • Database Preservation Toolkit (a tool for exporting relational databases as SIARD 2.0 or other formats); • ESSArch Tools for Producer (a tool for SIP creation); • ESSArch Tools for Archive (a tool for SIP ingestion); • RODA-in (a tool for SIP creation); • Universal Archiving Module (a tool for SIP creation). In addition, an overview of Pre-Ingest and Ingest processes will be provided by this report which will help to understand the tools and their use

Recruitment of Glycosyl Hydrolase Proteins in a Cone Snail Venomous Arsenal: Further Insights into Biomolecular Features of Conus Venoms

Cone snail venoms are considered an untapped reservoir of extremely diverse peptides, named conopeptides, displaying a wide array of pharmacological activities. We report here for the first time, the presence of high molecular weight compounds that participate in the envenomation cocktail used by these marine snails. Using a combination of proteomic and transcriptomic approaches, we identified glycosyl hydrolase proteins, of the hyaluronidase type (Hyal), from the dissected and injectable venoms (“injectable venom” stands for the venom variety obtained by milking of the snails. This is in contrast to the “dissected venom”, which was obtained from dissected snails by extraction of the venom glands) of a fish-hunting cone snail, Conus consors (Pionoconus clade). The major Hyal isoform, Conohyal-Cn1, is expressed as a mixture of numerous glycosylated proteins in the 50 kDa molecular mass range, as observed in 2D gel and mass spectrometry analyses. Further proteomic analysis and venom duct mRNA sequencing allowed full sequence determination. Additionally, unambiguous segment location of at least three glycosylation sites could be determined, with glycans corresponding to multiple hexose (Hex) and N-acetylhexosamine (HexNAc) moieties. With respect to other known Hyals, Conohyal-Cn1 clearly belongs to the hydrolase-type of Hyals, with strictly conserved consensus catalytic donor and positioning residues. Potent biological activity of the native Conohyals could be confirmed in degrading hyaluronic acid. A similar Hyal sequence was also found in the venom duct transcriptome of C. adamsonii (Textilia clade), implying a possible widespread recruitment of this enzyme family in fish-hunting cone snail venoms. These results provide the first detailed Hyal sequence characterized from a cone snail venom, and to a larger extent in the Mollusca phylum, thus extending our knowledge on this protein family and its evolutionary selection in marine snail venoms

Bogatenje angiografskih slik z generativnimi nasprotniškimi modeli za izboljšano zaznavanje intrakranialnih anevrizem

The risks associated with intracranial aneurysms have motivated research in the past and still continue to do so today, this thesis being no exception. With a reported incidence of around 3.2% (in the absence of risk factors) and with the associated risk of rupture rising steeply in correlation with aneurysm size, the value of early detection, evaluation and potential treatments is crucial. This thesis focuses on computer-aided detection of intracranial aneurysms, which is a computer vision task. Computer vision as a field of research has progressed immensely in the past 10 years or so as a result of the availability and development of deep convolutional neural networks and associated hardware technology. It has become the default approach in most detection and segmentation tasks where non-trivial analytical approaches were employed prior to this era. However, as machine learning approaches require adequately sized training data, the same methods may excel at common problems, where learning sets are easily obtainable and plentiful, but show poor performance in niche fields where training data is scarce. Multiple approaches can be taken in order to tackle this issue, such as fine-tuning the existing models and/or augmentation of available data through the deep learning based creation of artificial data. The lack of publicly available medical image datasets is a problem that must be frequently tackled when applying deep learning machine vision principles in medical image analysis. This is due to multiple reasons such as policies on preserving patient privacy, cost of expert\u27s work when annotating/manually segmenting images and a general lack of initiative to produce large public datasets for scientific use just to name a few. We try to address this issue by proposing a problem-specific dataset augmentation technique, which is itself based on generative convolutional neural networks. Our proposal was to take a scarce dataset of 3D magnetic resonance images with corresponding vasculature and aneurysm segmentations, create several 2D projections at locations of interest from multiple view and train a general adversarial network (GAN) on that dataset so that the network would learn to impaint a randomly shaped and textured aneursym into the regions of healthy vasculature. This would allow us to transform a large number of healthy datasets into datasets depicting the pathology of interest, which would as such be suitable for training a standard U-net based detector network. We also opted for a U-net detector variation that employed a combination of the Tversky index and focal loss concepts in order to increase the performance for highly unbalanced datasets, a property inherent to our aneurysm datasets, where we are normally interested in a very small portion of an input image. Our GAN architecture of choice was an of the shelf Cycle-GAN with some customized cost function terms. Learning adversarial networks for a given problem has proven to be very challenging. In the case of the generative network, the impainted aneurysms were meaningful, but their fusion with the vessels was not optimal in all cases. Therefore, we applied additional processing of the input grayscale image of the aneurysm-free vessel and the synthetically generated aneurysm image to ultimately produce useful images depicting aneurysms and vessels for the synthetic training set. Concretely, the problem turned out to be that the images produced in this way varied greatly depending on the extracted patch of the vascular images at the input to the network. With standard morphological operations, Poisson fusion of the input and the synthetic images and manual visual evaluation, we managed to discard all textural and anatomical irregularities and thus obtain the final synthetic set. The process of creating the final set of training images limited the ability to scale the size of the training set due to involved manual inspection. We limited ourselves to just over 2000 synthetic images versus around 6000 real projections and trained our detector network on combinations of both synthetic and real images at various ratios to determine the contribution of synthetic images to the learning convergence and performance of the aneurysm detector model. From the convergence curves of the loss functions during learning and the results of the evaluation of the detector model, we concluded that learning on only synthetic images is insufficient for adequate detector learning. From the results of further experiments, we concluded that the addition of synthetic images to the real ones in the training set did not degrade the performance of the detector and in some cases even improved the convergence and generalization of the detector model.Možganske anevrizme in z njimi povezana zdravstvena tveganja so v preteklosti dali razloge in motivacijo za številne raziskave in pričujoče delo v tem pogledu ni izjema. Naj zadošča dejstvo, da je v obči populaciji že brez prisotnosti dejavnikov tveganja incidenca anevrizem 3.2%, tveganje za pok anevrizme pa strmo narašča z večanjem njihove velikosti. Zato je razvoj metod in procesov za čim prejšnje odkritje in čim boljše vrednotenje te patologije ključnega pomena. To delo se osredotoča na strojno podprto detekcijo možganskih anevrizem, torej problem s področja strojnega vida. Slednje je bilo v zadnjih desetih letih zaznamovano z izjemnimi preboji na račun razvoja tehnologij konvolucijskih nevronskih mrež in strojne opreme, ki jih je naredila v splošnem mnogo dostopnejše za uporabo številnim posameznikom in organizacijam. Strojni vid na osnovi nevronskih mrež se je uveljavil kot primarni pristop in osnovno merilo uspešnosti za številne probleme, kjer so se prej uporabljale nalogam prilagojene analitične rešitve. Skupna lastnost pristopov s konvolucijskimi nevronskimi mrežami je seveda potreba po primerno velikih in označenih učnih množicah. Mreža z izbrano arhitekturo lahko izjemno dobro reši problme, kjer se število učnih slik zadostno, medtem kot taista mreža lahko da neuporabne rezultate na nišnih področjih, kjer je učnih (in testnih) slik malo. Obstaja več načinov spopadanja s tem problemom, med drugim prenos uteži modela za sorodni problem in/ali doučenje modelov in umetno bogatenje obstoječe učne množice. Pri analizi medicinskih slik je pomanjkanje ustreznih, tudi javno dostopnih baz pogost in pereč problem. Javni dostop do kakršnih koli zdravstvenih podatkov običajno ščiti zakonodaja, ki ureja pacientove pravice do zasebnosti in varovanja podatkov, potrebna so predhodna soglasja in postopki anonimizacije. Za probleme detekcije struktur zanimanja v medicinskih slikah vedno potrebujemo slikam pridružene oznake, ki so običajno določene ročno s strani izkušenih radiologov, kar je časovno zahteven in tudi drag proces. K reševanju tega problema smo pristopili z metodo bogatenja podatkov, ki sama temelji na rabi generativnih konvolucijskih nevronskih mrež. Namenili smo se razširiti maloštevilno učno množico tridimenzionalnih magnetno resonančnih slik možganskega ožilja s pripadajočimi razgradnjami žilja in anevrizem z uporabo generativnih nasprotniških modelov. Uporabljali smo dvodimenzionalne projekcije omenjenih slik, učenje mreže pa je bilo zastavljen tako, da se je generativna nevronska mreža učila prevajati izseke slik zdravega ožilja v izseke z dodanimi anevrizmami. Naš cilj je bil omogočiti avtomatsko ustvarjanje velikega števila sintetičnih učnih slik, s katerimi bi pozneje lahko naučili konvolucijsko nevronsko mrežo za detekcijo anevrizem. Generativni nasprotniški model je imel arhitekturo uveljavljene Cycle-GAN mreže, ki smo jo nadgradili z dodatnimi kazenskimi členi v izgubni funkciji. Za detektor smo uporabili arhitekturo U-mreže s Focal-Tversky izgubno funkcijo, pri čemer je izbiro arhitekture motiviralo izrazito neravnovesje med številom vzorcev anevrizme in ozadja slike, in vrednotili kvaliteto detekcije intrakranialnih anevrizem v dvodimenzionalnih projekcijah. Takšen pristop se je izkazal za smiselnega pri iskanju anevrizem, ki predstavljajo le majhen del celotne vaskulature. Učenje nasprotniških mrež za dani problem se je izkazalo za zelo zahtevno. Pri generativni mreži so bile vrisane anevrizme smiselne, njihovo zlivanje z žiljem pa ni bilo v vseh primerih optimalno. Zato smo uporabili dodatno obdelavo vhodne sivinske slike z ožiljem brez anevrizme in sintentično generirane slike z anevrizmo in ožiljem, da smo na koncu proizvedli uporabne slike z vrisanimi anevrizmami za sintetično učno množico. Konkretno se je za težavo izkazalo, da so proizvedene slike močno variirale v odvisnosti od izseka slike ožilja na vhodu v mrežo. S standardnimi morfološkimi operacijami, Poissonovim zlivanjem vhodne in sintentične slike in vizualnim vrednotenjem smo uspeli zavreči vse slike s teksturnimi in anatomskimi nepravilnostmi v končni sintetični množici. Proces ustvarjanja končne množice učnih slik je zaradi ročnega vrednotenja omejil zmožnosti skaliranja velikosti učne množice. Omejili smo se na nekaj več kot 2000 sintetičnih slik proti okoli 6000 dejanskim projekcijam in našo detektorsko mrežo učili na kombinacijah tako sintentičnih kot dejanskih slik v različnih razmerjih, da bi ugotovili doprinos sintetičnih slik k učenju in zmogljivosti modela detektorja anevrizem. Iz potekov izgubnih funkcij med učenjem in rezultatov vrednotenja modela detektorja smo ugotovili, da je učenje na zgolj sintetičnih slikah nezadostno za ustrezno učenje detektorja. Iz rezultatov nadaljnjih poskusov smo zaključili, da dodatek sintetičnih slik k dejanskim v učni množici ni poslabšal zmogljivosti detektorja in je v nekaterih primerih celo izboljšal konvergenco in generalizacijo modela detektorja

CELLULAR BASIS FOR ROD-CONE INTERACTIONS IN THE OUTER RETINA

Background. At least twice daily our retinas move between a light adapted, cone-dominated (photopic) state and a dark-adapted, color-blind and highly light-sensitive roddominated (scotopic) state. In between is a rather ill-defined transitional state called the mesopic state in which retinal circuits express both rod and cone signals. Consequently, in the mesopic state the retinal output to the brain contained in the firing patterns of the ganglion cells consists of information derived from both rod and cone signals. Morphology, physiology and psychophysics all contributed to an understanding that the two systems are not independent but interact extensively via both pooling and mutual inhibition. This review lays down a rationale for such rod-cone interactions in the vertebrate retinas. It suggests that the important functional roles of rod-cone interactions is in that they shorten the duration of the mesopic state. As a result, the retina is maintained in either in the (rod-dominated) high sensitivity photon counting mode or in the second mode which emphasizes temporal transients and spatial resolution (the cone-dominated photopic state).Conclusions. Experimental evidence for pre- and postsynaptic mixing of rod and cone signals in the retina is shown together with the preeminent neuromodulatory role of both light and dopamine in controling inter-actions between rod and cone signals. Dopamine is shown to be both necessary and sufficient to mediate light adaptation in the retina.</p

Intracellular calcium stores drive slow non-ribbon vesicle release from rod photoreceptors

Rods are capable of greater slow release than cones contributing to overall slower release kinetics. Slow release in rods involves Ca(2+)-induced Ca(2+) release (CICR). By impairing release from ribbons, we found that unlike cones where release occurs entirely at ribbon-style active zones, slow release from rods occurs mostly at ectopic, non-ribbon sites. To investigate the role of CICR in ribbon and non-ribbon release from rods, we used total internal reflection fluorescence microscopy as a tool for visualizing terminals of isolated rods loaded with fluorescent Ca(2+) indicator dyes and synaptic vesicles loaded with dextran-conjugated pH-sensitive rhodamine. We found that rather than simply facilitating release, activation of CICR by ryanodine triggered release directly in rods, independent of plasma membrane Ca(2+) channel activation. Ryanodine-evoked release occurred mostly at non-ribbon sites and release evoked by sustained depolarization at non-ribbon sites was mostly due to CICR. Unlike release at ribbon-style active zones, non-ribbon release did not occur at fixed locations. Fluorescence recovery after photobleaching of endoplasmic reticulum (ER)-tracker dye in rod terminals showed that ER extends continuously from synapse to soma. Release of Ca(2+) from terminal ER by lengthy depolarization did not significantly deplete Ca(2+) from ER in the perikaryon. Collectively, these results indicate that CICR-triggered release at non-ribbon sites is a major mechanism for maintaining vesicle release from rods and that CICR in terminals may be sustained by diffusion of Ca(2+) through ER from other parts of the cell

TRPV1 and Endocannabinoids: Emerging Molecular Signals that Modulate Mammalian Vision

Transient Receptor Potential Vanilloid 1 (TRPV1) subunits form a polymodal cation channel responsive to capsaicin, heat, acidity and endogenous metabolites of polyunsaturated fatty acids. While originally reported to serve as a pain and heat detector in the peripheral nervous system, TRPV1 has been implicated in the modulation of blood flow and osmoregulation but also neurotransmission, postsynaptic neuronal excitability and synaptic plasticity within the central nervous system. In addition to its central role in nociception, evidence is accumulating that TRPV1 contributes to stimulus transduction and/or processing in other sensory modalities, including thermosensation, mechanotransduction and vision. For example, TRPV1, in conjunction with intrinsic cannabinoid signaling, might contribute to retinal ganglion cell (RGC) axonal transport and excitability, cytokine release from microglial cells and regulation of retinal vasculature. While excessive TRPV1 activity was proposed to induce RGC excitotoxicity, physiological TRPV1 activity might serve a neuroprotective function within the complex context of retinal endocannabinoid signaling. In this review we evaluate the current evidence for localization and function of TRPV1 channels within the mammalian retina and explore the potential interaction of this intriguing nociceptor with endogenous agonists and modulators