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Isolated Ti(III) Species on the Surface of a Pre-active Ziegler Natta Catalyst. Dataset related to publication. Version: 1
<p>Origin of the data: Experimental spectroscopic measurements; Computer modelling. </p> <p>Data type: experimental measurements, models.</p> <p>The obtained data are in txt, DCS, DTA, SPC and PAR format.</p> <p>Data <strong>generated</strong> by a machine. Information on the machine: firm which produced the machine: <em>BRUKER</em>; version of the machine: <em>ESP 300E for measurements at X-band frequency and Elexys E580 for measurements at Q-band frequency</em>.</p> <p>Methodology used to collect the data and to process the data: <strong><em>EPR Spectroscopy</em></strong></p> <p>Analytical and procedural information: <strong><em>EPR Spectroscopy</em></strong></p> <p>Definitions of variables: <strong><em>Magnetic field and temperature.</em></strong></p> <p>Units of measurement: <strong><em>milli Tesla (mT), Kelvin (K), Megahertz (MHz).</em></strong></p> <p>Abbreviation <strong>mg70 </strong>for the data related to the binary system MgCl<sub>2</sub>/TiCl<sub>4</sub>,</p> <p>Abbreviation <strong>mg21 </strong> for the data related to the ternary system MgCl<sub>2</sub>/TiCl<sub>4</sub>/monoether</p> <p>Abbreviation <strong>mg36 </strong> for the data related to the ternary system MgCl<sub>2</sub>/TiCl<sub>4</sub>/diester.</p> <p>Specialized abbreviations:</p> <p><strong><em>EPR</em></strong><em> – Electron Paramagnetic Resonance,</em> <em><strong>CW</strong>– Continuous Wave,</em> <em><strong>ESE – </strong>Electron Spin Echo,</em> <strong><em>ESEEM </em></strong><br> <em>– Electron Spin Echo Envelope Modulation,</em> <em><strong>HYSCORE</strong> – Hyperfine Sublevel Correlation, </em><strong><em>ZN catalyst</em></strong><br> <em>– Ziegler-Natta catalyst</em>, <em><strong>ID – </strong>internal donor.</em></p> <p>To read the data, OriginPro 9.0 and Matlab R2019a have been used on Windows platform. For simulation Easyspin 5.2.28 simulation package has been used. To read the data, also Python can be used based on the convenience of the users.</p> <p>To process the data, OriginPro 9.0 and Matlab R2019a have been used in Windows platform. Python can also be used based on the convenient of users.</p> <p>Where to get <em>OriginPro 9.0, Matlab R2019a and Easyspin 5.2.28: </em></p> <ul> <li>MATLAB R2019a: <a href="https://nl.mathworks.com/downloads/web_downloads/select_release?mode=gwylf">https://nl.mathworks.com/downloads/web_downloads/select_release?mode=gwylf</a></li> <li>OriginPro 9.0: <a href="https://www.originlab.com/index.aspx?go=PRODUCTS/Origin">https://www.originlab.com/index.aspx?go=PRODUCTS/Origin</a></li> <li>Easyspin 5.2.28: <a href="https://easyspin.org/easyspin/documentation/index.html">https://easyspin.org/easyspin/documentation/index.html</a></li> </ul> <p>Where to get the licenses:</p> <p>For MATLAB R2019a the website to download the licence is: <a href="https://nl.mathworks.com/downloads/web_downloads/select_release?mode=gwylf">https://nl.mathworks.com/downloads/web_downloads/select_release?mode=gwylf</a></p> <p>For OriginPro 9.0: <a href="https://www.originlab.com/index.aspx?go=PURCHASE/LicensingOptions">https://www.originlab.com/index.aspx?go=PURCHASE/LicensingOptions</a></p>
Development and field evaluation of a motion sensor activated suction trap to study vector-host interactions
<p>1. Researchers elucidating vectors of zoonotic diseases encounter problems with inefficient surveillance techniques leading to underestimation of the importance of some species, and the overestimation of the importance of others. Carbon dioxide-baited light traps are the most widely used traps for sampling vector groups. However aspirating directly from the hosts is the most accurate method to incriminate vectors.</p> <p>2. A novel vector trapping system was developed, consisting of a suction trap, activated by a motion sensor, and controlled by a microcontroller, which activates automatically when host animals approach. The prototype was tested in two field experiments with ungulates and biting midges (Diptera: Ceratopogonidae) at a preserve in Florida. We measured the biting midge community collected at traps near and far from hosts and compared communities using diversity metrics and abundance curves.</p> <p>3. Traps activated in the presence of host animals with 94% accuracy. Diversity and richness of <i>Culicoides</i> species differed between sensor and control traps with 11 species captured by control traps and 7 species by sensor traps. Vector species were captured in significantly greater numbers in sensor traps, while more non-vector species were caught in control traps.</p> <p>4. Results confirm that vector species can be underrepresented in light trap collections, likely due to their tight associations with vertebrate hosts, a finding that should be taken into consideration when incriminating arbovirus vectors. Our novel trap system was a first attempt at solving the issue of collecting vector species from non-tame animals, effectively aspirating questing midges. Simple modifications of the system could be made to target other vector-host systems.</p>
PCB126 exposure revealed alterations in m6A RNA modifications in transcripts associated with AHR activation
<p>Chemical modifications of proteins, DNA and RNA moieties play critical roles in regulating gene expression. Emerging evidence suggests the RNA modifications (epitranscriptomics) have substantive roles in basic biological processes. One of the most common modifications in mRNA and noncoding RNAs is <i>N</i><sup>6</sup>-methyladenosine (m6A). In a subset of mRNAs, m<sup>6</sup>A sites are preferentially enriched near stop codons, in 3′ UTRs, and within exons, suggesting an important role in the regulation of mRNA processing and function including alternative splicing and gene expression. Very little is known about the effect of environmental chemical exposure on m6A modifications. As many of the commonly occurring environmental contaminants alter gene expression profiles and have detrimental effects on physiological processes, it is important to understand the effects of exposure on this important layer of gene regulation. Hence, the objective of this study was to characterize the acute effects of developmental exposure to PCB126, an environmentally relevant dioxin-like PCB, on m6A methylation patterns. We exposed zebrafish embryos to PCB126 for 6 hours starting from 72 hours post-fertilization and profiled m6A RNA using methylated RNA immunoprecipitation followed by sequencing (MeRIP-seq). Our analysis revealed 117 and 217 m6A peaks in the DMSO and PCB126 samples (FDR 5%), respectively. The majority of the peaks were preferentially located around the 3'UTR and stop codons. Statistical analysis revealed 15 m6A marked transcripts to be differentially methylated by PCB126 exposure. These include transcripts that are known to be activated by AHR agonists (e.g., <i>ahrra</i>, <i>tiparp</i>, <i>nfe2l2b) as well as others that are important for normal development (vgf, cebpd, sned1). These results suggest that</i> environmental chemicals such as dioxin-like PCBs could affect developmental gene expression patterns by altering m6A levels. Further studies are necessary to understand the functional consequences of exposure-associated alterations in m6A levels.</p>
Eye size and investment in frogs and toads correlate with adult habitat, activity pattern and breeding ecology
<p></p><p>Frogs and toads (Amphibia: Anura) display diverse ecologies and behaviours, which are often correlated with visual capacity in other vertebrates. Additionally, anurans exhibit a broad range of relative eye sizes, which have not previously been linked to ecological factors in this group. We measured relative investment in eye size and corneal size for 220 species of anurans representing all 55 currently recognized families and tested whether they were correlated with six natural history traits hypothesized to be associated with the evolution of eye size. Anuran eye size was significantly correlated with habitat, with notable decreases in eye investment among fossorial, subfossorial and aquatic species. Relative eye size was also associated with mating habitat and activity pattern. Compared to other vertebrates, anurans have relatively large eyes for their body size, indicating that vision is probably of high importance. Our study reveals the role that ecology and behaviour may have played in the evolution of anuran visual systems and highlights the usefulness of museum specimens, and importance of broad taxonomic sampling, for interpreting macroecological patterns.</p><p></p>
On the impact of release policies on bug handing activity: A case study of Eclipse
<p>This repository contains the data used in our paper entitled ["On the impact of release policies on bug handing activity: A case study of Eclipse"]</p> <p># Structure</p> <p>The repository contains :</p> <p>1. `raw_data`: a repository containing all the raw data of our study before processing.<br> 2. `data_processing`: a repository containing all the raw data of our study during processing.<br> 3. `data`: a repository containing all the data of our study after all processing steps.<br> 4. `figs`: The generated figures from the notebooks for the paper.<br> 5. The various Jupyter notebooks containing all the scripts used to gather the data and answer each research question.</p>
A Miniature Shielded-loop Active H-field Probe Design for Near-field Measurement
<p>In the "A Miniature Shielded-loop Active H-field Probe Design for Near-field Measurement", this includes all the figures.</p>
Liquiritigenin reduces osteoclast activity in zebrafish model of glucocorticoid-induced osteoporosis
<p>Drug and therapies currently used to treat human bone diseases have a lot of severe side effects. Liquiritigenin is a flavonoid extracted from Glycyrrhiza glabra roots which has been reported to have positive effects in vitro on osteoblasts activity and bone mineralization as well as inhibitory effect on osteoclasts differentiation and activity in vitro. The present study was aimed to evaluate the in vivo effects of liquiritigenin on bone structure and metabolism in physiological and pathological conditions using Danio rerio as experimental animal model. Treatments with liquiritigenin were performed on embryos to evaluate the osteogenesis during skeletal development. Other treatments were performed on adult fish affected by glucocorticoid-induced osteoporosis to assay the therapeutic potential of liquiritigenin in the reversion of bone-loss phenotype in scale model. Liquiritigenin treatment of zebrafish embryo significantly enhances the osteogenesis during development in a dose-dependent manner. In addition, liquiritigenin inhibits the formation of the osteoporotic phenotype in adult zebrafish model of glucocorticoid-induced osteoporosis preventing osteoclast activation in scales. Interestingly, liquiritigenin does not counteract the loss of osteoblastic activity in scales. The liquiritigenin exhibits in vivo anti-osteoporotic activity on adult fish scale model. It can be considered a good candidate to develop new drugs against osteoporosis.</p>
Dataset: Toothbrushing Data and Analysis of its Potential Use in Human Activity Recognition Applications
<p>This is the dataset that accompanies the paper 'Dataset: Toothbrushing Data and Analysis of its Potential Use in Human Activity Recognition Applications'.</p> <p>In this paper, we describe and analyze a time-series dataset from toothbrushing activity using brush-attached and wearable sensors. The data was collected from 17 participants when they brushed their teeth over one week in 5 different locations. The dataset consists of 62 toothbrushing sessions for each of the brush-attached and wearable sensor approaches, using both electric and manual brushes. The average duration of each session is 2 minutes. One sensor device was attached to the handle of the brush while the other was worn by the participants as a wrist-watch. We collected the data from a 3-axis accelerometer and a 3-axis gyroscope at a 200 Hz sampling rate.</p> <p>Accompanying code can be found at our GitHub repository <a href="https://github.com/icl-mq/toothbrushing-dataset">https://github.com/icl-mq/toothbrushing-dataset</a>. This repository contains example code demonstrating how to process the data along with the file metadata.</p>
Inhibition of Cdc42 activity extends lifespan and decreases circulating inflammatory cytokines in aged female C57BL/6 mice
<p>Cdc42 is a small RhoGTPase regulating multiple functions in eukaryotic cells. The activity of Cdc42 is significantly elevated in several tissues of aged mice, while the Cdc42 gain-of-activity mouse model presents with a pre-mature aging-like phenotype and with decreased lifespan. These data suggest a causal connection between elevated activity of Cdc42, aging and reduced lifespan. Here, we demonstrate that systemic treatment of aged (75-week old) female C57BL/6 mice with a Cdc42 activity specific inhibitor (CASIN) for 4 consecutive days significantly extends average and maximum lifespan. Moreover, aged CASIN-treated animals displayed a youthful level of the aging-associated cytokines IL-1b, IL-1a and INFg in serum and a significantly younger epigenetic clock as based on DNA methylation levels in blood cells. Overall, our data show that systemic administration of CASIN to reduce Cdc42 activity in aged mice extends murine lifespan.</p>
Characterization, functional properties and antioxidant activities of macromolecular extracts isolated from Porphyra yezoensis
<p><em>Porphyra yezoensis</em> is one of the most economical seaweed species in China. Determining how to make full use of mature <em>P. yezoensis </em>and exploring new ways to increase the value of its resources are important subjects of research for the development of the laver breeding industry. In this study, we provide a simple method to comprehensively extract the bioactive substances from <em>P. yezoensis</em>. The characteristics, functional properties and antioxidant activities of two types of biopolymer extract from <em>P. yezoensis</em> were studied and analyzed relatively. Based on the characterization of water-soluble concentrate (WPC) and alkali-soluble concentrate (APC), obtained via chemical analysis, FT-IR, TGA and differential scanning calorimetry (DSC), they both had typical polysaccharide and protein characteristics and steady composition. APC showed higher nitrogen solubility, water holding capacity and foaming ability. APC could not only be used as a protein supplement; it also performs well in improving the properties of foods in terms of water-holding and fat-absorption. The emulsifying activity and oil-holding capacity of WPC were observed to be higher than those of APC; thus, WPC has the potential to be used as an emulsifier. Surprisingly, WPC and APC have radical scavenging capacity in vitro, which broadens the direction of their application.</p>
Improved antibacterial activity of hemp fibres by covalent grafting of quaternary ammonium groups
<p><span><span>In this study, novel antibacterial hemp fibres grafted with </span></span>quaternary<span><span> ammonium groups, named HF-GTA, were prepared by alkalisation, oxidation, amination, and quaternisation multistage reactions. The chemical structure and micromorphology of the fibres were characterised by Fourier-transform infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, thermogravimetric analysis, and X-ray diffraction. The grafting and reaction mechanisms were proved to be successful, which indicated that the grafting reaction primarily occurred on the </span></span>hydroxyl <span><span>groups of cellulose and hemicellulose in the hemp fibres, where they retained good fibrous morphology, thermal stability and hygroscopicity. The antibacterial activity of HF-GTA exhibited the best antibacterial activity, where the antibacterial ratios against <i><span>E. coli </span></i>and <i><span>S. aureus</span></i> were 95.41% and 99.64%, respectively. <span>E</span><span>ven </span><span>a</span><span>fter 30 times of washing</span><span>, </span><span>the antibacterial activity </span><span>wa</span><span>s</span><span> retained</span><span> at </span><span>89.78% and 91.12%</span><span>,</span><span> indicating that HF-GTA </span><span>wa</span><span>s</span><span> endowed with good washing resistance. </span>The antibacterial mechanism occurred because the electrostatic reaction reduced the electrochemical potential on the cell membrane, leading to the release of cytoplasmic substances and the dissolution of cells, thereby producing antibacterial activity. It would be significantly important to guarantee textile quality and prevent disease transmission. </span></span></p>
BAFF 60-mer and BAFF 60-mer-dissociating activities in serum, cord blood and cerebrospinal fluid
<p>This dataset is related to "BAFF 60-mer, and differential BAFF 60-mer dissociating activities in human serum, cord blood and cerebrospinal fluid" (Eslami M, Meinl E, Eibel H, Willen L, Donzé O, Distl O, Schneider H, Speiser DE, Tsiantoulas D, Yalkinoglu Ö, Samy E, Schneider P).</p>
Data supporting tables and figures in t ms Tidal influence on seismic activity during the 2011-2013 El Hierro volcanic unrest
<p>Introduction</p> <p>This set of files contains data supporting the tables and figures featured in the journal article.<br> <br> File Ts01.xlsx shows data from the earthquake cluster C1 defined in the manuscript, as well as tidal stress phases and amplitudes obtained for each event using the methodology explained in the text. <br> Files Ts02.xlsx, Ts03.xlsx and Ts04.xlsx are datasets analog to File Ts01.xlsx, but using data from clusters C2, C3 and C4 respectively. <br> Data of Files Ts01.xlsx, Ts02.xlsx, Ts03.xlsx and Ts04.xlsx have been used to compose Tables 1, 2, 3, 4, 5, 6 in the manuscript, Figures 3, 5, 8, 9 in the manuscript, <br> plus Figures S9, S10, S11, S12, S13, S14, S15, S16 in the Supporting Information.</p> <p>File Ts05.xlsx features tidal strain calculated for the setting of the shallow magma reservior in Phase 1 of the volcanic crisis, at two-hour intervals, between 2011-07-01 and 2011-10-31. <br> Data from File Ts05.xlsx has been used for composition of Figures 6 and 7 in the manuscript.<br> Files Ts06.xlsx, Ts07.xlsx and Ts08.xlsx are datasets analog to File Ts05.xlsx, but calculating tidal strain for the locations of events belonging to clusters C2, C3 and C4 respectively.<br> Data of Files Ts06.xlsx, Ts07.xlsx and Ts08.xlsx have been used to compose Figures S1, S2, S3, S4, S5, S6 in the Supporting Information. </p> <p>File Ts09.xlsx shows tidal confining stress values corresponding to the events in cluster C1. <br> File Ts10.xlsx features values of tidal stress taken hourly for the location corresponding to an event belonging to subcluster C1A. <br> File Ts11.xlsx features values of tidal stress taken hourly for the location corresponding to an event belonging to subcluster C1B. <br> Figure S7 in the Supporting Information has been produced using data from Files Ts09.xlsx, Ts10.xlsx and Ts11.xlsx. </p> <p>File Ts12.xlsx collects all events in four clusters C1-C4, and shows the amplitudes of the tidal confining stress half cycles in which the events occur, considering only ocean tides or only body tides. <br> These data were used for stating the predominance of ocean tidal loading against body tides in Chapter 5 - Discussion.</p> <p>File Ts13.xlsx shows data from the earthquake cluster C1 defined in the manuscript, as well as tidal stress phases and amplitudes, but considering only those events with M >= 2.<br> Files Ts14.xlsx, Ts15.xlsx and Ts16.xlsx are datasets analog to File Ts13.xlsx, but using data from clusters C2, C3 and C4 (events with M >= 2 only) respectively.<br> Data of Files Ts13.xlsx, Ts14.xlsx, Ts15.xlsx and Ts16.xlsx have been used to compose Table S3 in the Supporting Information.</p> <p>File Ts17.xlsx features the 4 declustered catalogs D1, D2, D3 and D4 which are described in Tables S4 and S5 in the Supporting Information.</p> <p>File Ts18.xlsx collects all events in four clusters C1-C4, and shows the results of tidal tilt (North-South and East-West components). <br> These data were used to compose Figures S27 and S28 in the Supporting Information. </p> <p>File Ts19.xlsx features tidal stress calculated for the setting of the shallow magma reservior in Phase 1 of the volcanic crisis, at two-hour intervals, between 2011-07-01 and 2011-10-31. <br> Data from File Ts19.xlsx has been used for composition of Figure S17 in the manuscript.<br> Files Ts20.xlsx, Ts21.xlsx and Ts22.xlsx are datasets analog to File Ts19.xlsx, but calculating tidal stress for the locations of events belonging to clusters C2, C3 and C4 respectively.<br> Data of Files Ts20.xlsx, Ts21.xlsx and Ts22.xlsx have been used to compose Figures S18, S19 and S20 in the Supporting Information.</p> <p>File Ts23.xlsx shows data from the earthquake cluster C1 defined in the manuscript, as well as tidal stress phases and amplitudes obtained for each event using the methodology explained in the text,<br> but considering only ocean tides in the calculations. <br> Files Ts24.xlsx, Ts25.xlsx and Ts26.xlsx are datasets analog to File Ts23.xlsx, but using data from clusters C2, C3 and C4 respectively. <br> Data of Files Ts01.xlsx, Ts02.xlsx, Ts03.xlsx and Ts04.xlsx have been used to compose Figures S21 and S22 in Supporting Information.</p> <p>File Ts27.xlsx features horizontal tidal stress (Earth tides only) calculated for the setting of the shallow magma reservior in Phase 1 of the volcanic crisis, at two-hour intervals, <br> between 2011-07-01 and 2011-10-31. Data from File Ts27.xlsx has been used for composition of Figure S23 in the manuscript.<br> Files Ts28.xlsx, Ts29.xlsx and Ts30.xlsx are datasets analog to File Ts27.xlsx, but calculating horizontal tidal stress for the locations of events belonging to clusters C2, C3 and C4 respectively.<br> Data of Files Ts28.xlsx, Ts29.xlsx and Ts30.xlsx have been used to compose Figures S24, S25 and S26 in the Supporting Information.</p> <p>1. Ts01.xlsx Data used to detect tidal stress correlations in Phase 1 of the volcanic crisis.</p> <p>1.1 Column "Year", y.<br> 1.2 Column "Month", m.<br> 1.3 Column "Day", d.<br> 1.4 Column "Hour", h.<br> 1.5 Column "Minute", min.<br> 1.6 Column "Second", s.<br> 1.7 Column "Latitude", deg, latitude north of equator.<br> 1.8 Column "Longitude", deg, longitude east of Greenwich.<br> 1.9 Column "Depth", km.<br> 1.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 1.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 1.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 1.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 1.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 1.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 1.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 1.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 1.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 1.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 1.20 Column "Magnitude", earthquake magnitude.<br> 1.21 Column "Autonum", autonumeric code.</p> <p>2. Ts02.xlsx Data used to detect tidal stress correlations in Phase 2 of the volcanic crisis.</p> <p>2.1 Column "Year", y.<br> 2.2 Column "Month", m.<br> 2.3 Column "Day", d.<br> 2.4 Column "Hour", h.<br> 2.5 Column "Minute", min.<br> 2.6 Column "Second", s.<br> 2.7 Column "Latitude", deg, latitude north of equator.<br> 2.8 Column "Longitude", deg, longitude east of Greenwich.<br> 2.9 Column "Depth", km.<br> 2.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 2.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 2.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 2.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 2.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 2.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 2.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 2.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 2.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 2.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 2.20 Column "Magnitude", earthquake magnitude.<br> 2.21 Column "Autonum", autonumeric code.</p> <p>3. Ts03.xlsx Data used to detect tidal stress correlations in Phase 3 of the volcanic crisis.</p> <p>3.1 Column "Year", y.<br> 3.2 Column "Month", m.<br> 3.3 Column "Day", d.<br> 3.4 Column "Hour", h.<br> 3.5 Column "Minute", min.<br> 3.6 Column "Second", s.<br> 3.7 Column "Latitude", deg, latitude north of equator.<br> 3.8 Column "Longitude", deg, longitude east of Greenwich.<br> 3.9 Column "Depth", km.<br> 3.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 3.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 3.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 3.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 3.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 3.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 3.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 3.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 3.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 3.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 3.20 Column "Magnitude", earthquake magnitude.<br> 3.21 Column "Autonum", autonumeric code.</p> <p>4. Ts04.xlsx Data used to detect tidal stress correlations in Phase 4 of the volcanic crisis.</p> <p>4.1 Column "Year", y.<br> 4.2 Column "Month", m.<br> 4.3 Column "Day", d.<br> 4.4 Column "Hour", h.<br> 4.5 Column "Minute", min.<br> 4.6 Column "Second", s.<br> 4.7 Column "Latitude", deg, latitude north of equator.<br> 4.8 Column "Longitude", deg, longitude east of Greenwich.<br> 4.9 Column "Depth", km.<br> 4.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 4.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 4.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 4.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 4.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 4.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 4.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 4.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 4.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 4.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 4.20 Column "Magnitude", earthquake magnitude.<br> 4.21 Column "Autonum", autonumeric code.</p> <p>5. Ts05.xlsx Tidal strain calculated for the shallow magma reservior between 2011-07-01 and 2011-10-31.</p> <p>5.1 Column "Latitude", deg, latitude north of equator.<br> 5.2 Column "Longitude", deg, longitude east of Greenwich.<br> 5.3 Column "Depth", km.<br> 5.4 Column "Date", date in format yyyymmdd.<br> 5.5 Column "Time", time in format hour : minute : second.<br> 5.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 5.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 5.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 5.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>6. Ts06. Tidal strain calculated for the location of an event belonging to cluster C2.</p> <p>6.1 Column "Latitude", deg, latitude north of equator.<br> 6.2 Column "Longitude", deg, longitude east of Greenwich.<br> 6.3 Column "Depth", km.<br> 6.4 Column "Date", date in format yyyymmdd.<br> 6.5 Column "Time", time in format hour : minute : second.<br> 6.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 6.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 6.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 6.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>7. Ts07. Tidal strain calculated for the location of an event belonging to cluster C3.</p> <p>7.1 Column "Latitude", deg, latitude north of equator.<br> 7.2 Column "Longitude", deg, longitude east of Greenwich.<br> 7.3 Column "Depth", km.<br> 7.4 Column "Date", date in format yyyymmdd.<br> 7.5 Column "Time", time in format hour : minute : second.<br> 7.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 7.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 7.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 7.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>8. Ts08. Tidal strain calculated for the location of an event belonging to cluster C4.</p> <p>8.1 Column "Latitude", deg, latitude north of equator.<br> 8.2 Column "Longitude", deg, longitude east of Greenwich.<br> 8.3 Column "Depth", km.<br> 8.4 Column "Date", date in format yyyymmdd.<br> 8.5 Column "Time", time in format hour : minute : second.<br> 8.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 8.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 8.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 8.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>9. File Ts09.xlsx Tidal confining stress values corresponding to the events in cluster C1.</p> <p>9.1 Column "Latitude", deg, latitude north of equator.<br> 9.2 Column "Longitude", deg, longitude east of Greenwich.<br> 9.3 Column "Depth", km.<br> 9.4 Column "Date", date in format yyyymmdd.<br> 9.5 Column "Time", time in format hour : minute : second.<br> 9.6 Column "Tides", Pa, tidal confining stress.</p> <p>10. Ts10.xlsx Hourly values of tidal confining stress obtained for the location of an earthquake belonging to subcluster C1A.</p> <p>10.1 Column "Year", y.<br> 10.2 Column "Month", m.<br> 10.3 Column "Day", d.<br> 10.4 Column "Hour", h.<br> 10.5 Column "Minute", min.<br> 10.6 Column "Second", s.<br> 10.7 Column "Latitude", deg, latitude north of equator.<br> 10.8 Column "Longitude", deg, longitude east of Greenwich.<br> 10.9 Column "Depth", m.<br> 10.10 Column "Tides", Pa, tidal confining stress.</p> <p>11. Ts11.xlsx Hourly values of tidal confining stress obtained for the location of an earthquake belonging to subcluster C1B.</p> <p>11.1 Column "Year", y.<br> 11.2 Column "Month", m.<br> 11.3 Column "Day", d.<br> 11.4 Column "Hour", h.<br> 11.5 Column "Minute", min.<br> 11.6 Column "Second", s.<br> 11.7 Column "Latitude", deg, latitude north of equator.<br> 11.8 Column "Longitude", deg, longitude east of Greenwich.<br> 11.9 Column "Depth", m.<br> 11.10 Column "Tides", Pa, tidal confining stress.</p> <p>12. Ts12.xlsx Data used to compare ocean tides to body tides</p> <p>12.1 Column "Cluster", number of the cluster (C1-C4).<br> 12.2 Column "Year", y.<br> 12.3 Column "Month", m.<br> 12.4 Column "Day", d.<br> 12.5 Column "Hour", h.<br> 12.6 Column "Minute", min.<br> 12.7 Column "Second", s.<br> 12.8 Column "Latitude", deg, latitude north of equator.<br> 12.9 Column "Longitude", deg, longitude east of Greenwich.<br> 12.10 Column "Depth", km.<br> 12.11 Column "Autonum", autonumeric code.<br> 12.12 Column "Ampl_ocean_hc", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress, ocean tides only.<br> 12.13 Column "Ampl_body_hc", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress, body tides only.</p> <p>13. Ts13.xlsx Data in Cluster C1 with M>=2</p> <p>13.1 Column "Year", y.<br> 13.2 Column "Month", m.<br> 13.3 Column "Day", d.<br> 13.4 Column "Hour", h.<br> 13.5 Column "Minute", min.<br> 13.6 Column "Second", s.<br> 13.7 Column "Latitude", deg, latitude north of equator.<br> 13.8 Column "Longitude", deg, longitude east of Greenwich.<br> 13.9 Column "Depth", km.<br> 13.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 13.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 13.12 Column "Magnitude", earthquake magnitude.<br> 13.13 Column "Autonum", autonumeric code.</p> <p>14. Ts14.xlsx Data in Cluster C2 with M>=2</p> <p>14.1 Column "Year", y.<br> 14.2 Column "Month", m.<br> 14.3 Column "Day", d.<br> 14.4 Column "Hour", h.<br> 14.5 Column "Minute", min.<br> 14.6 Column "Second", s.<br> 14.7 Column "Latitude", deg, latitude north of equator.<br> 14.8 Column "Longitude", deg, longitude east of Greenwich.<br> 14.9 Column "Depth", km.<br> 14.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 14.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 14.12 Column "Magnitude", earthquake magnitude.<br> 14.13 Column "Autonum", autonumeric code.</p> <p>15. Ts15.xlsx Data in Cluster C3 with M>=2</p> <p>15.1 Column "Year", y.<br> 15.2 Column "Month", m.<br> 15.3 Column "Day", d.<br> 15.4 Column "Hour", h.<br> 15.5 Column "Minute", min.<br> 15.6 Column "Second", s.<br> 15.7 Column "Latitude", deg, latitude north of equator.<br> 15.8 Column "Longitude", deg, longitude east of Greenwich.<br> 15.9 Column "Depth", km.<br> 15.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 15.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 15.12 Column "Magnitude", earthquake magnitude.<br> 15.13 Column "Autonum", autonumeric code.</p> <p>16. Ts16.xlsx Data in Cluster C4 with M>=2</p> <p>16.1 Column "Year", y.<br> 16.2 Column "Month", m.<br> 16.3 Column "Day", d.<br> 16.4 Column "Hour", h.<br> 16.5 Column "Minute", min.<br> 16.6 Column "Second", s.<br> 16.7 Column "Latitude", deg, latitude north of equator.<br> 16.8 Column "Longitude", deg, longitude east of Greenwich.<br> 16.9 Column "Depth", km.<br> 16.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 16.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 16.12 Column "Magnitude", earthquake magnitude.<br> 16.13 Column "Autonum", autonumeric code.</p> <p>17. Ts17.xlsx Declustered datasets D1, D2, D3 and D4</p> <p>17.1 Column "Dataset", number of the declustered dataset (D1-D4).<br> 17.2 Column "Year", y.<br> 17.3 Column "Month", m.<br> 17.4 Column "Day", d.<br> 17.5 Column "Latitude", deg, latitude north of equator.<br> 17.6 Column "Longitude", deg, longitude east of Greenwich.<br> 17.7 Column "Magnitude", earthquake magnitude.<br> 17.8 Column "Depth", km.<br> 17.9 Column "Cluster". It takes the value "+" if the event does not belong to any cluster identified during the declustering process. <br> Otherwise, the event is the largest in a cluster identified by the code shown in Table S4 in Supporting Information. </p> <p><br> 18. Ts18.xlsx Data used to detect tidal tilt correlations</p> <p>18.1 Column "Cluster", number of the cluster (C1-C4).<br> 18.2 Column "Year", y.<br> 18.3 Column "Month", m.<br> 18.4 Column "Day", d.<br> 18.5 Column "Hour", h.<br> 18.6 Column "Minute", min.<br> 18.7 Column "Second", s.<br> 18.8 Column "Latitude", deg, latitude north of equator.<br> 18.9 Column "Longitude", deg, longitude east of Greenwich.<br> 18.10 Column "Depth", km.<br> 18.11 Column "Autonum", autonumeric code.<br> 18.12 Column "Phase_tilt_NS", deg, tidal phase angle assigned to the event, calculated for tidal tilt (North-South component).<br> 18.13 Column "Ampl_tilt_NS", nrad, amplitude of the tidal half cycle in which the event occurs, calculated for tidal tilt (North-South component).<br> 18.14 Column "Phase_tilt_EW", deg, tidal phase angle assigned to the event, calculated for tidal tilt (East-West component).<br> 18.15 Column "Ampl_tilt_NS", nrad, amplitude of the tidal half cycle in which the event occurs, calculated for tidal tilt (East-West component).</p> <p>19. Ts19.xlsx Tidal stress calculated for the shallow magma reservior between 2011-07-01 and 2011-10-31.</p> <p>19.1 Column "Latitude", deg, latitude north of equator.<br> 19.2 Column "Longitude", deg, longitude east of Greenwich.<br> 19.3 Column "Depth", km.<br> 19.4 Column "Date", date in format yyyymmdd.<br> 19.5 Column "Time", time in format hour : minute : second.<br> 19.6 Column "East-West stress", Pa, tidal East-West stress.<br> 19.7 Column "North-South stress", Pa, tidal North-South stress.<br> 19.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>20. Ts20.xlsx Tidal stress calculated for the location of an event belonging to cluster C2.</p> <p>20.1 Column "Latitude", deg, latitude north of equator.<br> 20.2 Column "Longitude", deg, longitude east of Greenwich.<br> 20.3 Column "Depth", km.<br> 20.4 Column "Date", date in format yyyymmdd.<br> 20.5 Column "Time", time in format hour : minute : second.<br> 20.6 Column "East-West stress", Pa, tidal East-West stress.<br> 20.7 Column "North-South stress", Pa, tidal North-South stress.<br> 20.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>21. Ts21.xlsx Tidal stress calculated for the location of an event belonging to cluster C3.</p> <p>21.1 Column "Latitude", deg, latitude north of equator.<br> 21.2 Column "Longitude", deg, longitude east of Greenwich.<br> 21.3 Column "Depth", km.<br> 21.4 Column "Date", date in format yyyymmdd.<br> 21.5 Column "Time", time in format hour : minute : second.<br> 21.6 Column "East-West stress", Pa, tidal East-West stress.<br> 21.7 Column "North-South stress", Pa, tidal North-South stress.<br> 21.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>22. Ts22.xlsx Tidal stress calculated for the location of an event belonging to cluster C4.</p> <p>22.1 Column "Latitude", deg, latitude north of equator.<br> 22.2 Column "Longitude", deg, longitude east of Greenwich.<br> 22.3 Column "Depth", km.<br> 22.4 Column "Date", date in format yyyymmdd.<br> 22.5 Column "Time", time in format hour : minute : second.<br> 22.6 Column "East-West stress", Pa, tidal East-West stress.<br> 22.7 Column "North-South stress", Pa, tidal North-South stress.<br> 22.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>23. Ts23.xlsx Data used to detect tidal stress correlations in Phase 1 of the volcanic crisis (ocean tides only).</p> <p>23.1 Column "Year", y.<br> 23.2 Column "Month", m.<br> 23.3 Column "Day", d.<br> 23.4 Column "Hour", h.<br> 23.5 Column "Minute", min.<br> 23.6 Column "Second", s.<br> 23.7 Column "Latitude", deg, latitude north of equator.<br> 23.8 Column "Longitude", deg, longitude east of Greenwich.<br> 23.9 Column "Depth", km.<br> 23.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 23.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 23.12 Column "Magnitude", earthquake magnitude.<br> 23.13 Column "Autonum", autonumeric code.</p> <p>24. Ts24.xlsx Data used to detect tidal stress correlations in Phase 2 of the volcanic crisis (ocean tides only).</p> <p>24.1 Column "Year", y.<br> 24.2 Column "Month", m.<br> 24.3 Column "Day", d.<br> 24.4 Column "Hour", h.<br> 24.5 Column "Minute", min.<br> 24.6 Column "Second", s.<br> 24.7 Column "Latitude", deg, latitude north of equator.<br> 24.8 Column "Longitude", deg, longitude east of Greenwich.<br> 24.9 Column "Depth", km.<br> 24.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 24.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 24.12 Column "Magnitude", earthquake magnitude.<br> 24.13 Column "Autonum", autonumeric code.</p> <p>25. Ts25.xlsx Data used to detect tidal stress correlations in Phase 3 of the volcanic crisis (ocean tides only).</p> <p>25.1 Column "Year", y.<br> 25.2 Column "Month", m.<br> 25.3 Column "Day", d.<br> 25.4 Column "Hour", h.<br> 25.5 Column "Minute", min.<br> 25.6 Column "Second", s.<br> 25.7 Column "Latitude", deg, latitude north of equator.<br> 25.8 Column "Longitude", deg, longitude east of Greenwich.<br> 25.9 Column "Depth", km.<br> 25.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 25.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 25.12 Column "Magnitude", earthquake magnitude.<br> 25.13 Column "Autonum", autonumeric code.</p> <p>26. Ts26.xlsx Data used to detect tidal stress correlations in Phase 4 of the volcanic crisis (ocean tides only).</p> <p>26.1 Column "Year", y.<br> 26.2 Column "Month", m.<br> 26.3 Column "Day", d.<br> 26.4 Column "Hour", h.<br> 26.5 Column "Minute", min.<br> 26.6 Column "Second", s.<br> 26.7 Column "Latitude", deg, latitude north of equator.<br> 26.8 Column "Longitude", deg, longitude east of Greenwich.<br> 26.9 Column "Depth", km.<br> 26.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 26.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 26.12 Column "Magnitude", earthquake magnitude.<br> 26.13 Column "Autonum", autonumeric code.</p> <p>27. Ts27.xlsx Horizontal tidal stress (Earth tides only) calculated for the shallow magma reservior between 2011-07-01 and 2011-10-31.</p> <p>27.1 Column "Latitude", deg, latitude north of equator.<br> 27.2 Column "Longitude", deg, longitude east of Greenwich.<br> 27.3 Column "Depth", km.<br> 27.4 Column "Date", date in format yyyymmdd.<br> 27.5 Column "Time", time in format hour : minute : second.<br> 27.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p> <p>28. Ts28.xlsx Horizontal tidal stress (Earth tides only) calculated for the location of an event belonging to cluster C2.</p> <p>28.1 Column "Latitude", deg, latitude north of equator.<br> 28.2 Column "Longitude", deg, longitude east of Greenwich.<br> 28.3 Column "Depth", km.<br> 28.4 Column "Date", date in format yyyymmdd.<br> 28.5 Column "Time", time in format hour : minute : second.<br> 28.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p> <p>29. Ts29.xlsx Horizontal tidal stress (Earth tides only) calculated for the location of an event belonging to cluster C3.</p> <p>29.1 Column "Latitude", deg, latitude north of equator.<br> 29.2 Column "Longitude", deg, longitude east of Greenwich.<br> 29.3 Column "Depth", km.<br> 29.4 Column "Date", date in format yyyymmdd.<br> 29.5 Column "Time", time in format hour : minute : second.<br> 29.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p> <p>30. Ts30.xlsx Horizontal tidal stress (Earth tides only) calculated for the location of an event belonging to cluster C4.</p> <p>30.1 Column "Latitude", deg, latitude north of equator.<br> 30.2 Column "Longitude", deg, longitude east of Greenwich.<br> 30.3 Column "Depth", km.<br> 30.4 Column "Date", date in format yyyymmdd.<br> 30.5 Column "Time", time in format hour : minute : second.<br> 30.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p>
Inhibition of firefly luciferase activity by a HIF prolyl hydroxylase inhibitor
<p>The three hypoxia-inducible factor (HIF) prolyl-4-hydroxylase domain (PHD) 1–3 enzymes confer oxygen sen-sitivity to the HIF pathway and are novel therapeutic targets for treatment of renal anemia. Inhibition of thePHDs may further be beneficial in other hypoxia-associated diseases, including ischemia and chronic in-flammation. Several pharmacologic PHD inhibitors (PHIs) are available, but our understanding of their selectivity and its chemical basis is limited.We here report that the PHI JNJ-42041935 (JNJ-1935) is structurally similar to the firefly luciferase substrate D-luciferin. Our results demonstrate that JNJ-1935 is a novel inhibitor of firefly luciferase enzymatic activity. In contrast, the PHIs FG-4592 (roxadustat) and FG-2216 (ICA,BIQ,IOX3,YM311) did not affect firefly luciferase. The JNJ-1935 mode of inhibition is competitive with a Ki of 1.36 μM. D-luciferin did not inhibit the PHDs, despite its structural similarity to JNJ-1935. This study provides insights into a previously unknown JNJ-1935 off-target effect as well as into the chemical requirements for firefly luciferase and PHD inhibitors and may inform the development of novel compounds targeting these enzymes.</p>
Phylogenomics of scorpions reveal contemporaneous diversification of scorpion mammalian predators and mammal-active sodium channel toxins
<p>Scorpions constitute a charismatic lineage of arthropods and comprise more than 2,500 described species. Found throughout various tropical and temperate habitats, these predatory arachnids have a long evolutionary history, with a fossil record that began in the Silurian. While all scorpions are venomous, the asymmetrically diverse family Buthidae harbors nearly half the diversity of extant scorpions, and all but one of the 58 species that are medically significant to humans. However, the lack of a densely sampled scorpion phylogeny has hindered broader inferences of the diversification dynamics of scorpion toxins. As redress, we assembled a phylogenomic dataset of 100 scorpion venom transcriptomes and/or genomes, emphasizing the sampling of highly toxic buthid genera. To infer divergence times of venom gene families, we applied a phylogenomic node dating approach for the species tree in tandem with phylostratigraphic bracketing to estimate minimum ages of mammal-specific toxins. Our analyses establish a robustly supported phylogeny of scorpions, particularly with regard to relationships between medically significant taxa. Analysis of venom gene families shows that mammal-specific sodium channel toxins have independently evolved in five lineages within Buthidae. Temporal windows of mammal-specific toxin origins are correlated with the basal diversification of major scorpion mammal predators such as carnivores, shrews, bats and rodents. These results suggest an evolutionary model of relatively recent diversification of buthid sodium channel toxin (NaTx) homologs in response to diversification of scorpion predators.</p>
Data from: Drivers of assemblage-wide calling activity in tropical anurans and the role of temporal resolution
<p>1. Temporal scale in animal communities is often associated with seasonality, despite the large variation in species activity during a diel cycle. A gap thus remains in understanding the dynamics of short-term activity in animal communities.</p> <p>2. Here we assessed calling activity of tropical anurans and addressed how species composition varied during night activity in assemblages along gradients of local and landscape environmental heterogeneity.</p> <p>3. We investigated 39 anuran assemblages in the Pantanal wetlands (Brazil) with passive acoustic monitoring during the peak of one breeding season and first determined changes in species composition between night periods (early, mid, and late) using two temporal resolutions (1-hour and 3-hour intervals). Then, we addressed the role of habitat structure (local and landscape heterogeneity variables from field-based and remote sensing metrics) and ecological context (species richness and phylogenetic relatedness) in determining changes in species composition (i) between night periods and (ii) across days.</p> <p>4. Nocturnal calling activity of anuran assemblages varied more within the 1-hour resolution than the 3-hour resolution. Differences in species composition between early and late-night periods were related to local habitat structure and phylogenetic relatedness, while a low variation in compositional changes across days was associated with low-heterogeneous landscapes. None of these relationships were observed using the coarser temporal resolution (3-hour).</p> <p>5. Our findings on the variation of calling activity in tropical anuran assemblages suggest potential trades-off mediated by fine-temporal partitioning. Local and landscape heterogeneity may provide conditions for spatial partitioning, while the relatedness among co-signaling species provides cues on the ecological overlap of species with similar requirements. These relationships suggest a role of niche dimensional complementarity on the structuring of these anuran assemblages over fine temporal scales. We argue that fine-temporal differences between species in breeding activity can influence the outcome of species interaction and that addressing temporal scaling issues can improve our understanding of dynamics of animal communities.</p>
Dataset related to article "Control of Cytoskeletal Dynamics by β-Arrestin1/Myosin Vb Signaling Regulates Endosomal Sorting and Scavenging Activity of the Atypical Chemokine Receptor ACKR2"
<p>This record contains raw data related to article "Control of Cytoskeletal Dynamics by β-Arrestin1/Myosin Vb Signaling Regulates Endosomal Sorting and Scavenging Activity of the Atypical Chemokine Receptor ACKR2"</p> <p>The atypical chemokine receptor ACKR2, formerly named D6, is a scavenger chemokine receptor with a non-redundant role in the control of inflammation and immunity. The scavenging activity of ACKR2 depends on its trafficking properties, which require actin cytoskeleton rearrangements downstream of a β-arrestin1-Rac1-PAK1-LIMK1-cofilin-dependent signaling pathway. We here demonstrate that in basal conditions, ACKR2 trafficking properties require intact actin and microtubules networks. The dynamic turnover of actin filaments is required to sustain ACKR2 constitutive endocytosis, while both actin and microtubule networks are involved in processes regulating ACKR2 constitutive sorting to rapid, Rab4-dependent and slow, Rab11-dependent recycling pathways, respectively. After chemokine engagement, ACKR2 requires myosin Vb activity to promote its trafficking from Rab11-positive recycling endosomes to the plasma membrane, which sustains its scavenging activity. Other than cofilin phosphorylation, induction of the β-arrestin1-dependent signaling pathway by ACKR2 agonists also leads to the rearrangement of microtubules, which is required to support the myosin Vb-dependent ACKR2 upregulation and its scavenging properties. Disruption of the actin-based cytoskeleton by the apoptosis-inducing agent staurosporine results in impaired ACKR2 internalization and chemokine degradation that is consistent with the emerging scavenging-independent activity of the receptor in apoptotic neutrophils instrumental for promoting efficient efferocytosis during the resolution of inflammation. In conclusion, we provide evidence that ACKR2 activates a β-arrestin1-dependent signaling pathway, triggering both the actin and the microtubule cytoskeletal networks, which control its trafficking and scavenger properties.</p>
Tactic-specific antimicrobial activity suggests a parental care function for accessory glands in a marine toadfish
<p>Males of some species possess extra reproductive organs called accessory glands which are outgrowths of the testes or sperm duct. These organs have a well-established role in reproduction; however, they also appear to have other important functions that are less understood. Here we investigate the function of the highly complex accessory glands of a marine toadfish, <em>Porichthys notatus, </em>a fish with two reproductive male types: large care-providing 'guarder' males and small non-caring 'sneaker' males. While both male types have accessory glands, guarder male accessory glands are much larger relative to their body size. We show that accessory gland fluids strongly inhibit the growth of bacterial genera associated with unhealthy eggs and have no effect on the growth of strains isolated from healthy eggs. This antibacterial effect was particularly pronounced for extracts from guarder males. Furthermore, we demonstrate that both healthy and unhealthy plainfin midshipman eggs have diverse but distinct microbial communities that differ in their composition and abundance. The highly specific inhibitory capacity of accessory gland fluid on bacteria from unhealthy eggs was robust across a wide range of ecologically relevant temperatures and salinities. Collectively, these ecological and molecular observations suggest a care function for the accessory gland, mediated by antimicrobial agents.</p>
Data from: Combining correlative and mechanistic niche models with human activity data to elucidate the invasive potential of a sub-Antarctic insect
<p>Aim</p> <p>Correlative Species Distribution Models (SDMs) are subject to substantial spatio-temporal limitations when historical occurrence records of data-poor species provide incomplete and outdated information for niche modelling. Complementary mechanistic modelling techniques can, therefore, offer a valuable contribution to underpin more physiologically-informed predictions of biological invasions, the risk of which is often exacerbated by climate change. In this study we integrate physiological and human pressure data to address the uncertainties and limitations of correlative SDMs and to better understand, predict, and manage biological invasions.</p> <p>Location</p> <p>Western archipelagos of the Southern Ocean and martime Antarctica</p> <p>Taxon</p> <p>Eretmoptera murphyi (Chironomidae), invertebrates.</p> <p>Methods</p> <p>Mahalanobis Distances were used for correlative SDM construction for a species with few records. A mechanistic SDM was built around different fitness components (larval survival and life stage progression) as a function of temperature. SDM predictions were combined with human activity levels in Antarctica to generate a site vulnerability index to the colonization of E. murphyi. Future scenarios of ecophysiological suitability were built around the warming trends in the region.<br> Results Both SDMs converge to predict high environmental suitability in the species' native and introduced ranges. However, the mechanistic model indicates a slightly larger invasive potential based on larval performance at different temperatures. Human activity levels across the Antarctic Peninsula play a key role in discerning site vulnerabilities. Niche suitability in Antarctica grows considerably under long-term climate scenarios, leading to a substantially higher invasive threat to the Antarctic ecosystems. In turn changing conditions result on growing physiological mismatches with the environment in the native range on South Georgia.</p> <p>Main conclusions</p> <p>Long-term studies of invasion potential under climate benefit from integrating correlative predictions with physiological experiments, as the invasion potential varies depending on the area and the timescale examined. This study also highlights a conservation paradox whereby the accidental introduction of an insect represents a threat to the Antarctic ecoystems that contrasts with its endangered status at the native range.</p>
Dataset and analyses for: Active foot placement control ensures stable gait: Effect of constraints on foot placement and ankle moments
<p>For this dataset kinematics, ground reaction forces and electromyography data were collected during human treadmill walking. Participants walked on the treadmill at normal and slow walking speed, across different conditions. Apart from unconstrained steady-state walking, these conditions included imposed foot placement and ankle moment constraints. In our analysis we focused on the coupling between CoM kinematic state and foot placement, as well as correlations with muscle activity, reflecting active foot placement control.</p> <p>The results have been presented and discussed in the paper entitled: Active foot placement control ensures stable gait: Effect of constraints on foot placement and ankle moments.</p> <p>Guidelines on how to use this code can be found in the word file "Documentation", which can be found in the zipped folder.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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OpenNeuro
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