UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

This study aims to investigate users’ knowledge sharing intention and collaborative stickiness intention towards social network sites (SNS). SNS offer an opportunity for users to interact and form relationships, while knowledge is accrued by integrating user’s information, experience, and practice. However, there have been few systematic studies that ask why people use SNS to share knowledge. We adopt social capital theory, social identity theory, as well as use and gratification theory to explore the determinants of members’ knowledge sharing intention in SNS. The survey was conducted on two education VCs of facebook, while most members were teachers and educators. Data analysis was carried out to validate our research model, and SmartPLS were used to analyze users’ collaborative stickiness intention. The result shows that social capital and social identity have impact on teacher’s knowledge sharing intention, in turn, influence on collaborative stickiness intention toward on SNS. Our findings not only help researchers interpret why members sharing their knowledge in VC, but also assist practitioners in developing better SNS strategy

Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its modality-aligned setting, i.e., the audio and visual modality are both assumed to signal the prediction target. With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored unaligned setting, where the goal is to recognize audio and visual events in a video with only weak labels observed. Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both). To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed Visual-Audio Label Elaboration (VALOR), is innovated to harvest modality labels for the training events. Empirical studies show that the harvested labels significantly improve an attentional baseline by 8.0 in average F-score (Type@AV). Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality. Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin (+5.4 F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well. Code is available at: https://github.com/Franklin905/VALOR

Molecular population genetics and gene expression analysis of duplicated CBF genes of Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p><it>CBF/DREB </it>duplicate genes are widely distributed in higher plants and encode transcriptional factors, or CBFs, which bind a DNA regulatory element and impart responsiveness to low temperatures and dehydration.</p> <p>Results</p> <p>We explored patterns of genetic variations of <it>CBF1, -2</it>, and -<it>3 </it>from 34 accessions of <it>Arabidopsis thaliana</it>. Molecular population genetic analyses of these genes indicated that <it>CBF2 </it>has much reduced nucleotide diversity in the transcriptional unit and promoter, suggesting that <it>CBF2 </it>has been subjected to a recent adaptive sweep, which agrees with reports of a regulatory protein of <it>CBF2</it>. Investigating the ratios of K_a/K_sbetween all paired <it>CBF </it>paralogus genes, high conservation of the AP2 domain was observed, and the major divergence of proteins was the result of relaxation in two regions within the transcriptional activation domain which was under positive selection after <it>CBF </it>duplication. With respect to the level of <it>CBF </it>gene expression, several mutated nucleotides in the promoters of <it>CBF3 </it>and <it>-1 </it>of specific ecotypes might be responsible for its consistently low expression.</p> <p>Conclusion</p> <p>We concluded from our data that important evolutionary changes in <it>CBF1, -2</it>, and -<it>3 </it>may have primarily occurred at the level of gene regulation as well as in protein function.</p

景觀生態中塊區結構指數與鳥類物種歧異度相關性之研究

本研究之目的乃探討景觀生態結構中各種指標對鳥類物種歧異度之影響，研究以獅頭山風景區內不同景觀生態結構地區為測試樣點，劃定物種調查區域及範圍，進行物種調查。利用ArcGIS 8.3版，整合整體的空間資訊，套疊繪製景觀生態結構。並以FRAGSTATS 2.0運算景觀結構之各塊區相關指數，包括面積(AREA)、塊區數目(NP)、平均塊區大小(MPS)、平均形狀指標(MSI)、平均塊區碎形維度(MPFD)、塊區密度(PD)、面積權重之形狀指標平均值(A八趴4S1)及面積權重之平均塊區碎形維度(AWMPFD)等八項指標，並進行變項之間相關性分析。研究結果顯示景觀生態結構與鳥類物種指標之間相關性，以水體結構最為相關，林地次之，農地和人工地盤最少。The purpose of this study is exploring a sustainable environment on the point of landscape ecology, which could benefits bird diversities. The theory of landscape ecological structure analyses were applies to test the wildlife diversity in different landscape settings. Twelve investigation sites were chosen from the Lion's Head Mountain Scenery Area, which contains various kinds landscape ecological structures. The ArcGIS8.3 was used to digitize the landscape ecological structure, calculated with the FRAGSTATS for Arc View. The selected variables includes the Area(AREA), Number of Patches(NP), Mean Patch Size(MPS), Mean Size Index(MSI), Mean Patch Fragmentation Dimension(MPED),Patch Density(PD),Area-Weighted Mean Size Index(AWMSI), and Area-Weighted Mean Patch Fragmentation Dimension(AWMPFD). The relationships among the landscape ecological structure indices and the bird's ecological indices were tested. The result shows the relationship between landscape ecological and bird diversity indices. The water body has most significant relation effects on bird diversity followed by woods, farms, and build areas respectively

Counting Crowds in Bad Weather

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.Comment: including supplemental materia

UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

Abstract This study aims to investigate users&apos; knowledge sharing intention and collaborative stickiness intention towards social network sites (SN

Liver angiosarcoma, a rare liver malignancy, presented with intraabdominal bleeding due to rupture- a case report

Liver angiosarcoma is a rare disease, however it still ranks as the third of most common primary liver maligancies. The prognosis of liver angiosarcoma is very poor with almost all patients with this kind of disease die within 2 years after diagnosis. No specific symptoms and signs are closely associated with this disease. Here, we report a case presenting shock status at first due to rupture of liver angiosarcoma- induced internal bleeding. After emergent transarterial embolization (TAE), she received partial hepatectomy two weeks later. 4 months after operation, she is still with a good performance status without obvious recurrence or metastasis identified

Protein kinase A-dependent Neuronal Nitric Oxide Synthase Activation Mediates the Enhancement of Baroreflex Response by Adrenomedullin in the Nucleus Tractus Solitarii of Rats

<p>Abstract</p> <p>Background</p> <p>Adrenomedullin (ADM) exerts its biological functions through the receptor-mediated enzymatic mechanisms that involve protein kinase A (PKA), or neuronal nitric oxide synthase (nNOS). We previously demonstrated that the receptor-mediated cAMP/PKA pathway involves in ADM-enhanced baroreceptor reflex (BRR) response. It remains unclear whether ADM may enhance BRR response via activation of nNOS-dependent mechanism in the nucleus tractus solitarii (NTS).</p> <p>Methods</p> <p>Intravenous injection of phenylephrine was administered to evoke the BRR before and at 10, 30, and 60 min after microinjection of the test agents into NTS of Sprague-Dawley rats. Western blotting analysis was used to measure the level and phosphorylation of proteins that involved in BRR-enhancing effects of ADM (0.2 pmol) in NTS. The colocalization of PKA and nNOS was examined by immunohistochemical staining and observed with a laser confocal microscope.</p> <p>Results</p> <p>We found that ADM-induced enhancement of BRR response was blunted by microinjection of NPLA or Rp-8-Br-cGMP, a selective inhibitor of nNOS or protein kinase G (PKG) respectively, into NTS. Western blot analysis further revealed that ADM induced an increase in the protein level of PKG-I which could be attenuated by co-microinjection with the ADM receptor antagonist ADM_22-52or NPLA. Moreover, we observed an increase in phosphorylation at Ser1416 of nNOS at 10, 30, and 60 min after intra-NTS administration of ADM. As such, nNOS/PKG signaling may also account for the enhancing effect of ADM on BRR response. Interestingly, biochemical evidence further showed that ADM-induced increase of nNOS phosphorylation was prevented by co-microinjection with Rp-8-Br-cAMP, a PKA inhibitor. The possibility of PKA-dependent nNOS activation was substantiated by immunohistochemical demonstration of co-localization of PKA and nNOS in putative NTS neurons.</p> <p>Conclusions</p> <p>The novel finding of this study is that the signal transduction cascade that underlies the enhancement of BRR response by ADM in NTS is composed sequentially of cAMP/PKA and nNOS/PKG pathways.</p

Crystallization of Adenylylsulfate Reductase from Desulfovibrio gigas: A Strategy Based on Controlled Protein Oligomerization

Adenylylsulfate reductase (adenosine 5′-phosphosulfate reductase, APS reductase or APSR, E.C.1.8.99.2) catalyzes the conversion of APS to sulfite in dissimilatory sulfate reduction. APSR was isolated and purified directly from massive anaerobically grown Desulfovibrio gigas, a strict anaerobe, for structure and function investigation. Oligomerization of APSR to form dimers–α_2β_2, tetramers–α_4β_4, hexamers–α_6β_6, and larger oligomers was observed during purification of the protein. Dynamic light scattering and ultracentrifugation revealed that the addition of adenosine monophosphate (AMP) or adenosine 5′-phosphosulfate (APS) disrupts the oligomerization, indicating that AMP or APS binding to the APSR dissociates the inactive hexamers into functional dimers. Treatment of APSR with β-mercaptoethanol decreased the enzyme size from a hexamer to a dimer, probably by disrupting the disulfide Cys156—Cys162 toward the C-terminus of the β-subunit. Alignment of the APSR sequences from D. gigas and A. fulgidus revealed the largest differences in this region of the β-subunit, with the D. gigas APSR containing 16 additional amino acids with the Cys156—Cys162 disulfide. Studies in a pH gradient showed that the diameter of the APSR decreased progressively with acidic pH. To crystallize the APSR for structure determination, we optimized conditions to generate a homogeneous and stable form of APSR by combining dynamic light scattering, ultracentrifugation, and electron paramagnetic resonance methods to analyze the various oligomeric states of the enzyme in varied environments

LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following

End-to-end Transformers have demonstrated an impressive success rate for Embodied Instruction Following when the environment has been seen in training. However, they tend to struggle when deployed in an unseen environment. This lack of generalizability is due to the agent's insensitivity to subtle changes in natural language instructions. To mitigate this issue, we propose explicitly aligning the agent's hidden states with the instructions via contrastive learning. Nevertheless, the semantic gap between high-level language instructions and the agent's low-level action space remains an obstacle. Therefore, we further introduce a novel concept of meta-actions to bridge the gap. Meta-actions are ubiquitous action patterns that can be parsed from the original action sequence. These patterns represent higher-level semantics that are intuitively aligned closer to the instructions. When meta-actions are applied as additional training signals, the agent generalizes better to unseen environments. Compared to a strong multi-modal Transformer baseline, we achieve a significant 4.5% absolute gain in success rate in unseen environments of ALFRED Embodied Instruction Following. Additional analysis shows that the contrastive objective and meta-actions are complementary in achieving the best results, and the resulting agent better aligns its states with corresponding instructions, making it more suitable for real-world embodied agents. The code is available at: https://github.com/joeyy5588/LACMA.Comment: EMNLP 202