Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell‐free synthesis and assembly of prolyl 4‐hydroxylase: the role of the beta‐subunit (PDI) in preventing misfolding and aggregation of the alpha‐subunit.

Prolyl 4‐hydroxylase (P4‐H) catalyses a vital post‐translational modification in the biosynthesis of collagen. The enzyme consists of two distinct polypeptides forming an alpha 2 beta 2 tetramer (alpha = 64 kDa, beta = 60 kDa), the beta‐subunit being identical to the multifunctional enzyme protein disulfide isomerase (PDI). By studying the cell‐free synthesis of the rat alpha‐subunit of P4‐H we have shown that the alpha‐subunit can be translocated, glycosylated and the signal peptide cleaved by dog pancreatic microsomal membranes to yield both singly and doubly glycosylated forms. When translations were carried out under conditions which prevent disulfide bond formation, the product synthesized formed aggregates which were associated with the immunoglobulin heavy chain binding protein (BiP). Translations carried out under conditions that promote disulfide bond formation yielded a product that was not associated with BiP but formed a complex with the endogenous beta‐subunit (PDI). Complex formation was detected by co‐precipitation of the newly synthesized alpha‐subunit with antibodies raised against PDI, by sucrose gradient centrifugation and by chemical cross‐linking. When microsomal vesicles were depleted of PDI, BiP and other soluble endoplasmic reticulum proteins, no complex formation was observed and the alpha‐subunit aggregated even under conditions that promote disulfide bond formation. We have therefore demonstrated that the enzyme P4‐H can be assembled at synthesis in a cell‐free system and that the solubility of the alpha‐subunit is dependent upon its association with PDI.
QUANTUM KOLMOGOROV COMPLEXITY AND ITS APPLICATIONS

Kolmogorov complexity is a measure of the information contained in a binary string. We investigate here the notion of quantum Kolmogorov complexity, a measure of the information required to describe a quantum state. We show that for any definition of quantum Kolmogorov complexity measuring the number of classical bits required to describe a pure quantum state, there exists a pure n-qubit state which requires exponentially many bits of description. This is shown by relating the classical communication complexity to the quantum Kolmogorov complexity. Furthermore, we give some examples of how quantum Kolmogorov complexity can be applied to prove results in different fields, such as quantum computation and thermodynamics, and we generalize it to the case of mixed quantum states.
Influence of herring (Clupea harengus) and herring fractions on metabolic status in rats fed a high energy diet

Aim: Few dietary studies have looked beyond fish oil to explain the beneficial metabolic effects of a fish‐containing diet. Our aim was to study whether addition of herring, or sub‐fractions of herring, could counteract negative metabolic effects known to be induced by a high‐fat, high‐sugar diet.
Insectivorous Bat Pollinates Columnar Cactus More Effectively per Visit than Specialized Nectar Bat

Plant-pollinator interactions are great model systems to investigate mutualistic relationships. We compared pollinator effectiveness between facultative and obligate nectar-feeding bats to determine how foraging specialization influences mutualistic interactions in a bat-adapted cactus. We predicted that a specialized nectarivorous bat would deliver more pollen than an opportunistic nectar-feeding bat because of specialized adaptations to nectar feeding that indicate close association with their food plants. Counter to our predictions, the opportunistic Antrozous pallidus delivered significantly more pollen grains per visit than the specialized Leptonycteris yerbabuenae. Higher pollinator effectiveness, based on visitation rates and pollen deposition levels, varied between species by site, and although A. pallidus visits flowers much less frequently than L. yerbabuenae over all sites, it is likely an effective and reliable pollinator of Pachycereus pringlei in Baja, Mexico. Our results suggest that morphological adaptations and dietary specialization on nectar do not necessarily confer advantages for pollination over less specialized plant visitors and highlight the reciprocally exploitative nature of mutualisms.
A 1.2V low power CMOS receiver for Bluetooth

A 1.2 V Bluetooth receiver was implemented in a 0.13 ¿m CMOS process. The receiver supports both the basic data rate at 1 Mb/s and the enhanced date rate (EDR) at 2 Mb/s and 3 Mb/s Bluetooth modes. The presented receiver is highly integrated with only 13 mA current consumption at 1.2 V supply. At this low power consumption, the receiver achieves a NF less than 15 dB and IIP3 of -20.8 dBm. The receiver provides a maximum voltage gain of 95 dB and 86 dB for the basic date rate and the enhanced date rate modes respectively.
Development of a system for measuring power and phase distributions of radio waves

A system for measuring power and phase distributions of a radio wave incident on the surface of a thin metamaterial absorber is developed. By measuring the voltages on an array of lumped resistors interconnecting surface patches on a mushroom-type metasurface at its resonance frequency, incident power and phase distributions are obtained. The effectiveness of the technique is evaluated by measuring a radio wave transmitted from a dipole antenna, on the basis of the comparison between the simulation and the actual measurement.
DEFINING THE CRITERIA TO SELECT THE WAVELET TYPE FOR THE ASSESSMENT OF SURFACE QUALITY

The paper deals with one of the most important problems in the wavelet analysis – the methodology for mother wavelet selection. In this study, the surface texture of machine parts was assessed applying a wavelet transform. The calculations involved in roughness profile evaluation were based on three selection criteria: an autocorrelation test, a cross-correlation function and an entropy-based test. All the calculations and diagrams were produced using the Wavelet toolbox of the MATLAB package.
A new method for image segmentation

A method is presented for finding a threshold surface which involves the ideas used in other methods but attempts to overcome some of their disadvantages. The method uses the gradient map of the image to point at well-defined portions of object boundaries in it. Both the location and gray levels at these boundary points make them good choices for local thresholds. These point values are then interpolated, yielding the threshold surface. A method for fitting a surface to this set of points, which are scattered in a manner unknown in advance, becomes necessary. Several possible approaches are discussed, and the implementation of one of them is described in detail. Two versions of the C.K. Chow and T. Kaneko algorithm (1972) and the present algorithm are applied to a few images, and the latter is shown to give better results, matching human performance quite well. <<ETX>>
Engagement in Online Social Networks: The Impact of Self-Disclosure and Humor

This study proposes an engagement model that supports acceptance and use of course-based online social networks for engaging student, hence improving the instructor's credibility. This research demonstrates that instructors who create course-based online social networks can increase student engagement in these online social networks and improve the instructor's credibility. This increase in engagement is seen when the instructor posts private information related to the course and when the instructor makes humorous posts. However, it is not seen when the instructor posts private information unrelated to the course. These results should be useful for instructors who are trying to improve student engagement and to enhance their own credibility. This research utilizes communication privacy management theory and instructional humor processing theory to expand our understanding of how instructor self-disclosure and use of humor via a course-based social network impacts student outcomes. The research also contributes to the theory by providing an engagement model that is unique to online educational settings.
Security challenges and risk evaluation framework for industrial wireless sensor networks

Due to flexibility, low cost and rapid deployment, wireless sensor networks (WSNs)have been drawing more and more interest from governments, researchers, application developers, and manufacturers in recent years. Nowadays, we are in the age of industry 4.0, in which the traditional industrial control systems will be connected with each other and provide intelligent manufacturing. Therefore, WSNs can play an extremely crucial role to monitor the environment and condition parameters for smart factories. Nevertheless, the introduction of the WSNs reveals the weakness, especially for industrial applications. Through the vulnerability of IWSNs, the latent attackers were likely to invade the information system. Risk evaluation is an overwhelmingly efficient method to reduce the risk of information system in order to an acceptable level. This paper aim to study the security issues about IWSNs as well as put forward a practical solution to evaluate the risk of IWSNs, which can guide us to make risk evaluation process and improve the security of IWSNs through appropriate countermeasures.
Detection & Classification of Lung Cancer at an Early Stage by Applying Feature Extraction-Optimization and Neural Network on Hybrid Structure

As of now the detection and classification of lung cancer disease is one of the most tedious tasks in the field of medical area. In the diversified sector of medical industry usage of technology plays a very important role. Detection and diagnosis of the lung cancer at an early stage with more accuracy is the most challenging task. So, in this research article 400 set of images has been used for this experiment. Best feature extraction technique and best feature optimization technique has been analyzed on the basis of parameter minimum execution time with minimum error rate. Then finest selection of features leads to an optimal classification. In this context, one of the best classification algorithm the support vector machine has been proposed in this hybrid model for the binary classification. Further Feed forward back propagation neural network has been implemented with SVM. This proposed hybrid model reduces the complexity of the system on the basis of minimum execution time that is 1.94 sec. with minimum error rate 29.25. Further better classification accuracy 99.6507% has been achieved by using this unique hybrid model
Concept Similarity Calculation in Ontology Mapping

Ontology is an explicit specification of a conceptualization. With the development of semantic Web, there are more and more ontologies. How to achieve the reusing and sharing of knowledge between ontologies has become the key to the development of semantic Web. Based on the analysis of the current technologies, a computation method of parsing the feature of concept in the ontologies and making use of the semantic similarity measurement techniques is proposed in the paper, in order to find the same or similar concepts pairs among different ontologies and then can be used in the ontology mapping, which has been proved feasible by experiments.
Very Large-Scale Linear Programming: A Case Study in Combining Interior Point and Simplex Methods

Experience with solving a 12.753. 313 variable linear program is described. This problem is the linear programming relaxation of a set partitioning problem arising from an airline crew scheduling application. A scheme is described that requires successive solutions of small subproblems, yielding a procedure that has little growth in solution time in terms of the number of variables. Experience using the simplex method as implemented in CPLEX, an interior point method as implemented in OBI, and a hybrid interior point/simplex approach is reported. The resulting procedure illustrates the power of an interior point/simplex combination for solving very large-scale linear programs.
Notch pathway regulation of chondrocyte differentiation and proliferation during appendicular and axial skeleton development

The role of Notch signaling in cartilage differentiation and maturation in vivo was examined. Conditional Notch pathway gain and loss of function was achieved using a Cre/loxP approach to manipulate Notch signaling in cartilage precursors and chondrocytes of the developing mouse embryo. Conditional overexpression of activated Notch intracellular domain (NICD) in the chondrocyte lineage results in skeletal malformations with decreased cartilage precursor proliferation and inhibited hypertrophic chondrocyte differentiation. Likewise, expression of NICD in cartilage precursors inhibits sclerotome differentiation, resulting in severe axial skeleton abnormalities. Furthermore, conditional loss of Notch signaling via RBP-J gene deletion in the chondrocyte lineage results in increased chondrocyte proliferation and skeletal malformations consistent with the observed increase in hypertrophic chondrocytes. In addition, the Notch pathway inhibits expression of Sox9 and its target genes required for normal chondrogenic cell proliferation and differentiation. Together, our results demonstrate that appropriate Notch pathway signaling is essential for proper chondrocyte progenitor proliferation and for the normal progression of hypertrophic chondrocyte differentiation into bone in the developing appendicular and axial skeletal elements.
Biosynthesis of linalyl acetate and other terpenes in lemon mint (Mentha aquatica var. citrata, Lamiaceae) glandular trichomes

The potential to produce and accumulate mono- and sesquiterpenes in lemon mint (Mentha aquatica var. citrata) glandular trichomes was evaluated. Volatiles were extracted using methyl-tert-butyl ether or solid phase micro extraction and determined by gas chromatography-mass spectrometry. The main components of the essential oil of lemon mint are the monoterpene alcohol linalool and its ester linalyl acetate. Sesquiterpenes, such as elemol, (E)-caryophyllene, and germacrene D, as well as the monoterpenes 1,8-cineole, β-myrcene, and β-(E)-ocimene, are also prominent. Most of the essential oil is localized in the glands, as leaves devoid of trichomes had very low essential oil levels. The quantities of all these components per leaf increased during leaf development until reaching the third whorl, and then the levels remained steady. Conversely, the essential oil content per gram fresh weight decreased with leaf development, but the decrease was not statistically significant at p = 0.05. Desalted cell-free ext.
Real-time update of multi-state system reliability using prognostic data-driven techniques

Multi-state system (MSS) reliability has been an active topic of research in the recent past, given its relevance to modeling power systems and other mechanical equipment which can be classified to operate at different states of performance (more than two) ranging from completely functional to partial degradation (wear-out) to system failure. The assessment of MSS involves the use of the Markov chain to estimate the transient and steady state probabilities given the transition rates between different states and a user-defined threshold demand from the system which defines reliability. In the case of systems which are not new and have been tested in the past (similar units operated before), the transition rates can be estimated from the historical records of the data. However, for new systems or those with minimal past data, the estimates of the state transition rates can be uncertain, which undermines the value of modeling systems from a multi-state perspective. This study proposes a particle filter (PF) based technique that capitalizes on real-time sensor-based degradation data of the system to estimate the future trend of wear-out and estimates the transition rates and their confidence bounds, which get updated every time a new data is measured. This study is aimed at bringing together the fields of MSS and prognostics and health management (PHM) to model and estimate the reliability of engineering systems more accurately and dynamically, moving away from the traditional practice of relying on historical failure data. The proposed technique would be handy for condition monitoring, lifetime estimation and maintenance scheduling of new systems or imperfect repair systems where prior knowledge of state transition rates is seldom available.
The Shikani Optical Stylet: a useful adjunct to airway management in a neonate with popliteal pterygium syndrome

The craniofacial abnormalities of popliteal pterygium syndrome present unique challenges to the pediatric anesthetist. Congenital fibrous bands between the maxilla and mandible limit mouth opening and the tongue may be fused to the palate. We describe the use of the Shikani Optical Stylet, a novel fiberoptic endoscope, in the airway management of a neonate with popliteal pterygium syndrome and syngnathia.
Cost effective portable system for sign language gesture recognition

This paper presents a standalone system that converts static gestures into voice. It is also known as GesTALK. The primary input device is the self design and cost effective Data Glove. It operates in two basic modes. In first mode the system speaks a single alphabet for a static gestures made by the user, while in second mode the system speaks complete string by concatenating words. Various gestures of alphabets are made to spell out the words involved in string. This system has been shown to work for both American Sign Language (ASL) and Pakistan Sign Language (PSL) and other language models. Results show that the system recognizes 24 out of 26 letters; an overall accuracy of 90% is achieved.
Inferring brain deformation during open neurosurgery using CBCT angiography

During neurosurgery the brain can deform, which is known as brain shift. The deformation can be as large as 20 mm and limits the accuracy of image-guided surgery. In this work we use pre- and intra-operative CBCT angiography to infer this deformation. We first segment cortex and vessels in both images and then perform surface registration between the pre- and intra-operative segmentations. The resulting deformation field is assumed to correspond to the brain deformation and can be used to update other pre-operative images. Since the intra-operative image quality is deteriorated by streak artifacts originating from the metallic stereotactic frame, our approach explores robust segmentation and registration methods that reduce the influence of these artifacts. Our method is tested on a clinical data set of 8 patients who underwent neurosurgery. We also compare with a recently published method and rigid registration as a baseline method, showing the improved accuracy of our approach.
Focusing Conservation Efforts on Ecosystem Service Supply May Increase Vulnerability of Socio-Ecological Systems

Growing concern about the loss of ecosystem services (ES) promotes their spatial representation as a key tool for the internalization of the ES framework into land use policies. Paradoxically, mapping approaches meant to inform policy decisions focus on the magnitude and spatial distribution of the biophysical supply of ES, largely ignoring the social mechanisms by which these services influence human wellbeing. If social mechanisms affecting ES demand, enhancing it or reducing it, are taken more into account, then policies are more effective. By developing and applying a new mapping routine to two distinct socio-ecological systems, we show a strong spatial uncoupling between ES supply and socio-ecological vulnerability to the loss of ES, under scenarios of land use and cover change. Public policies based on ES supply might not only fail at detecting priority conservation areas for the wellbeing of human societies, but may also increase their vulnerability by neglecting areas of currently low, but highly valued ES supply.
Investigation of the role of similarity measure and ranking algorithm in mining social networks

Social networks have attracted much attention recently. Different studies have been conducted to automatically extract social networks among various kinds of entities from the web. Social network analysis finds its application in many current business areas. In this paper we demonstrate how the choice of the similarity measure affects ranking results of entities in a social network extracted from the web. We use different similarity measures in order to build different social networks. By applying formulas described below for each of the networks we derive a new network which is different from the original one by edge weights. Subsequently, in the derived networks we rank entities again. Finally we compare the results.
ROBUST LEAST SQUARE SUPPORT VECTOR REGRESSION FOR CONTAMINATED DATA MODELING

Weighted least squares support vector machine (WLSSVM) is a robust version of least squares support vector machine (LS-SVM). It adds weights on error variables to eliminate the influence of outliers. But the weights, which largely depend on the original regression errors from unweighted LS-SVM, might be unreliable for correcting the biased estimation of LS-SVM, especially for the training data set with large deviation outliers. In this paper, a twostage weighting strategy is proposed. This approach derives from the idea of spatial rank of feature vector, and down-weights these large deviation outliers firstly. Then the weights are updated by these regression errors of WLSSVM with the weights obtained in the first weighting stage. Finally, WLS-SVM is again employed to further improve the prediction performance. The effectiveness of the proposed robust LS-SVM is validated by two artificial data examples and a soft sensor modeling problem.