HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces

Recent advances in neural implicit surfaces for multi-view 3D reconstruction primarily focus on improving large-scale surface reconstruction accuracy, but often produce over-smoothed geometries that lack fine surface details. To address this, we present High-Resolution NeuS (HR-NeuS), a novel neural implicit surface reconstruction method that recovers high-frequency surface geometry while maintaining large-scale reconstruction accuracy. We achieve this by utilizing (i) multi-resolution hash grid encoding rather than positional encoding at high frequencies, which boosts our model's expressiveness of local geometry details; (ii) a coarse-to-fine algorithmic framework that selectively applies surface regularization to coarse geometry without smoothing away fine details; (iii) a coarse-to-fine grid annealing strategy to train the network. We demonstrate through experiments on DTU and BlendedMVS datasets that our approach produces 3D geometries that are qualitatively more detailed and quantitatively of similar accuracy compared to previous approaches

piRNN: deep learning algorithm for piRNA prediction

Piwi-interacting RNAs (piRNAs) are the largest class of small non-coding RNAs discovered in germ cells. Identifying piRNAs from small RNA data is a challenging task due to the lack of conserved sequences and structural features of piRNAs. Many programs have been developed to identify piRNA from small RNA data. However, these programs have limitations. They either rely on extracting complicated features, or only demonstrate strong performance on transposon related piRNAs. Here we proposed a new program called piRNN for piRNA identification. For our software, we applied a convolutional neural network classifier that was trained on the datasets from four different species (Caenorhabditis elegans, Drosophila melanogaster, rat and human). A matrix of k-mer frequency values was used to represent each sequence. piRNN has great usability and shows better performance in comparison with other programs. It is freely available at https://github.com/bioinfolabmu/piRNN

The Adoption of Wikipedia: A Community- and Information Quality-Based View

The Web 2.0 model has aroused vast attention as it alters the traditional role of Internet users as pure information receivers. Wikipedia, as one of the most successful case of the Web 2.0 model, creates an online encyclopedia through the collective efforts of volunteers. Shared freely by all Internet users, it forms an online community platform on which users can seek and share knowledge. This study investigates the factors that affect the adoption of Wikipedia. Based on the TAM of Davis (1989), perceived critical mass, community identification, and perceived information quality were incorporated into the research model to explain the intention and usage of Wikipedia. This research is a work-in-progress and a questionnaire survey will be executed, targeting at Internet users who had prior experiences with knowledge seeking on Wikipedia

Bayesian Model Updating of a Simply-Supported Truss Bridge Based on Dynamic Responses

Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 224)This study intends to investigate the application of model updating based on forced vibration data to a simply-supported truss bridge. A fast Bayesian FFT method was used to perform the modal identification obtained from field tests, and the Transitional Markov Chain Monte Carlo (TMCMC) algorithm is employed to generate samples. Although updating as many parameters as possible is the ideal model update process, it is not practical to identify all the parameters because of limitation of the experimental data. The bridge was thus divided into several clusters, and the values of the updated parameters of the members in the same cluster are assumed to be equal. Two model updating schemes were discussed as an example to investigate the effect of parameter selection, such as how to model the spring at each support, in model updating process. It was observed that although models with more parameters tend to fit better, the updated result often showed a different trend from the engineering prediction

MARS: Message Passing for Antenna and RF Chain Selection for Hybrid Beamforming in MIMO Communication Systems

In this paper, we consider a prospective receiving hybrid beamforming structure consisting of several radio frequency (RF) chains and abundant antenna elements in multi-input multi-output (MIMO) systems. Due to conventional costly full connections, we design an enhanced partially-connected beamformer employing low-density parity-check (LDPC) based structure. As a benefit of LDPC-based structure, information can be exchanged among clustered RF/antenna groups, which results in a low computational complexity order. Advanced message passing (MP) capable of inferring and transferring data among different paths is designed to support LDPC-based hybrid beamformer. We propose a message passing enhanced antenna and RF chain selection (MARS) scheme to minimize the operational power of antennas and RF chains of the receiver. Furthermore, sequential and parallel MP for MARS are respectively designed as MARS-S and MARS-P schemes to address convergence speed issue. Simulations have validated the convergence of both the MARS-P and the MARS-S algorithms. Owing to asynchronous information transfer of MARS-P, it reveals that higher power is required than that of MARS-S, which strikes a compelling balance between power consumption, convergence, and computational complexity. It is also demonstrated that the proposed MARS scheme outperforms the existing benchmarks using heuristic method of fully-/partially-connected architectures in open literature in terms of the lowest power and highest energy efficiency

Observation of Majorana fermions with spin selective Andreev reflection in the vortex of topological superconductor

Majorana fermion (MF) whose antiparticle is itself has been predicted in condensed matter systems. Signatures of the MFs have been reported as zero energy modes in various systems. More definitive evidences are highly desired to verify the existence of the MF. Very recently, theory has predicted MFs to induce spin selective Andreev reflection (SSAR), a novel magnetic property which can be used to detect the MFs. Here we report the first observation of the SSAR from MFs inside vortices in Bi2Te3/NbSe2 hetero-structure, in which topological superconductivity was previously established. By using spin-polarized scanning tunneling microscopy/spectroscopy (STM/STS), we show that the zero-bias peak of the tunneling differential conductance at the vortex center is substantially higher when the tip polarization and the external magnetic field are parallel than anti-parallel to each other. Such strong spin dependence of the tunneling is absent away from the vortex center, or in a conventional superconductor. The observed spin dependent tunneling effect is a direct evidence for the SSAR from MFs, fully consistent with theoretical analyses. Our work provides definitive evidences of MFs and will stimulate the MFs research on their novel physical properties, hence a step towards their statistics and application in quantum computing.Comment: 4 figures 15 page

FAM129B, an antioxidative protein, reduces chemosensitivity by competing with Nrf2 for Keap1 binding.

BackgroundThe transcription factor Nrf2 is a master regulator of antioxidant response. While Nrf2 activation may counter increasing oxidative stress in aging, its activation in cancer can promote cancer progression and metastasis, and confer resistance to chemotherapy and radiotherapy. Thus, Nrf2 has been considered as a key pharmacological target. Unfortunately, there are no specific Nrf2 inhibitors for therapeutic application. Moreover, high Nrf2 activity in many tumors without Keap1 or Nrf2 mutations suggests that alternative mechanisms of Nrf2 regulation exist.MethodsInteraction of FAM129B with Keap1 is demonstrated by immunofluorescence, colocalization, co-immunoprecipitation and mammalian two-hybrid assay. Antioxidative function of FAM129B is analyzed by measuring ROS levels with DCF/flow cytometry, Nrf2 activation using luciferase reporter assay and determination of downstream gene expression by qPCR and wester blotting. Impact of FAM129B on in vivo chemosensitivity is examined in mice bearing breast and colon cancer xenografts. The clinical relevance of FAM129B is assessed by qPCR in breast cancer samples and data mining of publicly available databases.FindingsWe have demonstrated that FAM129B in cancer promotes Nrf2 activity by reducing its ubiquitination through competition with Nrf2 for Keap1 binding via its DLG and ETGE motifs. In addition, FAM129B reduces chemosensitivity by augmenting Nrf2 antioxidative signaling and confers poor prognosis in breast and lung cancer.InterpretationThese findings demonstrate the important role of FAM129B in Nrf2 activation and antioxidative response, and identify FMA129B as a potential therapeutic target. FUND: The Chang Gung Medical Foundation (Taiwan) and the Ministry of Science and Technology (Taiwan)

Molecular role of GATA binding protein 4 (GATA-4) in hyperglycemia-induced reduction of cardiac contractility

<p>Abstract</p> <p>Background</p> <p>Diabetic cardiomyopathy, a diabetes-specific complication, refers to a disorder that eventually leads to left ventricular hypertrophy in addition to diastolic and systolic dysfunction. In recent studies, hyperglycemia-induced reactive oxygen species (ROS) in cardiomyocytes have been linked to diabetic cardiomyopathy. GATA binding protein 4 (GATA-4) regulates the expression of many cardio-structural genes including cardiac troponin-I (cTnI).</p> <p>Methods</p> <p>Streptozotocin-induced diabetic rats and H9c2 embryonic rat cardiomyocytes treated with a high concentration of glucose (a D-glucose concentration of 30 mM was used and cells were cultured for 24 hr) were used to examine the effect of hyperglycemia on GATA-4 accumulation in the nucleus. cTnI expression was found to be linked to cardiac tonic dysfunction, and we evaluated the expression levels of cTnI and GATA-4 by Western blot analysis.</p> <p>Results</p> <p>Cardiac output was lowered in STZ-induced diabetic rats. In addition, higher expressions of cardiac troponin I (cTnI) and phosphorylated GATA-4 were identified in these rats by Western blotting. The changes were reversed by treatment with insulin or phlorizin after correction of the blood sugar level. In H9c2 cells, ROS production owing to the high glucose concentration increased the expression of cTnI and GATA-4 phosphorylation. However, hyperglycemia failed to increase the expression of cTnI when GATA-4 was silenced by small interfering RNA (siRNA) in H9c2 cells. Otherwise, activation of ERK is known to be a signal for phosphorylation of serine105 in GATA-4 to increase the DNA binding ability of this transcription factor. Moreover, GSK3β could directly interact with GATA-4 to cause GATA-4 to be exported from the nucleus. GATA-4 nuclear translocation and GSK3β ser9 phosphorylation were both elevated by a high glucose concentration in H9c2 cells. These changes were reversed by tiron (ROS scavenger), PD98059 (MEK/ERK inhibitor), or siRNA of GATA-4. Cell contractility measurement also indicated that the high glucose concentration decreased the contractility of H9c2 cells, and this was reduced by siRNA of GATA-4.</p> <p>Conclusions</p> <p>Hyperglycemia can cause systolic dysfunction and a higher expression of cTnI in cardiomyocytes through ROS, enhancing MEK/ERK-induced GATA-4 phosphorylation and accumulation in the cell nucleus.</p

Effective identification of terrain positions from gridded DEM data using multimodal classification integration

Terrain positions are widely used to describe the Earth’s topographic features and play an important role in the studies of landform evolution, soil erosion and hydrological modeling. This work develops a new multimodal classification system with enhanced classification performance by integrating different approaches for terrain position identification. The adopted classification approaches include local terrain attribute (LA)-based and regional terrain attribute (RA)-based, rule-based and supervised, and pixel-based and object-oriented methods. Firstly, a double-level definition scheme is presented for terrain positions. Then, utilizing a hierarchical framework, a multimodal approach is developed by integrating different classification techniques. Finally, an assessment method is established to evaluate the new classification system from different aspects. The experimental results, obtained at a Loess Plateau region in northern China on a 5 m digital elevation model (DEM), show reasonably positional relationship, and larger inter-class and smaller intra-class variances. This indicates that identified terrain positions are consistent with the actual topography from both overall and local perspectives, and have relatively good integrity and rationality. This study demonstrates that the current multimodal classification system, developed by taking advantage of various classification methods, can reflect the geographic meanings and topographic features of terrain positions from different levels

Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework

The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities. Despite these empirical advances, there remain fundamental research questions: How can we quantify the interactions that are necessary to solve a multimodal task? Subsequently, what are the most suitable multimodal models to capture these interactions? To answer these questions, we propose an information-theoretic approach to quantify the degree of redundancy, uniqueness, and synergy relating input modalities with an output task. We term these three measures as the PID statistics of a multimodal distribution (or PID for short), and introduce two new estimators for these PID statistics that scale to high-dimensional distributions. To validate PID estimation, we conduct extensive experiments on both synthetic datasets where the PID is known and on large-scale multimodal benchmarks where PID estimations are compared with human annotations. Finally, we demonstrate their usefulness in (1) quantifying interactions within multimodal datasets, (2) quantifying interactions captured by multimodal models, (3) principled approaches for model selection, and (4) three real-world case studies engaging with domain experts in pathology, mood prediction, and robotic perception where our framework helps to recommend strong multimodal models for each application.Comment: Code available at: https://github.com/pliang279/PI