Notes

Exploring the topology along with vibrant development qualities of co-invention systems and also engineering job areas.
All simulated setups were in line with in vitro experiments and in human measurements and gave detailed insight into determinants of local impedance changes as well as the relation between values measured with two different devices.

The in silico environment proved to be capable of resembling clinical scenarios and quantifying local impedance changes.

The tool can assists the interpretation of measurements in humans and has the potential to support future catheter development.
The tool can assists the interpretation of measurements in humans and has the potential to support future catheter development.We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC (random sample and consensus) that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods in accuracy. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling (a) automatic computation of vessel change metrics such as vessel density and caliber, and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.Interacting with virtual objects via haptic feedback using the user's hand directly (virtual hand haptic interaction) provides a natural and immersive way to explore the virtual world. It remains a challenging topic to achieve 1 kHz stable virtual hand haptic simulation with no penetration amid hundreds of hand-object contacts. In this paper, we advocate decoupling the high-dimensional optimization problem of computing the graphic-hand configuration, and progressively optimizing the configuration of the graphic palm and fingers, yielding a decoupled-and-progressive optimization framework. We also introduce a method for accurate and efficient hand-object contact simulation, which constructs a virtual hand consisting of a sphere-tree model and five articulated cone frustums, and adopts a configuration-based optimization algorithm to compute the graphic-hand configuration under non-penetration contact constraints. Experimental results show both high update rate and stability for a variety of manipulation behaviors. Non-penetration between the graphic hand and complex-shaped objects can be maintained under diverse contact distributions, and even for frequent contact switches. The update rate of the haptic simulation loop exceeds 1 kHz for the whole-hand interaction with about 250 contacts.With the dramatic increase in the amount of multimedia data, cross-modal similarity retrieval has become one of the most popular yet challenging problems. Hashing offers a promising solution for large-scale cross-modal data searching by embedding the high-dimensional data into the low-dimensional similarity preserving Hamming space. However, most existing cross-modal hashing usually seeks a semantic representation shared by multiple modalities, which cannot fully preserve and fuse the discriminative modal-specific features and heterogeneous similarity for cross-modal similarity searching. In this paper, we propose a joint specifics and consistency hash learning method for cross-modal retrieval. Specifically, we introduce an asymmetric learning framework to fully exploit the label information for discriminative hash code learning, where 1) each individual modality can be better converted into a meaningful subspace with specific information, 2) multiple subspaces are semantically connected to capture consistent information, and 3) the integration complexity of different subspaces is overcome so that the learned collaborative binary codes can merge the specifics with consistency. Then, we introduce an alternatively iterative optimization to tackle the specifics and consistency hashing learning problem, making it scalable for large-scale cross-modal retrieval. Extensive experiments on five widely used benchmark databases clearly demonstrate the effectiveness and efficiency of our proposed method on both one-cross-one and one-cross-two retrieval tasks.Growing studies have shown that miRNAs are inextricably linked with many human diseases, and a great deal of effort has been spent on identifying their potential associations. Compared with traditional experimental methods, computational approaches have achieved promising results. In this article, we propose a graph representation learning method to predict miRNA-disease associations. learn more Specifically, we first integrate the verified miRNA-disease associations with the similarity information of miRNA and disease to construct a miRNA-disease heterogeneous graph. Then, we apply a graph attention network to aggregate the neighbor information of nodes in each layer, and then feed the representation of the hidden layer into the structure-aware jumping knowledge network to obtain the global features of nodes. The output features of miRNAs and diseases are then concatenated and fed into a fully connected layer to score the potential associations. Through five-fold cross-validation, the average AUC, accuracy and precision values of our model are 93.30%, 85.18% and 88.90%, respectively. In addition, for three case studies of the esophageal tumor, lymphoma and prostate tumor, 46, 45 and 45 of the top 50 miRNAs predicted by our model were confirmed by relevant databases. Overall, our method could provide a reliable alternative for miRNA-disease association prediction.Nitrogenase employs a sophisticated electron transfer system and a Mo-Fe-S-C cofactor, designated the M-cluster [(cit)MoFe7 S9 C]), to reduce atmospheric N2 to bioaccessible NH3 . Previously, we have shown that the cofactor-free form of nitrogenase can be repurposed as a protein scaffold for the incorporation of a synthetic Fe-S cluster [Fe6 S9 (SEt)2 ]4- . Here, we demonstrate the utility of an asymmetric Mo-Fe-S cluster [Cp*MoFe5 S9 (SH)]3- as an alternative artificial cofactor upon incorporation into the cofactor-free nitrogenase scaffold. The resultant semi-artificial enzyme catalytically reduces C2 H2 to C2 H4 , and CN- into short-chain hydrocarbons, yet it is clearly distinct in activity from its [Fe6 S9 (SEt)2 ]4- -reconstituted counterpart, pointing to the possibility to employ molecular design and cluster synthesis strategies to further develop semi-artificial or artificial systems with desired catalytic activities.Single-molecule assays often require functionalized surfaces. One approach for microtubule assays renders surfaces hydrophobic and uses amphiphilic blocking agents. However, the optimal hydrophobicity is unclear, protocols take long, produce toxic waste, and are susceptible to failure. Our method uses plasma activation with hydrocarbons for hexamethyldisilazane (HMDS) silanization in the gas phase. We measured the surface hydrophobicity, its effect on how well microtubule filaments were bound to the surface, and the number of nonspecific interactions with kinesin motor proteins. Additionally, we tested and discuss the use of different silanes and activation methods. We found that even weakly hydrophobic surfaces were optimal. Our environmentally friendly method significanty reduced the overall preparation effort and resulted in reproducible, high-quality surfaces with low variability. We expect the method to be applicable to a wide range of other single-molecule assays.Eukaryotic RNA polymerase I (Pol I) products play fundamental roles in ribosomal assembly, protein synthesis, metabolism and cell growth. Abnormal expression of both Pol I transcription-related factors and Pol I products causes a range of diseases, including ribosomopathies and cancers. However, the factors and mechanisms governing Pol I-dependent transcription remain to be elucidated. Here, we report that transcription factor IIB-related factor 1 (BRF1), a subunit of transcription factor IIIB required for RNA polymerase III (Pol III)-mediated transcription, is a nucleolar protein and modulates Pol I-mediated transcription. We showed that BRF1 can be localized to the nucleolus in several human cell types. BRF1 expression correlates positively with Pol I product levels and tumour cell growth in vitro and in vivo. Pol III transcription inhibition assays confirmed that BRF1 modulates Pol I-directed transcription in an independent manner rather than through a Pol III product-to-45S pre-rRNA feedback mode. Mechanistically, BRF1 binds to the Pol I transcription machinery components and can be recruited to the rDNA promoter along with them. Additionally, alteration of BRF1 expression affects the recruitment of Pol I transcription machinery components to the rDNA promoter and the expression of TBP and TAF1A. These findings indicate that BRF1 modulates Pol I-directed transcription by controlling the expression of selective factor 1 subunits. In summary, we identified a novel role of BRF1 in Pol I-directed transcription, suggesting that BRF1 can independently regulate both Pol I- and Pol III-mediated transcription and act as a key coordinator of Pol I and Pol III.Photocatalytic N2 fixation has emerged as one of the most useful ways to produce NH3, a useful asset for chemical industries and a carbon-free energy source. Recently, significant progress has been made toward designing efficient photocatalysts to achieve this objective. Here, we introduce a highly active type-II heterojunction fabricated via integrating two-dimensional (2D) nanosheets of exfoliated g-C3N5 with nickel-chromium layered double hydroxide (NiCr-LDH). With an optimized loading of NiCr-LDH on exfoliated g-C3N5, excellent performance is realized for green ammonia synthesis under ambient conditions without any noble metal cocatalyst(s). Indeed, the g-C3N5/NiCr-LDH heterostructure with 2 wt % of NiCr-LDH (CN-NCL-2) exhibits an ammonia yield of about 2.523 mmol/g/h, which is about 7.51 and 2.86 times higher than that of solo catalysts, i.e., NiCr-LDH (NC-L) and exfoliated g-C3N5 (CN-5), respectively, where methanol is used as a sacrificial agent. The enhancement of NH3 evolution by the g-C3N5/NiCr-LDH heterostructure can be attributed to the efficient charge transfer, a key factor to the photocatalytic N2 fixation rate enhancement. Additionally, N2 vacancies present in the system help adsorb N2 on the surface, which improves the ammonia production rate further. The best-performing heterostructure also shows long-term stability with the NH3 production rate remaining nearly constant over 20 h, demonstrating the excellent robustness of the photocatalyst.
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