ビッグデータと深層学習によるオンライン政治コミュニケーションについての研究

要約のみTohoku University浜田宏課

Digitisation of Weather Records of Seungjeongwon Ilgi: A Historical Weather Dynamics Dataset of the Korean Peninsula (1623-1910)

Introduction This study has exploited the daily weather records of Seungjeongwon Ilgi from the NIKH database (http://sjw.history.go.kr/main.do). Seungjeongwon Ilgi is a daily record of the Seungjeongwon, the Royal Secretariat of the Joseon Dynasty of Korea. These diaries span from 1623 to 1910 and generally involve daily weather records in the entry header. Their observational site would be located in Seoul (N37°35′, E126°59′). We have exploited the weather records from the NIKH database and classified the daily weather using text mining method. We have also converted the report dates from the traditional lunisolar calendar to the Gregorian calendar, to better contextualise our data into the contemporary daily measurements. Data We provide different formats (csv, xlsx, json) to facilitate the usage of data. The main contents of data are listed as below. ID: The unique identifier of a specific record in the metadata, which can also serve as the identifier to merge with external data in the NIKH digital database. Traditional calendar: The original lunar dates in the NIKH digital database, which are listed in data format "YYYY-MM-DD". More specifically, "L0" implies the leap year and "L1" implies the common year. Leap: The identifier of a leap year. Gregorian calendar: The Gregorian calendar date that converted by the traditional calendar date. Weather Text: The text that describe the weather conditions. Specifically, multiple weather descriptions of the same day have been put together. Flag: The computed value that indicates different combinations of weather conditions. Volume: The volume of text in the original record. Herbal Volume: The volume of text in the herbal record. Sunny: A dummy variable that represents whether the weather description contains the expression of sunny. Cloudy: A dummy variable that represents whether the weather description contains the expression of cloudy. Rainy: A dummy variable that represents whether the weather description contains the expression of rainy. Snow: A dummy variable that represents whether the weather description contains the expression of snow. Wind: A dummy variable that represents whether the weather description contains the expression of wind. Import Data # Python # CSV file import pandas as pd data=pd.read_csv('~/SJWilgi_Seoul_Weather_YR1623_1910.csv',encoding="utf-8") # JSON file data=pd.read_json('~/SJWilgi_Seoul_Weather_YR1623_1910.json',encoding="utf-8") # Excel file data=pd.read_excel('~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx') # Excel file # R # CSV file library(readr) data<- read_csv("~/SJWilgi_Seoul_Weather_YR1623_1910.csv") # Excel file library(readxl) data <- read_excel("~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx"

CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

Pathological image analysis is a crucial field in computer-aided diagnosis, where deep learning is widely applied. Transfer learning using pre-trained models initialized on natural images has effectively improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, which has great potential to overcome the challenge of expensive annotations. Thus, studies focusing on pathological SSL pre-training call for a comprehensive and standardized dataset, similar to the ImageNet in computer vision. This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization. The CPIA dataset contains 21,427,877 standardized images, covering over 48 organs/tissues and about 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). A four-scale WSI standardization process is proposed based on the uniform resolution in microns per pixel (MPP), while the ROIs are divided into three scales artificially. This multi-scale dataset is built with the diagnosis habits under the supervision of experienced senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. The CPIA dataset along with baselines is available at https://github.com/zhanglab2021/CPIA_Dataset

The NVIDIA AI City Challenge

Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video cameras around the world capture data, albeit low-quality, very little automated analysis and value creation results. Lack of labeled data, and trained models that can be deployed at the edge of the city fabric, ensure that most traffic video data goes through little or no automated analysis. Real-time and batch analysis of this data can provide vital breakthroughs in real-time traffic management as well as pedestrian safety. The NVIDIA AI City Challenge brought together 29 teams from universities in 4 continents to collaboratively annotate a 125 hour data set and then compete on detection, localization and classification tasks as well as traffic and safety application analytics tasks. The result is the largest high quality annotated data set, a set of models trained using NVIDIA AI City Edge to Cloud platform and ready to be deployed at the edge solving traffic and safety problems for cities worldwide

Glycopolypeptide hydrogels with adjustable enzyme-triggered degradation: A novel proteoglycans analogue to repair articular-cartilage defects

Proteoglycans (PGs), also known as a viscous lubricant, is the main component of the cartilage extracellular matrix (ECM). The loss of PGs is accompanied by the chronic degeneration of cartilage tissue, which is an irreversible degeneration process that eventually develops into osteoarthritis (OA). Unfortunately, there is still no substitute for PGs in clinical treatments. Herein, we propose a new PGs analogue. The Glycopolypeptide hydrogels in the experimental groups with different concentrations were prepared by Schiff base reaction (Gel-1, Gel-2, Gel-3, Gel-4, Gel-5 and Gel-6). They have good biocompatibility and adjustable enzyme-triggered degradability. The hydrogels have a loose and porous structure suitable for the proliferation, adhesion, and migration of chondrocytes, good anti-swelling, and reduce the reactive oxygen species (ROS) in chondrocytes. In vitro experiments confirmed that the glycopolypeptide hydrogels significantly promoted ECM deposition and up-regulated the expression of cartilage-specific genes, such as type-II collagen, aggrecan, and glycosaminoglycans (sGAG). In vivo, the New Zealand rabbit knee articular cartilage defect model was established and the hydrogels were implanted to repair it, the results showed good cartilage regeneration potential. It is worth noting that the Gel-3 group, with a pore size of 122 ​± ​12 ​μm, was particularly prominent in the above experiments, and provides a theoretical reference for the design of cartilage-tissue regeneration materials in the future

Double Perovskite Mn⁴⁺-Doped La₂CaSnO₆/La₂MgSnO₆ Phosphor for Near-Ultraviolet Light Excited W-LEDs and Plant Growth

Non-rare earth doped oxide phosphors with far-red emission have become one of the hot spots of current research due to their low price and excellent physicochemical stability as the red component in white light-emitting diodes (W-LEDs) and plant growth. Herein, we report novel Mn4+-doped La2CaSnO6 and La2MgSnO6 phosphors by high-temperature solid-phase synthesis and analyzed their crystal structures by XRD and Rietveld refinement. Their excitation spectra consist of two distinct excitation bands with the dominant excitation range from 250 to 450 nm, indicating that they possess strong absorption of near-ultraviolet light. Their emission is located around 693 and 708 nm, respectively, and can be absorbed by the photosensitive pigments Pr and Pfr, proving their great potential for plant growth. Finally, the prepared samples were coated with 365 nm UV chips to fabricate far-red LEDs and W-LEDs with low correlation color temperature (CCT = 4958 K/5275 K) and high color rendering index (Ra = 96.4/96.6). Our results indicate that La2CaSnO6:Mn4+ and La2MgSnO6:Mn4+ red phosphors could be used as candidate materials for W-LED lighting and plant growth

The NVIDIA AI City Challenge

Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video cameras around the world capture data, albeit low-quality, very little automated analysis and value creation results. Lack of labeled data, and trained models that can be deployed at the edge of the city fabric, ensure that most traffic video data goes through little or no automated analysis. Real-time and batch analysis of this data can provide vital breakthroughs in real-time traffic management as well as pedestrian safety. The NVIDIA AI City Challenge brought together 29 teams from universities in 4 continents to collaboratively annotate a 125 hour data set and then compete on detection, localization and classification tasks as well as traffic and safety application analytics tasks. The result is the largest high quality annotated data set, a set of models trained using NVIDIA AI City Edge to Cloud platform and ready to be deployed at the edge solving traffic and safety problems for cities worldwide.This is a manuscript of a proceeding published as Naphade, M., Anastasiu, D. C., Sharma, A., Jagrlamudi, V., Jeon, H., Liu et al. "The NVIDIA AI City Challenge." In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). (2017):1-6. DOI: 10.1109/UIC-ATC.2017.8397673. Posted with permission.</p

Risk-stratified multi-round PSA screening for prostate cancer integrating the screening reference level and subgroup-specific progression indicators

Abstract Background Although prostate-specific antigen (PSA) is widely used in prostate cancer (PCa) screening, nearly half of PCa cases are missed and less than one-third of cases are non-lethal. Adopting diagnostic criteria in population-based screening and ignoring PSA progression are presumed leading causes. Methods A total of 31,942 participants with multi-round PSA tests from the PLCO trial were included. Time-dependent receiver-operating-characteristic curves and area under curves (tdAUCs) were performed to determine the screening reference level and the optimal subgroup-specific progression indicator. Effects of risk-stratified multi-round PSA screening were evaluated with multivariable Cox regression and measured with hazard ratio [HR (95%CIs)]. Results After a median follow-up of 11.6 years, a total of 3484 PCa cases and 216 PCa deaths were documented. The tdAUC of 10-year incidence PCa with PSA was 0.816, and the cut-off value was 1.61 ng/ml. Compared to subgroup with stable negative PSA in both first-round (FR) and last-round (LR) tests [FR(−)/LR(−)], HRs (95%CI) of PCa incidence were 1.66 (1.20–2.29), 8.29 (7.25–9.48), and 14.52 (12.95–16.28) for subgroups with loss of positive PSA[FR(+)/LR(−)], gain of positive PSA[FR(−)/LR(+)], and stable positive PSA[FR(+)/LR(+)]; while HRs(95%CI) of PCa mortality were 1.47 (0.52–4.15), 5.71 (3.68–8.86), and 5.01 (3.41–7.37). After excluding regressive PSA [(namely FR(+)/LR(−)], absolute velocity was the shared optimal progression indicator for subgroups with FR(−)/LR(−), FR(−)/LR(+), and FR(+)/LR(+), with tdAUCs of 0.665, 0.681 and 0.741, and cut-off values of 0.07, 0.21, and 0.33 ng/ml/year. After reclassifying participants into groups with positive and negative progression based on subgroup-specific progression indicators, incidence HR (95%CI) were 2.41 (1.87–3.10), 2.91 (2.43–3.48), and 3.16 (2.88–3.46) for positive progression compared to negative progression within subgroups of FR(−)/LR(−), FR(−)/LR(+), and FR(+)/LR(+), while mortality HR (95%CI) were 2.22 (0.91–5.38), 2.37 (1.28–4.38), and 2.98 (1.94–4.59). To improve screening performances by excluding regressive PSA and low-risk positive progression in FR(−)/LR(−), optimized screening strategy not only significantly reduce 32.4% of missed PCa (54.0% [1881/3484] vs. 21.6% [754/3484], P < 0.001), but also detected additional 8.0% of high-grade PCa (Gleason score 7–10: 36.0% [665/1849] vs. 28.0% [206/736], P < 0.001) than traditional screening strategy. Conclusions Risk-stratified multi-round PSA screening strategy integrating the screening reference level and the optimal subgroup-specific progression indicator of PSA could be recommended as a fundamental strategy to reduce missed diagnosis and improve the detection of high-grade PCa cases

Neuroprotection against ischemic stroke requires a specific class of early responder T cells in mice

Immunomodulation holds therapeutic promise against brain injuries, but leveraging this approach requires a precise understanding of mechanisms. We report that CD8+CD122+CD49dlo T regulatory-like cells (CD8+ TRLs) are among the earliest lymphocytes to infiltrate mouse brains after ischemic stroke and temper inflammation; they also confer neuroprotection. TRL depletion worsened stroke outcomes, an effect reversed by CD8+ TRL reconstitution. The CXCR3/CXCL10 axis served as the brain-homing mechanism for CD8+ TRLs. Upon brain entry, CD8+ TRLs were reprogrammed to upregulate leukemia inhibitory factor (LIF) receptor, epidermal growth factor-like transforming growth factor (ETGF), and interleukin 10 (IL-10). LIF/LIF receptor interactions induced ETGF and IL-10 production in CD8+ TRLs. While IL-10 induction was important for the antiinflammatory effects of CD8+ TRLs, ETGF provided direct neuroprotection. Poststroke intravenous transfer of CD8+ TRLs reduced infarction, promoting long-term neurological recovery in young males or aged mice of both sexes. Thus, these unique CD8+ TRLs serve as early responders to rally defenses against stroke, offering fresh perspectives for clinical translation