Analysis of Ocean in Situ Observations and Web-Based Visualization

The sparsity of observations poses a challenge common to various ocean disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks undersample a broad spectrum of scales. This situation is generally more severe for chemical and biological parameters because such sensors are less widely deployed. The present chapter describes the analysis tool DIVA (Data-Interpolating Variational Analysis) which is designed to generate gridded fields from in situ observations. DIVA has been applied to various physical (temperature and salinity), chemical (concentration of nitrate, nitrite and phosphate) and biological parameters (abundance of a species). The chapter also shows the technologies used to visualize the gridded fields. Visualization of analyses from in situ observations provide a unique set of challenges since the accuracy of the analysed field is not spatially uniform as it strongly depends on the location of the observations. In addition, an adequate treatment of the depth and time dimensions is essential.

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A web interface for griding arbitrarily distributed in situ data based on Data-Interpolating Variational Analysis (DIVA)

Advances in Geosciences ◽

10.5194/adgeo-28-29-2010 ◽

2010 ◽

Vol 28 ◽

pp. 29-37 ◽

Cited By ~ 22

Author(s):

A. Barth ◽

A. Alvera-Azcárate ◽

C. Troupin ◽

M. Ouberdous ◽

J.-M. Beckers

Keyword(s):

Variational Analysis ◽

Spatial Interpolation ◽

Google Earth ◽

Analysis Tool ◽

Web Interface ◽

Web Map ◽

In Situ Data ◽

Common Task ◽

Ascii Format

Abstract. Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). It is often used to create climatological maps for physical, biological or chemical parameters representing e.g. monthly or seasonally averaged fields. Since instantaneous observations can not be directly related to a field representing an average, simple spatial interpolation of observations is in general not acceptable. DIVA (Data-Interpolating Variational Analysis) is an analysis tool which takes the error in the observations and the typical spatial scale of the underlying field into account. Barriers due to the coastline and the topography in general and also currents estimates (if available) are used to propagate the information of a given observation spatially. DIVA is a command-line driven application written in Fortran and Shell Scripts. To make DIVA easier to use, a web interface has been developed (http://gher-diva.phys.ulg.ac.be). Installation and compilation of DIVA is therefore not required. The user can directly upload the data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are presented as different layers using the Web Map Service protocol. They are visualized in the browser using the Javascript library OpenLayers allowing the user to interact with layers (for example zooming and panning). Finally, the results can be downloaded as a NetCDF file, Matlab/Octave file and Keyhole Markup Language (KML) file for visualization in applications such as Google Earth.

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In situ observations of electromigration induced void behavior

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100137598 ◽

1995 ◽

Vol 53 ◽

pp. 244-245

Author(s):

T. Marieb ◽

J. C. Bravman ◽

P. Flinn ◽

D. Gardner ◽

M. Madden

Keyword(s):

Electron Microscopy ◽

Void Nucleation ◽

Plan View ◽

Transmission Electron ◽

Nucleation Sites ◽

Microelectronics Industry ◽

In Situ Observations ◽

Backscatter Electron Detector ◽

Voltage Scanning

Electromigration and stress voiding have been active areas of research in the microelectronics industry for many years. While accelerated testing of these phenomena has been performed for the last 25 years[1-2], only recently has the introduction of high voltage scanning electron microscopy (HVSEM) made possible in situ testing of realistic, passivated, full thickness samples at high resolution.With a combination of in situ HVSEM and post-testing transmission electron microscopy (TEM) , electromigration void nucleation sites in both normal polycrystalline and near-bamboo pure Al were investigated. The effect of the microstructure of the lines on the void motion was also studied.The HVSEM used was a slightly modified JEOL 1200 EX II scanning TEM with a backscatter electron detector placed above the sample[3]. To observe electromigration in situ the sample was heated and the line had current supplied to it to accelerate the voiding process. After testing lines were prepared for TEM by employing the plan-view wedge technique [6].

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In situ observations of coral spawning and spawn slick at Lankayan Island, Sabah, Malaysia

Marine Biodiversity ◽

10.1007/s12526-020-01158-5 ◽

2021 ◽

Vol 51 (1) ◽

Author(s):

Sze Hoon Gan ◽

Zarinah Waheed ◽

Fung Chen Chung ◽

Davies Austin Spiji ◽

Leony Sikim ◽

...

Keyword(s):

Coral Spawning ◽

In Situ Observations

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Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

Remote Sensing ◽

10.3390/rs13071250 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1250

Author(s):

Yanxing Hu ◽

Tao Che ◽

Liyun Dai ◽

Lin Xiao

Keyword(s):

Machine Learning ◽

Northern Hemisphere ◽

Snow Depth ◽

Learning Algorithm ◽

Random Forest Regression ◽

Machine Learning Methods ◽

Long Time ◽

In Situ Observations ◽

Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

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Fluorescent in situ hybridization (FISH): A useful diagnostic tool for childhood conjunctival melanoma

European Journal of Ophthalmology ◽

10.1177/11206721211030775 ◽

2021 ◽

pp. 112067212110307

Author(s):

Raquel María Moral ◽

Carlos Monteagudo ◽

Javier Muriel ◽

Lucía Moreno ◽

Ana María Peiró

Keyword(s):

In Situ Hybridization ◽

Fluorescent In Situ Hybridization ◽

Pediatric Population ◽

Diagnostic Technique ◽

Histopathological Diagnosis ◽

Fish Analysis ◽

Conjunctival Melanoma ◽

Adequate Treatment ◽

First Case

Introduction: Conjunctival melanoma is extremely rare in children and has low rates of resolution. Definitive histopathological diagnosis based exclusively on microscopic findings is sometimes difficult. Thus, early diagnosis and adequate treatment are essential to improve clinical outcomes. Clinical case: We present the first case in which the fluorescent in situ hybridization (FISH) diagnostic technique was applied to a 10-year-old boy initially suspected of having amelanotic nevi in his right eye. Based on the 65% of tumor cells with 11q13 (CCND1) copy number gain and 33% with 6p25 (RREB1) gain as measured by the FISH analysis, and on supporting histopathological findings, the diagnosis of conjunctival melanoma could be made. Following a larger re-excision, adjuvant therapy with Mitomycin C (MMC), cryotherapy and an amniotic membrane graft, the patient has remained disease-free during 9 years of long-term follow-up. Case discussion: Every ophthalmologist should remember to consider and not forget the possibility of using FISH analyses during the differential diagnosis of any suspicious conjunctival lesions. Genetic techniques, such as FISH, have led to great advances in the classification of ambiguous lesions. Evidence-based guidelines for diagnosing conjunctival melanoma in the pediatric population are needed to determine the most appropriate strategy for this age group.

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Patient’s point of view on the use of telemedicine in multiple sclerosis: a web-based survey

Neurological Sciences ◽

10.1007/s10072-021-05398-6 ◽

2021 ◽

Author(s):

Doriana Landi ◽

Marta Ponzano ◽

Carolina Gabri Nicoletti ◽

Gaia Cola ◽

Gianluca Cecchi ◽

...

Keyword(s):

Multiple Sclerosis ◽

Point Of View ◽

Physical Parameters ◽

Chronic Medical Conditions ◽

Socioeconomic Conditions ◽

Web Based ◽

Healthcare Facilities ◽

Advantages And Disadvantages ◽

Disease Characteristics ◽

Degree Of Disability

AbstractRestrictions in the access to healthcare facilities during COVID-19 pandemic have raised the need for remote monitoring of chronic medical conditions, including multiple sclerosis (MS). In order to enable the continuity of care in these circumstances, many telemedicine applications are currently tested. While physicians’ preferences are commonly investigated, data regarding the patients’ point of view are still lacking. We built a 37 items web-based survey exploring patients’ propensity, awareness, and opinions on telemedicine with the aim to evaluate the sustainability of this approach in MS. Analysing 613 questionnaires out of 1093 that were sent to persons with MS followed at the Multiple Sclerosis Center of Tor Vergata University, Rome, we found that more than half of respondents (54%) were open to having a televisit. Propensity toward telemedicine significantly depended on having a higher income (p = 0.037), living farther from the center (p = 0.038), using computer and tablet (p = 0.010) and using the Internet for other remote activities (p < 0.001), conversely it was not influenced by any specific disease characteristics (i.e. degree of disability). The main advantages and disadvantages of televisit reported by participants were respectively saving time (70%) and impossibility to measure physical parameters (71%). Although the majority of respondents are in favour of televisit, so far this approach is restricted to those displaying better socioeconomic conditions and higher familiarity with technology. Implications of the study are that telemedicine platforms should be better tailored to patients’ demands in order to spread the use of telemedicine, to enhance usability and to increase patients’ adherence.

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In situ observations of spiral growth on ice crystal surfaces

Physical Review Materials ◽

10.1103/physrevmaterials.2.093402 ◽

2018 ◽

Vol 2 (9) ◽

Cited By ~ 1

Author(s):

Ken-ichiro Murata ◽

Ken Nagashima ◽

Gen Sazaki

Keyword(s):

Spiral Growth ◽

Ice Crystal ◽

Crystal Surfaces ◽

In Situ Observations

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Pelagic deep-sea fauna observed on video transects in the southern Norwegian Sea

Polar Biology ◽

10.1007/s00300-021-02840-5 ◽

2021 ◽

Author(s):

Philipp Neitzel ◽

Aino Hosia ◽

Uwe Piatkowski ◽

Henk-Jan Hoving

Keyword(s):

First Record ◽

Observation System ◽

In Situ Observation ◽

Norwegian Sea ◽

Video Recordings ◽

In Situ Observations ◽

Abundance And Distribution ◽

Oceanic Food Webs ◽

Deep Sea Fauna

AbstractObservations of the diversity, distribution and abundance of pelagic fauna are absent for many ocean regions in the Atlantic, but baseline data are required to detect changes in communities as a result of climate change. Gelatinous fauna are increasingly recognized as vital players in oceanic food webs, but sampling these delicate organisms in nets is challenging. Underwater (in situ) observations have provided unprecedented insights into mesopelagic communities in particular for abundance and distribution of gelatinous fauna. In September 2018, we performed horizontal video transects (50–1200 m) using the pelagic in situ observation system during a research cruise in the southern Norwegian Sea. Annotation of the video recordings resulted in 12 abundant and 7 rare taxa. Chaetognaths, the trachymedusaAglantha digitaleand appendicularians were the three most abundant taxa. The high numbers of fishes and crustaceans in the upper 100 m was likely the result of vertical migration. Gelatinous zooplankton included ctenophores (lobate ctenophores,Beroespp.,Euplokamissp., and an undescribed cydippid) as well as calycophoran and physonect siphonophores. We discuss the distributions of these fauna, some of which represent the first record for the Norwegian Sea.

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