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).

PurposeTo assess the potential of histogram metrics of diffusion-tensor imaging (DTI)-derived indices in revealing neurodegeneration and its progression in spinocerebellar ataxia type 2 (SCA2).Materials and methodsNine SCA2 patients and 16 age-matched healthy controls, were examined twice (SCA2 patients 3.6±0.7 years and controls 3.3±1.0 years apart) on the same 1.5T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1000 s/mm2) images. Cerebrum and brainstem-cerebellum regions were segmented using FreeSurfer suite. Histogram analysis of DTI-derived indices, including mean diffusivity (MD), fractional anisotropy (FA), axial (AD) / radial (RD) diffusivity and mode of anisotropy (MO), was performed.ResultsAt baseline, significant differences between SCA2 patients and controls were confined to brainstem-cerebellum. Median values of MD/AD/RD and FA/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (1.11/1.30/1.03×10−3 mm2/s and 0.14/0.19) than in controls (0.80/1.00/0.70×10−3 mm2/s and 0.20/0.41). Also, peak location values of MD/AD/RD and FA were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.91/1.11/0.81×10−3 mm2/s and 0.12) than in controls (0.71/0.91/0.63×10−3 mm2/s and 0.18). Peak height values of FA and MD/AD/RD/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.20 and 0.07/0.06/0.07×10−3 mm2/s/year /0.07) than in controls (0.15 and 0.14/0.11/0.12/×10−3 mm2/s/year /0.09). The rate of change of MD median values was significantly (p<0.001) higher (i.e., increased) in SCA2 patients (0.010×10−3 mm2/s/year) than in controls (-0.003×10−3 mm2/s/year) in the brainstem-cerebellum, whereas no significant difference was found for other indices and in the cerebrum.ConclusionHistogram analysis of DTI-derived indices is a relatively straightforward approach which reveals microstructural changes associated with pontocerebellar degeneration in SCA2 and the median value of MD is capable to track its progression.

Hong Kong DHL: DTI: Air Trade Volume Index: Air Import data was reported at 50.000 NA in Aug 2018. This records a decrease from the previous number of 50.900 NA for May 2018. Hong Kong DHL: DTI: Air Trade Volume Index: Air Import data is updated quarterly, averaging 47.300 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 37.600 NA in May 2016. Hong Kong DHL: DTI: Air Trade Volume Index: Air Import 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).

Diffusion tensor imaging (DTI) studies have provided much evidence of white and subcortical gray matter changes during late childhood and early adolescence that suggest increasing myelination, axon density, and/or fiber coherence. Neurite orientation dispersion and density imaging (NODDI) can be used to further characterize development in white and subcortical grey matter regions in the brain by improving specificity of the MRI signal compared to conventional DTI. We used measures from NODDI and DTI to examine white and subcortical gray matter development in a group of 27 healthy participants aged 8–13 years. Neurite density index (NDI) was strongly correlated with age in nearly all regions, and was more strongly associated with age than fractional anisotropy (FA). No significant correlations were observed between orientation dispersion index (ODI) and age. This suggests that white matter and subcortical gray matter changes during late childhood and adolescence are dominated by changes in neurite density (i.e., axon density and myelination), rather than increasing coherence of axons. Within brain regions, FA was correlated with both ODI and NDI while mean diffusivity was only related to neurite density, providing further information about the structural variation across individuals. Data-driven clustering of the NODDI parameters showed that microstructural profiles varied along layers of white matter, but that that much of the white and subcortical gray matter matured in a similar manner. Clustering highlighted isolated brain regions with decreasing NDI values that were not apparent in region-of-interest analysis. Overall, these results help to more specifically understand patterns of white and gray matter development during late childhood and early adolescence.

The GQI indices (QA and ISO) showed more clear trends compared with the DTI indices (FA and MD) in the all four compartments.

FA = fractional anisotropy, MD = mean diffusivity, RD = radial diffusivity, WT = wild type.[Lat ventricle = lateral ventricle; Hippo CA3 = hippocampus CA3, Hippo CA1 = hippocampus CA1, Hippo dent gyr = hippocampus dentate gyrus, Hippo gen = hippocampus general, Olfact system = olfactory system, Perirh cortex = perirhinal cortex, Entorhi cortex = entorhinal cortex, Caud putamen = caudate putamen, B gang general = basal ganglia general, Ant commissure = anterior commissure, Lat olfact tract = lateral olfactory tract, Cereb aqueduct = cerebral aqueduct, Sup+Inf colli = superior and inferior collicus, Periaqued gray = periaqueductal gray matter, Cereb general = cerebellum general, Somatosen cortex = somatosensory cortex, Cereb peduncle = cerebral peduncle].Average ± SD values from DTI indices (FA, MD, and RD) and normalized volume from gray and white matter regions of the brain between wild type (n = 8) and NL-3 (n = 5) mice at 70 days of age.

ObjectiveTo identify independent risk factors for Parkinson disease mild cognitive impairment (PD-MCI) and develop a prediction model integrating clinical indicators, blood biomarker, and neuroimaging data, aiding in detection and intervention.MethodsA retrospective study was conducted with 150 PD patients. The PD-MCI group (n = 64) and PD with normal cognition (PD-NC, n = 86) were identified using the Montreal Cognitive Assessment scale. Data on demographics, motor symptoms, cognitive function, quality of life, blood markers, and diffusion tensor imaging along perivascular spaces (DTI-ALPS) were collected. Univariate analysis identified significant variables, and multivariate logistic regression identified independent risk factors. A nomogram prediction model was developed using R software. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, bootstrap resampling calibration curves, and decision curve analysis (DCA).ResultsSignificant differences between the groups were found in levodopa equivalent daily dose (LEDD), PD Quality of Life Questionnaire, creatinine, cystatin C, and ALPS index. Multivariate regression identified higher LEDD (OR = 1.01, 95%CI 1.00–1.03, p = 0.005) and creatinine levels (OR = 1.34, 95%CI 1.10–1.66, p = 0.005) as independent risk factors. The nomogram model demonstrated strong discriminatory ability (AUC = 0.864, 95%CI 0.807–0.922) and good calibration. DCA showed a significant net benefit within clinical threshold ranges.ConclusionThis study developed a PD-MCI prediction model incorporating DTI-ALPS and clinical blood biomarkers. It confirmed that LEDD and creatinine levels are independent risk factors, with high clinical value for early screening and individualized treatment.

The percentage coefficient of variation (%COV) of the DTI indices obtained with the original protocol, with diffusion-weighting applied in 60 directions, is reported separately for grey matter (GM), white matter (WM) and whole cord.

Correlation Coefficients between DTI indices and CASI scores.

DTI indices obtained within the lumbosacral enlargement (LSE); mean (SD) values in grey matter (GM), white matter (WM) and the whole cord in 14 healthy volunteers.

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).

Results of the linear mixed models examining variation in means of the DTI indices within each tissue-type between different diffusion encoding directions; significant and non-significant values are shown along with the percentage size of the largest difference for each measurement.

Cleaned BindingDB Dataset for DTI Modeling

This dataset contains a curated and cleaned version of BindingDB, focused on drug-target interaction (DTI) prediction tasks. The dataset includes compound SMILES strings, protein sequences, and their corresponding binding affinities (pKd values).

📁 Contents

• compound_iso_smiles.csv: Contains the SMILES representation of small molecules.
• target_sequence.csv: Contains protein sequences in FASTA format.
• labels.csv: Contains the pKd binding affinity values between compound–target pairs.
• Optional: .pt files for PyTorch-ready input.

📊 Dataset Stats

• Total drug-target pairs: ~X (fill in actual number)
• Unique compounds: ~Y
• Unique proteins: ~Z
• Label: pKd (negative log of dissociation constant Kd)

🧠 Use Cases

• Deep learning DTI regression models
• Graph neural networks (e.g., GIN, GAT)
• Sequence-based protein encoding (e.g., ProtBERT)
• Virtual screening and drug repurposing

⚙️ Recommended Models

• GIN + CNN / ProtBERT hybrid
• GraphDTA variants
• DeepAffinity, DeepDTA, or your custom transformer/CNN architectures

📌 Notes

• Duplicates, incomplete entries, and compounds with missing SMILES or targets without valid sequences have been removed.
• Binding affinity values are log-transformed to pKd scale.
• Suitable for both regression and binary classification (if you define thresholds).

🔗 Source

• BindingDB

📥 Citation

If you use this dataset, consider citing the original BindingDB paper:

Liu, T. et al. BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Research, 2007.

🛠️ Maintainer

Raj Aryan
Email: rajaryan2315@gmail.com
LinkedIn: www.linkedin.com/in/h4ck3r0

Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI). Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal imaging features in a partial least-square regression framework (percent error 15.0%). Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.

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).

ObjectiveThis study aimed to evaluate the glymphatic system in childhood absence epilepsy (CAE) using diffusion tensor image analysis along the paravascular space (DTI-ALPS) index. Methods: Forty-two CAE patients and 50 age- and gender-matched healthy controls (HC) were included in this study. All participants underwent scanning using a Siemens 3.0 T magnetic resonance scanner, and the DTI-ALPS index was calculated. The study compared the differences of DTI-ALPS index between CAE patients and the healthy controls. Additionally, this study also assessed the relationship between the DTI-ALPS index and clinical characteristics such as age, seizure frequency, and duration of epilepsy.ResultsThe DTI-ALPS index was lower in CAE patients compared to the healthy controls (1.45 ± 0.36 vs. 1.66 ± 0.30, p < 0.01). The DTI-ALPS index showed a negative correlation with the duration of epilepsy (r = −0.48, p < 0.01) and a positive correlation with age (r = 0.766, p < 0.01) in CAE patients. However, no significant correlation was observed between the DTI-ALPS index and seizure frequency.ConclusionThe results of this study indicate that children with CAE exhibit dysfunction in the glymphatic system of the brain, which might contribute to understanding the pathophysiological mechanism of CAE. The DTI-ALPS, as a non-invasive diagnostic marker, can be used to assess the function of the glymphatic system in CAE patients, providing promising applications in the diagnosis and research of CAE.

Correlations between NODDI and DTI parameters, controlling for age and sex.

Sex differences in changes in DTI metrics between baseline and 40 minutes after induction of anesthesia.

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address hamu@aurora.dti.ne.jp..

Radiation encephalopathy (RE) refers to radiation-induced brain necrosis and is a life-threatening complication in patients with nasopharyngeal carcinoma (NPC) after radiotherapy (RT), and radiation-induced pre-symptomatic glymphatic alterations have not yet been investigated. We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) index to examine the pre-symptomatic glymphatic alterations in NPC patients following RT. A total of 109 patients with NPC consisted of Pre-RT (n = 35) and Post-RT (n = 74) cohorts were included. The post-RT NPC patients, with normal-appearing brain structure at the time of MRI, were further divided into Post-RT-RE- (n = 58) and Post-RT-RE+ (n = 16) subgroups based on the detection of RE in follow-up. We observed lower DTI-ALPS left index, DTI-ALPS right index and DTI-ALPS whole brain index in post-RT patients than that in pre-RT patients (p