Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency representation in the emu basilar papilla

The frequency representation on the basilar papilla of the emu was investigated by recording from single auditory afferents and labeling them iontophoretically with horseradish peroxidase. Successfully labeled fibers covered a range of characteristic frequencies from 0.05 to 3.3 kHz, which corresponded to nearly 90% of the total papillar length. The termination sites of labeled fibers within the basilar papilla correlated with their characteristic frequency, the lowest frequencies being represented apically, the highest basally. The best mathematical description of the frequency distribution was given by an exponential regression, with a uniform mapping constant of 0.8 mm/octave. This function deviates from the more commonly observed pattern of increasing space constant towards higher frequencies. Most labeled fibers innervated only one hair cell; those contacting 4 or more hair cells were all of low characteristic frequency (⩽0.11 kHz). All labeled fibers terminated within the neural 56% of the basilar papilla’s width. This innervation pattern is consistent with that observed in other bird species. There was no conclusive evidence for a change of sensitivity and/or frequency selectivity with innervational position across the papilla’s width.
Large-Scale Structure in the Universe: Results from the Stromlo-APM Redshift Survey

We present results on the clustering of galaxies in the Stromlo-APM redshift survey, which consists of 1787 galaxies sampled at a rate of 1 in 20 at random from the APM Galaxy Survey to a magnitude limit of b J =17.15. An analysis of counts in cells, and a direct estimate of the two-point correlation function, indicate more large-scale structure than expected in a biased, scale-invariant, Ω=1, cold dark matter (CDM) model
Effect of Corn Origin on Broiler Performance, Processing Yield, and Nutrient Digestibility from 1 to 35 Days of Age

Simple Summary Understanding the physical, chemical, and nutritional variability of corn and its impact on broiler performance is crucial for poultry nutritionists and feed mills that use corn of different origins. In the present study, broilers were fed commercial diets formulated to only differ in corn origin (United States, Argentina, and Brazil) to assess its impact on growth performance, carcass traits, and nutrient digestibility. The results indicated the impact of corn origin on feed efficiency and breast weight. Likewise, corn origin had an impact on mineral digestibility. Abstract This experiment studied the effect of feeding corn from the United States (USA), Argentina (ARG), and Brazil (BRA) on broiler performance, processing yield, and nutrient digestibility from 1 to 35 d of age. A total of 900-day old YPM × Ross 708 male broilers were randomly sorted in 36 floor pens, each containing 25 chicks and subjected to three experimental diets, resulting in 12 replications per dietary treatment. Starter, grower, and finisher diets were formulated to be only different in corn origin. Data were analyzed as a one-way ANOVA and means were separated using Tukey’s HSD test, with statistical significance considered at p ≤ 0.05. No significant differences (p > 0.05) were found in body weight, body weight gain, and feed intake from 1 to 35 d of age among the treatments. However, broilers fed diets with the inclusion of corn from the USA and BRA had a lower feed conversion ratio (FCR) compared with broilers fed diets with corn from ARG from to 1 to 35 d of age (1.434 and 1.434 vs. 1.452 g:g; p = 0.002). Broilers fed diets with the inclusion of corn from BRA had a higher breast weight compared with broilers fed diets with the inclusion of corn from ARG (575 vs. 553 g; p = 0.036), but did not differ in breast weight of broilers fed diets with corn from the USA (575 vs. 556 g; p > 0.05). Corn origin did not influence (p > 0.05) crude protein and fat digestibility. However, broilers fed diets with corn from the USA and BRA had greater phosphorus (P) (63.37, 62.23 vs. 55.26%; p = 0.0003), calcium (Ca) (41.59, 43.85 vs. 30.23%; p = 0.0003), and potassium (K) (88.98, 87.97 vs. 86.04%; p = 0.001) digestibility compared with broilers fed diets with corn from ARG. Overall, corn origin influenced FCR, breast weight, and P, Ca, and K digestibility of broilers from 1 to 35 d of age.
Plaque Macrophage‐Targeting Nanosystems with Cooperative Co‐Regulation of ROS and TRAF6 for Stabilization of Atherosclerotic Plaques

Chronic inflammatory microenvironment is the predominant milieu that promotes the progression of atherosclerosis (AS). However, targeted regulation of the chronic proinflammatory milieu still needs to be improved. In this study, macrophage‐targeting nanosystems composed of 37pA peptide embedded–Pt lipid nanoparticles (37pA‐PtLNP) and tumor necrosis factor‐associated factor 6 (TRAF6) inhibitor (6877002)‐loaded poly(ε‐caprolactone)‐b‐polyethylene glycol‐b‐poly(ε‐caprolactone) (PCEC) are constructed with 37pA peptide embedded–lipid coating (37pA‐LNP/6877002), to stabilize atherosclerotic lesions. The cooperative regulation effects of these nanosystems in achieving reactive oxygen species (ROS) scavenging and inflammatory signaling inhibition are investigated. 37pA‐PtLNP effectively scavenges several ROS, however, also promotes the expression of iNOS. The introduction of 37pA‐LNP/6877002 inhibits the activities of TRAF6, a downstream intracellular factor of the CD40L‐CD40‐TRAF6 axis, to decrease the proportion of the proinflammatory M1 macrophage phenotype, which is further downregulated by combined treatment with 37pA‐PtLNP. The combination of 37pA‐PtLNP and 37pA‐LNP/6877002 not only counteracts 37pA‐PtLNP–induced iNOS upregulation but also alleviates the chronic inflammatory microenvironment by reducing the expression of proinflammatory cytokines and chemokines. The anti‐AS efficacy in vivo on ApoE−/− mice further demonstrates that the combination of 37pA‐PtLNP and 37pA‐LNP/6877002 targetedly modulates the atherosclerotic plaque microenvironment, achieving stabilization of lesions with minimal progression. This study provides a promising strategy for AS management.
Legal Entity Extraction: An Experimental Study of NER Approach for Legal Documents

—In legal domain Name Entity Recognition serves as the basis for subsequent stages of legal artificial intelligence. In this paper, the authors have developed a dataset for training Name Entity Recognition (NER) in the Indian legal domain. As a first step of the research methodology study is done to identify and establish more legal entities than commonly used named entities such as person, organization, location, and so on. The annotators can make use of these entities to annotate different types of legal documents. Variety of text annotation tools are in existence finding the best one is a difficult task, so authors have experimented with various tools before settling on the best one for this research work. The resulting annotations from unstructured text can be stored into a JavaScript Object Notation (JSON) format which improves data readability and manipulation simple. After annotation, the resulting dataset contains approximately 30 documents and approximately 5000 sentences. This data further used to train a spacy pre-trained pipeline to predict accurate legal name entities. The accuracy of legal names can be increased further if the pre-trained models are fine-tuned using legal texts.
What Money Can Do: Examining the Effects of Rewards on Online Romance Fraudsters’ Deceptive Strategies

ABSTRACT With the advent of the internet, romance fraud – or instances wherein individuals use fake identities and sham romantic relationships to defraud others – has moved online. Victims of this crime experience harms to their financial, social, and personal well-being. While researchers have made strides in exploring this crime from the perspective of victims, little research has investigated it using data drawn from the persons committing the crime. Moreover, little is known about how these offenders may alter their strategies in response to variations in perceived reward. In this study, we explore these processes utilizing data collected via a series of sequential e-mail exchanges with 94 online romance fraudsters. More specifically, we investigate the deception strategies used by these fraudsters and examine whether and how these strategies shift in response to changes in the prospect of receiving financial reward from a victim. Our results add to current understanding of online romance fraud and have implications for theories of interpersonal deception and rational choice.
Temperature dependence of magnetic anisotropy in Mn-substituted cobalt ferrite

The temperature variation of magnetic anisotropy and coercive field of magnetoelastic manganese-substituted cobalt ferrites (CoMnxFe2? xO4 with 0 ? x ? 0.6) was investigated. Major magnetic hysteresis loops were measured for each sample at temperatures over the range 10–400 K, using a superconducting quantum interference device magnetometer. The high-field regimes of the hysteresis loops were modeled using the law of approach to saturation equation, based on the assumption that at sufficiently high field only rotational processes remain, with an additional forced magnetization term that was linear with applied field. The cubic anisotropy constant K1 was calculated from the fitting of the data to the theoretical equation. It was found that anisotropy increases substantially with decreasing temperature from 400 to 150 K, and decreases with increasing Mn content. Below 150 K, it appears that even under a maximum applied field of 5 T, the anisotropy of CoFe2O4 and CoMn0.2Fe1. 8O4 is so high as to prevent complete approach to saturation, thereby making the use of the law of approach questionable in these cases.
HADOOP : Solution for Big Data Challenges in Bioinformatics and its Prospective in India

Data becomes big data while its volume, variety, and velocity exceed abilities of our systems architecture and algorithm. Data volume is increasingly triggered by recent advancements in high-throughput technologies like Next Generation Sequencing (NGS), the discovery of gene expression and personalized medicine. Hadoop open source platform is magnificent to obtain information from raw biological big data with providing massive storage, advanced security, and fast data processing platform. In this review, comprehensive applicability of Hadoop being an open source platform is discussed with its global status to overcome big data challenges and its perspective in India.
Expectation and variance of genetic gain in open and closed nucleus and progeny testing schemes

Open and closed nucleus and conventional and modern progeny testing schemes were compared for expectation and variance of genetic gain. Generation intervals were optimized, with minimum values of 2 and 6 years (progeny test results available) for males in nucleus and progeny testing schemes, respectively. Females had a minimum generation interval of 2 years, except in the conventional progeny testing schemes, which had a minimum of 4 years (one individual record available). Apart from the generation intervals and the progeny test, open nucleus and progeny testing schemes were identical, since ‘nucleus females’ are also born in progeny testing schemes, being full-sibs of the young bulls and dispersed over commercial herds. The number of nucleus sires (bull sires) selected was varied between four and 32. Selection was for milk production. A deterministic model was used, that accounted for variance reduction due to selection and the effects of finite size and family structure on the selection differentials. Prediction of the variance of the selection response accounted for selection of full- and paternal half-sibs. Closed nucleus schemes gave a factor 0·03, 0·13 and 0·19 larger response rates than open nucleus and modern and conventional progeny testing schemes, respectively. Reduction of genetic variance of open nucleus schemes was larger than that of closed nucleus schemes, which caused the slightly higher response rates of closed nucleus schemes. Standard deviations of selection responses of closed nucleus schemes were a factor 0·46, 0·79 and 0·86 larger, respectively. Preference for the schemes was assessed using a quadratic utility function expressing risk and inbreeding aversion. The increase in genetic gain due to shortening of generation intervals more than compensated for its increased variance. Whether the increased genetic gain due to closing the nucleus compensated for its increased variance depended on the amount of risk aversion. Selection of four sires and eight to 16 sires had the highest utility in progeny testing and nucleus schemes, respectively.
Impact of Ensemble Size on Ensemble Prediction

Abstract The impact of ensemble size on the performance of the European Centre for Medium-Range Weather Forecasts ensemble prediction system (EPS) is analyzed. The skill of ensembles generated using 2, 4, 8, 16, and 32 perturbed ensemble members are compared for a period of 45 days—from 1 October to 15 November 1996. For each ensemble configuration, the skill is compared with the potential skill, measured by randomly choosing one of the 32 ensemble members as verification (idealized ensemble). Results are based on the analyses of the prediction of the 500-hPa geopotential height field. Various measures of performance are applied: skill of the ensemble mean, spread–skill relationship, skill of most accurate ensemble member, Brier score, ranked probability score, relative operating characteristic, and the outlier statistic. The relation between ensemble spread and control error is studied using L2, L8, and L∞ norms to measure distances between ensemble members and the control forecast or the verification. I.
A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis

—Log data from computers used for network forensic analysis is ineffective at identifying specific security threats. Log data limitations include the difficulty in reconstructing communication patterns between nodes and the inability to identify more advanced security threats. By combining traditional log data analysis methods with a more effective combination of approaches, a more comprehensive view of communication patterns can be achieved. This combined approach can then help identify potential security threats more effectively. It's difficult to determine the specific benefits of combining Packet Capture (PCAP) and Social Network Analysis (SNA) when performing forensics. This article proposes a new approach to forensic analysis that combines PCAP and social network analysis to overcome some of the limitations of traditional methods. The purpose of this discovery is to improve the accuracy of network forensic analysis by combining PCAP and social network analysis to provide a more comprehensive view of network communication patterns. Network forensics, which combines pcap analysis and social network analysis, provides more comprehensive results. PCAP analysis is used to analyze network traffic, conversation statistics, protocol distribution, packet content and round-trip times. Social network analysis maps communication patterns between nodes and identifies the most influential key players within the network. PCAP analysis efficiently captures and analyzes network packets, and SNA provides insight into relationships and communication patterns between devices on the network.
A2AR as a Prognostic Marker and a Potential Immunotherapy Target in Human Glioma

Gliomas are considered one of the most malignant tumors in the body. The immune system has the ability to control the initiation and development of tumors, including gliomas. Thus, immune cells find themselves controlled by various molecular pathways, inhibiting their activation, such as the immunosuppressive adenosine 2A receptor (A2AR). Our objective was to establish the expression profile and role of A2AR at the transcriptomic level, using real-time RT-PCR in Moroccan glioma patients, in addition to TCGA and CGGA cohorts. The real-time RT-PCR results in Moroccan patients showed that high expression of this gene was associated with poor survival in males. Our study on the CGGA cohort corroborated these results. In addition, there was a positive association of A2AR with T-cell exhaustion genes. A2AR also correlated strongly with genes that are primarily enriched in focal adhesion and extracellular matrix interactions, inducing epithelial mesenchymal transition, angiogenesis, and glioma growth. However, in the TCGA cohort, the A2AR showed results that were different from the two previously examined cohorts. In fact, this gene was instead linked to a good prognosis in patients with the astrocytoma histological type. The correlation and enrichment results reinforced the prognostic role of A2AR in this TCGA cohort, in which its high expression was shown to be related to lymphocyte differentiation and a successful cytolytic response, suggesting a more efficient anti-tumor immune response. Correlations and differential analyses based on A2AR gene expression, to understand the cause of the association of this gene with two different prognoses (CGGA males and TCGA Astrocytoma), showed that the overexpression of A2AR in Chinese male patients could be associated with the overexpression of extracellular adenosine, which binds to A2AR to induce immunosuppression and consequently a poor prognosis. However, in the second group (TCGA astrocytomas), the overexpression of the gene could be associated with an adenosine deficiency, and therefore this receptor does not undergo activation. The absence of A2AR activation in these patients may have protected them from immunosuppression, which could reflect the good prognosis. A2AR can be considered a promising therapeutic target in male CGGA and Moroccan patients with gliomas.
Diagnostic Analysis and Computational Strategies for Estimating Single Spell Discrete Time Duration Models – A Monte Carlo Study

This extensive Monte Carlo study re-examines properties of the nonparametric maximum likelihood estimator of discrete duration models with unobserved heterogeneity and unknown duration dependence. Alternative specifications and computation strategies are compared. We find: i) The inherent complexity of mixture models poses the major estimation hurdle. ii) It is important to choose a flexible specification for duration dependence. Polynomial specifications with a fixed number of terms can lead to systematically biased estimates of the hazard. iii) In estimation, simulated annealing is found to dominate all other optimization algorithms. Common applied research problems, such as near-boundary false optima are eliminated. iv) A bootstrap procedure is suggested to help choose the number of support points of unobserved heterogeneity. v) Likelihood ratio tests may still be appropriate especially when there are many common parameters in the components of the model. vi) Gateaux derivatives do not appear to help optimize the likelihood function.
Physicochemical and Antibacterial Evaluation of TiO2/CNT Mesoporous Nanomaterials Prepared by High-Pressure Hydrothermal Sol–Gel Method under an Ultrasonic Composite Environment

TiO2 has attracted significant research interest, principally due to its nontoxicity, high stability, and abundance. Carbon-doped TiO2 can improve light absorption efficiency. In order to prepare high-efficiency photocatalysts, carbon-doped composites were prepared by hydrothermal reaction in a high-pressure reactor, and then TiO2/CNT mesoporous composites were prepared by the sol–gel method in an ultrasonic environment. Characterized by SEM and TEM, the composite materials contained TiO2 nanoparticles as well as CNT. After phase analysis, it was the anatase-doped phase. The following infrared light absorption performance and Escherichia coli bactericidal performance tests showed that it had better infrared and visible light absorption performance than pure TiO2. The TiO2/CNT mesoporous nanomaterials synthesized in this work are possible for clean industrial productions.
Multiple Classifier System for Writer Independent Offline Handwritten Signature Verification using Hybrid Features

Offline handwritten signature verification is a very challenging area of research as the handwriting of two people may bear similarity whereas handwriting of a person may vary at different times. The accuracy of handwritten signature verification system depends on the classifier system and the way of feature extraction. Keeping this point of view, four types of hybrid feature sets and three types of classifiers specifically support vector machine with polynomial kernel, support vector machine with quadratic kernel and decision tree are investigated for writer-independent offline handwritten signature verification in the present work. To obtain hybrid feature sets, local oriented statistical information booster, discrete wavelet transform, and histogram of oriented gradient feature descriptors are extracted and are coupled with each other. To create multiple classifier system, the training set is partitioned into subsets and these training subsets are used to train the classifiers of multiple classifier system using same training algorithm for all classifiers. The performance analysis is carried out using two scenarios. In the first scenario, genuine and random forgery signatures are used to train the classifiers whereas genuine, random, unskilled and simulated forgery signatures are used to train the classifiers in the second scenario. False rejection rate 8.00 and false acceptance rate 0.00 for all types of forgeries are reported as the best result of the experiments.
Ethacrynic Acid: A Promising Candidate for Drug Repurposing as an Anticancer Agent

Ethacrynic acid (ECA) is a diuretic that inhibits Na-K-2Cl cotransporter (NKCC2) present in the thick ascending loop of Henle and muculo dens and is clinically used for the treatment of edema caused by excessive body fluid. However, its clinical use is limited due to its low bioavailability and side effects, such as liver damage and hearing loss at high doses. Despite this, ECA has recently emerged as a potential anticancer agent through the approach of drug repositioning, with a novel mechanism of action. ECA has been shown to regulate cancer hallmark processes such as proliferation, apoptosis, migration and invasion, angiogenesis, inflammation, energy metabolism, and the increase of inhibitory growth factors through various mechanisms. Additionally, ECA has been used as a scaffold for synthesizing a new material, and various derivatives have been synthesized. This review explores the potential of ECA and its derivatives as anticancer agents, both alone and in combination with adjuvants, by examining their effects on ten hallmarks of cancer and neuronal contribution to cancer. Furthermore, we investigated the trend of synthesis research of a series of ECA derivatives to improve the bioavailability of ECA. This review highlights the importance of ECA research and its potential to provide a cost-effective alternative to new drug discovery and development for cancer treatment.
Analysis of algorithms of the Arrow-Hurwicz type

Explicit and implicit two-parameter iterative Arrow-Hurwicz algorithms for linear block saddle-point problems are considered. Such problems arise, for example, in finite element discretization of Stokes-type and elasticity equations and in mixed finite element discretization. Criteria for convergence and estimates for the rate of convergence are provided in terms of four constants. These constants correspond to the preconditioner for the upper left block and to the preconditioner for the Uzawa algorithm. We present precise criteria for convergence. Estimates for the convergence rate of the explicit algorithm can be obtained by analyzing the resolvent of the iteration operator. The eigenvalues and norm of the resolvent are estimated. The introduction of two parameters can change the asymptotic behavior of the spectral radius of the iteration operator for the implicit Arrow-Hurwicz algorithm. Such a selection of parameters is specified.