Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Detection of Host Response Antibodies to Borrelia burgdorferi Using Immuno-PCR

ABSTRACT Lyme disease is the fastest-growing zoonotic disease in North America. Current methods for detection of Borrelia burgdorferi infection are challenged by analysis subjectivity and standardization of antigen source. In the present study, we developed an immuno-PCR (iPCR)-based approach employing recombinant in vivo-expressed B. burgdorferi antigens for objective detection of a host immune response to B. burgdorferi infection. iPCR is a liquid-phase protein detection method that combines the sensitivity of PCR with the specificity and versatility of immunoassay-based protocols. Use of magnetic beads coated with intact spirochetes provided effective antigen presentation and allowed detection of host-generated antibodies in experimentally infected mice at day 11 postinoculation, whereas host-generated antibodies were detected at day 14 by enzyme-linked immunosorbent assay (ELISA) and day 21 by immunoblotting. Furthermore, magnetic beads coated with recombinant B. burgdorferi in vivo-expressed antigen OspC or BmpA demonstrated positive detection of host-generated antibodies in mice at day 7 postinoculation with markedly increased iPCR signals above the background, with the quantification cycle (Cq) value for each sample minus the mean background Cq plus 3 standard deviations (ΔCq) being 4 to 10, whereas ΔCq was 2.5 for intact spirochete-coated beads. iPCR demonstrated a strong correlation (Spearman rank correlation = 0.895, P < 0.0001) with a commercial ELISA for detection of host antibodies in human Lyme disease patient sera using the B. burgdorferi VlsE C6 peptide. In addition, iPCR showed potential applicability for direct detection of spirochetes in blood. The results presented here indicate that our iPCR assay has the potential to provide an objective format that can be used for sensitive detection of multiple host response antibodies and isotypes to B. burgdorferi infection.
Halite: Fast and Scalable Multiresolution Local-Correlation Clustering

This paper proposes Halite, a novel, fast, and scalable clustering method that looks for clusters in subspaces of multidimensional data. Existing methods are typically superlinear in space or execution time. Halite's strengths are that it is fast and scalable, while still giving highly accurate results. Specifically the main contributions of Halite are: 1) Scalability: it is linear or quasi linear in time and space regarding the data size and dimensionality, and the dimensionality of the clusters' subspaces; 2) Usability: it is deterministic, robust to noise, doesn't take the number of clusters as an input parameter, and detects clusters in subspaces generated by original axes or by their linear combinations, including space rotation; 3) Effectiveness: it is accurate, providing results with equal or better quality compared to top related works; and 4) Generality: it includes a soft clustering approach. Experiments on synthetic data ranging from five to 30 axes and up to 1 \rm million points were performed. Halite was in average at least 12 times faster than seven representative works, and always presented highly accurate results. On real data, Halite was at least 11 times faster than others, increasing their accuracy in up to 35 percent. Finally, we report experiments in a real scenario where soft clustering is desirable.
On time-frequency mask estimation for MVDR beamforming with application in robust speech recognition

Acoustic beamforming has played a key role in the robust automatic speech recognition (ASR) applications. Accurate estimates of the speech and noise spatial covariance matrices (SCM) are crucial for successfully applying the minimum variance distortionless response (MVDR) beamforming. Reliable estimation of time-frequency (TF) masks can improve the estimation of the SCMs and significantly improve the performance of the MVDR beamforming in ASR tasks. In this paper, we focus on the TF mask estimation using recurrent neural networks (RNN). Specifically, our methods include training the RNN to estimate the speech and noise masks independently, training the RNN to minimize the ASR cost function directly, and performing multiple passes to iteratively improve the mask estimation. The proposed methods are evaluated individually and overally on the CHiME-4 challenge. The results show that the proposed methods improve the ASR performance individually and also work complementarily. The overall performance achieves a word error rate of 8.9% with 6-microphone configuration, which is much better than 12.0% achieved with the state-of-the-art MVDR implementation.
RED-UCATION: A Novel CNN Architecture Based on Denoising Nonlinearities

Image denoising is the most fundamental image enhancement task, and many algorithms have been proposed over the years for its solution. Interestingly, such an image denoising “engine” can be used to solve general inverse problems. Indeed, in our recent work we have presented the Regularization by Denoising (RED) framework: using a denoising engine in defining the regularization of any inverse problem. We have shown how this scheme leads to well-founded iterative algorithms in which the denoiser is applied in each iteration. In this work we describe how a learned version of RED defines a novel convolutional neural network architecture, where the commonly used point-wise nonlinearities are replaced by a denoising engine. We show how this network can be optimized end- to-end using a back - propagation that relies on guided denoising algorithms. As a case-study, we concentrate on the image deblurring problem and show the superiority of the trainable variant of RED over its analytic form.
Bag Constrained Structure Pattern Mining for Multi-Graph Classification

This paper formulates a multi-graph learning task. In our problem setting, a bag contains a number of graphs and a class label. A bag is labeled positive if at least one graph in the bag is positive, and negative otherwise. In addition, the genuine label of each graph in a positive bag is unknown, and all graphs in a negative bag are negative. The aim of multi-graph learning is to build a learning model from a number of labeled training bags to predict previously unseen test bags with maximum accuracy. This problem setting is essentially different from existing multi-instance learning (MIL), where instances in MIL share well-defined feature values, but no features are available to represent graphs in a multi-graph bag. To solve the problem, we propose a Multi-Graph Feature based Learning (gMGFL) algorithm that explores and selects a set of discriminative subgraphs as features to transfer each bag into a single instance, with the bag label being propagated to the transferred instance. As a result, the multi-graph bags form a labeled training instance set, so generic learning algorithms, such as decision trees, can be used to derive learning models for multi-graph classification. Experiments and comparisons on real-world multi-graph tasks demonstrate the algorithm performance.
Japanese Guideline for Food Allergy

ABSTRACT Food allergy is defined as “a phenomenon in which adverse reactions (symptoms in skin, mucosal, digestive, respiratory systems, and anaphylactic reactions) are caused in living body through immunological mechanisms after intake of causative food. ” Various symptoms of food allergy occur in many organs. Food allergy falls into four general clinical types; 1) neonatal and infantile gastrointestinal allergy, 2) infantile atopic dermatitis associated with food allergy, 3) immediate symptoms (urticaria, anaphylaxis, etc.), and 4) food-dependent exercise-induced anaphylaxis and oral allergy syndrome (i.e., specific forms of immediate-type food allergy). Therapy for food allergy includes treatments of and prophylactic measures against hypersensitivity like anaphylaxis. A fundamental prophylactic measure is the elimination diet. However, elimination diets should be conducted only if they are inevitable because they places a burden on patients. For this purpose, it is highly important that causative foods are accurately identified. Many means to determine the causative foods are available, including history taking, skin prick test, antigen specific IgE antibodies in blood, basophil histamine release test, elimination diet test, oral food challenge test, etc. Of these, the oral food challenge test is the most reliable. However, it should be conducted under the supervision of experienced physicians because it may cause adverse reactions such as anaphylaxis.
Seroepidemiology of Human Papillomavirus 16 (HPV16) L2 and Generation of L2-Specific Human Chimeric Monoclonal Antibodies

ABSTRACT Presently, the seroprevalence of human papillomavirus (HPV) minor capsid antigen L2-reactive antibody is not well understood, and no serologic standard exists for L2-specific neutralizing antibodies. Therefore, we screened a total of 1,078 serum samples for HPV16 L2 reactivity, and these were obtained from four prior clinical studies: a population-based (n = 880) surveillance study with a high-risk HPV DNA prevalence of 10.8%, a cohort study of women (n = 160) with high-grade cervical intraepithelial neoplasia (CIN), and two phase II trials in women with high-grade vulvar intraepithelial neoplasia (VIN) receiving imiquimod therapy combined with either photodynamic therapy (PDT) (n = 19) or vaccination with a fusion protein comprising HPV16 L2, E7, and E6 (TA-CIN) (n = 19). Sera were screened sequentially by HPV16 L2 enzyme-linked immunosorbent assay (ELISA) and then Western blot. Seven of the 1,078 serum samples tested had L2-specific antibodies, but none were detectably neutralizing for HPV16. To develop a standard, we substituted human IgG1 sequences into conserved regions of two rodent monoclonal antibodies (MAbs) specific for neutralizing epitopes at HPV16 L2 residues 17 to 36 and 58 to 64, creating JWW-1 and JWW-2, respectively. These chimeric MAbs retained neutralizing activity and together reacted with 33/34 clinically relevant HPV types tested. In conclusion, our inability to identify an HPV16 L2-specific neutralizing antibody response even in the sera of patients with active genital HPV disease suggests the subdominance of L2 protective epitopes and the value of the chimeric MAbs JWW-1 and JWW-2 as standards for immunoassays to measure L2-specific human antibodies.
DNA Vaccination Elicits Protective Immune Responses against Pandemic and Classic Swine Influenza Viruses in Pigs

ABSTRACT Swine influenza is a highly contagious viral infection in pigs that significantly impacts the pork industry due to weight loss and secondary infections. There is also the potential of a significant threat to public health, as was seen in 2009 when the pandemic H1N1 influenza virus strain emerged from reassortment events among avian, swine, and human influenza viruses within pigs. As classic and pandemic H1N1 strains now circulate in swine, an effective vaccine may be the best strategy to protect the pork industry and public health. Current inactivated-virus vaccines available for swine influenza protect only against viral strains closely related to the vaccine strain, and egg-based production of these vaccines is insufficient to respond to large outbreaks. DNA vaccines are a promising alternative since they can potentially induce broad-based protection with more efficient production methods. In this study we evaluated the potentials of monovalent and trivalent DNA vaccine constructs to (i) elicit both humoral and gamma interferon (IFN-γ) responses and (ii) protect pigs against viral shedding and lung disease after challenge with pandemic H1N1 or classic swine H1N1 influenza virus. We also compared the efficiency of a needle-free vaccine delivery method to that of a conventional needle/syringe injection. We report that DNA vaccination elicits robust serum antibody and cellular responses after three immunizations and confers significant protection against influenza virus challenge. Needle-free delivery elicited improved antibody responses with the same efficiency as conventional injection and should be considered for development as a practical alternative for vaccine administration.
Wage, Work Environment, and Staffing: Effects on Nurse Outcomes

Research has shown that hospitals with better nurse staffing and work environments have better nurse outcomes—less burnout, job dissatisfaction, and intention to leave the job. Many studies, however, have not accounted for wage effects, which may confound findings. By using a secondary analysis with cross-sectional administrative data and a four-state survey of nurses, we investigated how wage, work environment, and staffing were associated with nurse outcomes. Logistic regression models, with and without wage, were used to estimate the effects of work environment and staffing on burnout, job dissatisfaction, and intent to leave. We discovered that wage was associated with job dissatisfaction and intent to leave but had little influence on burnout, while work environment and average patient-to-nurse ratio still have considerable effects on nurse outcomes. Wage is important for good nurse outcomes, but it does not diminish the significant influence of work environment and staffing on nurse outcomes.
Understanding short texts through semantic enrichment and hashing

Clustering short texts by their meaning is a challenging task. The semantic hashing approach encodes the meaning of a text into a compact binary code. Thus, to tell if two texts have similar meanings, we only need to check if they have similar codes. The encoding is created by a deep neural network, which is trained on texts represented by word-count vectors. Unfortunately, for short texts such as search queries, such representations are insufficient to capture the underlying semantics. We propose a method to add more semantic signals to enrich short texts. Furthermore, we introduce a simplified deep learning network constructed by stacked auto-encoders to do semantic hashing. Experiments show that our method significantly improves the understanding of short texts, including text retrieval, classification and other general text-related tasks.
SEDEX: Scalable Entity Preserving Data Exchange

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. Generally, data exchnge is performed using schema mapping, representing high level relations between source and target schemas. In this paper, we argue that data exchange solely based on schema level information limits the ability to express semantics in data exchange. We show such schema level mappings not only may result in entity fragmentation, they are unable to resolve some ambiguous data exchange scenarios. To address this problem, we propose Scalable Entity Preserving Data Exchange (SEDEX), a hybrid method based on data and schema mapping that employs similarities between relation trees of source and target relations to find the best relations that can host source instances. Our experiments show SEDEX outperforms other methods in terms of quality and scalability of data exchange.
Reduced Frequencies and Heightened CD103 Expression among Virus-Induced CD8+ T Cells in the Respiratory Tract Airways of Vitamin A-Deficient Mice

ABSTRACT Vitamin A deficiency (VAD) has profound effects on immune responses in the gut, but its effect on other mucosal responses is less well understood. Sendai virus (SeV) is a candidate human parainfluenza virus type 1 (hPIV-1) vaccine and a candidate vaccine vector for other respiratory viruses. A single intranasal dose of SeV elicits a protective immune response against hPIV-1 within days after vaccination. To define the effect of VAD on acute responses toward SeV, we monitored both antibodies and CD8+ T cells in mice. On day 10 following SeV infection, there was a trend toward lower antibody activities in the nasal washes of VAD mice than in those of controls, while bronchoalveolar lavage (BAL) fluid and serum antibodies were not reduced. In contrast, there was a dramatic reduction of immunodominant CD8+ T cell frequencies in the lower respiratory tract (LRT) airways of VAD animals. These T cells also showed unusually high CD103 (the αE subunit of αEβ7) expression patterns. In both VAD and control mice, E-cadherin (the ligand for αEβ7) was better expressed among epithelial cells lining the upper respiratory tract (URT) than in LRT airways. The results support a working hypothesis that the high CD103 expression among T cell populations in VAD mice alters mechanisms of T cell cross talk with URT and LRT epithelial cells, thereby inhibiting T cell migration and egress into the lower airway. Our data emphasize that the consequences of VAD are not limited to gut-resident cells and characterize VAD influences on an immune response to a respiratory virus vaccine.
Inference of Regular Expressions for Text Extraction from Examples

A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work, we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behavior. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators. A prototype is available as a web application at <;uri xlink:type="simple">http://regex.inginf. units.it<;/uri>.
Antibodies to Borrelia burgdorferi OspA, OspC, OspF, and C6 Antigens as Markers for Early and Late Infection in Dogs

ABSTRACT Lyme disease in the United States is caused by Borrelia burgdorferi sensu stricto, which is transmitted to mammals by infected ticks. Borrelia spirochetes differentially express immunogenic outer surface proteins (Osp). Our aim was to evaluate antibody responses to Osp antigens to aid the diagnosis of early infection and the management of Lyme disease. We analyzed antibody responses during the first 3 months after the experimental infection of dogs using a novel multiplex assay. Results were compared to those obtained with two commercial assays detecting C6 antigen. Multiplex analysis identified antibodies to OspC and C6 as early as 3 weeks postinfection (p.i.) and those to OspF by 5 weeks p.i. Antibodies to C6 and OspF increased throughout the study, while antibodies to OspC peaked between 7 and 11 weeks p.i. and declined thereafter. A short-term antibody response to OspA was observed in 3/8 experimentally infected dogs on day 21 p.i. Quant C6 enzyme-linked immunosorbent assay (ELISA) results matched multiplex results during the first 7 weeks p.i. ; however, antibody levels subsequently declined by up to 29%. Immune responses then were analyzed in sera from 125 client-owned dogs and revealed high agreement between antibodies to OspF and C6 as robust markers for infection. Results from canine patient sera supported that OspC is an early infection marker and antibodies to OspC decline over time. The onset and decline of antibody responses to B. burgdorferi Osp antigens and C6 reflect their differential expression during infection. They provide valuable tools to determine the stage of infection, treatment outcomes, and vaccination status in dogs.
Cross-Reactivity of Antipneumococcal Surface Protein C (PspC) Antibodies with Different Strains and Evaluation of Inhibition of Human Complement Factor H and Secretory IgA Binding via PspC

ABSTRACT Pneumococcal surface protein C (PspC) is an important candidate for a cost-effective vaccine with broad coverage against pneumococcal diseases. Previous studies have shown that Streptococcus pneumoniae is able to bind to both human factor H (FH), an inhibitor of complement alternative pathway, and human secretory IgA (sIgA) via PspC. PspC was classified into 11 groups based on variations of the gene. In this work, we used three PspC fragments from different groups (PspC3, PspC5, and PspC8) to immunize mice for the production of antibodies. Immunization with PspC3 induced antibodies that recognized the majority of the clinical isolates as analyzed by Western blotting of whole-cell extracts and flow cytometry of intact bacteria, while anti-PspC5 antibodies showed cross-reactivity with the paralogue pneumococcal surface protein A (PspA), and anti-PspC8 antibodies reacted only with the PspC8-expressing strain. Most of the isolates tested showed strong binding to FH and weaker interaction with sIgA. Preincubation with anti-PspC3 and anti-PspC5 IgG led to some inhibition of binding of FH, and preincubation with anti-PspC3 partially inhibited sIgA binding in Western blotting. The analysis of intact bacteria through flow cytometry showed only a small decrease in FH binding after incubation of strain D39 with anti-PspC3 IgG, and one clinical isolate showed inhibition of sIgA binding by anti-PspC3 IgG. We conclude that although anti-PspC3 antibodies were able to recognize PspC variants from the majority of the strains tested, partial inhibition of FH and sIgA binding through anti-PspC3 antibodies in vitro could be observed for only a restricted number of isolates.
An Approach to Cold-Start Link Prediction: Establishing Connections between Non-Topological and Topological Information

Cold-start link prediction is a term for information starved link prediction where little or no topological information is present to guide the determination of whether links to a node will form. Due to the lack of topological information, traditional topology-based link prediction methods cannot be applied to solve the cold-start link prediction problem. Therefore, an effective approach is presented through establishing connections between non-topological and topological information. In the approach, topological information is first extracted by a latent-feature representation model, then a logistic model is proposed to establish the connections between topological and non-topological information, and finally the linking possibility between cold-start users and existing users is calculated. Experiments with three types of real-world social networks Weibo, Facebook, and Twitter show that the proposed approach is more effective in solving the cold-start link prediction problem and establishing connections between topological and non-topological information.