The City of Austin’s “DTI 2060 Population and Employment Forecast” is a long-range, small-area population and employment forecast produced by the Demographics and Data Division in the Planning Department in conjunction with representatives from multiple City departments making up the DTI Work Group. DTI stands for Delphi, Trends, and Imagine Austin, and the "DTI 2060 Population and Employment Forecast” is an update to the "Population Projections 2040". The DTI work group produced population and employment forecasts within each polygon in the study area for the year 2025 and the decades from 2030 to 2060, using the year 2020 as the baseline and half of 2010’s migration trends. Potential population and employment growth were forecast within Imagine Austin activity centers and along mixed-use corridors using City staff knowledge of the trends within current development patterns and practices. The DTI 2060 forecast incorporates urban-centric future growth and development and accounts for widely-dispersed, low-density suburban development.

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug–target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug–target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

BackgroundDiffusion tensor imaging (DTI) has proven valuable in assessing structural and architectural features of white matter (WM) in postnatal development. Diffusion tensor-based morphometry (DTBM) uses DTI data to measure local volume changes and has been demonstrated in previous studies to be informative in the evaluation of specific WM pathways in several neurological disorders. In this study, we assess DTBM volume changes during postnatal brain development in typically developing children. In addition, we evaluate in each pathway the relationship between changes in volume and DTI metrics.MethodWe included DTI data from 182 healthy participants in the age range of 0–21 years, from the publicly available database: the NIH Pediatric MRI Data (NIHPD). Data were processed using the TORTOISE pipeline and age-specific templates were created using the diffusion tensor-based registration tool DRTAMAS. Region of interests (ROIs) were defined on a study-specific, young-adult reference template (18–21 years). Individual brains were registered to the reference template using a two-step process involving age-specific templates. ROI values for volume and DTI metrics were normalized to the median values of the 18-21-year group. Developmental trajectories were analyzed in two age segments; Segment 1: data between 0 and 2.69 years and Segment 2: for the remaining age range.ResultsThe results show that volumetric developmental trajectories varied largely among WM regions. The estimated volume at birth ranged: 12–53% of the adult value; where the rate of growth ranged: 3–30% of the adult value per year, in Segment 1; and 0–4% afterwards (Segment 2). The Corticospinal Tract, for example, showed protracted growth into young adulthood, while the Corpus Callosum growth was almost completed in the first 3 years. The magnitude of changes in local volume were generally larger than the magnitude of changes in diffusion metrics. Moreover, volumetric changes were more protracted, i.e., for many regions volume continued to increase even when diffusion metrics had reached a plateau.ConclusionIn conclusion, DTBM has shown developmental trajectories for WM volume in the human brain that are pathway specific and distinct from those obtained for DTI metrics. In future studies, DTBM should be performed in larger cohorts to assess correlation with cognitive and behavioral changes as well as establish ranges for normative values.

Hong Kong DHL: DTI: Markets: Americas data was reported at 50.000 NA in Aug 2018. This records a decrease from the previous number of 51.000 NA for May 2018. Hong Kong DHL: DTI: Markets: Americas data is updated quarterly, averaging 47.500 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 52.000 NA in Feb 2015 and a record low of 33.000 NA in May 2016. Hong Kong DHL: DTI: Markets: Americas data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Hong Kong DHL: DTI: Markets: Asia Pacific data was reported at 47.000 NA in Aug 2018. This records a decrease from the previous number of 49.000 NA for May 2018. Hong Kong DHL: DTI: Markets: Asia Pacific data is updated quarterly, averaging 46.000 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 53.000 NA in Feb 2015 and a record low of 35.000 NA in Aug 2016. Hong Kong DHL: DTI: Markets: Asia Pacific data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Each participants first session contains - a 3D MP2RAGE T1w data set of the whole brain. - a DTI dataset of a partial volume of the brain focused on the basal ganglia and basal forebrain.

DTI data in session 1 is read out with phase encoding direction anterior to posterior.

As far as provided, session 2 contains DTI data of the same brain volume with phase incoding direction posterior to anterior.

Sequence protocol for DTI data: b = 1000 s/mm�, TR = 3000 ms, TE = 67 ms, 25 axial slices, voxel size 1.4 x 1.4 x 1.4 mm�, 64 directions spanning the whole sphere, 2 averages, intermingled volumes with a b-value of 0 sec/mm�.

DTI data acquisition parameters for the different MRI scanners utilized in the current study.

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The EDSD data includes 474 Diffusion Tensor Imaging (DTI) and 474 structural MRI scans (MPRAGE) from patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Elderly Subjects. The EDSD is a cross-sectional multicenter study.

Source Data and dataset for the Digital Brain Project: Source data for Figure 2 (Source_Data_Figure_2.zip), Extended Data Figure 1 (Source_Data_Extended_Data_Figure_1.zip), Extended Data Figure 2 (Source_Data_Extended_Data_Figure_2.zip)), and Extended Data Table1, and the dataset for the construction of Digital Brain (Source_Data_Extended_Table_1_and_Dataset_for_DB.zip). Please note that this dataset for Digital Brain exactly includes the source data for Extended Data Table 1.

Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a “GO and KEGG enrichment score” method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection “minimum redundancy maximum relevance (mRMR)” method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions.

Hong Kong DHL: DTI: Markets: Europe data was reported at 48.000 NA in Aug 2018. This records a decrease from the previous number of 49.000 NA for May 2018. Hong Kong DHL: DTI: Markets: Europe data is updated quarterly, averaging 45.500 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 52.000 NA in Feb 2018 and a record low of 32.000 NA in Feb 2016. Hong Kong DHL: DTI: Markets: Europe data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Hong Kong DHL: DTI: Markets: Rest of the World data was reported at 39.000 NA in Aug 2018. This records a decrease from the previous number of 55.000 NA for May 2018. Hong Kong DHL: DTI: Markets: Rest of the World data is updated quarterly, averaging 47.000 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 55.000 NA in May 2018 and a record low of 32.000 NA in Feb 2016. Hong Kong DHL: DTI: Markets: Rest of the World data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Hong Kong DHL: DTI: Attributes: Sales Volume data was reported at 49.000 NA in Aug 2018. This records a decrease from the previous number of 51.000 NA for May 2018. Hong Kong DHL: DTI: Attributes: Sales Volume data is updated quarterly, averaging 48.000 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 55.000 NA in Nov 2014 and a record low of 37.000 NA in May 2016. Hong Kong DHL: DTI: Attributes: Sales Volume data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Hong Kong DHL: DTI: Attributes: Shipment Urgency data was reported at 49.000 NA in Aug 2018. This stayed constant from the previous number of 49.000 NA for May 2018. Hong Kong DHL: DTI: Attributes: Shipment Urgency data is updated quarterly, averaging 47.000 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 52.000 NA in Nov 2014 and a record low of 39.000 NA in Aug 2016. Hong Kong DHL: DTI: Attributes: Shipment Urgency data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Hong Kong DHL: DTI: Air-Freighted Commodities: Electronic Products & Parts data was reported at 51.000 NA in Aug 2018. This records a decrease from the previous number of 52.000 NA for May 2018. Hong Kong DHL: DTI: Air-Freighted Commodities: Electronic Products & Parts data is updated quarterly, averaging 49.000 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 56.000 NA in Feb 2018 and a record low of 37.000 NA in Aug 2016. Hong Kong DHL: DTI: Air-Freighted Commodities: Electronic Products & Parts data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Hong Kong DHL: DTI: Air-Freighted Commodities: Food & Beverage data was reported at 54.000 NA in Aug 2018. This records a decrease from the previous number of 62.000 NA for May 2018. Hong Kong DHL: DTI: Air-Freighted Commodities: Food & Beverage data is updated quarterly, averaging 53.500 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 62.000 NA in May 2018 and a record low of 33.000 NA in Aug 2016. Hong Kong DHL: DTI: Air-Freighted Commodities: Food & Beverage data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Hong Kong DHL: DTI: Air Trade Volume Index: Air Re-Export data was reported at 46.500 NA in Aug 2018. This records a decrease from the previous number of 48.800 NA for May 2018. Hong Kong DHL: DTI: Air Trade Volume Index: Air Re-Export data is updated quarterly, averaging 45.950 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 50.500 NA in Feb 2018 and a record low of 33.600 NA in Feb 2016. Hong Kong DHL: DTI: Air Trade Volume Index: Air Re-Export data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

Imports: MM: Electrical and Electronic Equipment data was reported at 9,268,946,721.000 ZAR in May 2018. This records an increase from the previous number of 8,193,044,081.000 ZAR for Apr 2018. Imports: MM: Electrical and Electronic Equipment data is updated monthly, averaging 7,794,143,354.000 ZAR from Jan 2009 (Median) to May 2018, with 113 observations. The data reached an all-time high of 14,341,001,377.000 ZAR in Oct 2015 and a record low of 3,959,828,064.000 ZAR in Dec 2009. Imports: MM: Electrical and Electronic Equipment data remains active status in CEIC and is reported by Department of Trade and Industry. The data is categorized under Global Database’s South Africa – Table ZA.JA015: Imports: incl BLNS: DTI: Harmonized System: by Commodity.