Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of trypsin by cowpea thionin: Characterization, molecular modeling, and docking

Higher plants produce several families of proteins with toxic properties, which act as defense compounds against pests and pathogens. The thionin family represents one family and comprises low molecular mass cysteine‐rich proteins, usually basic and distributed in different plant tissues. Here, we report the purification and characterization of a new thionin from cowpea (Vigna unguiculata) with proteinase inhibitory activity. Cowpea thionin inhibits trypsin, but not chymotrypsin, binding with a stoichiometry of 1:1 as shown with the use of mass spectrometry. Previous annotations of thionins as proteinase inhibitors were based on their erroneous identification as homologues of Bowman‐Birk family inhibitors. Molecular modeling experiments were used to propose a mode of docking of cowpea thionin with trypsin. Consideration of the dynamic properties of the cowpea thionin was essential to arrive at a model with favorable interface characteristics comparable with structures of trypsin‐inhibitor complexes determined by X‐ray crystallography. In the final model, Lys11 occupies the S1 specificity pocket of trypsin as part of a canonical style interaction. Proteins 2002;48:311–319. © 2002 Wiley‐Liss, Inc.
A Review on Estimation of Defocus Blur from a Single Image

Defocus estimation is an important part in high quality image processing field, which mainly includes edge detection, image deblurring, and measuring image quality. Wrong lens setting or shallow depth of field which would produce defocus blur. Defocus blur is most of the times present in natural images. In this paper we have discussed some of the methods like local contrast prior, defocus magnification and spectrum contrast for estimating the defocus blur map.
Remote ischaemic postconditioning protects the heart during acute myocardial infarction in pigs

Background: Ischaemic preconditioning results in a reduction in ischaemic-reperfusion injury to the heart. This beneficial effect is seen both with direct local preconditioning of the myocardium and with remote preconditioning of easily accessible distant non-vital limb tissue. Ischaemic postconditioning with a comparable sequence of brief periods of local ischaemia, when applied immediately after the ischaemic insult, confers benefits similar to preconditioning. Objective: To test the hypothesis that limb ischaemia induces remote postconditioning and hence reduces experimental myocardial infarct size in a validated swine model of acute myocardial infarction. Methods: Acute myocardial infarction was induced in 24 pigs with 90 min balloon inflations of the left anterior descending coronary artery. Remote ischaemic postconditioning was induced in 12 of the pigs by four 5 min cycles of blood pressure cuff inflation applied to the lower limb immediately after the balloon deflation. Infarct size was assessed by measuring 72 h creatinine kinase release, MRI scan and immunohistochemical analysis. Results: Area under the curve of creatinine kinase release was significantly reduced in the postconditioning group compared with the control group with a 26% reduction in the infarct size (p<0.05). This was confirmed by MRI scanning and immunohistochemical analysis that revealed a 22% (p<0.05) and a 47.52% (p<0.01) relative reduction in the infarct size, respectively. Conclusion: Remote ischaemic postconditioning is a simple technique to reduce infarct size without the hazards and logistics of multiple coronary artery balloon inflations. This type of conditioning promises clear clinical potential.
Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm

We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA) algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.
Spectrum of type IIB supergravity on AdS5×T11: Predictions on N=1SCFT's

We derive the full Kaluza-Klein spectrum of type IIB supergravity compactified on ${\mathrm{AdS}}_{5}{\ifmmode\times\else\texttimes\fi{}\mathrm{T}}^{11}$ with ${\mathrm{T}}^{11}=SU(2)\ifmmode\times\else\texttimes\fi{}\mathrm{SU}(2)/U(1). $ From the knowledge of the spectrum and general multiplet shortening conditions, we make a refined test of the AdS-CFT correspondence, by comparison between various shortenings of $SU(2,2|1)$ supermultiplets on ${\mathrm{AdS}}_{5}$ and different families of boundary operators with protected dimensions. Additional towers of long multiplets with rational dimensions, that are not protected by supersymmetry, are also predicted from the supergravity analysis to occur in the SCFT at leading order in N and ${g}_{s}N. $
Experimental and theoretical analysis of PCF

An effective index guiding photonic crystal fiber (PCF) is proposed by introducing square air holes in the cladding and circular air holes in the core for getting flattened dispersion and low confinement loss. Dispersion and Confinement loss of the photonic crystal fibers are analyzed in detail using Finite Difference Time Domain (FDTD) method. Through the simulation results we observed that the flattened dispersion and low confinement losses is obtained in a wavelength range of 1.5 to 2.0 micro-meter from 6 rings of a PCF. From the 6 rings, inner three rings are designed as circular and outer three rings are designed by using square or linear air holes. By using FDTD method we calculate confinement loss of the proposed PCF structure in the wavelength range from 0.2 micro-meter to 2.0 micro-meter.
Design and Simulation Tools for Multivariable Control of Unmanned Underwater Vehicles

The development of a control system for an unmanned underwater vehicle (UUV) is widely agreed to be a demanding task. On the one hand, UUVs may exhibit a substantial number of actuators of diverse type, such as propellers, flaps and thrusters. On the other hand, frequently a complex navigation system has to be implemented to combine measurements from various sensors. Thus, from a control engineering point of view, they are distinctly multi input, multi output systems. This fact alone makes the development of a control system a complex problem, irrespective of other issues like nonlinearities, environmental disturbances or model uncertainties. We present an integrated simulation and control design framework which is intended to reduce the overall development and testing time for multivariable UUV control systems. This is achieved by maintaining complete independence between simulation and control design tools on the one hand and individual vehicle actuator configurations and particular control tasks on the other hand. We demonstrate how model based control design methods can be used within this approach in a flexible way and, particularly, that this framework, once established, can be easily extended to other vehicles and control modes
Information-Lookahead Planning for AUV Mapping

Exploration for robotic mapping is typically handled using greedy entropy reduction. Here we show how to apply information lookahead planning to a challenging instance of this problem in which an Autonomous Underwater Vehicle (AUV) maps hydrothermal vents. Given a simulation of vent behaviour we derive an observation function to turn the planning for mapping problem into a POMDP. We test a variety of information state MDP algorithms against greedy, systematic and reactive search strategies. We show that directly rewarding the AUV for visiting vents induces effective mapping strategies. We evaluate the algorithms in simulation and show that our information lookahead method outperforms the others.
Wireless Batteryless Implantable Blood Pressure Monitoring Microsystem for Small Laboratory Animals

A novel, wireless, batteryless, implantable blood pressure monitoring microsystem for small laboratory animals is developed for advanced biological and system biology research. The system employs an instrumented elastic circular cuff, wrapped around a blood vessel, for real-time blood pressure monitoring. The elastic circular cuff is made of soft bio-compatible silicone material, which is filled with bio-compatible insulating fluid with an immersed microelectromechanical systems (MEMS) pressure sensor and integrated electronic system to detect a down-scaled vessel blood pressure waveform. This technique avoids vessel penetration and substantially minimizes vessel restriction due to the soft cuff elasticity, thus attractive for long-term monitoring. A large-model engineering experiment is first developed to verify and demonstrate the concept. A miniature prototype monitoring cuff is then fabricated and implanted in two laboratory rats to evaluate its functionality. A wireless and batteryless monitoring microsystem is then implanted and characterized in a laboratory rat. The measured in vivo blood pressure waveform by the microsystem and a reference waveform recorded by a commercial catheter-tip transducer are closely matched in shape with a constant scaling factor, demonstrating a blood pressure signal with high fidelity can be wirelessly obtained by the implantable monitoring microsystem. The overall implant dissipates 300 ¿W, which is powered by an external adaptive RF powering source.
A Randomized Controlled Study of Cognitive Behavior Therapy and Behavioral Family Therapy for Anorexia Nervosa Patients

Very few studies have examined the role of cognitive behavior therapy (CBT) in the outpatient treatment of anorexia nervosa. This study used a randomized, controlled design to evaluate a 12-month, manual based program of CBT, with behavioral family therapy as the comparison group. Twenty-five adolescents and young adults with anorexia nervosa, currently living with their families, were recruited into the study with both treatment groups receiving 21–25 sessions of therapy. Outcome measures included nutritional status, eating behaviors, mood, self-esteem, and family communication. Sixty percent of the total sample and 72% of treatment completers had “good” outcome (defined as maintaining weight within 10% of average body weight and regular menstrual cycles) at post-treatment and at six months follow-up. No significant differences between treatment groups were found and the majority of patients did not reach symptomatic recovery. While limited by the small sample size, the findings compliment and extend previous research.
A three dimensional model for land mobile-HAP-MIMO fading channels

A three dimensional (3-D) cylinder scattering model is proposed for multiple-input multiple-output (MIMO) Land Mobile Stratospheric multipath fading channels. Analytical expressions for the space-time correlation function between two arbitrary sub-channels are derived in non-isotropic environments. This model can be used to estimate the required high altitude platform (HAP) antenna separation to achieve an uncorrelated HAP-MIMO channel matrix. Theoretical derivations have been derived through extensive computer simulations. Our procedure provides an important framework for designing and testing future HAP-MIMO based communication systems and studying the channel capacity of HAP-MIMO based channels, with LOS and NLOS components.
Computation of Heteroclinic Arcs with Application to the Volume Preserving Hénon Family

Let $f:\mathbb{R}^3\rightarrow\mathbb{R}^3$ be a diffeomorphism with $p_0,p_1\in\mathbb{R}^3$ distinct hyperbolic fixed points. Assume that $W^u(p_0)$ and $W^s(p_1)$ are two-dimensional manifolds which intersect transversally at a point q. Then the intersection is locally a one-dimensional smooth arc $\tilde{\gamma}$ through q, and points on $\tilde{\gamma}$ are orbits heteroclinic from $p_0$ to $p_1$. We describe and implement a numerical scheme for computing the jets of $\tilde{\gamma}$ to arbitrary order. We begin by computing high order polynomial approximations of some functions $P_u,P_s:\mathbb{R}^2\rightarrow\mathbb{R}^3$, and domain disks $D_u,D_s\subset\mathbb{R}^2$, such that $W_{loc}^u(p_0)=P_u(D_u)$ and $W_{loc}^s(p_1)=P_s(D_s)$ with $W_{loc}^u(p_0)\cap W_{loc}^s(p_1)\neq\emptyset$. Then the intersection arc $\tilde{\gamma}$ solves a functional equation involving $P_s$ and $P_u$. We develop an iterative numerical scheme for solving the functional equation, resulting in a high order Taylor expa.
Stereo-based stochastic mapping with context using probabilistic PCA for noise robust automatic speech recognition

In this paper we investigate stereo-based stochastic mapping (SSM) with context for the noise robustness of automatic speech recognition, especially under unseen conditions. Probabilistic PCA (PPCA) is used in the SSM framework to reduce the high dimensionality of the noisy speech features with context and derive an eigen representation in the noisy feature space for the prediction of clean features. To reduce the computational cost in training, an approximation by single-pass re-training is considered for the estimation of joint GMM. We also show that the SSM estimate under the minimum mean square error (MMSE) in a space where low dimensional representation of clean speech and uncorrelated additive noise can be assumed is related to the subspace speech enhancement. Experiments on large vocabulary continuous speech recognition tasks observe gains from the proposed approach under the conditions with seen, unseen and real noise.
Wave field correlations in weakly mismatched random media

This paper concerns the derivation of a Fokker-Planck equation for the correlation of two high frequency wave fields propagating in two different random media. The mismatch between the random media need be small, on the order of the wavelength, and their correlation length need be large relative to the wavelength. The loss of correlation caused by the mismatch in the random media is quantified and the limit process for the phase difference is obtained. The derivation is based on a random Liouville equation to model high frequency correlations and on the method of characteristics to characterize mixing in the random Liouville equation. Applications of such correlation loss include the monitoring in time of random media and the analysis of time reversed waves in changing heterogeneous domains.
Cost effective Distributed Generation Placement and sizing using Water Flow-like Algorithm

Distributed Generation (DG) is been a very interesting concept in recent years for its inheritance benefits when integrated to grid. It can reduce real losses, voltage deviation and congestion merely when proper location and sizes of DG were provided. But such connection of DG should also have maximum cost saving over the time. For optimal locations, real loss index, voltage index were calculated and given as inputs to Fuzzy inference system which will take decision based on the rule base framed, to give DG Placement index (DGPI) as output. Maximum values of DGPI pose optimal location to DG placement. In this paper an objective function representing Cost of Maximum Saving is formulated which is maximized by optimizing the size of DG using a novice algorithm i.e. Water Flow-like Algorithm (WFA). This algorithm is based on the flowing behaviour of water from higher altitude to lower level and is governed by parameter like gravitational force, momentum of water etc. The proposed method is tested on IEEE 33 bus system and IEEE 69 bus systems, and the result obtained were presented.
Massive parallel sequencing and digital gene expression analysis reveals potential mechanisms to overcome therapy resistance in pulmonary neuroendocrine tumors

Background: Lung cancer is the leading cause of cancer-related deaths worldwide. 25% show neuroendocrine differentiation (typical/atypical carcinoids, large-/small-cell neuroendocrine carcinomas). Carcinoids present with long survival rates, but metastatic carcinoids correlate with decreased survival and are commonly insensitive to standard chemotherapy or radiation. Therefore, novel therapeutic strategies are urgently needed. Material and methods: 70 representative tumor specimens were used for next-generation sequencing analysis of 14 genes related to therapy response. Additionally, mRNA-expression profiles of 60 matching samples were determined for 13 selected drug targets by using the NanoString nCounter technology. Results: A number of features known to sensitize tumors for different targeted therapies could be identified, which hopefully improve the clinical management of this subgroup of lung neoplasias. In particular, EGFR expression was observed in the investigated tumors in a noteworthy manner. Additionally, MDM2 was strongly expressed in the majority of all samples whereas the expression of its physiological inhibitor, CDKN2A, was nearly absent in all low-grade tumors. TP53 showed a high frequency of variants in high-grade tumors but mutations were rare in carcinoids. Conclusion: Based on our results, therapeutic approaches with MDM2-inhibitors and monoclonal anti-EGFR antibodies may be promising in pulmonary carcinoid tumors.
Repeated Labilization-Reconsolidation Processes Strengthen Declarative Memory in Humans

The idea that memories are immutable after consolidation has been challenged. Several reports have shown that after the presentation of a specific reminder, reactivated old memories become labile and again susceptible to amnesic agents. Such vulnerability diminishes with the progress of time and implies a re-stabilization phase, usually referred to as reconsolidation. To date, the main findings describe the mechanisms associated with the labilization-reconsolidation process, but little is known about its functionality from a biological standpoint. Indeed, two functions have been proposed. One suggests that destabilization of the original memory after the reminder allows the integration of new information into the background of the original memory (memory updating), and the other suggests that the labilization-reconsolidation process strengthens the original memory (memory strengthening). We have previously reported the reconsolidation of human declarative memories, demonstrating memory updating in the framework of reconsolidation. Here we deal with the strengthening function attributed to the reconsolidation process. We triggered labilization-reconsolidation processes successively by repeated presentations of the proper reminder. Participants learned an association between five cue-syllables and their respective response-syllables. Twenty-four hours later, the paired-associate verbal memory was labilized by exposing the subjects to one, two or four reminders. The List-memory was evaluated on Day 3 showing that the memory was improved when at least a second reminder was presented in the time window of the first labilization-reconsolidation process prompted by the earlier reminder. However, the improvement effect was revealed on Day 3, only when at least two reminders were presented on Day2 and not as a consequence of only retrieval. Therefore, we propose central concepts for the reconsolidation process, emphasizing its biological role and the parametrical constrains for this function to be operative.
Towards a Method for Evaluating the Precision of Software Measures (Short Paper)

Software measurement currently plays a crucial role in software engineering given that the evaluation of software quality depends on the values of the measurements carried out. One important quality attribute is measurement precision. However, this attribute is frequently used indistinctly and confused with accuracy in software measurement. In this paper, we clarify the meaning of precision and propose a method for assessing the precision of software measures in accordance with ISO 5725. This method was used to assess a functional size measurement procedure. A pilot study was designed for the purpose of revealing any deficiencies in the design of our study.
Improving visibility and fidelity of underwater images using an adaptive restoration algorithm

When light is transmitted in water from a subject to an observer, it is scattered and absorbed by the unstable environment such as suspended particles and turbid water. Due to these phenomena, underwater images usually have poor quality including low contrast, blurring, darkness, and color diminishing. In this paper, we propose a new underwater image restoration algorithm that consists of two major phases: visibility restoration and fidelity restoration. In the first phase, underwater images are observed similar to haze images because they have the same problems of low contrast and color shifting. This motivated us to use the haze removal technique, namely, dark channel prior, to dehaze underwater images. Subsequently, in the second phase, we equalize the color mean in each RGB (red, green, blue) channel to balance the color. Then transform the color space from RGB to HSV (hue, saturation, value) color space to adjust S channel to make the image color more natural. Finally, we adjust V channel according to the brightness value of RGB to enhance the contrast. Preliminary results indicated that the proposed method effectively improved visibility and fidelity of underwater images.
Panchromatic image processing using hyperspectral unmixing method

In the paper, we consider the probability of applying hyper-spectral image (HSI) processing methods to panchromatic images (PIs), which is a novel yet crucial issue for further analyses. To achieve the purpose, we propose an effective approach for handling PI with HSI unmixing methods. In the approach, HSI simulating process is first implemented to obtain a synthetic HSI from PI. After that, a hyperspectral unmixing algorithm, vertex component analysis, is then applied to extract endmembers that comprise the vertices of the data simplex. Meanwhile, we calculate abundances of HSI by employing least squares method. We will see that the unmixing results, namely endmembers and abundances, can be used for target detection and other applications. Different objects such as ships and cars were successfully extracted from the backgrounds, which demonstrates the efficacy of the proposed approach.