Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics and multiple stable binding modes of DNA intercalators revealed by single-molecule force spectroscopy.

By combining single molecule force spectroscopy with simple buffer exchange, we isolated the effects of binding and intercalation of YOYO and YOPRO. We showed that force-enhanced intercalation can occur from a reservoir of bound dye that was not fully intercalated, yet remained out of equilibrium with free dye for periods long compared to many single molecule experiments (>5 min for YOPRO and >2 hr for YOYO). Moreover, for YOYO, this reservoir of polycyclic moieties that is not bis-intercalated accounts for the most of the decay due to the force-enhanced intercalation. Our work highlights that binding/unbinding and intercalation/de-intercalation are distinct processes that can occur on very different time scales.
A quality map of transfer printing

Transfer printing is a nanofabrication technique that involves an assembly process by which a printable layer can be transferred from a transfer substrate to a device substrate. Future application of transfer printing toward a roll-to-roll printing process of flexible devices hinges upon the understanding on the mechanisms governing transfer printing quality, which is far from mature. So far, the quality control of transfer printing has been mainly explored via massive experimental trials, which are both time consuming and cost prohibitive. In this paper, we conduct systematic computational modeling to investigate the governing mechanisms of the transfer printing process. While the existing understanding of transfer printing mainly relies on the differential interfacial adhesion, our results suggest that both interfacial defects (e.g., cracks) and differential interfacial adhesion play pivotal roles in the transfer printing quality. The outcomes of this study define a quality map of transfer printing in t.
A Geometric Newton Method for Oja's Vector Field

Newton's method for solving the matrix equation runs up against the fact that its zeros are not isolated. This is due to a symmetry of F by the action of the orthogonal group. We show how differential-geometric techniques can be exploited to remove this symmetry and obtain a geometric Newton algorithm that finds the zeros of F. The geometric Newton method does not suffer from the degeneracy issue that stands in the way of the original Newton method.
Extracellular Nucleic Acids in Urine: Sources, Structure, Diagnostic Potential

Cell-free nucleic acids (cfNA) may reach the urine through cell necrosis or apoptosis, active secretion of nucleic acids by healthy and tumor cells of the urinary tract, and transport of circulating nucleic acids (cir- NA) from the blood into primary urine. Even though urinary DNA and RNA are fragmented, they can be used to detect marker sequences. MicroRNAs are also of interest as diagnostic probes. The stability of cfNA in the urine is determined by their structure and packaging into supramolecular complexes and by nuclease activity in the urine. This review summarizes current data on the sources of urinary cfNA, their structural features, diagnostic potential and factors affecting their stability.
Eliminating redundancy among protein sequences using submodular optimization

Motivation Submodular optimization, a discrete analogue to continuous convex optimization, has been used with great success in many fields but is not yet widely used in biology. We apply submodular optimization to the problem of removing redundancy in protein sequence data sets. This is a common step in many bioinformatics and structural biology workflows, including creation of non-redundant training sets for sequence and structural models as well as selection of “operational taxonomic units” from metagenomics data. Results We demonstrate that the submodular optimization approach results in representative protein sequence subsets with greater structural diversity than sets chosen by existing methods. In particular, we compare to a widely used, heuristic algorithm implemented in software tools such as CD-HIT, as well to as a variety of standard clustering methods, using as a gold standard the SCOPe library of protein domain structures. In this setting, submodular optimization consistently yields protein sequence subsets that include more SCOPe domain families than sets of the same size selected by competing approaches. We also show how the optimization framework allows us to design a mixture objective function that performs well for both large and small representative sets. The framework we describe is theoretically optimal under some assumptions, and it is flexible and intuitive because it applies generic methods to optimize one of a variety of objective functions. This application serves as a model for how submodular optimization can be applied to other discrete problems in biology. Availability Source code is available at https://github.com/mlibbrecht/submodular_sequence_repset. Contact william-noble@uw.edu
Neutralization of LINGO-1 during In Vitro Differentiation of Neural Stem Cells Results in Proliferation of Immature Neurons

Identifying external factors that can be used to control neural stem cells division and their differentiation to neurons, astrocytes and oligodendrocytes is of high scientific and clinical interest. Here we show that the Nogo-66 receptor interacting protein LINGO-1 is a potent regulator of neural stem cell maturation to neurons. LINGO-1 is expressed by cortical neural stem cells from E14 mouse embryos and inhibition of LINGO-1 during the first days of neural stem cell differentiation results in decreased neuronal maturation. Compared to neurons in control cultures, which after 6 days of differentiation have long extending neurites, neurons in cultures treated with anti-LINGO-1 antibodies retain an immature, round phenotype with only very short processes. Furthermore, neutralization of LINGO-1 results in a threefold increase in βIII tubulin-positive cells compared to untreated control cultures. By using BrdU incorporation assays we show that the immature neurons in LINGO-1 neutralized cultures are dividing neuroblasts. In contrast to control cultures, in which no cells were double positive for βIII tubulin and BrdU, 36% of the neurons in cultures treated with anti-LINGO-1 antibodies were proliferating after three days of differentiation. TUNEL assays revealed that the amount of cells going through apoptosis during the early phase of differentiation was significantly decreased in cultures treated with anti-LINGO-1 antibodies compared to untreated control cultures. Taken together, our results demonstrate a novel role for LINGO-1 in neural stem cell differentiation to neurons and suggest a possibility to use LINGO-1 inhibitors to compensate for neuronal cell loss in the injured brain.
Photosynthetic Gas Exchange and Discrimination against 13CO2 and C18O16O in Tobacco Plants Modified by an Antisense Construct to Have Low Chloroplastic Carbonic Anhydrase

The physiological role of chloroplastic carbonic anhydrase (CA) was examined by antisense suppression of chloroplastic CA (on average 8% of wild type) in Nicotiana tabacum. Photosynthetic gas-exchange characteristics of low-CA and wild-type plants were measured concurrently with short-term, on-line stable isotope discrimination at varying vapor pressure deficit (VPD) and light intensity. Low-CA and wild-type plants were indistinguishable in the responses of assimilation, transpiration, stomatal conductance, and intercellular CO2 concentration to changing VPD or light intensity. At saturating light intensity, low-CA plants had lower discrimination against 13CO2 than wild-type plants by 1.2 to 1.8[per mille (thousand) sign]. Consequently, tissue of the low-CA plants was higher in 13C than the control plants. It was calculated that low-CA plants had chloroplast CO2 concentrations 13 to 22 [mu]mol mol-1 lower than wild-type plants. Discrimination against C18O16O in low-CA plants was 20% of that of the wild type, confirming a role of chloroplastic CA in the mechanism of discrimination against C18O16O ([delta]C18O16O). As VPD increased, stomatal closure caused a reduction in chloroplastic C02 concentration, and since VPD and chloroplastic CO2 concentration act in opposing directions on [delta]C18O16O, no effect of VPD was seen on [delta]C18O16O.
A New Hidden Markov Model with Application to Classification

A new hidden Markov model was proposed by using the framework of a Markov chain to deal with classification. Based on this framework, a new estimation method for the transition probabilities among the hidden states was discussed, which avoids the local maximum led by the learning method of the traditional hidden Markov model. Using the stationary distribution of the hidden states, a classifier was proposed with observations being easily classified. Numerical examples were given to demonstrate the initial use of the model by using the standard data set. The result shows the effectiveness of the model-based classifier. The new model-based classification method can be widely used in statistical time series analysis such as speech recognition and handwritten characters recognition
An Algorithm to Improve the Performance of Watermarking Systems

Random permutation and sharpening are basic methods of image processing. In some digital image watermarking schemes, if original image is pre-processed with random permutation before embedding and the watermarked image is pre-processed with sharpening before detecting, the performance of watermarking systems can be improved, that means the images can seem better, or the veracity rate of detection is higher. In this article, this method is validated in Barni’s semi-blind watermarking system. The method is suitable for some semi-blind or blind watermarking systems, whether in spatial domain or in other transform domain.
An Advanced Honeypot System for Efficient Capture and Analysis of Network Attack Traffic

A Honeypot is an information system resource used to divert attackers and hackers away from critical resources as well as a tool to study an attacker’s methods. One of the most widely used tools is honeyd for creating honeypots. The logs generated by honeyd can grow very large in size when there is heavy attack traffic in the system, thus consuming a lot of disk space. The huge log size poses difficulty when they are processed and analyzed by security analysts as they consume a lot of time and resources. We propose a system which addresses these issues. It has two important modules. The first module is to capture packets in the network ie either lan or web. The second module is a analyzer the captured packets in order to generate summarized captured packet information and graphs for the security administrators. This application also monitors packet information regarding web traffic. The experimental results show that the space required by log file reduces significantly and reports generated dynamically as per user needs.
Emotion Work in the Arab Context

The research investigated the relationship of emotion work directed towards customers and towards coworkers with job satisfaction in Saudi Arabia. Emotion work means the requirement to display particular emotions as part of the job and includes surface acting where actual emotions differ from displayed emotions and deep acting where displayed and felt emotions are congruent. Participants were 147 flight attendants (31 men, 116 women; mean age = 36.9 years, SD = 7.5) employed by a major Saudi Arabian airline, who were either Saudi nationals or nationals of other Gulf Arab countries. Data were collected with questionnaires. Analysis was based on the General Linear Model and indicated that deep acting towards customers and towards coworkers was positively related to job satisfaction. On the other hand, the hypothesized negative relationship between surface acting and job satisfaction was not supported. Organizational trust moderated the relationship of emotion work with job satisfaction. The findings indicate the importance of considering emotion work in interactions with coworkers along with the influence of national culture in the relationships of emotion work with key variables.
Maximum entropy based Chinese-Japanese word alignment

Word alignment can be used for numerous applications in natural language processing, such as lexicography, machine translation and so on. In this paper, we present an efficient context-dependent word alignment model based on maximum entropy (ME) approach. All knowledge sources are treated as feature functions in this model, such as source words, POS information and bilingual dictionary. This approach allows easy integration of more context-dependent information. We perform experiments on a Chinese-Japanese parallel corpus and the results are compared with a manually produced reference alignment. We achieve good alignment accuracy in a very noisy environment using unsupervised train method. And the effects of different features are also evaluated.
A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming

A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolutionary competition between simultaneously matched classifiers. The evolutionary algorithm (GP) is therefore searching for compact fuzzy rule bases which are simultaneously general, accurate and co-adapted. Additional extensions to the proposed framework are suggested.
Feature-based registration of confocal fluorescence endomicroscopy images

In this paper, we propose a feature-based registration algorithm for the confocal fluorescence images captured by an endomicroscopy system. We first extract a number of feature points from the endomicroscopy images by applying the binarization-thinning process and using the Rutovitz crossing number. These feature points are then post-processed to eliminate the spurious ones. After that we use the purified feature points to complete the image registration between every two consecutive slice images. The aligned image stack will be finally utilized to reconstruct and visualize the 3D structure of the living cell and tissue in real time, which provides the opportunity for the clinicians to diagnose various diseases including the early-stage cancers.
Alleviation of depression in teenagers using light- sound technology

Teenage years are a time of rapid psychological transition from child to adult. The myth of universal teenage turmoil must not cloud professional judgment to the point that teenagers are not treated seriously. Today Light and Sound Stimulation are very much effective for the teenage to stimulate the connative aspect and decrease depression of an individual. The study highlights research about the effect of light and sound stimulation to alleviate depression in teenagers. A Sample of 120 students was selected from teenage group that is, 13 to 19 years of both sexes belonging to middle socio-economic status by purposive random sampling technique. The experimental conditions were further divided into three experimental groups and were administered three different treatments that is, sound, light and light-sound through mind power music and mind machines. Pre and post treatment tests were administered to all subjects, using the self-rating Depression Scale as the dependent variable. Independent ‘t’ statistic at the 0.01 level revealed a significant difference in the mean pre and post scores of the experimental and control groups. The finding of the present study reveals that after giving light-sound stimulation to the teenagers showed alleviation in depression.
Post-wimp User Interfaces U Nlike the Steady, Predictable Hardware Price/performance

characterized by four distinguishable interface styles, each lasting for many years and optimized to the hardware available at the time. In the first period, the early 1950s and 1960s, computers were used in batch mode with punched-card input and line-printer output; there were essentially no user interfaces because there were no interactive users (although some of us were privileged to be able to do console debugging using switches and lights as our “user interface”). The second period in the evolution of interfaces (early 1960s through early 1980s) was the era of timesharing on mainframes and minicomputers using mechanical or “glass” teletypes (alphanumeric displays), when for the first time users could interact with the computer by typing in commands with parameters. Note that this era persisted even through the age of personal microcomputers with such operating systems as DOS and Unix with their command line shells. During the 1970s, timesharing and manual command lines remained deeply entrenched, but at Xerox PARC the third age of user interfaces dawned. Raster graphics-based networked workstations and “pointand-click” WIMP GUIs (graphical user interfaces based on windows, icons, menus, and a pointing device, typically a mouse) are the legacy of Xerox PARC that we’re still using today. WIMP GUIs were popularized by the Macintosh in 1984 and later copied by Windows on the PC and Motif on Unix workstations. Applications today have much the same look and feel as the early desktop applications (except for the increased “realism” achieved through the use of drop shadows for buttons and other UI widgets); the main advance lies in the shift from monochrome displays to color and in a large set of software-engineering tools for building WIMP interfaces. I find it rather surprising that the third generation of WIMP user interfaces has been so dominant for more than two decades; they are apparently sufficiently good for conventional desktop tasks that the field is stuck comfortably in a rut. I argue in this essay that the status quo does not suffice—that the newer forms of computing and computing devices available today necessitate new thinking t h e h u m a n c o n n e c t i o n Andries van Dam
A Japanese Predicate Argument Structure Analysis using Decision Lists

This paper describes a new automatic method for Japanese predicate argument structure analysis. The method learns relevant features to assign case roles to the argument of the target predicate using the features of the words located closest to the target predicate under various constraints such as dependency types, words, semantic categories, parts of speech, functional words and predicate voices. We constructed decision lists in which these features were sorted by their learned weights. Using our method, we integrated the tasks of semantic role labeling and zero-pronoun identification, and achieved a 17% improvement compared with a baseline method in a sentence level performance analysis.
Spatial recognition model by extracting correlated information between vision and motion information using neural-network

Spatial recognition ability can be obtained by learning from visual experiments. The author thinks that this ability is to extract correlated information between vision and motion signals. Learning to extract correlated information among multiple inputs can be done without supervisor. The author built a robot with a vision sensor in a computer and it was confirmed that the robot was able to recognize the distance vector of two dimensions from itself to a target object using neural-network without any supervisors.
Send Orders of Reprints at Reprints@benthamscience.org Selected Topics in Homogenization of Transport Processes in Historical Masonry Structures

The paper reviews several topics associated with the homogenization of transport processed in historical masonry structures. Since these often experience an irregular or random pattern, we open the subject by summarizing essential steps in the formulation of a suitable computational model in the form of Statistically Equivalent Periodic Unit Cell (SEPUC). Accepting SEPUC as a reliable representative volume element is supported by application of the Fast Fourier Transform to both the SEPUC and large binary sample of real masonry in search for effective thermal conductivities limited here to a steady state heat conduction problem. Fully coupled non-stationary heat and moisture transport is addressed next in the framework of two-scale first-order homogenization approach with emphases on the application of boundary and initial conditions on the meso-scale.
Comparing wavelet transforms for recognizing cardiac patterns

The authors' study made use of wavelet transforms to describe and recognize isolated cardiac beats. The choice of the wavelet family as well as the selection of the analyzing function into these families are discussed. The criterion used in the first case was the correct classification rate, and in the second case, the correlation coefficient between the original pattern and the reconstructed one. Two types of description have been considered-the energy-based representation and the extrema distribution estimated at each decomposition level-and their quality has been assessed by using principal component analysis. Their capability of discrimination between normal, premature ventricular contraction, and ischemic beats has been studied by means of linear discriminant analysis. This work leads also, for the problem at hand, to the identification of the most relevant resolution levels. >
The Influence of Adult Attachment on Patient Self-Management in Primary Care - The Need for a Personalized Approach and Patient-Centred Care

Objective Self-management strategies are essential elements of evidence-based treatment in patients with chronic conditions in primary care. Our objective was to analyse different self-management skills and behaviours and their association to adult attachment in primary care patients with multiple chronic conditions. Methods In the apricare study (Adult Attachment in Primary Care) we used a prospective longitudinal design to examine the association between adult attachment and self-management in primary care patients with multimorbidity. The attachment dimensions avoidance and anxiety were measured using the ECR-RD. Self-management skills were measured by the FERUS (motivation to change, coping, self-efficacy, hope, social support) and self-management-behaviour by the DSMQ (glucose management, dietary control, physical activity, health-care use). Clinical diagnosis and severity of disease were assessed by the patients’ GPs. Multivariate analyses (GLM) were used to assess the relationship between the dimensions of adult attachment and patient self-management. Results 219 patients in primary care with multiple chronic conditions (type II diabetes, hypertension and at least one other chronic condition) between the ages of 50 and 85 were included in the study. The attachment dimension anxiety was positively associated with motivation to change and negatively associated with coping, self-efficacy and hope, dietary control and physical activity. Avoidance was negatively associated with coping, self-efficacy, social support and health care use. Conclusion The two attachment dimensions anxiety and avoidance are associated with different components of self-management. A personalized, attachment-based view on patients with chronic diseases could be the key to effective, individual self-management approaches in primary care.