Meteorological data (near-surface air temperature, relative humidity and air pressure) measured by the ice unit of the unmanned ice station during the MOSAiC expedition

Air temperature and relative humidity (Vaisala HMP155A), as well as barometric pressure (Vaisala CS106), were measured at 1.5 m height above the initial snow surface

Temperature_UIS

The temperature data in °C measured by the thermistors chain of the ice unit. The depth refers the initial snow-ice interface

CT and CTD data measured by the ocean unit of the unmanned ice station during the MOSAiC expedition

The UIS ocean unit (CT package) consisted of five conductivity & temperature sensors (RBR duo CT), one conductivity, and temperature & depth (pressure) sensor (RBR concerto CTD). The ocean unit were used to measure upper ocean at the depths of about 5-40 m, with the initial depths of 5.4, 10.4, 15.4, 20.4, 25.4, and 40.4 m. The ice and ocean units of UIS were deployed 10 m apart. The changes in the depths of CT sensors were estimated based on their initial depths and the depth measured by the CTD at the bottom of CT package

Location of the unmanned ice station during the MOSAiC expedition

One unmanned ice station (UIS) has been deployed at the L3 site (85.13ºN, 135.68ºE) of the Distributed Network (DN) of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign on 10 October 2019. The measurement of the UIS ice unit lasted until 15 June 2020 when the buoy drifted to 82.28°N; while the ocean unit lasted until 28 September 2020 and finally failed at 74.09°N

The ice mass balance and CT package data of the buoy unmanned ice station collected in Arctic Ocean during the MOSAiC expedition 2019/2020

One unmanned ice station (UIS) has been deployed at the L3 site (85.13ºN, 135.68ºE) of the Distributed Network (DN) of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign on 10 October 2019. The UIS is a new prototype of IMB assembled by the Polar Research Institute of China, which consists of two separate units (ice and ocean) to measure physical parameters of the air-snow-sea ice-ocean system. For the ice unit, two acoustic sensors (Campbell SR50A and Teledyne-Benthos PSA916, respectively) are used to measure the relative changes in the position of the air/snow and ice/water interfaces. Thermistors (Maxim Integrated DS28EA00) mounted at 0.03 m spacing along a 4.5-m thermistor chain were used to measure temperature profiles. Air temperature and relative humidity (Vaisala HMP155A), as well as barometric pressure (Vaisala CS106), were measured at 1.5 m height above the initial snow surface. The UIS ocean unit (CT package) consisted of five conductivity & temperature sensors (RBR duo CT), one conductivity, and temperature & depth (pressure) sensor (RBR concerto CTD). The ocean unit were used to measure upper ocean at the depths of about 5-40 m, with the initial depths of 5.4, 10.4, 15.4, 20.4, 25.4, and 40.4 m. The ice and ocean units of UIS were deployed 10 m apart. The initial ice thickness and snow depth at the buoy deployment site were 1.53 and 0.15 m, respectively. The changes in ice thickness was determined using measurements by the underwater acoustic sounder. The measuring noise of the acoustic sounder has been removed. Since the acoustic sensor at the surface was invalid very soon after the deployment, the evolution of the air/snow interface was determined using the temperature profiles. Overall, the measurement accuracy was 0.1 K for temperature, 0.03 m for the snow or ice surface, and 0.01 m for the ice bottom, respectively. After the snow cover melted over, the negative values for the snow depth indicate the onset of ice surface melt. The changes in the depths of CT sensors were estimated based on their initial depths and the depth measured by the CTD at the bottom of CT package. The measurement of the UIS ice unit lasted until 15 June 2020 when the buoy drifted to 82.28°N; while the ocean unit lasted until 28 September 2020 and finally failed at 74.09°N

Snow depth and ice thickness

Snow depth and ice thickness estimated from the temperature profile or measured by the acoustic sounder

The Influencing Factors of Biomedical R&D Cooperation in Three Major Urban Agglomerations of China Based on Cooperative Patents

Due to the particularity of the biomedical industry, it has become necessary for biomedical enterprises to seek innovative research and development (R&D) cooperation to maintain advanced technologies and products in multiple fields. Under such circumstance, the biomedical industry has gradually formed a certain cluster to promote the development of the industry. So far, the biomedical industry cluster has formed in China, mainly within the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei three urban agglomerations. Within the industrial clusters, the frequency of innovation cooperation among enterprises, universities, research institutions, and other relevant organizations in the biomedical area is high, and the capacity for innovation cooperation is strong as well. This paper used the representative cross-section data of cooperative patents from the medical science and technology patent database of China National Knowledge Infrastructure (CNKI), researching the R&D coopertion within the three major urban agglomerations in China from 2008 to 2016 (Yangtze River Delta Urban Agglomeration, Pearl River Delta Urban Agglomeration, Beijing-Tianjin-Hebei Urban Agglomeration) on total 36 cities’ spatial pattern characteristics of biomedical cooperation and the influencing factors. The spatial interaction model was used to study the spatial, economic, political, and R&D influencing factors of cross-city cooperation. The degree of aggregation showed that cross-city R&D cooperation mainly occurred in well-developed and central cities of urban agglomerations. Econometric results revealed that spatial, economic, political, and R&D bias factors did have a significant impact on the frequency of biomedical R&D cooperation across cities

Eyes-Open/Eyes-Closed Dataset Sharing for Reproducibility Evaluation of Resting State fMRI Data Analysis Methods

The multi-scan resting state fMRI (rs-fMRI) dataset was recently released; thus the test-retest (TRT) reliability of rs-fMRI measures can be assessed. However, because this dataset was acquired only from a single group under a single condition, we cannot directly evaluate whether the rs-fMRI measures can generate reproducible between-condition or between-group results. Because the modulation of resting state activity has gained increasing attention, it is important to know whether one rs-fMRI metric can reliably detect the alteration of the resting activity. Here, we shared a public Eyes-Open (EO)/Eyes-Closed (EC) dataset for evaluating the split-half reproducibility of the rs-fMRI measures in detecting changes of the resting state activity between EO and EC. As examples, we assessed the split-half reproducibility of three widely applied rs-fMRI metrics: amplitude of low frequency fluctuation, regional homogeneity, and seed-based correlation analysis. Our results demonstrated that reproducible patterns of EO-EC differences can be detected by all three measures, suggesting the feasibility of the EO/EC dataset for performing reproducibility assessment for other rs-fMRI measures

Functional brain hubs and their test-retest reliability: A multiband resting-state functional MRI study

Resting-state functional MRI (R-fMRI) has emerged as a promising neuroimaging technique used to identify global hubs of the human brain functional connectome. However, most R-fMRI studies on functional hubs mainly utilize traditional R-fMRI data with relatively low sampling rates (e.g., repetition time [TR] = 2 s). R-fMRI data scanned with higher sampling rates are important for the characterization of reliable functional connectomes because they can provide temporally complementary information about functional integration among brain regions and simultaneously reduce the effects of high frequency physiological noise. Here, we employed a publicly available multiband R-fMRI dataset with a sub-second sampling rate (TR = 645 ms) to identify global hubs in the human voxel-wise functional networks, and further examined their test-retest (TRT) reliability over scanning time. We showed that the functional hubs of human brain networks were mainly located at the default-mode regions (e.g., medial prefrontal and parietal cortex as well as the lateral parietal and temporal cortex) and the sensorimotor and visual cortex. These hub regions were highly anatomically distance-dependent, where short-range and long-range hubs were primarily located at the primary cortex and the multimodal association cortex, respectively. We found that most functional hubs exhibited fair to good TRT reliability using intraclass correlation coefficients. Interestingly, our analysis suggested that a 6-minute scan duration was able to reliably detect these functional hubs. Further comparison analysis revealed that these results were approximately consistent with those obtained using traditional R-fMRI scans of the same subjects with TR = 2500 ms, but several regions (e.g., lateral frontal cortex, paracentral lobule and anterior temporal lobe) exhibited different TRT reliability. Finally, we showed that several regions (including the medial/lateral prefrontal cortex and lateral temporal cortex) were identified as brain hubs in a high frequency band (02-03 Hz), which is beyond the frequency scope of traditional R-fMRI scans. Our results demonstrated the validity of multiband R4MRI data to reliably detect functional hubs in the voxel-wise whole-brain networks, which motivated the acquisition of high temporal resolution R-fMRI data for the studies of human brain functional connectomes in healthy and diseased conditions. (C) 2013 Elsevier Inc. All rights reserved.</p