Miracle-dz/UniFAR-FTUs huggingface dataset:
Proteomic analysis of proteins involved in mitochondria-to-nucleus retrograde response in human cancer cells

All tumors examined to date contain mutations in mitochondrial DNA (mtDNA). In addition, depletion of mtDNA is reported in a variety of tumors. Mitochondrial dysfunction resulting from changes in mtDNA invokes mitochondria-to-nucleus retrograde response in human cells. To identify proteins involved in retrograde response and their potential role in tumorigenesis, we carried out a comparative proteomic analysis using a cell line in which the mitochondrial genome was completely depleted (? 0 cells lacking all mtDNA-encoded protein subunits), a cybrid cell line in which mtDNA was restored, and the parental cell line. Our comparative proteomic approach revealed marked changes in the cellular proteome and led us to identify quantitative changes in expression of several proteins. We found that subunits of complex I and complex III, molecular chaperones, and a protein involved in cell cycle control were down-regulated and Inosine 5’-monophosphate dehydrogenase type 2 (IMPDH2) involved in nucleotides biosynthesis was up-regulated in ? 0 cells. Our findings demonstrate that the expression of proteins is restored to wild type level by transfer of wild type mitochondria to ? 0 cells, suggesting that these proteins play key roles in retrograde response. To determine a potential role for identified retrograde responsive proteins in tumorigenesis, we analyzed the expression of UQCRC1 gene (encoding ubiquinol cytochrome-c reductase core protein I) in breast and ovarian tumors. We found that 1) UQCRC1 was highly expressed in breast (74%) and ovarian tumors (34%) and 2) the expression positively correlated with cytochrome c-oxidase (COXII) encoded by mtDNA. Our study opens an avenue for identification of retrograde proteins as potential tumor suppressors or oncogenes involved in carcinogenesis.
Road Crack Detection Using Deep Neural Network with Receptive Field Block

Cracks are common pavement diseases that affect pavement performance. To maintain the road in good condition, localizing and fixing the cracks is a vital responsibility for transportation maintenance department. However, traditional manual detection methods are considerably tedious and require domain expertise. Therefore, the research on automatic detection and identification of pavement crack is of great significance for ensuring traffic safety and pavement maintenance decisions. In this paper, we propose an automatic pavement crack detection network based on the Single Shot MultiBox Detector(SSD) deep learning framework, and introduce the receptive field module to enhance the feature extraction capability of the network, which ensures real-time crack detection and also improves the performance of accuracy in pavement crack detection.
Distribution of Daily Breathing Rates For Use in California's Air Toxics Hot Spots Program Risk Assessments

Data were combined from a study measuring breathing rates at various activities and two activity pattern studies to generate breathing rate distributions for children and adults. The children and adult breathing rate distributions were combined using a Monte Carlo technique to generate a breathing rate distribution for a lifetime spanning ages 0 to 70. The children's breathing rate distribution has a mean, standard deviation, median and 95th percentile of 452, 67.7, 441, and 581 L/kg-day, respectively. The adult breathing rate distribution has a mean, standard deviation, median and 95th percentile of 232, 64.6, 209, and 381 L/kg-day, respectively. The simulated 70-year distribution has a mean, standard deviation, median and 95th percentile of 271, 57.9, 253, and 393 L/kg-day, respectively. The adult breathing rate distribution is based on 24-hour recall activity data that would not necessarily capture average activity patterns and therefore breathing rates. We utilized the human energy expenditure literature to validate the breathing rate distribution. We conclude that the breathing rate distribution is reasonable for chronic long-term risk assessment in California's Air Toxics Hot Spots program.
Small mammal responses to environmental change: integrating past and present dynamics

Abstract Forecasting the response of species and communities to environmental change is a priority for multiple disciplines in the natural sciences. In looking toward the future, much can be learned from examining faunal response under past episodes of environmental change. Typically, retrospective approaches are limited to one spatial and temporal scale. Here, we illustrate how integrating across spatiotemporal scales can provide powerful insights into faunal response, and can inform conservation and management. To do this we compare paleontological and neontological studies on the small mammal fauna of the Great Basin. Small mammal species and their assemblages have long been recognized as indicators of ecological change and ecosystem health. We use fossil data from two long-term owl roosts to reconstruct patterns of richness and the apportioning of abundance among functional groups across multiple episodes of warming during the Holocene (last 10,000 years). We then use these findings as a climate-only baseline against which to compare changes in richness and abundance in 2 independent mountain ranges over the past century. While the past century has been marked by climate warming, the modern day Great Basin landscape also has been subject to intense human land-use practices and the introduction of nonnative plant species. Our contrast highlights that for Great Basin small mammals, modern-day land-use practices are modifying climate-based expectations.
In-Band Full Duplex Wireless LANs: Medium Access Control Protocols, Design Issues and Their Challenges

In-band full duplex wireless medium access control (MAC) protocol is essential in order to enable higher layers of the protocol stack to exploit the maximum benefits from physical layer full duplex technology. Unlike half duplex wireless local area network, a full duplex MAC protocol has to deal with several unique issues and challenges that arise because of the dynamic nature of the wireless environment. In this paper, we have discussed several existing full duplex MAC protocols and have shown qualitative comparisons among these full duplex MAC protocols. Full duplex in-band wireless communication has the potential to double the capacity of wireless network. Inter-client Interference (ICI) is a hindrance in achieving double spectral efficiency of the in-band full-duplex wireless medium. In this paper, we have classified existing solutions to the ICI problem and compared the solutions with respect to the proposed approaches, their advantages and disadvantages.We have also identified and discussed several issues and challenges of designing a full duplex MAC protocol. Results of qualitative comparisons of various wireless full duplex MAC protocols may be applied to design new protocols as well as researchers may find the identified issues and challenges helpful to solve various problems of a full duplex MAC protocol.
Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics

“Egg pasta” is a kind of pasta prepared by adding eggs in the dough; the color of this product is often associated to its quality, as it is proportional to the quantity of egg present in the dough. A possible adulteration on this product is represented by the addition of turmeric (not reported in the label) in the dough. The inclusion of this ingredient (which is minimal, given the strong coloring power of this spice) fraudulently accentuates the yellow color of the product, making it more attractive to the consumer. Given this scenario, the aim of the present work is to develop an analytical approach suitable at detecting the presence of turmeric as an adulterant in egg pasta. One hundred samples of traditional and adulterated egg pasta were analyzed by NIR spectroscopy and PLS-DA (Partial Least Squares Discriminant Analysis) in order to discriminate adulterated and compliant pasta. The classification model provided a total correct classification rate of 97.5% in external validation (40 samples). Eventually, the adulterant was quantified by PLS. This strategy provided satisfying results, achieving a RMSEP (Root Mean Square Error in Prediction) of 0.112 (%-w/w) in external validation.
NON-STATIONARY TIME SERIES PREDICTION USING ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK MODELS

The main goal of non-stationary time series prediction is the construction, identification, configuration and verification of their models. The efficiency of using machine learning technologies for the analysis of non-stationary time series is shown due to their ability to model complex nonlinear dependencies in the behaviourof the time series from both depending on previous values and external factors, to analysefeatures, relationships and complex interactions. The features of time series prediction using a one-dimensional convolutional neural network are discussed. The features of the architecture and the training process when using a one-dimensional convolutional neural network are considered on the example of solving the problems to predictsales and build a forecast of company stock prices. To improve the quality of the prediction, the initial time series were pre-processed by the moving average method in the window. Computer modellingof the predicting problem using the one-dimensional convolutional neural network was performed in the Python programming language. The sales prediction using the proposed one-dimensional convolutional neural network model predicted volume sale of cars and commercial vehicles in Vietnam from two thousand and eleven to two thousand and eighteen. The one-dimensional convolutional neural network model has given a high accuracy of prediction with seasonal trend data. In stock prices prediction, another architecture of one-dimensional convolutional neural network model was launched, which corresponds to non-stationary data with large lengths of data series with small intervals between minutes, such as stock trading statistics per minute. In this project, data is taken from Amazon Nasdaq one hundred for forty thousand five hundred and sixty data points. The data is divided into training and test sets. The test suite is used to verify the actual performance of the model. It is shown that the model of a one-dimensional convolutional neural network gives good results in the presence of both seasonal and trend components of the time series with large data sizes.
Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument, Oregon, USA

We evaluated the current inventory and monitoring practices at John Day Fossil Beds National Monument (JODA) with an analysis of fossil yield based on locality data. While JODA covers a range of fossiliferous Cenozoic rock, most paleontological fieldwork conducted by the park staff occurs in the highly fossiliferous Oligocene Turtle Cove Member of the John Day Formation. To optimize the number of fossils collected, JODA established a schedule that cycled fieldwork every four years between the five most fossiliferous areas of the park. We digitized over 1000 field collections from 19992019 to evaluate whether the four-year return interval allowed enough time for new fossils to be exposed through erosion in an area between visits as intended by the schedule. We found no significant difference in fossil yield between areas that had not been visited for 1, 2, 3, 4, or 5+ years. Based on these results, we infer that the claystones of the Turtle Cove Member are erodible and fossiliferous enough that one winter of erosion will expose enough fossils to fully “recharge” an area that had been thoroughly inventoried the previous year. Because of limited staff and high fossil yield, not every fossil will be collected. Therefore, we use this analysis to shift JODA’s paleontological inventory and monitoring practices away from a rigid schedule and propose a flexible new system. The paleontology staff will use a tracker geodatabase of locality data from past years to set priorities each year based on past collection and current available staff and space. Anne E. Kort. Department of Earth and Atmospheric Sciences, Indiana University, 1001 East 10th Street, Bloomington, Indiana 47408, USA, aekort@iu.edu Nicholas A. Famoso. John Day Fossil Beds National Monument, U.S. National Park Service, 32651 Highway 19, Kimberly, Oregon 97848, USA, nicholas_famoso@nps.gov; ORCID: 0000-0002-1795-624X
Isolation and Characterization of a Novel cDNA Which Identifies Both Neural‐Specific and Ubiquitously Expressed GSα mRNAs

Abstract: Heterotrimeric G proteins consisting of α, β, and γ subunits couple sensory, hormone, and neurotransmit‐ ter receptors to intracellular and transmembrane effectors. Several splicing variants of the Gs (the G protein that stimulates adenylyl cyclase) α subunit (GSα) have been described. Some of these couple receptors to stimulation of adenylyl cyclase and Ca2+ channels, whereas others encode truncated proteins whose functions are not currently defined. We describe a 1321N1 human astrocytoma cDNA clone for a novel GSα isoform isolated from astrocytoma cells (Gastro) that is identical to GSα‐1 with the exception of a novel 5’ sequence extending into the previously described exon 1 of GSα, a single base change, and an alternative polyadenylation site. Analysis by northern blotting and reverse transcription/PCR confirms the presence of an mRNA corresponding to this cDNA in astrocytoma cells. Additional northern analysis indicates that Gastro recognizes two novel GSα mRNAs in the rat: a 2.0‐kb mRNA expressed only in neural and neuroendocrine tissues and a 1.8‐kb mRNA that is ubiquitously expressed. Functional analysis of Gastro is complicated by the apparent insertion of alphoid satellite DNA into the transcription unit. The resulting cDNA encodes a truncated protein that may be translated from the methionine in exon 2 as previously described.
Involvement of jasmonic acid signalling in bacterial wilt disease resistance induced by biocontrol agent Pythium oligandrum in tomato

When the biocontrol agent Pythium oligandrum (PO) colonizes the rhizosphere, it suppresses bacterial wilt disease in tomato (Solanum lycopersicum cv. Micro-Tom) caused by Ralstonia solanacearum, and a homogenate of its mycelia exhibits elicitor activity, inducing an ethylene (ET)-dependent defence response in Micro-Tom. Since salicylic acid (SA) and jasmonic acid (JA) play an important role in plant defence responses to pathogens, the involvement of SA- and JA-dependent signal transduction pathways in resistance to R. solanacearum was investigated in tomato roots treated with a mycelial homogenate of PO. Bacterial wilt disease was also suppressed in tomato cv. Moneymaker treated with the PO homogenate. However, the SA-inducible PR-1(P6) gene was not up-regulated in either Micro-Tom or Moneymaker. SA did not accumulate in homogenate-treated roots in comparison with distilled water-treated controls, even 24 h after inoculation. Induced resistance against R. solanacearum was not compromised in SA-non-accumulating NahG transgenic plants treated with the PO homogenate. On the other hand, the expression of the JA-responsive gene for the basic PR-6 protein was induced in both tomato cultivars treated with the PO homogenate. Furthermore, quantitative disease assays showed that the induced resistance against R. solanacearum was compromized in PO homogenate-treated jai1-1 mutant plants defective in JA signalling. These results indicated that the JA-dependent signalling pathway is required for PO-induced resistance against R. solanacearum in tomato.
Growth-Stage-Specific Kc of Greenhouse Tomato Plants Grown in Semi-Arid Mediterranean Region

Growth stage specific crop coefficient for greenhouse tomatoes grown in the semi arid Mediterranean was evaluated. The evaluation consisted of two simultaneous field trails: 1) Data accumulation of the water consumption of round and cherry tomato plants with the use of drainage lysimeters. 2) Irrigation of tomatoes planted in local sandy soil with either reduced or excess irrigation water doses using the lysimeter water consumption data as the reference criteria. Crop coefficients of between 0.3-1 for the time period from seedling establishment until first fruit harvest and 0.8 for the fruit harvest period were recorded for the winter season round tomato crop. During the summer season, coefficients of between 0.2-0.9 for the time period from seedling establishment until first fruit harvest and 0.8 for the fruit harvest period were measured for the round tomato crop. The cherry cluster cultivar showed a slightly different pattern of water consumption with a peak in crop coefficient at the middle of the fruit harvest stage and a continuous reduction in water consumption until the end of the growth period. Excess irrigation rates damaged the post harvest quality of the fruit of one of the tested cultivars. Reduced irrigation rates caused a reduction in the mean weight of the fruit.
The Effect of IPCC Reports and Regulatory Announcements on the Stock Market

This study explores U.S. public companies’ reactions to scientific announcements by the IPCC (Intergovernmental Panel on Climate Change) with respect to updated climate change knowledge and how it affects their stock valuations, given their carbon emission/environmental outlooks. Based on a sample of total daily returns collected for 10 industry indexes from the S&P 500 Index over the period 1990–2014, and using an event study approach, we analyze the connection between IPCC assessment report announcements and firms’ returns to evaluate panel data models. We found that various sectors, regardless of their carbon profiles, react abnormally to IPCC report announcements without remarkable long-run cumulative effects. The implications of these results are that there is no clear violation of the efficient markets hypothesis, yet short-term profits may be gained. Furthermore, the market still reacts to new scientific announcements, even though 24 years have passed since the first IPCC report. In addition, there is a negative relationship for low and medium carbon-intensive industries, especially in the short term.
A quick, easy and non‐intrusive method for underwater volume and surface area evaluation of benthic organisms by 3D computer modelling

In order to understand physiological, ecological and biological processes, it is often crucial to determine an organism's volume and surface area (SA). Most of the available methods require sacrificing the organism or at least removing it from its natural habitat, in order to measure these parameters. Advances in computer vision algorithms now allow us to determine these parameters using non‐destructive, three‐dimensional modelling. The addition of cloud computing and the availability of freeware make this tool widely accessible. Photographs of corals and sponges were taken in natura and used to create digital 3D models using the ‘structure‐from‐motion’ technique. Modelling was done online using 123D Catch freeware (Autodesk Inc.). Volume and SA of the corals and sponges were calculated from these 3D models. Comparing in situ 3D modelling to current measuring methods (e.g. water displacement, paraffin dipping) showed that volume calculation by 3D modelling gave fast results accurate to within 8% of estimated true volume. Using cloud computing enabled the creation of a 3D model in <30 min. SA accuracy was found to differ significantly, depending on the shape of the modelled object, with an accuracy ranging widely from 2% to 18%. We found that in situ volume and SA measurements created by 3D modelling enable easy, fast and non‐intrusive studies of benthic aquatic organisms, without removing the subject organisms from their habitat, thus enabling continuous study of natural growth over extended time periods. The freely available freeware, along with ease of use, makes this method accessible to many areas of research.
A reconstructive hermeneutic analysis: the distinctive role of body- and movement-based interventions with male offenders

ABSTRACT Body- and movement-based interventions are more and more being recognised as a therapeutic and pedagogical method. For an evidence-driven practice, scientific insight about how those interventions assist the process of change is essential. The present analysis provides a scientifically sound overview regarding therapeutic mechanisms of change when working with body- and movement-based interventions with male offenders. Based on the reconstructive-hermeneutic analysis, 23 publications were analysed regarding therapeutic factors and their orientation. Eight bodily therapeutic factors (BTF) and nine bodily orientations (BO) of body- and movement-based interventions were identified. An initial framework for the application of body- and movement-based interventions with male offenders in consideration of their processes is presented. This work suggests that body- and movement-based interventions are associated with defined bodily therapeutic factors and orientations. Further scientific research for further and practice-relevant theories in this field is suggested.
Characterization and Identification of Raw Beef, Pork, Chicken and Turkey Meats by Electrophoretic Patterns of Their Sarcoplasmic Proteins

Electrophoretic patterns of fresh beef, pork, chicken, and turkey sarcoplasmic proteins were studied using thin layer agarose gel electro-phoresis. Creatine kinase isozyme MM and myoglobin bands were used to identify the species. Binary mixtures (5/95, 25175, 50150, 751 25, 9515, % by weight of each species) were also examined by their electrophoretic and densitometric patterns. Electrophoretic patterns remained stable at elevated pH (7.5), but were affected by low pH (4.7). The relative ratios of the bands which characterized each species changed in proportion to the species content in each binary mixture. These ratios make it possible to predict the approximate fraction of each species present in such mixtures.
Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. In this study, satellite and ground‐based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground‐based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground‐based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.