THE IMPACT OF POWER BOUNDARY MANAGEMENT ON THE DESIGN OF COMPANY-INITIATED OPEN INNOVATION PLATFORM

Open innovation recognizes potential opportunities and advantages gained from leveraging knowledge and innovations found outside an organization‟s formal boundaries. With the intensive use of Internet-based tools, organizations are actively involved in using Open Innovation Platform (OIP) to attract external knowledge. However, developing a company-initiated OIP is a challenging task because usage of OIP depends on the voluntary participation of external users, which makes companies cannot follow the protocol of developing traditional IS. Furthermore, a company\u27s institutional properties may also impact the design company-initiated OIP. In this research, we focus on one type of organizational property, namely power boundary, and explore its impact on the design of a company-initiated OIP over time. From qualitative analysis of two versions of OIP in a single company, we develop a theoretical model depicting how the changes of power boundary of a firm influence the design of a company-initiated OIP over time. This result generates theoretical and empirical insights into the OIP design and power boundary and thus has important implications for both scholars and practitioners

Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space

In recent years, the task of weakly supervised audio-visual violence detection has gained considerable attention. The goal of this task is to identify violent segments within multimodal data based on video-level labels. Despite advances in this field, traditional Euclidean neural networks, which have been used in prior research, encounter difficulties in capturing highly discriminative representations due to limitations of the feature space. To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination. Our framework comprises a detour fusion module for multimodal fusion, effectively alleviating modality inconsistency between audio and visual signals. Additionally, we contribute two branches of fully hyperbolic graph convolutional networks that excavate feature similarities and temporal relationships among snippets in hyperbolic space. By learning snippet representations in this space, the framework effectively learns semantic discrepancies between violent and normal events. Extensive experiments on the XD-Violence benchmark demonstrate that our method outperforms state-of-the-art methods by a sizable margin.Comment: 8 pages, 5 figure

Roles of Extracellular Vesicles in Human Reproduction

Extracellular vesicles (EVs) are newly identified as cell-to-cell communication mediators that carry and transfer various regulatory molecules. Recent studies have shown that EVs play important roles in normal physiology and pathological conditions of human reproduction. In the female reproductive system, EVs in follicular fluid, oviduct fluid, and uterine luminal fluid are considered as vehicles to regulate follicular development, oocyte maturation and mediate embryo–maternal crosstalk to affect embryo implantation and pregnancy. In the male reproductive system, prostasomes and epididymosomes are involved in regulating sperm maturation, motility, capacitation, acrosome reaction, and fertilization. EVs transmitted cargos also play important roles in reproduction-related pathologies, such as polycystic ovarian syndrome, endometriosis, pregnancy complications, male infertility, and gynecological malignant tumors. In view of the important roles in the reproductive system, EVs may be used as biomarkers or therapeutic targets for reproductive abnormalities and related diseases. In this chapter, we sorted EVs in human reproduction through their physical/pathological functions and mechanisms, and listed several EVs as biomarkers and clinical therapeutic applications in the future

センイ キンゾク オヨビ キドルイ ゲンソ ヲ テンカ シタ シツオン キョウジセイ チッカブツ ハンドウタイ

研究ノー

Altered expression of Tim family molecules and an imbalanced ratio of Tim-3 to Tim-1 expression in patients with type 1 diabetes

BackgroundT-cell immunoglobulin and mucin domain (Tim) proteins are immunomodulatory molecules that play key roles in the regulation of T-cell activation. Published studies have reported that Tim molecules are involved in the pathogenesis of certain autoimmune diseases. Type 1 diabetes (T1D) is an autoimmune disease in which T cells mediate the destruction of islet β cells. However, the expression of Tim molecules in T1D remains unclear. In this study, we measured the expression of Tim family molecules as well as T-cell subset-specific transcription factors in T1D patients, and we explored the possible involvement of Tim molecules in the pathogenesis of T1D.MethodsNinety T1D patients, Thirty-six type 2 diabetes (T2D) patients and forty healthy controls (HCs) were recruited for this study. Peripheral blood mononuclear cells (PBMCs) were isolated, RNA was extracted from the PBMCs and reverse transcribed into cDNA, and gene expression patterns were analysed by RT–qPCR. The expression of Tim molecules in different T-cell subsets was analysed by flow cytometry.ResultsCompared with that in HCs, the mRNA expression of Tim-1 and RORC was increased in T1D patients (P=0.0355 and P=0.0423, respectively), while the expression of Tim-3 was decreased (P=0.0013). In addition, compared with HCs, the ratio of Tim-3 to Tim-1 expression in diabetic patients was decreased (P<0.0001 for T1D and P=0.0387 for T2D). The ratios of T-Bet to GATA3 expression and RORC to FOXP3 expression were higher in T1D patients than in HCs (P=0.0042 and P=0.0066, respectively). Furthermore, the T1D patients with defective islet function had more significant imbalances in the Tim-3/Tim-1 and RORC/FOXP3 ratios (P<0.0001, and P=0.001, respectively). Moreover, Both Tim-3 expression in CD4+ T cells and the Tim-3 to Tim-1 ratio were elevated in T1D in the remission phase compared to T1D.ConclusionOur study revealed altered expression of Tim molecules in T1D patients. The imbalanced ratios of Tim-3/Tim-1 expression were more pronounced in T1D patients with defective islet function. However, alterations in Tim molecule expression are mitigated in T1D in the remission phase. All these findings suggest that Tim family molecules may be involved in the pathogenesis of T1D

Solar-Driven Reduction of 1 atm of CO_2 to Formate at 10% Energy-Conversion Efficiency by Use of a TiO_2-Protected III–V Tandem Photoanode in Conjunction with a Bipolar Membrane and a Pd/C Cathode

A solar-driven CO_2 reduction (CO_2R) cell was constructed, consisting of a tandem GaAs/InGaP/TiO_2/Ni photoanode in 1.0 M KOH(aq) (pH = 13.7) to facilitate the oxygen-evolution reaction (OER), a Pd/C nanoparticle-coated Ti mesh cathode in 2.8 M KHCO_3(aq) (pH = 8.0) to perform the CO_2R reaction, and a bipolar membrane to allow for steady-state operation of the catholyte and anolyte at different bulk pH values. At the operational current density of 8.5 mA cm^(–2), in 2.8 M KHCO_3(aq), the cathode exhibited 94% Faradaic efficiency for the reduction of 1 atm of CO_2(g) to formate. The anode exhibited a 320 ± 7 mV overpotential for the OER in 1.0 M KOH(aq), and the bipolar membrane exhibited ∼480 mV voltage loss with minimal product crossovers and >90 and >95% selectivity for protons and hydroxide ions, respectively. The bipolar membrane facilitated coupling between two electrodes and electrolytes, one for the CO_2R reaction and one for the OER, that typically operate at mutually different pH values and produced a lower total cell overvoltage than known single-electrolyte CO_2R systems while exhibiting ∼10% solar-to-fuels energy-conversion efficiency

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers, conjunction, disjunction, and negation. We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI-2023

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

PURPOSE:We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).METHODS:A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram.RESULTS:The radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction nomogram. The model exhibited good calibration and discrimination in the training and validation cohorts (C-index [95% confidence interval]: 0.846 [0.787–0.905] and 0.844 [0.774–0.915], respectively). Its clinical usefulness was confirmed using a decision curve analysis.CONCLUSION:The radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC

Tumor-Intrinsic Sirpa Promotes Sensitivity to Checkpoint Inhibition Immunotherapy in Melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD