Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Cancer Cell Elasticity by Cell Type

Lower cellular elasticity is a distinguishing feature of cancer cells compared with normal cells. To determine whether cellular elasticity differs based on cancer cell type, cells were selected from three different cancer types including breast, cervix, and lung. For each cancer type, one counterpart normal cell and three types of cancer cells were selected, and their elasticity was measured using atomic force microscopy (AFM). The elasticity of normal cells was in the order of MCF10A > WI-38 ≥ Ect1/E6E7 which corresponds to the counterpart normal breast, lung, and cervical cancer cells, respectively. All cancer cells exhibited lower elasticity than their counterpart normal cells. Compared with the counterpart normal cells, the difference in cellular elasticity was the greatest in cervical cancer cells, followed by lung and breast cancer cells. This result indicates lower elasticity is a unique property of cancer cells; however, the reduction in elasticity may depend on the histological origin of the cells. The F-actin cytoskeleton of cancer cells was different in structure and content from normal cells. The F-actin is mainly distributed at the periphery of cancer cells and its content was mostly lower than that seen in normal cells.
Review of the genus Loimia Malmgren, 1866 (Annelida, Terebellidae) from China seas with recognition of two new species based on integrative taxonomy

Specimens of the genus Loimia (Annelida, Terebellidae) deposited in the Marine Biological Museum of the Chinese Academy of Sciences (MBMCAS) together with materials newly collected from China seas were examined. Based on morphological comparisons and molecular analysis, some specimens collected from the coasts of Shandong province and Guangxi province were confirmed as two new Loimia species respectively (Loimia borealis sp. n. and Loimia macrobranchia sp. n.). Morphologically, L. borealis sp. n. is distinguished from previously known species of this genus by having seven equal sized ventral shields, with length five times the width; this species was retrieved as sister to the clades of Loimia arborea Moore, 1903 and Loimia bandera Hutchings, 1990 in the phylogenetic tree, which was reconstructed based on mitochondrial COI gene. Loimia macrobranchia sp. n. differs from congeners by the large size of its first pair of branchiae with a thick main stem and about 18 dendritic branches arranged in two levels. A key to identifying Loimia species found in Chinese seas is given.
Prediction of human fetal–maternal blood concentration ratio of chemicals

Background The measurement of human fetal-maternal blood concentration ratio (logFM) of chemicals is critical for the risk assessment of chemical-induced developmental toxicity. While a few in vitro and ex vivo experimental methods were developed for predicting logFM of chemicals, the obtained experimental results are not able to directly predict in vivo outcomes. Methods A total of 55 chemicals with logFM values representing in vivo fetal-maternal blood ratio were divided into training and test datasets. An interpretable linear regression model was developed along with feature selection methods. Cross-validation on training dataset and prediction on independent test dataset were conducted to validate the prediction model. Results This study presents the first valid quantitative structure-activity relationship model following the Organisation for Economic Co-operation and Development (OECD) guidelines based on multiple linear regression for predicting in vivo logFM values. The autocorrelation descriptor AATSC1c and information content descriptor ZMIC1 were identified as informative features for predicting logFM. After the adjustment of the applicability domain, the developed model performs well with correlation coefficients of 0.875, 0.850 and 0.847 for model fitting, leave-one-out cross-validation and independent test, respectively. The model is expected to be useful for assessing human transplacental exposure.
ADAMs family members as amyloid precursor protein α‐secretases

In the non‐amyloidogenic pathway, the Alzheimer's amyloid precursor protein (APP) is cleaved within the amyloid‐β domain by α‐secretase precluding deposition of intact amyloid‐β peptide. The large ectodomain released from the cell surface by the action of α‐secretase has several neuroprotective properties. Studies with protease inhibitors have shown that α‐secretase is a zinc metalloproteinase, and several members of the adamalysin family of proteins, tumour necrosis factor‐α convertase (TACE, ADAM17), ADAM10, and ADAM9, all fulfil some of the criteria required of α‐secretase. We review the evidence for each of these ADAMs acting as the α‐secretase. What seems to be emerging from numerous studies, including those with mice in which each of the ADAMs has been knocked out, is that there is a team of zinc metalloproteinases able to cleave APP at the α‐secretase site. We also discuss how upregulation of α‐secretase activity by muscarinic agonists, cholesterol‐lowering drugs, steroid hormones, non‐steroidal anti‐inflammatory drugs, and metal ions may explain some of the therapeutic actions of these agents in Alzheimer's disease. © 2003 Wiley‐Liss, Inc.
Cheyletidae (Acarina: Trombidiformes) Associated with Citrus in Florida

This is the third in a series of papers (Muma 1955, 1960) dealing with predatory mites associated with citrus trees in Florida. Biological and ecological studies designed to evaluate and improve biological control of injurious insects and mites have created a need for named species. The present paper identifies and names the 12 species of cheyletids that have been collected on or under citrus trees in Florida. Mites of the family Cheyletidae, with the exception of ione species, Paracheyletia wellsi (Baker), are uncommon to rare on citrus trees. P. wellsi is relatively common, sometimes abundant, on fruit, leaves and bark, and is occasionally taken in the litter under the trees. Paracheyletia bakeri Ehara, a closely related species, is also found in the litter under the trees and on several occasions has decimated laboratory cultures of Tetranychidae and Tenuipalpidae. All mites were mounted in Hoyer's modification of a chloral hydrate preservative on microscope slides and examined with phase contrast illumination at magnifications varying from 125X to 50OX. Measurements were made at 125X and adjusted to the nearest 0.01 mm. Descriptive and key characters refer only to females; males were not sufficiently common to evaluate. The following key was constructed for those species that are associated with citrus trees in Florida. Diagnostic characters were not evaluated for generic or specific significance and therefore do not apply to forms not included in the key.
RECENT CHANGES IN POPULA TIONS OF RESIDENT GYPS VULTURES IN INDIA 1

Bombay Natural History Society, Hornbill House, S.B. Singh Road, Mumbai 400 001, Maharashtra, India. Conservation Science Group, Department of Zoology, University of Cambridge CB2 3EJ, UK. Email: reg29@cam.ac. uk Royal Society for the Protection of Birds, The Lodge, Sandy, Bedfordshire SG19 2DL, UK Institute of Zoology, Zoological Society of London, Regent’s Park, London, NW1 4RY, UK. Email: a.cunningham@ioz.ac. uk Email: jatayuprakash@sify.com Email: sachinranade@yahoo.com Email: sarvan_wlb@yahoo.com Email: jatayuprakash@sify.com Email: venkatsacon@yahoo.co.in Email: bnhs@bom3.vsnl. net.in Email: debbie.pain@rspb.org. uk Email: Richard.Cuthbert@rspb.org. uk
DYNAMIC PORTFOLIO OPTIMIZATION USING GENERALIZED DYNAMIC CONDITIONAL HETEROSKEDASTIC FACTOR MODELS

We model large panels of financial time series by means of generalized dynamic factor models with multivariate GARCH idiosyncratic components. Such models combine the features of dynamic factors with those of a generalized smooth transition conditional correlation (GSTCC) model, which belongs to the class of time-varying conditional correlation models. The model is applied to dynamic portfolio allocation with Value at Risk constraints on 6.5 years of daily TOPIX Sector Indexes. Results show that the proposed model yields better portfolio performance than other multivariate models proposed in the literature, including the traditional mean-variance approach.
Evaluation of the spouted bed dried leaf extract of Bauhinia forficata for the treatment of experimental diabetes in rats

Previously, we have demonstrated that treatment of experimental diabetes with a decoction of Bauhinia forficata leaves is beneficial. In this study, we prepared a two-fold concentrate of this extract and tested its effects on physiological, biochemical and toxicity markers in streptozotocin-diabetic rats. Dried and ground leaves were extracted with warm 70% hydroethanol and the filtrate concentrated by evaporation at 50°C. This solution was mixed with colloidal silicon dioxide (Tixosil-333 ® ) and dried in a spouted bed (BfT). Rats were treated with water, insulin and Tixosil particles at low or high doses, alone or coated with dried BfT. Animals were periodically weighed and monitored for water and food intake; urinary volume, glucose, urea and protein; blood glucose, serum lipids, liver toxicity markers transaminase and phosphatase and masses of adipose tissue and skeletal muscle. Insulin treatment gave best rat growth and lowest values for all other markers. No other treatment affected any diabetic marker, but the enzyme activities were changed by diabetes and BfT. Thus, BfT toxicity could arise from secondary products of plant constituents or Tixosil interaction. Therefore, BfT prepared in the spouted bed as described, is unsuitable for treatment of diabetes, which implies that the method of preparation of any medicine is critical for its efficacy and toxicity.
Genetic association of leaf rust resistance gene Lr34 with adult plant resistance to stripe rust in bread wheat

Near-isogenic Thatcher lines (viz., RL6058, RL6077, and Line 920) carrying gene Lr34 for leaf rust resistance, Frontana, and various other wheat cultivars carrying Lr34 displayed adult plant resistance to stripe rust in Mexico. F 2 populations or F 3 lines from several intercrosses of wheats carrying Lr34 did not segregate for susceptibility to stripe rust and leaf rust, indicating that at least one resistance gene was common in each parent (.)
Quantitative description of collagen structure in the articular cartilage of the young and adult equine distal metacarpus

The orientation and organisation of collagen fibrils play an important role in the mechanical functioning of the articular cartilage (AC) that covers the surfaces in the diarthrodial joints. In the adult animal, typically an arcade like 'Benninghoff structure' is found. Because the remodelling capacity of the collagen network in the adult animal is limited, this Benninghoff structure needs to develop before the animal reaches maturity, and it needs to develop correctly. The aim of this study is to use quantitative polarised light microscopy (qPLM) and scanning electron microscopy (SEM) techniques to investigate if this Benninghoff structure is already present in the young animal, and to quantitatively investigate possible differences in collagen structure in the equine distal metacarpus of the young and adult animal. In total, 21 forelimbs of 13 horses are used. In animals of age 10 months and older, we find an arcade like Benninghoff structure for the collagen fibril network in both the qPLM and SEM study. The qPLM study shows that the collagen's predominant orientation is parallel to the articular surface throughout the entire cartilage depth in two animals directly after birth. These findings are supported by SEM results on a foal. We conclude that structural remodelling of the collagen network in AC occurs in the first months after birth. Because animals start with collagen parallel to the articular surface and need to remodel this structure to a Benninghoff architecture, and because collagen structure is an important parameter for AC mechanics and mechanobiology, these results suggest implications for AC epigenetics.
The importance of optimal parameter setting for pitch extraction.

In this study we present a performance comparison for five pitch extraction algorithms: Auto Correlation, Cross Correlation, and Sub-Harmonic Summation (as implemented in PRAAT [Boersma and Weenick (2010)]), the Robust Algorithm for Pitch Tracking implemented in ESPS [Talkin (1995)], and SWIPE' [Camacho (2007)]. Recent research showed that SHS and SWIPE' outperformed the other algorithms on two speech databases with EGG reference values [Camacho (2007)]. That study, however, used a fixed search range of 40-800 Hz for all speakers, regardless of sex or speaker-specific pitch characteristics. In the current study, we adopt the parameter optimization strategy from De Looze and Rauzy (2009) to calculate specific pitch floor and ceiling values for each speaker. Our results show a substantial improvement in accuracy of the AC, CC, and RAPT algorithms when the optimized parameters are used (especially for the female speakers), and all five algorithms show similar performance. The gross error rate for all five algorithms ranges from 0.1% to 0.3% (N=18 098) on the FDA database [Bagshaw (1994)] and from 0.2% to 0.4% (N=11 527) on the Keele database [Plante et al. (1995)]. Our study thus highlights the importance of pre-processing the speech signal to determine optimal speaker-specific parameters for pitch extraction.
Active Learning for Drug Discovery

by a generous gift from Ray and Stephanie Lane in support of graduate education in computational biology. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, donors or the U.S. Government. To my sweet wife and dear children. Abstract The use of high throughput screening methods has aided the drug discovery process allowing for the testing of numerous compounds for effects on a single target protein. However, by focusing primarily on a single target during high throughput screening, undesirable secondary effects are often detected late in the development process after substantial investment has been made. In order to better detect effects on a system, high content screening methods have been developed utilizing imaging technology in conjunction with machine learning methods to detect effects on living systems as a result of exposure to a drug stimulus. These have primarily been applied in animal systems, and we therefore explored approaches to extending high content screening methods to plant cells. A pilot high content screening approach was developed and used to test the effects of nine compounds on protoplasts from six lines of Arabidopsis thaliana expressing different fluorescently-tagged proteins. Various image analysis and machine learning techniques were used to determine which compounds affected the subcellular distributions of the proteins. Both high throughput and high content screening methods are primarily limited in that very few target proteins are measured directly in these experiments. An alternative approach would be to do a more global screen against many undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive due to the number of combinations of potential drugs and secondary targets. Methods for making this global approach feasible through active machine learning were therefore developed. The active learning approach iteratively constructs models to predict the results of unobserved experiments and utilizes these models to guide experimentation efforts. Such methods were developed and applied to screening data for 20,000 compounds on 177 assays. It was shown through simulations that nearly 60% of all hits (compounds that have an effect on a particular assay) could be identified after exploring only 3% of the experimental space. Finally, an automated approach to creating NIH 3T3 cell lines expressing fluorescently-tagged proteins via CD-tagging and identifying the tagged protein was developed. This was used …
Stocking density and erythrocytic parameters of pacu raised in cages

O objetivo neste trabalho foi avaliar o manejo, as caracteristicas eritrocitarias e o nivel de glicose sanguinea de pacus (Piaractus mesopotamicus) estocados em diferentes densidades em tanques-rede. Foram utilizados 2.700 peixes com peso inicial de 142,11 ± 10,54 g, distribuidos em nove tanques-rede de 5 m3 de volume util durante 240 dias. O delineamento utilizado foi o inteiramente casualizado, com tres densidades (200, 300 e 400 peixes por tanque), cada uma com tres repeticoes. Somente o peso final e o ganho de peso diminuiram com o aumento da densidade de estocagem. A biomassa final aumenta de acordo com o numero de animais estocados por unidade produtiva. As densidades de criacao nao influenciaram o rendimento de carcaca, mas afetaram o comprimento total, que foi maior nos peixes criados em baixa densidade, que nao diferiram daqueles cultivados em densidade intermediaria. Os peixes criados em menor densidade apresentaram maior deposicao lipidica, mas nao diferiram significativamente daqueles estocados em densidade intermediaria. As variaveis eritrocitarias, como eritrocitos totais, hemoglobina, hematocrito, volume corpuscular medio e concentracoes de hemoglobina corpuscular media e de hemoglobina corpuscular media, nao apresentaram diferencas estatisticas entre as densidades utilizadas e permaneceram na faixa normal para a especie. Os niveis de glicose no sangue nao diferiram significativamente entre as densidades trabalhadas. A densidade de estocagem nao influencia os parâmetros eritrocitarios, o nivel de glicose nem o rendimento de carcaca de pacus. A densidade de 200 peixes/tanque resulta em maior peso final e ganho de peso, porem reduz a produtividade por area e aumenta a deposicao lipidica dos peixes.
An Extended Review on Internet of Things (IoT) and its Promising Applications

At present IoT is immensely a descriptive term of a vision that everything should be connected to the internet. IoT applications have been widely used in several fields of social living such as healthcare and social products, industrial automation and energy. In this scenario, there are more than 14 billion interconnected digital and electronic devices in operation worldwide, the equivalent of almost two devices for every human being on earth. The IoT connects different nonliving objects through the internet and enables them to share information with their community network to automate processes for human beings and makes their lives convenient. Subsequently, objects are being amalgamated with internet connectivity and powerful data analysis capabilities that promise to change the way we work and live. The Internet is a worldwide system of interconnected computer networks that use the standard Internet protocol suite (TCP/IP) to serve billions of users globally. The most vital characteristics of IoT include connectivity, active engagement, connectivity, sensors, artificial intelligence, and small device use. This paper provides an overview of existing Internet of Things (IoT), technical details, and applications in this new emerging area as well as we are thoroughly analyzing the layer about the IoT. However, this manuscript will give a better understanding for the new researchers, who want to do research in this field of Internet of Things.
PredCheck: Detecting Predatory Behaviour in Scholarly World

High solicitation for publishing a paper in scientific journals has led to the emergence of a large number of open-access predatory publishers. They fail to provide a rigorous peer-review process, thereby diluting the quality of research work and charge high article processing fees. Identification of such publishers has remained a challenge due to the vast diversity of the scholarly publishing ecosystem. Earlier works utilises only the objective features such as metadata. In this work, we aim to explore the possibility of identifying predatory behaviour through text-based features. We propose PredCheck, a four-step classificaton pipeline. The first classifier identifies the subject of the paper using TF-IDF vectors. Based on the subject of the paper, the Doc2Vec embeddings of the text are found. These embeddings are then fed into a Naive Bayes classifier that identifies the text to be predatory or non-predatory. Our pipeline gives a macro accuracy of 95% and an F1-score of 0.89.
Inter-subdiscipline Analysis Based on Mathematical Statements

A mathematical paper contains various mathematical statements, including definitions, theorems, lemmas, and so on. The mining of mathematical literature currently focuses on formulas and disregards statements. The present study investigates the (automatic) subdiscipline classification for mathematical statements. The classification results are applied into inter-subdiscipline analysis, including proportion and dependency analyses. First, a statement learning data is directly compiled from mathematical textbooks with a little human labeling to train an effective subdiscipline classifier. Second, a relatively large corpus, namely, analysis data, is compiled from mathematical journals. The classification results on the analysis data are subsequently used to quantify the inter-subdisciplinary relationships and conduct proportion analysis. Lastly, the dependency of different subdisciplines is analyzed and dependency chains among subdisciplines can be obtained.
Towards Deep Network & Application Integration: Possibilities, Challenges, and Research Directions

The collaboration between networks and applications provides opportunities to both applications to improve their performances and network service providers to increase business offering. Although many systems are proposed to support such collaborations, they are point or incremental solutions. In this paper, we propose the exploration of a more integrated architecture with huge possibilities taking a network-application integration (NAI) approach. Specifically, we explore the NAI possibilities in two concrete aspects: application-aware networking and network-aware applications. We review recent progress in these two aspects, and identify the key challenges in systematically realizing such a deep integration. To address these challenges, we present the initial design of a generic NAI possibilities exposure and discovery framework, called PED, based on satisfiability modulo theories (SMT). The key components of PED include a unified, abstract representation of network information using mathematical programming constraints, a declarative language for applications to express their intents on discovering network information, and an efficient compiler to translate application intents to constraint programming problems and discover corresponding network information. Preliminary evaluation results demonstrate the potentials of the PED framework. At the end of the paper, we also discuss a series of key future research directions toward deep NAI.