Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass spectrometry pesticide screening with paper-based microfluidic cassette for 2D paper chromatography and electrospray ionization

This paper presents a novel paper-based microfluidic cassette for 2D paper chromatography and electrospray ionization (ESI) for rapid mass spectrometry screening of pesticides in vegetable. Instead of using conventional bulky HPLC/MS systems, liquid sample is directly applied on the cassette for sample separation with paper chromatography and then enriched with the second paper chromatography for enhancing the sensing performance of ESI-MS detections. The time and cost for food-safety examination can be dramatically reduced with the developed method. Results show that the ion intensity is up to 100 times enhanced after the 2nd chromatography process for sample enrichment such that the limit of detection (LOD) is as low as 1.0 ppb for detecting acetaminophen and vitamin B2. The developed system also detects two pesticides in the fresh green onion obtained from the market. The developed method has shown its potentials on rapid pesticide screening in vegetables.
Chinese spam filtering based on online active learning methods

In this paper, new active learning methods are proposed to filter Chinese spam. It is time-consuming and expensive to label the spam emails in the large datasets. Active learning methods can conspicuously reduce labeling cost by identifying informative examples and speed up online Logistic Regression filter. The experiments illustrate that our methods not only decrease the number of label requests, but also improve the classification performance of spam filtering.
Panorama to cube: a content-aware representation method

As panoramas provide a brand new viewpoint for the public, relevant cameras and software such as RICOH Theta, Microsoft Photosynth are embracing more and more users. However, the display methods for panoramas remain monotonous. In this paper, we propose a novel representation method called Content-Aware Cube Unwrapping using the effective and interactive techniques of orientational rectification, image modification and energy estimation. Thus, a number of fascinating applications will come into reality. For instance, six surfaces of a Rubiks cube can be automatically rendered from a vertically oriented panorama, without cutting any person or significant object apart. Moreover, seam carving and inserting are applied to each surface to enhance the key content and to make the scenery more consistent.
Surface Trees: Interactive Hierarchical Surface Modeling (papers 0295)

A method is described for representing and manipulating a hierarchy of surface editing operations, in the context of an interactive shape modeling tool. Surface deformations are cast as dynamic geometric textures, applied to locally-parameterized regions of the surface which can be interactively manipulated, and also layered. This use of dynamically layered deformation is characterized as “surface compositing”, and leads to the definition of the surface tree a hierarchical, procedural representation of a 3D surface. Like CSG trees, surface trees allow any deformation to be manipulated at any time. Editing operations higher in the model tree are automatically recomputed, with relative position of layered elements maintained by “anchoring” them in the parameter space of lower layers. In addition to surface deformation, dynamic holes and handles can be created between (possibly non-manifold) surfaces. These techniques are demonstrated in an interactive “drag-and-drop” mesh editing system. To efficiently implement this system, a novel procedural mesh data structure is described. CR Categories: I.3. 5 [Computer Graphics]: Computational Geometry and Object Modeling—Curve, surface, solid, and object representations I.3. 6 [Computer Graphics]: Methodology and Techniques—Interaction Techniques
Cooperative task allocation in heterogeneous wireless sensor networks

In a wireless sensor network, sensor nodes are strictly energy and capacity constrained, which makes it necessary for them to collaboratively execute a complex task. Thus, task allocation becomes a fundamental and crucial issue in wireless sensor networks. Most previous studies developed centralized methods to solve this problem. In addition, a common assumption is that all the sensor nodes are homogeneous, which is unfavorable in many real applications. In this article, a distributed task allocation strategy which can handle the problem in a heterogeneous wireless sensor network is proposed. The task is propagated from nodes to nodes and each node matches its own capacity with the required capacities until all the demanded capacities of the task are obtained. Building on this, an enhanced task allocation strategy based on self-organization is developed. By utilizing previous assigning information, the nodes with proper capacities will be selected as candidate nodes, then the paths to these nodes will be optimized. In so doing, a new arriving task can be allocated directly and quickly. Simulation results show the feasibility of the proposed approach. Furthermore, the overall performance of the self-organization-based strategy is validated through a comparison with a particle swarm optimization–based centralized method and the fundamental method.
CONVOLUTIONAL NETWORK FOR TOOL DISCRIMINATION

The present paper discusses the use of deep learning techniques, in particular a convolutional neural network, which is trained to identify, in an image, a surgical cutting tool located on a plane. Initially a database is established regarding the tool with different rotations and after this, the base structure of the convolutional network for its training is determined. It is possible to obtain an average identification percentage of 89% with respect to its discrimination in a group of tools also of surgical cut.
Fault-tolerant voting mechanism and recovery scheme for TMR FPGA-based systems

This paper presents an original approach to the implementation of a fault-tolerant FPGA-based system. In particular, we consider the conventional Triple Modular Redundancy fault-tolerance technique and address practical problems related to its actual implementation into FPGA devices. All possible functional faults affecting the used FPGAs are either tolerated or on-line detected. Differently from conventional VLSI fault-tolerant systems, here the FPGA possible reconfigurability is exploited to ensure the continuity of operation for a high number of possible internal faults, without requiring further replications (besides the three basic copies) of the considered device.
Conception, design and development of a low-cost intelligent prosthesis for one-sided transfemoral amputees

Introduction: Modern transfemoral knee prostheses are designed to offer comfort and self-confidence to amputees. These prostheses are mainly based upon either a passive concept, with a damping system, or an active computational intelligent design to control knee motion during the swing phase. In Brazil, most lower extremity amputees are unable to afford modern prostheses due to their high cost. In this work, we present the conception, design and development of a low-cost intelligent prosthesis for one-sided transfemoral amputees. Methods: The concept of the prosthesis is based on a control system with sensors for loads, which are installed on the amputee's preserved leg and used as a mirror for the movement of the prosthesis. Mechanical strength analysis, using the Finite Element Method, electromechanical tests for the sensors and actuators and verification of data acquisition, signal conditioning and data transferring to the knee prosthesis were performed. Results: The laboratory tests performed showed the feasibility of the proposed design. The electromechanical concept that was used enabled a controlled activation of the knee prosthesis by the two load cells located on the shoe sole of the preserved leg. Conclusions: The electromechanical design concept and the resulting knee prosthesis show promising results concerning prosthesis activation during walking tests, thereby showing the feasibility of a reduced manufacturing cost compared to the modern prostheses available on the market.
Statistical models for neuroimaging meta-analytic inference

A statistical meta-analysis combines the results of several studies that address a set of related research hypotheses, thus increasing the power and reliability of the inference. Meta-analytic methods are over 50 years old and play an important role in science; pooling evidence from many trials to provide answers that any one trial would have insufficient samples to address. On the other hand, the number of neuroimaging studies is growing dramatically, with many of these publications containing conflicting results, or being based on only a small number of subjects. Hence there has been increasing interest in using meta-analysis methods to find consistent results for a specific functional task, or for predicting the results of a study that has not been performed directly. Current state of neuroimaging meta-analysis is limited to coordinate-based meta-analysis (CBMA), i.e., using only the coordinates of activation peaks that are reported by a group of studies, in order to "localize" the brain regions that respond to a certain type of stimulus. This class of meta-analysis suffers from a series of problems and hence cannot result in as accurate results as desired. In this research, we describe the problems that existing CBMA methods are suffering from and introduce a hierarchical mixed-effects image-based metaanalysis (IBMA) solution that incorporates the sufficient statistics (i.e., voxel-wise effect size and its associated uncertainty) from each study. In order to improve the statistical-inference stage of our proposed IBMA method, we introduce a nonparametric technique that is capable of adjusting such an inference for spatial nonstationarity. Given that in common practice, neuroimaging studies rarely provide the full image data, in an attempt to improve the existing CBMA techniques we introduce a fully automatic model-based approach that employs Gaussian-process regression (GPR) for estimating the meta-analytic statistic image from its corresponding sparse and noisy observations (i.e., the collected foci). To conclude, we introduce a new way to approach neuroimaging meta-analysis that enables the analysis to result in information such as “functional connectivity” and networks of the brain regions’ interactions, rather than just localizing the functions.
EEG channel optimization via sparse common spatial filter

In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class, the classification accuracy is guaranteed by a constraint that the ratio of variances of signals in two different classes is lower bounded. Then, a sparse spatial filter is achieved by minimizing the l1-norm of filter coefficients and channels of EEG signals can be further optimized. The original nonconvex optimization problem is relaxed to a semidefinite program (SDP), which can be efficiently solved by well-developed numerical solvers. Experimental results demonstrate that the proposed algorithm can identify and discard about 50% channels with only 1% decrease of classification accuracy.
Violence as Mediating Variable in Mental Health Disparities Associated to Sexual Orientation Among Mexican Youths

ABSTRACT In this study, we explored the role of sex as an effect-modifying variable in the association between sexual orientation and mental health in Mexican youth. In addition, we tested if violent experiences in the family and the school and attitudes toward homosexuality could act as mediating variables in such association. Data from three representative surveys performed in 2007, 2009, and 2013 among Mexican high school students were analyzed. Two dimensions of sexual orientation were evaluated: romantic partnership and sexual behavior. The outcomes were negative and positive mood, suicidal ideation and intent, self-concept, and self-esteem. There were differences by gender because in males, there were more disparities in mental health associated with sexual orientation (suicidal ideation and attempt, negative and positive mood, negative self-concept, and family-related self-esteem) than in females (suicidal ideation and negative mood). Experiences of school violence were mediators in the relationship between sexual orientation and most health outcomes in males.
Enhanced Escherichia coli invasion of human brain microvascular endothelial cells is associated with alterations in cytoskeleton induced by nicotine

Although epidemiological studies have shown that exposure to tobacco smoking significantly increases the risk of bacterial meningitis, heretofore the pathogenic effects of smoking on this disease have been poorly understood. In order to dissect this issue, we have investigated the effects of nicotine, the major component of tobacco, on E. coli invasion of human brain microvascular endothelial cells (HBMEC). Our studies showed that E. coli invasion of HBMEC was significantly enhanced by nicotine in a dose‐dependent manner. The nicotine‐mediated enhancement was associated with actin cytoskeleton rearrangement and morphological changes in the eukaryotic host cell that are essential for bacterial entry. The recombinant IbeA protein and α‐bungarotoxin (a nicotinic acetylcholine receptor antagonist) were able to efficiently block the nicotine‐mediated cellular effects, suggesting the involvement of the IbeA and nicotinic receptors. Blocking of phosphatidylinositol 3‐kinase (PI3K) by LY294002 abolished the entry of E. coli in HBMECs treated with nicotine in a dose‐dependent manner. Inhibition of PI3K was associated with decreased phosphorylation of Akt and actin cytoskeletal rearrangement. In contrast to PI3K, blockage of Rho kinase (ROCK) by Y27632 upregulated both nicotine‐ and E. coli‐mediated cellular responses. Thus, this study provides experimental evidence for the first time that the major component of tobacco, nicotine, enhances meningitic E. coli invasion of HBMEC through modulation of cytoskeleton.
Transformation of sound pressure level from the free field to the eardrum in the horizontal plane.

Measurements of pressure transformation, azimuthal dependence, interaural level difference, and ear canal pressure distribution from 12 studies are brought together in a common framework. The pool of data covers 100 subjects, the majority male, measured in five countries over a 40‐yr period. Logical procedures are developed to identify the surfaces which best fit these essentially three‐dimensional distributions of data, making allowance for the many disparities between studies. Sheets of data are presented showing transformation to the eardrum, azimuthal dependence, and interaural difference as functions of frequency from 0.2 to 12 kHz at 45° intervals in azimuth. Other sheets show azimuthal dependence and interaural difference as functions of azimuth at 24 discrete frequencies. The logical procedures, data presentations, and review of disparities lead to the construction of self‐consistent families of curves best fitting the data and showing the average sound pressure transformation from the free field .
Women Physical Aggression (A Review)

Female aggression is a serious problem in most societies and is increasing these days in families. Female aggression has a negative effect on women as offender, their partners, children, and society in general. This paper aims to review the articles based on research that have been done on females' physical aggression. It attempts to show that females are also physically aggressive as males. According to the existing literatures, the rate of females' physical aggression is equal to those of males, and in some studies it is found to be higher than males. Based on these findings, it is concluded the rate of females' physical aggression is either equal to or higher than males, but not necessarily less than males. (Report and Opinion 2010;2(4):1-6). (ISSN:1553-9873).
Base line correction for handwritten word recognition

The authors have researched two-letter state name (state name abbreviation) recognition and full state name recognition. According to this research, they think that the accuracy of the character segmentation is essential to recognize the word correctly, and it depends on the normalization of the word image. The normalization includes smoothing, underline removal, spurious blob removal, slant and base line correction etc. They present a new base line correction algorithm for the off-line handwritten words, which include cursive (continuous or running) words and hand-printed words. It uses background region analysis with the lower convex hull which is background area closed for three directions (upper, right, left), and the upper and bottom profiles of the merged convex hull. The authors show that the new method of base line correction is very powerful for most word images for city names of the USPS mail address database. The resulting image is useful for the holistic approach, and it's effective even when the image includes parts under the base line, for example, "f", "g", "j", or a very large character.
Joint Latent Subspace Learning and Regression for Cross-Modal Retrieval

Cross-modal retrieval has received much attention in recent years. It is a commonly used method to project multi-modality data into a common subspace and then retrieve. However, nearly all existing methods directly adopt the space defined by the binary class label information without learning as the shared subspace for regression. In this paper, we first adopt the spectral regression method to learn the optimal latent space shared by data of all modalities based on the orthogonal constraints. Then we construct a graph model to project the multi-modality data into the latent space. Finally, we combine these two processes together to jointly learn the latent space and regress. We conduct extensive experiments on multiple benchmark datasets and our proposed method outperforms the state-of-the-art approaches.
Efficient , Scalable Clarification of Diverse Bioprocess Streams Using a Novel Pilot-Scale Tubular Bowl Centrifuge

NOVEMBER 2005 C entrifugation is ubiquitous in the biotechnology industry, primarily in cell harvest and cell lysate clarification. The technology provides advantages over microfiltration, particularly for large macromolecules such as vaccines that are either virus-like particles or loosely structured polysaccharides or nucleic acids. When large pore-size cutoffs are needed, membranes are susceptible to filter fouling by cell debris. Scale-up can become problematic due to uncertain modeling of the fouled condition, as witnessed by the development of complex operational algorithms such as Cwall (1) and optimized flow patterns such as backpulsing (2) and co-current flow (3). Clarification by centrifugation, on the other hand, circumvents the problem altogether and eliminates the cost of batch filter replacement. Several centrifuge designs are used to clarify biological streams, with scroll decanters, disc-stack centrifuges, and tubular bowl centrifuges being the most common. The choice for a given application depends on the nature and concentration of solid particles to be sedimented. For example, because of low sedimentation forces, the use of scroll decanters is limited to streams containing large particles such as mycelia or flocculated cells (4). Disc-stack centrifuges can generate much higher centrifugal forces, process large volumes of material, and handle large quantities of solids, discharging sediment as a wet slurry (5). That can be either ejected continuously (nozzle discharge) or intermittently (solids ejecting discharge). Disc-stack centrifuges fit a wide range of uses in bioprocessing and have long been used to harvest microbial cells and inclusion bodies (4) or to clarify cell lysates (12). A relatively new application for discstack centrifuges is in mammalian cell harvest, for which low expression rates of therapeutic proteins in cell culture (<2 g/L) require large volumes of cell cultures to be produced at manufacturing scale (>15,000-L bioreactors). The high throughput of disc-stack machines makes them a good choice at manufacturing scale (5– 11). But mammalian cells are shear sensitive (8), so the high-shear entrance region of such machines was redesigned with a hydrohermetic seal to eliminate a problematic gas–liquid interface. Even so, careful operation is critical to preventing high fluid stress. Centrifuges must generally be operated at less than full speed to achieve optimum centrate clarity in mammalian cell harvest (10, 11). Biotech companies must quickly convert new leads into purified therapeutics for clinical trials and, ultimately, for the market. A key element in that process is the dual role of pilot facilities: to make GMPcompliant material for clinical trials and demonstrate process scale-up B I O P R O C E S S TECHNICAL
Regional sea level change and variability in the Caribbean sea since 1950

Regional sea level change and variability in the Caribbean sea since 1950 We investigate the regional variability in sea level in the Caribbean Sea region over the past 60 years (1950-2009) using an Empirical Orthogonal Function (EOF)-based 2-dimensional past sea level reconstruction (a mean of 3 reconstructions based on few long tide gauge records and different sea level grids from satellite altimetry and ocean circulation models) and satellite altimetry data for the last two decades. We find that over the past 60 years, the mean rate of sea level rise in the region was similar to the global mean rise (~1.8 mm/yr). The interannual mean sea level of the placeCaribbean region appears highly correlated with El Nino-Southern Oscillation (ENSO) indices. Interpolation of the sea level reconstruction grid at different sites, in particular at the Caribbean Islands where tide gauge records are either very short or inexistent, shows that locally, the sea level trend is on the order of 2 mm/yr, i.e. only slightly larger than the mean trend over the region. Besides, correlation with ENSO is in general good, especially since the mid-1980s. We also find a significant correlation between the interannual variability in sea level and hurricane activity, especially over the past decade during which hurricane intensity and sea level interannual variability have both increased.
The distribution of temafloxacin in bronchial epithelial lining fluid, alveolar macrophages and bronchial mucosa.

The concentrations of temafloxacin, a new fluoroquinolone antimicrobial, in the potential sites of pulmonary infection were assessed by fibreoptic bronchoscopy with bronchoalveolar lavage. Fourteen patients received a course of temafloxacin, 600 mg twice daily, for three days prior to sampling. The mean serum concentration was 9.6 (SEM 1.2) mg.l-1, compared with 14.9(SEM 1.8) mg.kg-1 for bronchial mucosa, 26.5 (SEM 3.6) mg.l-1 for epithelial lining fluid and 83.0 (SEM 11.5) mg.l-1 for alveolar macrophage. In the ten patients who completed the protocol, site concentrations correlated well with serum concentrations. Temafloxacin was concentrated in each of the potential sites of infection examined and is, therefore, a promising new agent for the treatment of respiratory tract infection.