Signature Sequence of Intersection Curve of Two Quadrics for Exact Morphological Classification

We present an efficient method for classifying the morphology of the intersection curve of two quadrics (QSIC) in PR3, 3D real projective space; here, the term morphology is used in a broad sense to mean the shape, topological, and algebraic properties of a QSIC, including singularity, reducibility, the number of connected components, and the degree of each irreducible component, etc. There are in total 35 different QSIC morphologies with non-degenerate quadric pencils. For each of these 35 QSIC morphologies, through a detailed study of the eigenvalue curve and the index function jump we establish a characterizing algebraic condition expressed in terms of the Segre characteristics and the signature sequence of a quadric pencil. We show how to compute a signature sequence with rational arithmetic so as to determine the morphology of the intersection curve of any two given quadrics. Two immediate applications of our results are the robust topological classification of QSIC in computing B-rep surface representation in solid modeling and the derivation of algebraic conditions for collision detection of quadric primitives

KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]

The volume of time series data has exploded due to the popularity of new applications, such as data center management and IoT. Subsequence matching is a fundamental task in mining time series data. All index-based approaches only consider raw subsequence matching (RSM) and do not support subsequence normalization. UCR Suite can deal with normalized subsequence match problem (NSM), but it needs to scan full time series. In this paper, we propose a novel problem, named constrained normalized subsequence matching problem (cNSM), which adds some constraints to NSM problem. The cNSM problem provides a knob to flexibly control the degree of offset shifting and amplitude scaling, which enables users to build the index to process the query. We propose a new index structure, KV-index, and the matching algorithm, KV-match. With a single index, our approach can support both RSM and cNSM problems under either ED or DTW distance. KV-index is a key-value structure, which can be easily implemented on local files or HBase tables. To support the query of arbitrary lengths, we extend KV-match to KV-match$_{DP}$, which utilizes multiple varied-length indexes to process the query. We conduct extensive experiments on synthetic and real-world datasets. The results verify the effectiveness and efficiency of our approach.Comment: 13 page

Targeting the complex I and III of mitochondrial electron transport chain as a potentially viable option in liver cancer management

Abstract Liver cancer is one of the most common and lethal types of oncological disease in the world, with limited treatment options. New treatment modalities are desperately needed, but their development is hampered by a lack of insight into the underlying molecular mechanisms of disease. It is clear that metabolic reprogramming in mitochondrial function is intimately linked to the liver cancer process, prompting the possibility to explore mitochondrial biochemistry as a potential therapeutic target. Here we report that depletion of mitochondrial DNA, pharmacologic inhibition of mitochondrial electron transport chain (mETC) complex I/complex III, or genetic of mETC complex I restricts cancer cell growth and clonogenicity in various preclinical models of liver cancer, including cell lines, mouse liver organoids, and murine xenografts. The restriction is linked to the production of reactive oxygen species, apoptosis induction and reduced ATP generation. As a result, our findings suggest that the mETC compartment of mitochondria could be a potential therapeutic target in liver cancer

Solving the mystery of vanishing rivers in China

A major controversy was sparked worldwide by a recent national water census claiming that the number of Chinese rivers with watersheds ≥100 km2 was less than half the previous estimate of 50 000 rivers, which also stimulates debates on the potential causes and consequences. Here, we estimated the number of rivers in terms of stream-segmentation characteristics described by Horton, Strahler and Shreve stream-order rules, as well as their mixed mode for named rivers recorded in the Encyclopedia of Rivers and Lakes in China. As a result, the number of 'vanishing rivers' has been found to be highly relevant to statistical specifications in addition to the erroneous inclusion of pseudo-rivers primarily generated in arid or frost-thaw areas. The modified Horton stream-order scheme reasonably depicts the configuration of complete natural streams from headwater to destination, while the Strahler largely projects the fragmentation of the named river networks associated with human aggregation to the hierarchical river systems

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions. Multi-modal models are expected to benefit from cross-modal interactions on the basis of ensuring uni-modal feature learning. However, recent supervised multi-modal late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality. We prove that this phenomenon does hurt the model's generalization ability. To this end, we propose to choose a targeted late-fusion learning method for the given supervised multi-modal task from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on various multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40

Automatic Frequency-Based Flood Forecast From Numerical Weather Prediction Using A Service-Oriented Architecture

Destructive floods occurred more frequently in mountainous regions in China in recent years. However, the meteorological and hydrological station network in such regions is usually poor, and no long-series observations are available. Therefore, it is difficult to determine the hydrological parameters for flood discharge and stage forecast. This paper aims to propose an automatic frequency-based flood forecast framework from numerical weather prediction (NWP) using a Service Oriented Architecture (SOA). The proposed framework has 4 main steps. First, historical flood discharge is simulated by using a distributed hydrological model and satellite-derived rainfall dataset (e.g., the CMORPH and the TRMM), and the relationship between flood frequency and simulated flood discharge (i.e., the frequency curve) is established for each river reach. Second, by taking the advantages of the highly automatic SOA technology, the predicted rainfall data from the NWP (e.g., the TIGGE ensemble) are downloaded and interpreted automatically in real time. Third, a distributed hydrological model is automatically executed in the SOA environment to predict flow discharges of each river reach. And finally, the flood frequency is obtained from the simulated flow discharges by looking up the frequency curves, and warning information of possible floods is generated for potential sufferers. By using Web service in a social network, users can be informed such warning information at any time, and can make better preparation for the possible floods. Along with the real-time updates of the NWP, the latest warning information will always be available for users. From a sample demonstration, it can be concluded that the frequency-based flood forecast from the NWP is highly useful to enhance user awareness of flood risk, and the SOA and social network techniques are regarded as a feasible way for developing the automatic system

Direct determination of band-gap renormalization in degenerately doped ultrawide band gap β-Ga_{2}O_{3} semiconductor

Ga2O3 is emerging as a promising wide band-gap semiconductor for high-power electronics and deep ultraviolet optoelectronics. It is highly desirable to dope it with controllable carrier concentrations for different device applications. This work reports a combined photoemission spectroscopy and theoretical calculation study on the electronic structure of Si doped Ga_{2}O_{3} films with carrier concentration varying from 4.6×10^{18} cm^{−3} to 2.6×10^{20} cm^{−3}. Hard x-ray photoelectron spectroscopy was used to directly measure the widening of the band gap as a result of occupation of conduction band and band-gap renormalization associated with many-body interactions. A large band-gap renormalization of 0.3 eV was directly observed in heavily doped Ga_{2}O_{3}. Supplemented with hybrid density functional theory calculations, we demonstrated that the band-gap renormalization results from the decrease in energy of the conduction band edge driven by the mutual electrostatic interaction between added electrons. Moreover, our work reveals that Si is a superior dopant over Ge and Sn, because Si 3s forms a resonant donor state above the conduction band minimum, leaving the host conduction band mostly unperturbed and a high mobility is maintained though the doping level is high. Insights of the present work have significant implications in doping optimization of Ga_{2}O_{3} and realization of optoelectronic devices

Production of cold-adapted cellulase by Verticillium sp. isolated from Antarctic soils

Background: Cellulose can be converted to ethanol by simultaneous saccharification and fermentation (SSF). The difference between the optimal temperature of cellulase and microbial fermentation, however, has been identified as the critical problem with SSF. In this study, one fungal strain (AnsX1) with high cellulase activity at low temperature was isolated from Antarctic soils and identified as Verticillium sp. by morphological and molecular analyses. Results: The biochemical properties of crude AnsX1 cellulase samples were studied by filter paper cellulase assay. The maximum cellulase activity was achieved at low temperature in an acidic environment with addition of metal ions. Furthermore, AnsX1 cellulase demonstrated 54-63% enzymatic activity at ethanol concentrations of 5-10%. AnsX1 cellulase production was influenced by inoculum size, carbon and nitrogen sources, and elicitors. The optimal culture conditions for AnsX1 cellulase production were 5% inoculum, wheat bran as carbon source, (NH4)2SO4 as nitrogen source, and sorbitol added in the medium. Conclusions: Our present work has potential to enable the development of an economic and efficient cold-adapted cellulase system for bioconversion of lignocellulosic biomass into biofuels in future

Sorting nexin 12 interacts with BACE1 and regulates BACE1-mediated APP processing

Background: beta-site APP cleaving enzyme 1 (BACE1) cleaves beta-amyloid precursor protein (APP) to initiate the production of beta-amyloid (A beta), the prime culprit in Alzheimer's disease (AD). Dysregulation of the intracellular trafficking of BACE1 may affect A beta generation, contributing to AD pathology. In this study, we investigated whether BACE1 trafficking and BACE1-mediated APP processing/A beta generation are affected by sorting nexin 12 (SNX12), a member of the sorting nexin (SNX) family that is involved in protein trafficking regulation. Results: Herein, we find that SNX12 is widely expressed in brain tissues and is mainly localized in the early endosomes. Overexpression of SNX12 does not affect the steady-state levels of APP, BACE1 or gamma-secretase components, but dramatically reduces the levels of A beta, soluble APP beta and APP beta-carboxyl terminal fragments. Downregulation of SNX12 has the opposite effects. Modulation of SNX12 levels does not affect gamma-secretase activity or in vitro beta-secretase activity. Further studies reveal that SNX12 interacts with BACE1 and downregulation of SNX12 accelerates BACE1 endocytosis and decreases steady-state level of cell surface BACE1. Finally, we find that the SNX12 protein level is dramatically decreased in the brain of AD patients as compared to that of controls. Conclusion: This study demonstrates that SNX12 can regulate the endocytosis of BACE1 through their interaction, thereby affecting beta-processing of APP for A beta production. The reduced level of SNX12 in AD brains suggests that an alteration of SNX12 may contribute to AD pathology. Therefore, inhibition of BACE1-mediated beta-processing of APP by regulating SNX12 might serve as an alternative strategy in developing an AD intervention.Alzheimer's Association; National Natural Science Foundation of China [30973150, 81161120496, 81000540]; 973 Prophase Project [2010CB535004]; Natural Science Foundation of Fujian Province of China [2009J06022, 2010J01235]; Program for New Century Excellent Talents in Universities (NCET); Fundamental Research Funds for the Central Universities; Fok Ying Tung Education Foundatio