Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization

It is NP-complete to find non-negative factors W and H with fixed rank r from a non-negative matrix X by minimizing ||X-WH^Τ ||^2. Although the separability assumption (all data points are in the conical hull of the extreme rows) enables polynomial-time algorithms, the computational cost is not affordable for big data. This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013]. The design of quantum DCA (QDCA) is challenging. In the divide step, the random projections in DCA is completed by a quantum algorithm for linear operations, which achieves the exponential speedup. We then devise a heuristic post-selection procedure which extracts the information of anchors stored in the quantum states efficiently. Under a plausible assumption, QDCA performs efficiently, achieves the quantum speedup, and is beneficial for high dimensional problems.
Repairing ASR output by Artificial Development and Ontology based Learning

General purpose automatic speech recognition (gpASR) systems such as Google, Watson, etc. sometimes output inaccurate sentences when used in a domain specific scenario as it may not have had enough training samples for that particular domain and context. Further, the accent of the speaker and the environmental conditions in which the speaker speaks a sentence may influence the speech engine to recognize certain words inaccurately. Many approaches to improve the accuracy of ASR output exist. However, in the context of a domain and the environment in which a speaker speaks the sentences, gpASR output needs a lot of improvement in order to provide effective speech interfaces to domain-specific systems. In this paper, we demonstrate a method that combines bio-inspired artifi- cial development (ArtDev) with machine learning (ML) approaches to repair the output of a gpASR. Our method factors in the environment to tailor the repair process.
Targeting the MMP-14/MMP-2/integrin αvβ3 axis with multispecific N-TIMP2–based antagonists for cancer therapy

The pathophysiological functions of the signaling molecules matrix metalloproteinase-14 (MMP-14) and integrin αvβ3 in various types of cancer are believed to derive from their collaborative activity in promoting invasion, metastasis, and angiogenesis, as shown in vitro and in vivo. The two effectors act in concert in a cell-specific manner through the localization of pro-MMP-2 to the cell surface, where it is processed to intermediate and matured MMP-2. The matured MMP-2 product is localized to the cell surface via its binding to integrin αvβ3. The MMP-14/MMP-2/integrin αvβ3 axis thus constitutes an attractive putative target for therapeutic interventions, but the development of inhibitors that target this axis remains an unfulfilled task. To address the lack of such multitarget inhibitors, we have established a combinatorial approach that is based on flow cytometry screening of a yeast-displayed N-TIMP2 (N-terminal domain variant of tissue inhibitor of metalloproteinase-2) mutant library. On the basis of this screening, we generated protein monomers and a heterodimer that contain monovalent and bivalent binding epitopes to MMP-14 and integrin αvβ3. Among these proteins, the bi-specific heterodimer, which bound strongly to both MMP-14 and integrin αvβ3, exhibited superior ability to inhibit MMP-2 activation and displayed the highest inhibitory activity in cell-based models of a MMP-14-, MMP-2-, and integrin αvβ3-dependent glioblastoma and of endothelial cell invasiveness and endothelial capillary tube formation. These assays enabled us to show the superiority of the combined target effects of the inhibitors and to investigate separately the role each of the three signaling molecules in various malignant processes.
Theoretical Studies on Catalysis Mechanisms of Serum Paraoxonase 1 and Phosphotriesterase Diisopropyl Fluorophosphatase Suggest the Alteration of Substrate Preference from Paraoxonase to DFP

The calcium-dependent β-propeller proteins mammalian serum paraoxonase 1 (PON1) and phosphotriesterase diisopropyl fluorophosphatase (DFPase) catalyze the hydrolysis of organophosphorus compounds and enhance hydrolysis of various nerve agents. In the present work, the phosphotriesterase activity development between PON1 and DFPase was investigated by using the hybrid density functional theory method B3LYP. Based on the active-site difference between PON1 and DFPase, both the wild type and the mutant (a water molecule replacing Asn270 in PON1) models were designed. The results indicated that the substitution of a water molecule for Asn270 in PON1 had little effect on the enzyme activity in kinetics, while being more efficient in thermodynamics, which is essential for DFP hydrolysis. Structure comparisons of evolutionarily related enzymes show that the mutation of Asn270 leads to the catalytic Ca2+ ion indirectly connecting the buried structural Ca2+ ion via hydrogen bonds in DFPase. It can reduce the plasticity of enzymatic structure, and possibly change the substrate preference from paraoxon to DFP, which implies an evolutionary transition from mono- to dinuclear catalytic centers. Our studies shed light on the investigation of enzyme catalysis mechanism from an evolutionary perspective.
Chinese Poetry Generation with a Working Memory Model

As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge. In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line. After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both automatic and human evaluation results show that our model outperforms current state-of-the-art methods.
Getting Closer to Absolute Molar Masses of Technical Lignins

Abstract Determination of molecular weight parameters of native and, in particular, technical lignins are based on size exclusion chromatography (SEC) approaches. However, no matter which approach is used, either conventional SEC with a refractive index detector and calibration with standards or multi‐angle light scattering (MALS) detection at 488 nm, 633 nm, 658 nm, or 690 nm, all variants can be severely erroneous. The lack of calibration standards with high structural similarity to lignin impairs the quality of the molar masses determined by conventional SEC, and the typical fluorescence of (technical) lignins renders the corresponding MALS data rather questionable. Application of MALS detection at 785 nm by using an infrared laser largely overcomes those problems and allows for a reliable and reproducible determination of the molar mass distributions of all types of lignins, which has been demonstrated in this study for various and structurally different analytes, such as kraft lignins, milled‐wood lignin, lignosulfonates, and biorefinery lignins. The topics of calibration, lignin fluorescence, and lignin UV absorption in connection with MALS detection are critically discussed in detail, and a reliable protocol is presented. Correction factors based on MALS measurements have been determined for commercially available calibration standards, such as pullulan and polystyrene sulfonate, so that now more reliable mass data can be obtained also if no MALS system is available and these conventional calibration standards have to be resorted to.
A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning

The large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcement learning control algorithm with the one-layer neural network for each subsystem or called agents to adapt the variation of the networked infrastructures. Each controller includes a critic network and action network for approximating strategy utility function and desired control law, respectively. For avoiding a large number of trials and improving the stability, the training of action network introduces supervised learning mechanisms into reduction of long-term cost. The stability of the control system with learning algorithm is analyzed; the upper bound of the tracking error and neural network weights are also estimated. The effectiveness of our proposed controller is illustrated in the simulation; the results indicate the stability under communication delay and disturbances as well.
Opinion Diffusion and Campaigning on Society Graphs

We study the effects of campaigning, where the society is partitioned into voter clusters and a diffusion process propagates opinions in a network connecting those clusters. Our model is very general and can incorporate many campaigning actions, various partitions of the society into voter clusters, and very general diffusion processes. Perhaps surprisingly, we show that computing the cheapest campaign for rigging a given election can usually be done efficiently, even with arbitrarily-many voters.
The Significant Value of Preoperative Prognostic Nutritional Index for Survival in Pancreatic Cancers: A Meta-analysis

Abstract Nutritional status plays a significant value in the incidence of postoperative complications and survival rate after resection of cancers. The preoperative prognostic nutritional index (PNI) has recently been reported to be a simple, sensitive, and effective prognostic parameter in various cancers. We conducted a meta-analysis to explore the significant value of PNI in pancreatic cancers. We systematically searched electronic databases of PubMed, Embase, Cochrane Library, Web of Science, Wanfang Data, and Chinese National Knowledge Infrastructure. Then, relevant studies were selected to perform this meta-analysis. Eleven studies with a total of 2123 patients met the inclusion criteria of this meta-analysis. The pooled results revealed that a lower PNI was significantly correlated with poorer overall survival (hazard ratio, 1.57; 95% confidence interval, 1.40–1.77; P < 0.001). Preoperative PNI might be an efficient predictor of survival with pancreatic cancers.
Resting-State Functional Magnetic Resonance Imaging and Probabilistic Diffusion Tensor Imaging Demonstrate That the Greatest Functional and Structural Connectivity in the Hand Motor Homunculus Occurs in the Area of the Thumb

The primary hand motor region is classically believed to be in the "hand knob" area in the precentral gyrus (PCG). However, hand motor task-based activation is often localized outside this area. The purpose of this study is to investigate the structural and functional connectivity driven by different seed locations corresponding to the little, index, and thumb in the PCG using probabilistic diffusion tractography (PDT) and resting-state functional magnetic resonance imaging (rfMRI). Twelve healthy subjects had three regions of interest (ROIs) placed in the left PCG: lateral to the hand knob (thumb area), within the hand knob (index finger area), and medial to the hand knob (little finger area). Connectivity maps were generated using PDT and rfMRI. Individual and group level analyses were performed. Results show that the greatest hand motor connectivity between both hemispheres was obtained using the ROI positioned just lateral to the hand knob in the PCG (the thumb area). The number of connected voxels in the PCG between the two hemispheres was greatest in the lateral-most ROI (the thumb area): 279 compared with 13 for the medial-most ROI and 9 for the central hand knob ROI. Similarly, the highest white matter connectivity between the two hemispheres resulted from the ROI placed in the lateral portion of PCG (p < 0.003). The maximal functional and structural connectivity of the hand motor area between hemispheres occurs in the thumb area, located laterally at the "hand knob. " Thus, this location appears maximal for rfMRI and PDT seeding of the motor area.
Performance of Treponemal Tests for the Diagnosis of Syphilis

BACKGROUND Treponemal immunoassays are increasingly used for syphilis screening with the reverse sequence algorithm. There are few data describing performance of treponemal immunoassays compared to traditional treponemal tests in patients with and without syphilis. METHODS We calculated sensitivity and specificity of 7 treponemal assays: (1) ADVIA Centaur (chemiluminescence immunoassay [CIA]); (2) Bioplex 2200 (microbead immunoassay); (3) fluorescent treponemal antibody absorption test (FTA-ABS); (4) INNO-LIA (line immunoassay); (5) LIAISON CIA; (6) Treponema pallidum particle agglutination assay (TPPA); and (7) Trep-Sure (enzyme immunoassay [EIA]), using a reference standard combining clinical diagnosis and serology results. Sera were collected between May 2012-January 2013. Cases were characterized as: (1) current clinical diagnosis of syphilis: primary, secondary, early latent, late latent; (2) prior treated syphilis only; (3) no evidence of current syphilis, no prior history of syphilis, and at least 4 of 7 treponemal tests negative. RESULTS Among 959 participants, 262 had current syphilis, 294 had prior syphilis, and 403 did not have syphilis. FTA-ABS was less sensitive for primary syphilis (78.2%) than the immunoassays or TPPA (94.5%-96.4%) (all P ≤ .01). All immunoassays were 100% sensitive for secondary syphilis, 95.2%-100% sensitive for early latent disease, and 86.8%-98.5% sensitive in late latent disease. TPPA had 100% specificity. CONCLUSIONS Treponemal immunoassays demonstrated excellent sensitivity for secondary, early latent, and seropositive primary syphilis. Sensitivity of FTA-ABS in primary syphilis was poor. Given its high specificity and superior sensitivity, TPPA is preferred to adjudicate discordant results with the reverse sequence algorithm over the FTA-ABS.
GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations between their readings. Forecasting geo-sensory time series is of great importance yet very challenging as it is affected by many complex factors, i.e., dynamic spatio-temporal correlations and external factors. In this paper, we predict the readings of a geo-sensor over several future hours by using a multi-level attention-based recurrent neural network that considers multiple sensors' readings, meteorological data, and spatial data. More specifically, our model consists of two major parts: 1) a multi-level attention mechanism to model the dynamic spatio-temporal dependencies. 2) a general fusion module to incorporate the external factors from different domains. Experiments on two types of real-world datasets, viz., air quality data and water quality data, demonstrate that our method outperforms nine baseline methods.
Long-term In Vivo Tracking of Inflammatory Cell Dynamics Within Drosophila Pupae

During the rapid inflammatory response to tissue damage, cells of the innate immune system are quickly recruited to the injury site. Once at the wound, innate immune cells perform a number of essential functions, such as fighting infection, clearing necrotic debris, and stimulating matrix deposition. In order to fully understand the diverse signaling events that regulate this immune response, it is crucial to observe the complex behaviors of (and interactions that occur between) multiple cell lineages in vivo, and in real-time, with the high spatio-temporal resolution. The optical translucency and the genetic tractability of Drosophila embryos have established Drosophila as an invaluable model to live-image and dissect fundamental aspects of inflammatory cell behavior, including mechanisms of developmental dispersal, clearance of apoptotic corpses and/or microbial pathogens, and recruitment to wounds. However, more recent work has now demonstrated that employing a much later stage in the Drosophila lifecycle – the Drosophila pupa – offers a number of distinct advantages, including improved RNAi efficiency, longer imaging periods, and significantly greater immune cell numbers. Here we describe a protocol for imaging wound repair and the associated inflammatory response at the high spatio-temporal resolution in live Drosophila pupae. To follow the dynamics of both re-epithelialization and inflammation, we use a number of specific in vivo fluorescent markers for both the epithelium and innate immune cells. We also demonstrate the effectiveness of photo-convertible fluorophores, such as Kaede, for following the specific immune cell subsets, to track their behavior as they migrate to, and resolve from, the injury site.
Approximating Word Ranking and Negative Sampling for Word Embedding

CBOW (Continuous Bag-Of-Words) is one of the most commonly used techniques to generate word embeddings in various NLP tasks. However, it fails to reach the optimal performance due to uniform involvements of positive words and a simple sampling distribution of negative words. To resolve these issues, we propose OptRank to optimize word ranking and approximate negative sampling for bettering word embedding. Specifically, we first formalize word embedding as a ranking problem. Then, we weigh the positive words by their ranks such that highly ranked words have more importance, and adopt a dynamic sampling strategy to select informative negative words. In addition, an approximation method is designed to efficiently compute word ranks. Empirical experiments show that OptRank consistently outperforms its counterparts on a benchmark dataset with different sampling scales, especially when the sampled subset is small. The code and datasets can be obtained from https://github.com/ouououououou/OptRank.
Roles of Oral Infections in the Pathomechanism of Atherosclerosis

Oral infections occur frequently in humans and often lead to chronic inflammations affecting the teeth (i.e., caries), the gingival tissues surrounding the teeth (i.e., gingivitis and endodontic lesions), and the tooth-supporting structures (i.e., periodontitis). At least four basic pathogenic mechanisms have been proposed that involve oral inflammations in the pathogenesis of atherosclerosis: (1) low level bacteremia by which oral bacteria enter the blood stream and invade the arterial wall; (2) systemic inflammation induced by inflammatory mediators released from the sites of the oral inflammation into the blood stream; (3) autoimmunity to host proteins caused by the host immune response to specific components of oral pathogens; (4) pro-atherogenic effects resulting from specific bacterial toxins that are produced by oral pathogenic bacteria. In this narrative review, we summarize published experimental evidence related to these four mechanisms and discuss their impact on the pathogenesis of atherosclerosis.
A Pervasive Approach to EEG-Based Depression Detection

Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine,K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed thatK-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.
Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network

Predicting users’ activity and location preferences is of great significance in location based services. Considering that users’ activity and location preferences interplay with each other, many scholars tried to figure out the relation between users’ activities and locations for improving prediction performance. However, most previous works enforce a rigid human-defined modeling strategy to capture these two factors, either activity purpose controlling location preference or spatial region determining activity preference. Unlike existing methods, we introduce spatial-activity topics as the latent factor capturing both users’ activity and location preferences. We propose Multi-task Context Aware Recurrent Neural Network to leverage the spatial activity topic for activity and location prediction. More specifically, a novel Context Aware Recurrent Unit is designed to integrate the sequential dependency and temporal regularity of spatial activity topics. Extensive experimental results based on real-world public datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.
Comprehensive study of domain rearrangements of single-chain bispecific antibodies to determine the best combination of configurations and microbial host cells

ABSTRACT Small bispecific antibodies (bsAbs) are important therapeutic molecules and represent the first bsAb format approved by the United States Food and Drug Administration. Diabody (Db), a small bsAb format, has four possible domain orders; we previously reported the differences in the expression levels and cancer growth inhibition effects upon rearranging the domain order of this format. However, there have been no comprehensive reports on domain rearrangements of bispecific single-chain Db (scDb) and tandem single-chain Fv (taFv), which are widely used bsAb formats. In this study, we designed all possible domain orders for scDb and taFv (each with eight variants) with identical Fv pairs and individually expressed all 16 variants using Escherichia coli, Pichia pastoris, and Brevibacillus choshinensis. Comprehensive investigations showed that the intrinsic functions of the variants were similar to each other, regardless of the expression host system, but expression levels varied depending on the format as well as on the host cell. Among the 16 variants, we found a promising candidate that exhibited high activity and productivity. Furthermore, we determined that B. choshinensis is an attractive expression host because of its secretory production of recombinant proteins.