Aim Species distribution models (SDMs) are powerful tools for assessing suitable habitats across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species' realised distributions is often limited since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species' range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species' ranges and thereby range limits that are complementary to information offered by SDMs.

Innovation Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalisation. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our approach with an occurrence dataset for 49 bat species covering four biodiversity hotspots in the Eastern Mediterranean, Western Asia and Central Asia.

Main Conclusions Our approach offers a flexible method to integrate expert range maps with any combination of SDM modelling algorithms, thus facilitating the use of algorithm ensembles. In addition, it provides a novel, data-driven way to account for uncertainty in expert-defined ranges not requiring prior knowledge about their accuracy, which is often lacking. Integrating expert range maps into SDMs for bats resulted in more realistic predictions of distribution patterns that showed narrower niche breadths and smaller range overlaps between species compared to traditional SDMs. Our approach holds promise to improve assessments of species distributions, while our work highlights the overlooked potential of stacked generalisation as an ensemble method in species distribution modelling.

Executive Statement To support analysis of the Landsat long-term data record that began in 1972, the USGS Landsat data archive was reorganized into a formal tiered data collection structure. This structure ensures all Landsat Level 1 products provide a consistent archive of known data quality to support time-series analysis and data “stacking”, while controlling continuous improvement of the archive, and access to all data as they are acquired. Collection 1 Level 1 processing began in August 2016 and continued until all archived data was processed, completing May 2018. Newly-acquired Landsat 8 and Landsat 7 data continue to be processed into Collection 1 shortly after data is downlinked to USGS EROS.Learn more: https://www.usgs.gov/media/files/landsat-collection-1-level-1-product-definition

Using first principles methods, we calculated the entire -surface of the first-order pyramidal planes in -titanium. Slip on these planes involving dislocations with -type Burgers vectors is one means by which -titanium polycrystals may supplement slip on prism planes with -type Burgers vectors to maintain ductility. We find one low energy and one high energy stacking fault with energies of 163 and 681 , respectively. Contrary to previous suggestions, we do not find a stable stable stacking fault at .

The stacked catalog 4XMM-DR14s (XMMSTACK) has been compiled from 1,751 groups, comprising 10,336 overlapping XMM-Newton observations. They were selected from the public observations taken between 2000 February 1 and 2023 November 16 which overlap by at least one arcminute in radius. It contains 427,524 unique sources, 329,972 of them multiply observed, with positions and source parameters like fluxes in the XMM-Newton standard energy bands, hardness ratios, quality estimate, and information on inter-observation variability. The parameters are directly derived from the simultaneous fit, and, wherever applicable, additionally calculated for each contributing observation. Exposures that do not qualify for source detection, for example because of a high background level, are used for subsequent PSF photometry: source fluxes and flux-related parameters are derived for them at the source position and extent found during source detection. 4XMM-DR14s lists 1,807,316 individual flux measurements of the 427,524 unique sources. Stacked source detection aims at exploring the multiply observed sky regions and exploit their survey potential, in particular to study the long-term behavior of X-ray emitting sources. It thus makes use of the long(er) effective exposure time per sky area and offers the opportunity to investigate flux variability directly through the source detection process. The main catalog properties are summarized in the table below, the data processing and the stacked source detection are described in the processing summary. To ensure detection quality, background levels are assessed, and event-based astrometric corrections are applied before running source detection. After source detections, problematic detections and detection parameters are flagged by an automated algorithm. All detections are screened visually, and obviously spurious sources are flagged manually. This table contains the source parameters from the individual observations in the stacked catalog, 4XMM-DR14s. The parameters are derived from the simultaneous source-detection fit to all stacked observations at the common source position for each observation that covers a source, amounting to 1,807,316 measurements. The mean source parameters from stacked source detection are provided in the associated main table 4XMM-DR14s, referred to as XMMSTACK. The authors referred to the EPIC instruments with the following designations: PN, M1 (MOS1), and M2 (MOS2). The energy bands used in the 4XMM processing were the same as for the 3XMM catalog. The following are the basic energy bands:

 1: 0.2-0.5 keV 2: 0.5-1.0 keV 3: 1.0-2.0 keV 4: 2.0-4.5 keV 5: 4.5-12.0 keV 

All-EPIC values cover the energy range 0.2-12.0 keV. The full catalog documentation can be found at https://xmmssc.aip.de/. The following table gives an overview of the statistics of this catalog in comparison with the previous stacked catalogs, 4XMM-DR14s through 3XMM-DR7s:

 4XMM-DR14s 4XMM-DR13s 4XMM-DR12s 4XMM-DR11s 4XMM-DR10s 4XMM-DR9s 3XMM-DR7s Number of stacks 1,751 1,688 1,620 1,475 1,396 1,329 434 Number of observations 10,336 9,796 9,355 8,292 7,803 6,604 789 Time span first to last observation Feb 01, 2000 Feb 01, 2000 Feb 01, 2000 Feb 03, 2000 Feb 03, 2000 Feb 03, 2000 Feb 20, 2000 -- Nov 16,2023 -- Nov 29, 2022 -- Dec 04, 2021 -- Dec 17, 2020 -- Dec 14, 2019 -- Nov 13, 2018 -- Apr 02, 2016 Approximate sky coverage (sq. deg.) 685 650 625 560 540 485 150 Approximate multiply observed sky area(sq. deg) 440 420 400 350 335 300 100 Total number of sources 427,524 401,596 386,043 358,809 335,812 288,191 71,951 Sources with several contributing observations 329,972 310,478 298,626 275,440 256,213 218,283 57,665 Multiply observed sources with flag 0 or 1 276,058 262,842 252,445 233,542 216,999 191,497 55,450 Multiply observed with a total detection 266,129 251,555 241,880 224,178 208,921 181,132 49,935 likelihood of at least six Multiply observed with a total detection 226,219 213,812 205,394 189,556 176,680 153,487 42,077 likelihood of at least ten Total measurements 1,807,316 1,683,264 1,592,263 1,421,966 1,322,299 1,033,264 216,393 Maximum exposures per source 173 170 155 140 140 103 69 Maximum observations per source 77 77 70 65 65 40 23 Maximum on-time per source 2.8 Ms 2.8 Ms 2.8 Ms 2.8 Ms 2.8 Ms 1.9 Ms 1.3 Ms 

This database table was last updated by the HEASARC in July 2024. It contains the 4XMM-DR14s observations catalog, released by ESA on 2024-07-09 and obtained from the XMM-Newton Survey Science Center Consortium at https://xmmssc.aip.de/cms/catalogues/4xmm-dr14s/. It is also available as a gzipped FITS file. This is a service provided by NASA HEASARC .

It is shown that the enthalpy of any close packed structure for a given element can be characterized as a linear expansion in a set of continuous variables a_n, which describe the stacking configuration. This enables us to represent the infinite, discrete set of stacking sequences within a finite, continuous space of the expansion parameters H_n. These H_n determine the stable structure and vary continuously in the thermodynamic space of pressure, temperature, or composition. The continuity of both spaces means that only transformations between stable structures adjacent in the H_n space are possible, giving the model predictive as well as descriptive ability. We calculate the H_n using density functional theory and interatomic potentials for a range of materials. Some striking results are found: e.g. the Lennard-Jones potential model has 11 possible stable structures and over 50 phase transitions as a function of cutoff range. The very different phase diagrams of Sc, Tl, Y, and the lanthanides are understood within a single theory. We find that the widely reported 9R-fcc transition is not allowed in equilibrium thermodynamics, and in cases where it has been reported in experiments (Li, Na), we show that DFT theory is also unable to predict it.

Evaluating the population pharmacokinetic parameters, biological half-life (HL), and apparent volume of distribution (Vd) is important for identifying potential risks of chemicals. In this study, we developed a framework of stacking machine learning models for predicting the two parameters, providing more generalized prediction methods for data from diverse sources. We built a larger database containing experimental data for 2934 and 1787 substances for HL and Vd, respectively, and considered two different chemical featurization methods. We employed five individual algorithms (Support Vector Regression, Random Forest, Gaussian Process, Artificial Neural Network, and Extreme Gradient Boosting) to construct the base models, and then combined predictions using Multiple Linear Regression to obtain 4 stacking models. Our stacking models performed well and outperformed the corresponding base models, with the extended connectivity fingerprint-based stacking model achieving the best predictive performance. The accuracy of the models, as defined by the applicability domain, was further improved, retaining more than 60% of the test data. Finally, we developed a publicly accessible online Web site (http://tkpara.hhra.net), where users can easily and quickly utilize our models. Our work provides data support for human health risk assessment of chemicals and for the use and management of chemicals or industrial products.

1. Artificial night-lighting threatens to disrupt strongly conserved light-dependent processes in animals and may have cascading effects on ecosystems as species interactions become altered. Insectivorous bats and their prey have been involved in a nocturnal, coevolutionary arms race for millions of years. Lights may interfere with anti-bat defensive behaviours in moths, and disrupt a complex and globally ubiquitous interaction between bats and insects, ultimately leading to detrimental consequences for ecosystems on a global scale. 2. We combined experimental and mathematical approaches to determine effects of light pollution on a free-living bat–insect community. We compared prey selection by Cape serotine bats Neoromicia capensis in naturally unlit and artificially lit conditions using a manipulative field experiment, and developed a probabilistic model based on a suite of prey-selection factors to explain differences in observed diet. 3. Moth consumption by N. capensis was low under unlit conditions (mean percentage volume ± SD: 5.91 ± 6.25%), while moth consumption increased six-fold (mean percentage volume ± SD: 35.42 ± 17.90%) under lit conditions despite a decrease in relative moth abundance. Predictive prey-selection models which included high-efficacy estimates for eared-moth defensive behaviour found most support given diet data for bats in unlit conditions. Conversely, models which estimated eared-moth defensive behaviour as absent or low, found more support given diet data for bats in lit conditions. Our models therefore suggest the increase in moth consumption was a result of light-induced, decreased eared-moth defensive behaviour. 4. Policy implications. In the current context of unyielding growth in global light pollution, we predict that specialist moth-eating bats and eared-moths will face ever-increasing challenges to survival through increased resource competition and predation risk, respectively. Lights should be developed to be less attractive to moths, with the goal of reducing effects on moth behaviour. Unfortunately, market preference for broad-spectrum lighting and possible effects on other taxa make development of moth-friendly lighting improbable. Mitigation should therefore focus on the reduction of temporal, spatial, and luminance redundancy in outdoor lighting. Restriction of light inside nature reserves and urban greenbelts can serve as dark refugia for moth-eating bats and moths, and may become important for their persistence.

Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. On the basis of rigorous ab initio data, we developed simple predictive models of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. These models provide reliable predictions of the stacking ability of a given heterocycle based on readily computed heterocycle descriptors, eliminating the need for quantum chemical computations of stacked dimers. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual means for understanding stacking interactions in protein binding sites and tuning their strength in the context of drug design.

The invention relates to a composite material (1), for closure elements, mad from a support material (2) and a thermoplastically moulded layer (3) with projecting and recessed sections (4,5). According to the invention, the thermoplastically moulded layer (3) is thinner in the region of the recessed sections (4) than in the region of the projecting sections (5). Said surface structure may for example be produced by embossing by means of ribbed rollers or embossing plates. The composite material (1) is suitable for the production of sealing elements, such as for example, cup tops, usually stored in stacks. The cup elements stacked thus can easily be unstacked after stacking, precisely by means of said recessed or projecting sections(4, 5). On the side of the composite material facing the consumer a decorative and/or informative impression (6) can further be provided.

In this dataset cathodoluminescence (CL) spectroscopy and atom probe tomography (APT) have been used to investigate the luminescence characteristics and the relation between the distribution of impurities and stacking faults (SFs) in Mg-doped zincblende gallium nitride (zb-GaN:Mg). Several characteristic peaks have been identified in the CL emission spectra, related to donor-to-acceptor (DAP) transitions involving different Mg acceptor energy levels. These DAP peaks have been used to demonstrate a segregation of Mg close to SFs compared to the surrounding defect-free material, which was also supported by APT measurements. (For more details of the experiment see the related publication.)

File: Fig1c)-mean-spectrum File contains a representative mean CL spectrum for the area of the cubic GaN:Mg sample shown in figure 1(a).

Files: Fig4-PointSpectrum-point-A to Fig4-PointSpectrum-point-D These files contain point spectra taken from regions away from SFs (A, B) and at SF locations (C, D), as shown in figure 4 of the corresponding publication.

File: Fig-5b)-1D-concentration-profile This file contains 1D element concentration profiles taken across a SF in the APT dataset shown in Fig 5 of the corresponding publication.

Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.

Zincblende InGaN/GaN quantum wells grown along the [001] direction are free of the internal electric fields that limit the efficiency of conventional wurtzite InGaN-based quantum wells in the (0001) orientation. However, heteroepitaxial grown zincblende GaN often exhibit a significant density of stacking faults, which reduce the radiative recombination efficiency when they intersect the quantum wells (QWs). In this work we show that increasing the thickness of the buffer layer between substrate and InGaN/GaN quantum wells reduces the stacking faut density in the quantum wells and thereby increases the emission efficiency.

Three sets of samples have been investigated X-ray diffraction (XRD), cathodoluminescence (CL) spectroscopy and photoluminescence (PL) spectroscopy. The first set of samples (series 1) consists of a single GaN buffer layer with thicknesses between 0.6 µm and 3.0 µm grown on 3C-SiC-on-Si substrates. Sample series 2 is based on a similar set of buffer layers with varying thicknesses, which was overgrown with a single InGaN/GaN qunatum well. In series 2 an InGaN-based single quantum well (SQW) structure was grown on a set of buffer layers with total thicknesses between 0.8 µm and 3.2 µm. Finally, series 3 is based on a similar set of buffer layers with varying thicknesses, which was overgrown with a multiple quantum well (MQW) structure.

Further information on the sample structures and the experimental results can be found in the related research article.

File: “Fig. 02” contains data from the XRD phase analysis for the first set of samples, consisting of buffer layers only.

File: "Fig. 03 & 06" contains the HSPY files for the cathodoluminescence measurements on the SQW samples with varying buffer thickness between 0.8 µm to 3.2 µm. Python-based Jupyer Notebook is able to open the .hspy files, with HyperSpy and LumiSpy packages loaded. HyperSpy can be installed using Anaconda and LumiSpy using pip instructions. Jupyter Notebook will be installed automatically when installing Anaconda.

Please find details in the links below:

HyperSpy: https://hyperspy.org/hyperspy-doc/current/user_guide/install.html#anaconda-install LumiSpy: https://docs.lumispy.org/en/stable/user_guide/installation.html Anaconda: https://www.anaconda.com/download

File: "Fig. 04 & 05" contains statistical analyses of the CL data for the SQW samples of series 2.

The tab “Fig. 4a” contains the variation of density of SF-related dark stripes in CL maps with GaN buffer thickness parallel and perpendicular to the [1-10] miscut direction.

The data in tab “Fig. 4b” shows the variation of mean area per bright patch with buffer thickness.

The data in tab “Fig 5” are the room-temperature mean CL spectra of the zb-InGaN SQW samples of series 2.

File: “Fig. 07-09” is an Excel file that contains the data of the PL analyses performed on the MQW samples of the third set of samples.

The tab “Figure 7” contains low temperature (12 K) PL emission spectra measured at an excitation power density 10 W.cm^-2.

The tab “Figure 8” contains PL transients for photon energies spanning the emission band of the MQW sample with 3.2 µm buffer layer.

The tab “Figure 9”, contains the normalised temperature dependence of the spectrally integrated emission spectrum for the MQW sample with 3.2 μm buffer layer. The data of the inset show the intensity at 300 K relative to the intensity at 12 K for the MQW series as a function of buffer layer thickness.

Covalent organic frameworks (COFs), formed by reversible condensation of rigid organic building blocks, are crystalline and porous materials of great potential for catalysis and organic electronics. Particularly with a view of organic electronics, achieving a maximum degree of crystallinity and large domain sizes while allowing for a tightly π-stacked topology would be highly desirable. We present a design concept that uses the 3D geometry of the building blocks to generate a lattice of uniquely defined docking sites for the attachment of consecutive layers, thus allowing us to achieve a greatly improved degree of order within a given average number of attachment and detachment cycles during COF growth. Synchronization of the molecular geometry across several hundred nanometers promotes the growth of highly crystalline frameworks with unprecedented domain sizes. Spectroscopic data indicate considerable delocalization of excitations along the π-stacked columns and the feasibility of donor–acceptor excitations across the imine bonds. The frameworks developed in this study can serve as a blueprint for the design of a broad range of tailor-made 2D COFs with extended π-conjugated building blocks for applications in photocatalysis and optoelectronics.

Background and objectiveMost previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.MethodsThe ARIMA, STL+ARIMA, BP-ANN and LSTM network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017. We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. In addition, gradient-boosting regression trees (GBRTs) were used to combine the above four models. We also determined whether stacking structure improved the model prediction performance.ResultsThe root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. After using the stacking architecture combined with the above four models, the RMSE, MASE and MAD values of the ensemble model decreased to 6.810, 0.224 and 4.625, respectively.ConclusionsA novel ensemble model based on the robustness of structured prediction and model combination through stacking was developed. The findings suggest that the predictive performance of the final model is superior to that of the other four sub-models, indicating that stacking architecture may have significant implications in infectious disease prediction.

Protein-Protein, Genetic, and Chemical Interactions for GORASP2 (Homo sapiens) curated by BioGRID (https://thebiogrid.org); DEFINITION: golgi reassembly stacking protein 2, 55kDa

Dataset to run example script of the Valparaíso Stacking Analysis Tool (VSAT-1D). The Valparaíso Stacking Analysis Tool (VSAT) provides a series of tools for selecting, stacking, and analyzing 1D spectra. It is intended for stacking samples of spectra belonging to large extragalactic catalogs by selecting subsamples of galaxies defined by their available properties (e.g. redshift, stellar mass, star formation rate) being possible to generate diverse (e.g. median, average, weighted average, histogram) composite spectra. However, it is possible to also use VSAT on smaller datasets containing any type of astronomical object.

VSAT can be downloaded from the github repository link.

Dataset to run Example.py script of the Valparaíso Stacking Analysis Tool (VSAT-3D). The Valparaíso Stacking Analysis Tool (VSAT-3D) provides a series of tools for selecting, stacking, and analyzing 3D spectra. It is intended for stacking samples of datacubes extracted from interferometric datasets, belonging to large extragalactic catalogs by selecting subsamples of galaxies defined by their available properties (e.g. redshift, stellar mass, star formation rate) being possible to generate diverse (e.g. median, average, weighted average, histogram) composite spectra. However, it is possible to also use VSAT-3D on smaller datasets containing any type of astronomical object.

VSAT-3D can be downloaded from the github repository link.

Protein-Protein, Genetic, and Chemical Interactions for GORASP1 (Homo sapiens) curated by BioGRID (https://thebiogrid.org); DEFINITION: golgi reassembly stacking protein 1, 65kDa

N-Methyl mesoporphyrin IX (NMM) is exceptionally selective for G-quadruplexes (GQ) relative to duplex DNA and, as such, has found a wide range of applications in biology and chemistry. In addition, NMM is selective for parallel versus antiparallel GQ folds, as was recently demonstrated in our laboratory. Here, we present the X-ray crystal structure of a complex between NMM and human telomeric DNA dAGGG(TTAGGG)3, Tel22, determined in two space groups, P21212 and P6, at 1.65 and 2.15 Å resolution, respectively. The former is the highest resolution structure of the human telomeric GQ DNA reported to date. The biological unit contains a Tel22 dimer of 5′-5′ stacked parallel-stranded quadruplexes capped on both ends with NMM, supporting the spectroscopically determined 1:1 stoichiometry. NMM is capable of adjusting its macrocycle geometry to closely match that of the terminal G-tetrad required for efficient π–π stacking. The out-of-plane N-methyl group of NMM fits perfectly into the center of the parallel GQ core where it aligns with potassium ions. In contrast, the interaction of the N-methyl group with duplex DNA or antiparallel GQ would lead to steric clashes that prevent NMM from binding to these structures, thus explaining its unique selectivity. On the basis of the biochemical data, binding of NMM to Tel22 does not rely on relatively nonspecific electrostatic interactions, which characterize most canonical GQ ligands, but rather it is hydrophobic in nature. The structural features observed in the NMM–Tel22 complex described here will serve as guidelines for developing new quadruplex ligands that have excellent affinity and precisely defined selectivity.

Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive data set comprising parameters across multiple species. Building upon this groundwork, we introduced the PKStack ensemble model to predict PK parameters across diverse species. PKStack integrates a variety of base models and includes uncertainty in its predictions. We also manually collected PK data from animals as an external test set. We predicted a total of 45 tasks for nine PK parameters in five species, and in general, the prediction accuracy was better for intravenous injections, including parameters such as human Vd (R2 = 0.72, RMSE = 0.31), human CL (R2 = 0.52, RMSE = 0.32), and others. In addition to predictive accuracy, we also considered the interpretability of the results and the definition of the model’s application domain. Based on the findings, our model has great potential for practical applications in drug discovery.