ZeptoMetrix (United States)

Taking a step-by-step approach to modelling neurons and neural circuitry, this textbook teaches students how to use computational techniques to understand the nervous system at all levels, using case studies throughout to illustrate fundamental principles. Starting with a simple model of a neuron, the authors gradually introduce neuronal morphology, synapses, ion channels and intracellular signalling. This fully updated new edition contains additional examples and case studies on specific modelling techniques, suggestions on different ways to use this book, and new chapters covering plasticity, modelling extracellular influences on brain circuits, modelling experimental measurement processes, and choosing appropriate model structures and their parameters. The online resources offer exercises and simulation code that recreate many of the book's figures, allowing students to practice as they learn. Requiring an elementary background in neuroscience and high-school mathematics, this is an ideal resource for a course on computational neuroscience.

The technique of processing the results of factory experts’ reports has been developed to interpret the origin of nonmetallic inclusions (NMIs) detected in the defects of a welded pipe joints made of J55 API 5L (22GYu) steel. Thermodynamic simulation was performed to assess the possibility of the indigenous origin of these inclusions. The results of such a simulation were summarized using radar diagrams of four clusters uniting NMIs with similar composition of deoxidation and modification products. More than 130 compositions of actual inclusions detected on dozens of welded joints samples were processed and combined into four clusters in the same way. After a comparative analysis of these two types of radar diagrams, the origin of all clusters of actual inclusions was established, three of which are predominantly exogenous inclusions and the fourth is predominantly indigenous. None of the clusters contains only indigenous inclusions or only exogenous ones. The main source of inclusions in the discontinuities of welded joints is inclusions conglomerates, sorbed on the refractories surface, contacted with molten steel during its ladle treatment and pouring. These sintered conglomerates of indigenous and exogenous inclusions, that form three clusters of four ones, were out washed by the flow of steel melt into the crystallizer during steel pouring. The reasons for the formation of the fourth cluster are related to “cold welding”. The technique is implemented as a plug-in of Thixomet Pro image analysis system and allows to automate the process of metallographic conclusions generation in factory practice, as well as to accumulate all information in a database for its subsequent processing and analysis.

ObjectivesThis study aims to retrospectively assess C‐lectin‐like molecule 1 (CLL‐1) bimodal expression on CD34+ blasts in acute myeloid leukemia (AML) patients (total N = 306) and explore potential CLL‐1 bimodal associations with leukemia and patient‐specific characteristics.MethodsFlow cytometry assays were performed to assess the deeper immunophenotyping of CLL‐1 bimodality. Cytogenetic analysis was performed to characterize the gene mutation on CLL‐1‐negative subpopulation of CLL‐1 bimodal AML samples.ResultsThe frequency of a bimodal pattern of CLL‐1 expression of CD34+ blasts ranged from 8% to 65% in the different cohorts. Bimodal CLL‐1 expression was most prevalent in patients with MDS‐related AML (P = .011), ELN adverse risk (P = .002), NPM1 wild type (WT, P = .049), FLT3 WT (P = .035), and relatively low percentages of leukemia‐associated immunophenotypes (P = .006). Additional immunophenotyping analysis revealed the CLL‐1− subpopulation may consist of pre‐B cells, immature myeloblasts, and hematopoietic stem cells. Furthermore, (pre)‐leukemic mutations were detected in both CLL‐1+ and CLL‐1− subfractions of bimodal samples (N = 3).ConclusionsC‐lectin‐like molecule 1 bimodality occurs in about 25% of AML patients and the CLL‐1− cell population still contains malignant cells, hence it may potentially limit the effectiveness of CLL‐1‐targeted therapies and warrant further investigation.

<p>iSDAsoil dataset soil extractable Iron (Fe) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.fe_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron mean value,</li> <li>sol_log.fe_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.fe_mehlich3 R-square: 0.817 Fitted values sd: 0.497 RMSE: 0.235 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.0165 -0.1312 -0.0082 0.1238 2.5077 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.913522 1.869721 2.093 0.036344 * regr.ranger 0.856893 0.007912 108.306 < 2e-16 *** regr.xgboost 0.027856 0.007738 3.600 0.000318 *** regr.cubist 0.146095 0.007230 20.207 < 2e-16 *** regr.nnet -0.879348 0.402810 -2.183 0.029037 * regr.cvglmnet 0.005610 0.004470 1.255 0.209415 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2349 on 57526 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.8173 F-statistic: 5.148e+04 on 5 and 57526 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>

ABSTRACT This discussion focuses on photonic soldering, which uses high intensity flash lamps to overcome the disadvantages of laser soldering; while still enabling soldering on a wide range of substrates. Similar to laser soldering, photonic soldering utilizes selective absorption of light to enable conventional solders (such as SAC-305) to affix commercial packages (such as transistors, LEDs or resistors in traditional sizes) on the underlying thermally unstable substrate (such as PET, PEN or TPU). Microscopy, cross-sectional SEM and X-ray imaging of solder joints are used to compare the photonic soldering outcomes with the conventional reflow soldering results. Evaluated results indicate a close correlation of outcomes between the two techniques. Furthermore, photonic soldering allows wearables to be stretched, folded, bent and run through washing and drying cycles – results that are not possible with attachment methods such as adhesive tapes.

Abstract The use of on‐line mass spectrometry to measure aroma release from model systems, or from people consuming food or beverages, delivers time‐based aroma release curves that relate better to sensory perception. The technique allows the rapid analysis of the headspace above foods as well as the measurement of the performance of flavour systems in manufactured foods during consumption. The data obtained can give quick insights into the potential success of a flavouring and on‐line mass spectrometry is widely used in the food and flavour industries. The speed of data acquisition, sensitivity to a wide range of chemical compounds, and resolution of compounds with the same nominal mass are recurring issues for on‐line analysis. Here, we present a study of the performance of an atmospheric pressure chemical ionization (APCI) source connected to a triple quadrupole mass spectrometer. Fast data acquisition could be achieved with little loss of ion signal but with slightly increased noise. In ‘Multiple Reaction Monitoring’ mode, sensitivity increased appreciably for six out of the seven compounds tested, and changing the collision energy of the system produced different mass spectral patterns for six compounds with the same nominal mass. The application of some of these techniques to coffee analysis identified the challenges and approaches needed to provide an in‐depth analysis for real samples.

Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see NRCS Soil Survey Laboratory Methods Manual), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <strong>here</strong>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included. To access and visualize some of the maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, available.water.capacity = available water capacity in mm, usda.mm = determination method: Water Retention Difference in mm, m = mean value, 250m = spatial resolution / block support: 250 m, b0..10cm = vertical reference: 0-10 cm layer below surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1,

Le prix littéraire de la Porte Dorée célèbre en 2019 ses dix ans d’existence : dix éditions d’un prix qui n’a jamais démérité par le choix des lauréats et la qualité des dizaines de romans sélectionnés chaque année.

&lt;p&gt;Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001&ndash;2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the&nbsp;Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail &lt;strong&gt;&lt;a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN"&gt;here&lt;/a&gt;&lt;/strong&gt;. Antartica is not included.&lt;/p&gt; &lt;p&gt;To access and visualize maps use:&nbsp;&nbsp;&lt;a href="http://www.openlandmap.org/"&gt;OpenLandMap.org&lt;/a&gt;&lt;/p&gt; &lt;p&gt;If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Technical issues and questions about the code:&nbsp;&lt;a href="https://gitlab.com/openlandmap/global-layers/issues"&gt;https://gitlab.com/openlandmap/global-layers/issues&lt;/a&gt;&nbsp;&lt;/li&gt; &lt;li&gt;General questions and comments:&nbsp;&lt;a href="https://disqus.com/home/forums/landgis/"&gt;https://disqus.com/home/forums/landgis/&lt;/a&gt;&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;All files internally compressed using &quot;COMPRESS=DEFLATE&quot; creation&nbsp;option in GDAL. File naming convention:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;sol = theme: soil,&lt;/li&gt; &lt;li&gt;organic.carbon.stock = variable: soil organic carbon stock in kg/m2,&lt;/li&gt; &lt;li&gt;msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,&lt;/li&gt; &lt;li&gt;m = mean value,&lt;/li&gt; &lt;li&gt;250m = spatial resolution / block support: 250 m,&lt;/li&gt; &lt;li&gt;b0..30cm = vertical reference: standard layer 0-30 cm below surface,&lt;/li&gt; &lt;li&gt;2014 = time reference: year 2014,&lt;/li&gt; &lt;li&gt;v0.2 = version number: 0.2,&lt;/li&gt; &lt;/ul&gt;

Layers include: lithology (15) and landform (7) indicator maps (0-100%). Derived from the USGS Global Ecosystem Map, i.e. the EcoTapestry map. Water bodies masked out. Antartica is not included. All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: dtm = theme: digital terrain models / relief and soil, lithology = variable: lithological class, usgs.ecotapestry = determination method: USGS Global Ecosystem Map, p = probability 0-100%, 250m = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2014 = time reference: year 2014, v1.0 = version number: 1.0,