The 2017 Northern Ireland Multiple Deprivation Measures (NIMDM 2017) were published on 23rd November 2017. It provides information for 7 distinct types of deprivation, known as domains, along with an overall multiple deprivation measure (MDM). The NIMDM 2017 comprises of 38 indicators in total.

The NIMDM 2017 provide a mechanism for ranking areas within Northern Ireland in the order of the most deprived to the least deprived. However, they do not quantify the extent to which one area is more or less deprived than another.

The Northern Ireland Multiple Deprivation Measures 2017 report is available at https://www.nisra.gov.uk/publications/nimdm17-results

Dataset Name: Multiple Deprivation Measure Rank 2017Data Owner: NISRAContact: deprivation@nisra.gov.ukSource URL: https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017Uploaded to SPACE Hub: 31/07/2023Update Frequency: As released by NISRAScale Threshold: N/AProjection : Irish GridFormat: Esri Feature Layer (Hosted) Vector PolygonNotes: The updated deprivation measures were released on 23rd November 2017 replacing the NIMDM 2010 as the official measure of deprivation in Northern Ireland and were awarded a prestigious Campion Award for Excellence in Official Statistics by the Royal Statistical Society and the UK Statistics Authority in 2018. The measures, known as NIMDM 2017, were informed through public consultation and Steering Group agreement, and provide a mechanism for ranking the 890 Super Output areas (SOAs) in Northern Ireland from the most deprived (rank 1) to the least deprived (rank 890).They include ranks of the areas for each of 7 distinct types (or domains) of deprivation, which have been combined to produce an overall multiple deprivation measure (MDM) rank of the areas. The MDM ranks of the areas should be considered in conjunction with those for each of the 7 domains in order to gain a comprehensive picture of deprivation.The newly developed online NIMDM 2017 Analysis Package and interactive maps allow users to readily access the data and easily find the areas in which they are interested. Training on these packages is available on the NISRA Youtube Channel(external link opens in a new window / tab).

Las medidas de privación múltiple de Irlanda del Norte de 2017 (NIMDM 2017) se publicaron el 23 de noviembre de 2017. Proporciona información sobre 7 tipos distintos de privación, conocidos como dominios, junto con una medida general de privación múltiple (MDM). El NIMDM 2017 consta de 38 indicadores en total.

El NIMDM 2017 proporciona un mecanismo para clasificar las áreas dentro de Irlanda del Norte en el orden de las más desfavorecidas a las menos desfavorecidas. Sin embargo, no cuantifican en qué medida una zona está más o menos desfavorecida que otra.

El informe de 2017 sobre las medidas de privación múltiple en Irlanda del Norte está disponible en https://www.nisra.gov.uk/publications/nimdm17-results

Northern Ireland Multiple Deprivation Measures (NIMDM 2017) julkaistiin 23. marraskuuta 2017. Se tarjoaa tietoa seitsemästä erityyppisestä puutteesta, joita kutsutaan verkkotunnuksiksi, sekä yleisestä moninkertaisen puutteen toimenpiteestä (MDM). NIMDM 2017 sisältää yhteensä 38 indikaattoria.

NIMDM 2017 tarjoaa mekanismin, jonka avulla Pohjois-Irlannissa sijaitsevat alueet asetetaan vähävaraisimpien ja vähävaraisimpien väliseen järjestykseen. Niissä ei kuitenkaan kvantifioida sitä, missä määrin yksi alue on enemmän tai vähemmän heikossa asemassa kuin toinen.

Northern Ireland Multiple Deprivation Measures 2017 -raportti on saatavilla osoitteessa https://www.nisra.gov.uk/publications/nimdm17-results

ObjectiveTo evaluate the performance of MDM-score system in screening for mitochondrial diabetes mellitus (MDM) with m.3243A>G mutation in newly diagnosed diabetes.MethodsFrom 2015 to 2017, we recruited 5130 newly diagnosed diabetes patients distributed in 46 hospitals in China. Their DNA samples were subjected to targeted sequencing of 37 genes, including the mitochondrial m.3243A>G mutation. Based on this cohort, we analyzed the clinical characteristics of MDM and type 2 diabetes (T2DM), and evaluated the overall efficacy of the MDM-score through ROC curve analysis.ResultsMDM patients were diagnosed at a younger age (P =0.002) than T2DM patients. They also had a higher proportion of females, lower body mass index, lower height, lower weight, lower systolic blood pressure, and lower fasting C-peptide (P < 0.05). Among 48 MDM patients, the m.3243A>G heteroplasmy level was higher in MDM score ≥ 3 than in MDM score < 3 (P = 0.0281). There were 23 cases with MDM-score ≥ 3 in clinical T2DM, with an AUC of 0.612 (95% CI: 0.540-0.683, P

Russia Commercial Banks Income: Year to Date: MDM Bank data was reported at 493,084,322.000 RUB th in Sep 2018. This records an increase from the previous number of 376,395,764.000 RUB th for Jun 2018. Russia Commercial Banks Income: Year to Date: MDM Bank data is updated quarterly, averaging 117,679,403.500 RUB th from Jun 2003 (Median) to Sep 2018, with 62 observations. The data reached an all-time high of 698,666,252.000 RUB th in Sep 2017 and a record low of 543,101.000 RUB th in Mar 2004. Russia Commercial Banks Income: Year to Date: MDM Bank data remains active status in CEIC and is reported by The Central Bank of the Russian Federation. The data is categorized under Russia Premium Database’s Monetary and Banking Statistics – Table RU.KAJ002: Commercial Banks: Income: ytd.

Water level data were obtained from logging pressure transducers (Solinst Levelogger Edge 3001, Solinst Canada, Georgetown, Ontario, Canada) corrected for barometeric fluctuations using a barometer (Solinst Barologger Gold 3001). Each transducer was suspended on a steel cable inside a 2" PVC pipe 1.5 m in length installed to 1.4 m depth and screened at 1.3--1.45 m below the top of the piezometer casing. Piezometers were developed prior to measurement by repeatedly plunging and removing a solid rod into the piezometer tube. The details of how the surface datum was obtained are described in Cobb et al (2017, doi:10.1073/pnas.1701090114).

Russia Commercial Banks Net Profit: Year to Date: MDM Bank data was reported at 54,053,290.000 RUB th in Sep 2018. This records a decrease from the previous number of 59,706,137.000 RUB th for Jun 2018. Russia Commercial Banks Net Profit: Year to Date: MDM Bank data is updated quarterly, averaging 622,677.000 RUB th from Jun 2003 (Median) to Sep 2018, with 62 observations. The data reached an all-time high of 62,092,285.000 RUB th in Mar 2018 and a record low of -31,777,810.000 RUB th in Sep 2017. Russia Commercial Banks Net Profit: Year to Date: MDM Bank data remains active status in CEIC and is reported by The Central Bank of the Russian Federation. The data is categorized under Russia Premium Database’s Monetary and Banking Statistics – Table RU.KAJ001: Commercial Banks: Net Profit: ytd.

Temporal networks model how the interaction between elements in a complex system evolves over time. Just as complex systems display collective dynamics, here we interpret temporal networks as trajectories performing a collective motion in graph space, following a latent graph dynamical system. Under this paradigm, we propose a way to measure how the network pulsates and collectively fluctuates over time and space. To this aim, we extend the notion of linear correlation functions to the case of sequences of network snapshots, i.e., a network trajectory. We construct stochastic and deterministic graph dynamical systems and show that the emergent collective correlations are well captured by simple measures, and we illustrate how these patterns are revealed in empirical networks arising in different domains. .L. and V.M.E. acknowledge funding from projects DYNDEEP (EUR2021-122007), MISLAND (PID2020-114324GB-C22), and Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) all funded by MCIN/AEI/10.13039/501100011033. J.P.R. is supported by the Juan de la Cierva Formación program (Reference No. FJC2019-040622-I) funded by the Spanish Ministry of Science and Innovation.

Chi square linear trend test of DM Vs ROP severity.

Water level data were obtained from logging pressure transducers (Solinst Levelogger Edge 3001, Solinst Canada, Georgetown, Ontario, Canada) corrected for barometeric fluctuations using a barometer (Solinst Barologger Gold 3001). Each transducer was suspended on a steel cable inside a 2" PVC pipe 1.5 m in length installed to 1.4 m depth and screened at 1.3--1.45 m below the top of the piezometer casing. Piezometers were developed prior to measurement by repeatedly plunging and removing a solid rod into the piezometer tube. The details of how the surface datum was obtained are described in Cobb et al (2017, doi:10.1073/pnas.1701090114).

[Methods for processing the data] Once data (available at https://doi.org/XXX/DigitalCSIC/XXX) was validated, several processing steps were performed to ensure an optimal training process for the neural network models. First, all the data of the time series were re-sampled by averaging the data points obtaining a daily frequency. Afterwards, a standard feature-scaling procedure (min-max normalization) was applied to every feature (temperature, salinity and oxygen) and to pHT. Finally, we built our training and validations sets as tensors with dimensions (batchsize, windowsize, 𝑁features), where batchsize is the number of examples to train per iteration, windowsize is the number of past and future points considered and 𝑁features is the number of features used to predict the target series. Temperature values below 𝑇=12.5T=12.5 °C were discarded as they are considered outliers in sensor data outside the normal range in the study area. A BiDireccional Long Short-Term Memory (BD-LSTM) neural network was selected as the best architecture to reconstruct the pHT time series, with no signs of overfitting and achieving less than 1% error in both training and validation sets. Data corresponding to the Bay of Palma were used in the selection of the best neural network architecture. The code and data used to determine the best neural network architecture can be found in a GitHub repository mentioned in the context information. [Description of methods used for collection/generation of data] Data was acquired in both stations using a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63. Funding for this work was provided by the projects RTI2018-095441-B-C21, RTI2018-095441-B-C22 (SuMaEco) and Grant MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 and by the “ERDF A way of making Europe", the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS (https://pti-waterios.csic.es/). Peer reviewed

Clinical differences between ROP group and non-ROP group.

Panda Hill Tanzania Ltd (PHT), jointly owned by Tremont Investments Ltd (Tremont) and Cradle Resources Ltd (Cradle) is planning to construct a Panda Hill Niobium Project in Tanzania.The project involves the construction of a niobium processing facility with a processing capacity of 1 million tonnes per annum (MTPA).The project includes the construction of a production unit, a processing unit, a warehouse, an administrative space, a storage space and parking facility, and the installation of related machinery and equipment, safety and security systems.In March 2015, the pre-feasibility study work completed and definitive feasibility study (DFS) work completed in April 2016.In May 2016, MDM Engineering was awarded as the front-end engineering design (FEED) contract for the project.Hatch was selected as the preferred engineering, procurement, construction management (EPCM) contractor.In May 2017, Hatch conducted site investigations for the implementation of the project.In September 2017, MDM Engineering completed FEED and was reviewed by Hatch.In January 2018, PHT raised the US$2.9 million by issuing the shares to institutional and sophisticated investors.PHT is the process of securing funds to commence construction and simultaneously is in negotiations with the Tanzanian Government for the new mining legislation. Read More

Commercial Banks Income: ytd: MDM Bank在2018-09达493,084,322.000 RUB th，相较于2018-06的376,395,764.000 RUB th有所增长。Commercial Banks Income: ytd: MDM Bank数据按季度更新，2003-06至2018-09期间平均值为117,679,403.500 RUB th，共62份观测结果。该数据的历史最高值出现于2017-09，达698,666,252.000 RUB th，而历史最低值则出现于2004-03，为543,101.000 RUB th。CEIC提供的Commercial Banks Income: ytd: MDM Bank数据处于定期更新的状态，数据来源于The Central Bank of the Russian Federation，数据归类于Russia Premium Database的Monetary and Banking Statistics – Table RU.KAJ002: Commercial Banks: Income: ytd。