Data Augmentation and Dense-LSTM for Human Activity Recognition Using WiFi Signal

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data

Multiple mesodermal lineage differentiation of Apodemus sylvaticus embryonic stem cells in vitro

<p>Abstract</p> <p>Background</p> <p>Embryonic stem (ES) cells have attracted significant attention from researchers around the world because of their ability to undergo indefinite self-renewal and produce derivatives from the three cell lineages, which has enormous value in research and clinical applications. Until now, many ES cell lines of different mammals have been established and studied. In addition, recently, AS-ES1 cells derived from <it>Apodemus sylvaticus </it>were established and identified by our laboratory as a new mammalian ES cell line. Hence further research, in the application of AS-ES1 cells, is warranted.</p> <p>Results</p> <p>Herein we report the generation of multiple mesodermal AS-ES1 lineages via embryoid body (EB) formation by the hanging drop method and the addition of particular reagents and factors for induction at the stage of EB attachment. The AS-ES1 cells generated separately in vitro included: adipocytes, osteoblasts, chondrocytes and cardiomyocytes. Histochemical staining, immunofluorescent staining and RT-PCR were carried out to confirm the formation of multiple mesodermal lineage cells.</p> <p>Conclusions</p> <p>The appropriate reagents and culture milieu used in mesodermal differentiation of mouse ES cells also guide the differentiation of in vitro AS-ES1 cells into distinct mesoderm-derived cells. This study provides a better understanding of the characteristics of AS-ES1 cells, a new species ES cell line and promotes the use of Apodemus ES cells as a complement to mouse ES cells in future studies.</p

Association of Mitochondrial DNA Variations with Lung Cancer Risk in a Han Chinese Population from Southwestern China

Mitochondrial DNA (mtDNA) is particularly susceptible to oxidative damage and mutation due to the high rate of reactive oxygen species (ROS) production and limited DNA-repair capacity in mitochondrial. Previous studies demonstrated that the increased mtDNA copy number for compensation for damage, which was associated with cigarette smoking, has been found to be associated with lung cancer risk among heavy smokers. Given that the common and “non-pathological” mtDNA variations determine differences in oxidative phosphorylation performance and ROS production, an important determinant of lung cancer risk, we hypothesize that the mtDNA variations may play roles in lung cancer risk. To test this hypothesis, we conducted a case-control study to compare the frequencies of mtDNA haplogroups and an 822 bp mtDNA deletion between 422 lung cancer patients and 504 controls. Multivariate logistic regression analysis revealed that haplogroups D and F were related to individual lung cancer resistance (OR = 0.465, 95%CI = 0.329–0.656, p<0.001; and OR = 0.622, 95%CI = 0.425–0.909, p = 0.014, respectively), while haplogroups G and M7 might be risk factors for lung cancer (OR = 3.924, 95%CI = 1.757–6.689, p<0.001; and OR = 2.037, 95%CI = 1.253–3.312, p = 0.004, respectively). Additionally, multivariate logistic regression analysis revealed that cigarette smoking was a risk factor for the 822 bp mtDNA deletion. Furthermore, the increased frequencies of the mtDNA deletion in male cigarette smoking subjects of combined cases and controls with haplogroup D indicated that the haplogroup D might be susceptible to DNA damage from external ROS caused by heavy cigarette smoking

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S

Solvent-Free Selective Condensations Based on the Formation of the Olefinic (C=C) Bond Catalyzed by Organocatalyst

Pyrrolidine and its derivatives were used to catalyze aldol and Knoevenagel condensations for the formation of the olefinic (C=C) bond under solvent-free conditions. The 3-pyrrolidinamine showed high activity and afforded excellent yields of α,β-unsaturated compounds. The aldol condensation of aromatic/heterocyclic aldehydes with ketones affords enones in high conversion (99.5%) and selectivity (92.7%). Good to excellent yields of α,β-unsaturated compounds were obtained in the Knoevenagel condensation of aldehydes with methylene-activated substrates

Twitter user geolocation method based on single-point toponym matching and local toponym filtering

The availability of accurate toponyms in user tweets is crucial for geolocating Twitter users.However, existing methods for locating Twitter users often suffer from limited quantity and reliability of acquired toponyms, thus impacting the accuracy of user geolocation.To address this issue, a twitter user geolocation method based on single-point toponym matching and local toponym filtering was proposed.A toponym type discriminating algorithm based on the aggregation degree of locations of the toponym was designed.In the proposed algorithm, a single-point toponym database was generated to provide more reliable toponyms extracted from tweets.Then, according to a proposed local place name filtering algorithm based on the aggregation degree of user location, the aggregation degree of user location centered on the longitude and latitude of toponyms and the average longitude and latitude of users were calculated.This process helped in extracting local toponyms with a high aggregation degree, which enhances the reliability of toponyms used in geolocation.Finally, a user-toponym heterogeneous graph was constructed based on user social relationships and user mentions of toponyms, and users were located by graph representation learning and neural networks.A large number of user geolocation experiments were conducted based on two commonly used public datasets in this field, namely GEOTEXT and TW-US.Comparisons with nine existing typical methods for Twitter user geolocation, including HGNN, ReLP, and GCN, demonstrate that our proposed method achieves significantly higher geolocation accuracy.On the GEOTEXT dataset, the average error is reduced by 7.3～342.8 km, the median error is reduced by 2.4～354.4 km, and the accuracy of large area-level geolocation is improved by 1.3%～26.3%.On the TW-US dataset, the average error is reduced by 8.6～246.6 km, the median error is reduced by 5.7～149.7 km, and the accuracy of large area-level geolocation is improved by 1.5%～20.5%

Lattice Distortion and Phase Stability of Pd-Doped NiCoFeCr Solid-Solution Alloys

In the present study, we have revealed that (NiCoFeCr)100&#8722;xPdx (x= 1, 3, 5, 20 atom%) high-entropy alloys (HEAs) have both local- and long-range lattice distortions by utilizing X-ray total scattering, X-ray diffraction, and extended X-ray absorption fine structure methods. The local lattice distortion determined by the lattice constant difference between the local and average structures was found to be proportional to the Pd content. A small amount of Pd-doping (1 atom%) yields long-range lattice distortion, which is demonstrated by a larger (200) lattice plane spacing than the expected value from an average structure, however, the degree of long-range lattice distortion is not sensitive to the Pd concentration. The structural stability of these distorted HEAs under high-pressure was also examined. The experimental results indicate that doping with a small amount of Pd significantly enhances the stability of the fcc phase by increasing the fcc-to-hcp transformation pressure from ~13.0 GPa in NiCoFeCr to 20&#8315;26 GPa in the Pd-doped HEAs and NiCoFeCrPd maintains its fcc lattice up to 74 GPa, the maximum pressure that the current experiments have reached

Data Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data

Data Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere wireless signal propagation, that manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in certain speed and scale, recent works commonly have moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi based human activity recognition system that synthesize variant activities data through 8 CSI transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep learning model that cater to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data

Efficient methoxycarbonylation of diisobutylene over functionalized ZSM-5 supported cobalt complex catalysts

By grafting nitrogen-containing complexes onto ZSM-5 mesoporous material and then supporting a cobalt catalyst in situ, the methoxycarbonylation of diisobutylene (DIB) was achieved. Moreover, a series of functionalized ZSM-5 mesoporous materials containing different nitrogen complexes were synthesized and characterized by FT-IR, N2 adsorption–desorption isotherms, XRD, SEM, and X-ray photoelectron spectroscopy (XPS). Subsequently, the catalytic activity of functionalized ZSM-5 mesoporous materials and the reaction parameters in the methoxycarbonylation of DIB were investigated. The results revealed that the conversion of DIB was 88.3% and the selectivity for methyl isononanoate was 93.4% under solvent-free conditions at 6.0 MPa and 140 °C for 10 h by using the catalyst ZSM-5iCPdPy@Co2(CO)8. The potential mechanism for this catalytic reaction was also put forth. Admittedly, these inexpensive and easy-to-recover heterogeneous catalysts can replace the noble metal palladium complexes on a laboratory scale to achieve partial olefin carbonylation reactions