A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurrence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code is released on https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.Comment: Accepted in IJCAI2

The Stochastic Modeling of TiO 2 Memristor and Its Usage in Neuromorphic System Design

Abstract Memristor, the fourth basic circuit element, has shown great potential in neuromorphic circuit design for its unique synapse-like feature. However, though the continuous resistance state of memristor has been expected, obtaining and maintaining an arbitrary intermediate state cannot be well controlled in nowadays memristive system. In addition, the stochastic switching behaviors have been widely observed. To facilitate the investigation on memristor-based hardware implementation, we built a stochastic behavior model of TiO 2 memristive devices based on the real experimental results. By leveraging the stochastic behavior of memristors, a macro cell design composed of multiple parallel connecting memristors can be successfully used in implementing the weight storage unit and the stochastic neuron the two fundamental components in neural network (NN)s, providing a feasible solution in memristor-based hardware implementation

A nuclease specific to lepidopteran insects suppresses RNAi

More than 70% of all agricultural pests are insects in the order Lepidoptera, which, unlike other related insect orders, are not very sensitive to RNAi, limiting genetic studies of this insect group. However, the reason for this distinct lepidopteran characteristic is unknown. Previously, using transcriptome analysis of the Asian corn borer Ostrinia furnacalis, we identified a gene, termed up56, that is up-regulated in response to dsRNA. Here we report that this Lepidoptera-specific gene encodes a nuclease that contributes to RNAi insensitivity in this insect order. Its identity was experimentally validated, and sequence analysis indicated that up56 encodes a previously uncharacterized protein with homologous sequences in seven other lepidopteran species. Its computationally predicted three-dimensional structure revealed a high structural similarity to human exonuclease I. Exposure to dsRNA in O. furnacalis strongly up-regulated this gene's expression, and the protein could digest single-stranded RNA (ssRNA), dsRNA, and dsDNA both in vitro and in vivo. Of note, we found that this up-regulation of up56 expression is faster than that of the gene encoding the key RNAi-associated nuclease Dicer. up56 knockdown in O. furnacalis significantly enhanced RNAi efficiency. Moreover, up56 overexpression in Drosophila melanogaster suppressed RNAi efficiency. Finally, up56 knockdown significantly increased the amount and diversity of small RNAs. Therefore, we renamed this protein RNAi efficiency-related nuclease (REase). In conclusion, we propose that REase may explain why lepidopterans are refractory to RNAi and that it represents a target for further research of RNAi efficiency in this insect order

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer's disease.

PURPOSE Despite the revealed role of immunological dysfunctions in the development and progression of Alzheimer's disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort. METHODS We analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer's Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging. RESULTS TSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =-0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =-54.01, P = 0.003). CONCLUSION By integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction

A comprehensive evaluation method for the site selection of new healthcare facilities in geological hazard-prone areas

Healthcare facilities in geological hazard-prone areas not only are responsible for local basic medical services but also are the main provider of hazard emergency rescue work. The selection of their sites is further complicated by the need to consider both the equalization of regional medical services and resource allocation and the impact of geological hazards on site safety. Shimian County in Sichuan Province, a geological disaster-prone area, was chosen as the study area. First, suitability analysis of the construction land was used to determine the site alternatives for new healthcare facilities, and an evaluation index system of construction land suitability consisting of geological hazard susceptibility, slope and aspect was established. Then, the suitability was evaluated by the Ordered Weighted Averaging (OWA) operator, and the Analytic Hierarchy Process (AHP) and the quantitative method of Regular Increasing Monotone (RIM) were used to calculate the criterion weights and order weights in the Ordered Weighted Averaging operator respectively. The suitability results were classified into five levels: high, moderate, average, barely suitable, and unsuitable. Twelve site alternatives were identified in the highly and moderately suitable areas. Finally, a comprehensive evaluation index system consisting of indices such as construction land suitability and medical service accessibility was established, the PROMETHEE II method was conducted to comprehensively evaluate the site alternatives, and ranked results for the 12 site alternatives were obtained. These ranked results were analyzed by subindexes and Graphical Analysis for Interactive Aid (GAIA) to obtain a score for each alternative index and its similarity to the alternative, which could significantly help decision-making. This study achieves reasonable and scientific site selection for healthcare facilities in geological hazard-prone areas, and the results can provide references for relevant decision-makers

Superovulation and expression of follicle-stimulating hormone receptor in young rabbit females

[EN] To optimise the use of juvenile in vitro embryo transfer technologies in young rabbit females, superovulation was performed in New Zealand White young rabbit females at different ages and the expression mode of follicle-stimulating hormone receptor (FSHR) was explored using real-time quantitative polymerase chain reaction, and in vitro maturation (IVM) together with fertilisation (IVF) was conducted immediately after superovulation. The results showed that (1) the age factor significantly affected superovulation in young rabbit females, with 60 d as an optimal age; (2) the mRNA level of FSHR exhibited a rising trend, though it was lower at 30 to 40 d of age; (3) the maturation rate of the oocytes from 60 d old rabbits was significantly higher than in those from 50 d old rabbits; (4) the fertilisation rate of oocytes was not significantly different among rabbits 50, 60 and 70 d old.This work was supported by funding from the Key Natural Science programme of Jiangsu Higher Education Institutions (13KJA230001) and the Priority Academic Programme Development of Jiangsu Higher Education Institutions (PAPD 2011-137).Zhang, H.; Cheng, GH.; Li, YJ.; Cai, MY.; Guo, HY.; Qin, KL. (2017). Superovulation and expression of follicle-stimulating hormone receptor in young rabbit females. World Rabbit Science. 25(2):167-172. https://doi.org/10.4995/wrs.2017.4485SWORD16717225

Noise does not equal bias in assessing the evolutionary history of the angiosperm flora of China: A response to Qian (2019)

In response to our paper on the evolutionary history of the Chinese flora, Qian suggests that certain features of the divergence time estimation employed might have led to biased conclusions in Lu et al (2018). Here, we consider Qian’s specific criticisms, explore the extent of uncertainty in the data and demonstrate that (i) no systematic bias toward dates that are too young or too old is detected in Lu et al.; (ii) constraint of the crown age of angiosperms does not bias the generic ages estimated by Lu et al.; and (iii) ages derived from the Chinese regional phylogeny do not bias the conclusions reported by Lu et al. All these analyses confirm that the conclusions reported previously are robust. We argue that, like many large- scale biodiversity analyses, sources of noise in divergence time estimation are to be expected, but these should not be confused with bias.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163425/2/jbi13947.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163425/1/jbi13947_am.pd