Appendiceal neuroendocrine tumors(aNETs)are an uncommon neoplasm that is relatively indolent in most cases.They are typically diagnosed in younger patients than other neuroendocrine tumors and are often an incidental ...Appendiceal neuroendocrine tumors(aNETs)are an uncommon neoplasm that is relatively indolent in most cases.They are typically diagnosed in younger patients than other neuroendocrine tumors and are often an incidental finding after an appendectomy.Although there are numerous clinical practice guidelines on management of a NETs,there is continues to be a dearth of evidence on optimal treatment.Management of these tumors is stratified according to risk of locoregional and distant metastasis.However,there is a lack of consensus regarding tumors that measure 1-2 cm.In these cases,some histopathological features such as size,tumor grade,presence of lymphovascular invasion,or mesoappendix infiltration must also be considered.Computed tomography or magnetic resonance imaging scans are recommended for evaluating the presence of additional disease,except in the case of tumors smaller than 1 cm without additional risk factors.Somatostatin receptor scintigraphy or positron emission tomography with computed tomography should be considered in cases with suspected residual or distant disease.The main point of controversy is the indication for performing a completion right hemicolectomy after an initial appendectomy,based on the risk of lymph node metastases.The main factor considered is tumor size and 2 cm is the most common threshold for indicating a colectomy.Other factors such as mesoappendix infiltration,lymphovascular invasion,or tumor grade may also be considered.On the other hand,potential complications,and decreased quality of life after a hemicolectomy as well as the lack of evidence on benefits in terms of survival must be taken into consideration.In this review,we present data regarding the current indications,outcomes,and benefits of a colectomy.展开更多
Objective:To investigate the reproductive performance and milk quality to nutrition pre-pubertal plane in Kurdish female kids.Methods: Forty Kurdish female kids [aged (28.0±6.6) d and weighted (7.56±1.10) kg...Objective:To investigate the reproductive performance and milk quality to nutrition pre-pubertal plane in Kurdish female kids.Methods: Forty Kurdish female kids [aged (28.0±6.6) d and weighted (7.56±1.10) kg] were assigned randomly in pre-weaning period to one of two practical diets: low quality diet (LQD) [87 g CP/kg dray matter (DM) and 2.02 Mcal ME/kg DM], and high quality diet (HQD), (148 g CP/kg DM and 2.50 Mcal ME/kg DM). At weaning, from each group, one half of kids was separated randomly and allocated to LQD or HQD. Consequently, in post-weaning period, there were four treatment groups including: LQD pre and post-weaning (L-L), control group and LQD pre-weaning and HQD post-weaning (L-H);HQD pre-weaning and LQD post-weaning (H-L), HQD pre- and post-weanin (H-H). From 30 to 180 d of age, body weight and DM intake were determined every 2 wk.Results:Results showed that the HQD treatment enhanced body weight and DM intake during pre-weaning period, in comparison with the LQD treatment (P<0.01). During post-weaning, kids of H-H treatment had higher DM intake compared with other kid's treatments. Kids fed the HQD treatment had greater withers height compared to kids on the LQD treatment at 90 d of age (P<0.01). Kids in the L-H and H-H groups weighed more and were younger at puberty. In the period of pre-pubertal, diet plan was not significantly affected milk yield and reproductive performance at the first lactation.Conclusions:Overall, management strategies that have been used to availability of nutrition could increase growth and feed intake in Kurdish female kids. In addition, these strategic programs should be enhancing economic characteristics at the start of puberty of kid in goat husbandry.展开更多
A synthetizing material blended with two distinct proteins (collagen and casein) and mineral mixture, was developed in order to evaluate their properties suitable for possible applications in the biomedical such as in...A synthetizing material blended with two distinct proteins (collagen and casein) and mineral mixture, was developed in order to evaluate their properties suitable for possible applications in the biomedical such as inducing the regeneration of damaged bone, either due to an accident or illness. Samples were evaluated by 1) Mechanical properties tests under the bending, 2) Scanning electronic microscopy and 3) Infrared spectroscopy were carried out. The results showed that the developed material has breaking strength and structure characteristics associated with the protein used in their composition. This fact suggests that the used protein determines the resistance of the material, in such a way according to the required use, being able to choose appropriate strength and duration either short or long time. The material composition for specific use, in order to find the most suitable mixture for bone replacement, or induce bone recovery, according to the required properties similar to those of damaged living tissue.展开更多
Computational study of molecules and materials from first principles is a cornerstone of physics,chemistry,and materials science,but limited by the cost of accurate and precise simulations.In settings involving many s...Computational study of molecules and materials from first principles is a cornerstone of physics,chemistry,and materials science,but limited by the cost of accurate and precise simulations.In settings involving many simulations,machine learning can reduce these costs,often by orders of magnitude,by interpolating between reference simulations.This requires representations that describe any molecule or material and support interpolation.We comprehensively review and discuss current representations and relations between them.For selected state-of-the-art representations,we compare energy predictions for organic molecules,binary alloys,and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution,regression method,and hyper-parameter optimization.展开更多
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications,including superconductivity,catalysis,and thermoelectricity.Advancem...Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications,including superconductivity,catalysis,and thermoelectricity.Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data.For such applications,reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed.Here,we present a general,data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis.We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values.By extracting the most influential material properties from this model,we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.展开更多
We present the Novel-Materials-Discovery(NOMAD)Artificial-Intelligence(AI)Toolkit,a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable,accessible,interoperable,and reu...We present the Novel-Materials-Discovery(NOMAD)Artificial-Intelligence(AI)Toolkit,a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable,accessible,interoperable,and reusable(FAIR)data.The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive,the largest database of materials-science data worldwide,as well as locally stored,users’owned data.The NOMAD Oasis,a local,stand-alone server can be also used to run the AI Toolkit.By using Jupyter notebooks that run in a web-browser,the NOMAD data can be queried and accessed;data mining,machine learning,and other AI techniques can be then applied to analyze them.This infrastructure brings the concept of reproducibility in materials science to the next level,by allowing researchers to share not only the data contributing to their scientific publications,but also all the developed methods and analytics tools.Besides reproducing published results,users of the NOMAD AI toolkit can modify the Jupyter notebooks toward their own research work.展开更多
Singlet fission(SF),the conversion of one singlet exciton into two triplet excitons,could significantly enhance solar cell efficiency.Molecular crystals that undergo SF are scarce.Computational exploration may acceler...Singlet fission(SF),the conversion of one singlet exciton into two triplet excitons,could significantly enhance solar cell efficiency.Molecular crystals that undergo SF are scarce.Computational exploration may accelerate the discovery of SF materials.However,many-body perturbation theory(MBPT)calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening.We use the sure-independence-screening-and-sparsifying-operator(SISSO)machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons(PAH101).SISSO generates models by iteratively combining physical primary features.The best models are selected by linear regression with cross-validation.The SISSO models successfully predict the SF driving force with errors below 0.2 eV.Based on the cost,accuracy,and classification performance of SISSO models,we propose a hierarchical materials screening workflow.Three potential SF candidates are found in the PAH101 set.展开更多
Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve...Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision.Here,we present AI-STEM,an automatic,artificial-intelligence based method,for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy(STEM)images of polycrystalline materials.The method is based on a Bayesian convolutional neural network(BNN)that is trained only on simulated images.AI-STEM automatically and accurately identifies crystal structure,lattice orientation,and location of interface regions in synthetic and experimental images.The model is trained on cubic and hexagonal crystal structures,yielding classifications and uncertainty estimates,while no explicit information on structural patterns at the interfaces is included during training.This work combines principles from probabilistic modeling,deep learning,and information theory,enabling automatic analysis of experimental,atomic-resolution images.展开更多
In semi-arid regions,air temperatures have increased in the last decades more than in many other parts of the world.Mongolia has an arid/semi-arid climate and much of the population are herders whose livelihoods depen...In semi-arid regions,air temperatures have increased in the last decades more than in many other parts of the world.Mongolia has an arid/semi-arid climate and much of the population are herders whose livelihoods depend upon limited water resources that fluctuate with a variable climate.Herders were surveyed to identify their observations of changes in climate extremes for two soums of central Mongolia,Ikh-Tamir in the forest steppe north of the Khangai Mountains and Jinst in the desert steppe south of the mountains.The herders’indigenous knowledge of changes in climate extremes mostly aligned with the station-based analyses of change.Temperatures were warming with more warm days and nights at all stations.There were fewer cool days and nights observed at the mountain stations both in the summer and winter,yet more cool days and nights were observed in the winter at the desert steppe station.The number of summer days is increasing while the number of frost days is decreasing at all stations.The results of this study support further use of local knowledge and meteorological observations to provide more holistic analysis of climate change in different regions of the world.展开更多
文摘Appendiceal neuroendocrine tumors(aNETs)are an uncommon neoplasm that is relatively indolent in most cases.They are typically diagnosed in younger patients than other neuroendocrine tumors and are often an incidental finding after an appendectomy.Although there are numerous clinical practice guidelines on management of a NETs,there is continues to be a dearth of evidence on optimal treatment.Management of these tumors is stratified according to risk of locoregional and distant metastasis.However,there is a lack of consensus regarding tumors that measure 1-2 cm.In these cases,some histopathological features such as size,tumor grade,presence of lymphovascular invasion,or mesoappendix infiltration must also be considered.Computed tomography or magnetic resonance imaging scans are recommended for evaluating the presence of additional disease,except in the case of tumors smaller than 1 cm without additional risk factors.Somatostatin receptor scintigraphy or positron emission tomography with computed tomography should be considered in cases with suspected residual or distant disease.The main point of controversy is the indication for performing a completion right hemicolectomy after an initial appendectomy,based on the risk of lymph node metastases.The main factor considered is tumor size and 2 cm is the most common threshold for indicating a colectomy.Other factors such as mesoappendix infiltration,lymphovascular invasion,or tumor grade may also be considered.On the other hand,potential complications,and decreased quality of life after a hemicolectomy as well as the lack of evidence on benefits in terms of survival must be taken into consideration.In this review,we present data regarding the current indications,outcomes,and benefits of a colectomy.
文摘Objective:To investigate the reproductive performance and milk quality to nutrition pre-pubertal plane in Kurdish female kids.Methods: Forty Kurdish female kids [aged (28.0±6.6) d and weighted (7.56±1.10) kg] were assigned randomly in pre-weaning period to one of two practical diets: low quality diet (LQD) [87 g CP/kg dray matter (DM) and 2.02 Mcal ME/kg DM], and high quality diet (HQD), (148 g CP/kg DM and 2.50 Mcal ME/kg DM). At weaning, from each group, one half of kids was separated randomly and allocated to LQD or HQD. Consequently, in post-weaning period, there were four treatment groups including: LQD pre and post-weaning (L-L), control group and LQD pre-weaning and HQD post-weaning (L-H);HQD pre-weaning and LQD post-weaning (H-L), HQD pre- and post-weanin (H-H). From 30 to 180 d of age, body weight and DM intake were determined every 2 wk.Results:Results showed that the HQD treatment enhanced body weight and DM intake during pre-weaning period, in comparison with the LQD treatment (P<0.01). During post-weaning, kids of H-H treatment had higher DM intake compared with other kid's treatments. Kids fed the HQD treatment had greater withers height compared to kids on the LQD treatment at 90 d of age (P<0.01). Kids in the L-H and H-H groups weighed more and were younger at puberty. In the period of pre-pubertal, diet plan was not significantly affected milk yield and reproductive performance at the first lactation.Conclusions:Overall, management strategies that have been used to availability of nutrition could increase growth and feed intake in Kurdish female kids. In addition, these strategic programs should be enhancing economic characteristics at the start of puberty of kid in goat husbandry.
文摘A synthetizing material blended with two distinct proteins (collagen and casein) and mineral mixture, was developed in order to evaluate their properties suitable for possible applications in the biomedical such as inducing the regeneration of damaged bone, either due to an accident or illness. Samples were evaluated by 1) Mechanical properties tests under the bending, 2) Scanning electronic microscopy and 3) Infrared spectroscopy were carried out. The results showed that the developed material has breaking strength and structure characteristics associated with the protein used in their composition. This fact suggests that the used protein determines the resistance of the material, in such a way according to the required use, being able to choose appropriate strength and duration either short or long time. The material composition for specific use, in order to find the most suitable mixture for bone replacement, or induce bone recovery, according to the required properties similar to those of damaged living tissue.
基金Partially by National Natural Science Foundation Funds （10701010）, Key University Science Research Project of Anhui Province （KJ2011A138） and Ph. D. Science Research Fund of Anhui Normal University.
基金This work received funding from the European Union’s Horizon 2020 Research and Innovation Programme,Grant Agreements No.676580,the NOMAD Laboratory CoE,and No.740233,ERC:TEC1PIt was funded in part by the German Ministry for Education and Research as BIFOLD-Berlin Institute for the Foundations of Learning and Data(ref.01IS18025A and ref.01IS18037A)Part of the research was performed while the authors visited the Institute for Pure and Applied Mathematics(IPAM),which is supported by the National Science Foundation(Grant No.DMS-1440415).
文摘Computational study of molecules and materials from first principles is a cornerstone of physics,chemistry,and materials science,but limited by the cost of accurate and precise simulations.In settings involving many simulations,machine learning can reduce these costs,often by orders of magnitude,by interpolating between reference simulations.This requires representations that describe any molecule or material and support interpolation.We comprehensively review and discuss current representations and relations between them.For selected state-of-the-art representations,we compare energy predictions for organic molecules,binary alloys,and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution,regression method,and hyper-parameter optimization.
基金This work was funded by the NOMAD Center of Excellence(European Union’s Horizon 2020 research and innovation program,grant agreement No 951786)the ERC Advanced Grant TEC1p(European Research Council,grant agreement No 740233)the project FAIRmat(FAIR Data Infrastructure for Condensed-Matter Physics and the Chemical Physics of Solids,German Research Foundation,project No 460197019).T.A.R.P.would like to thank the Alexander von Humboldt(AvH)Foundation for their support through the AvH Postdoctoral Fellowship Program.This research used resources of the Max Planck Computing and Data Facility and the Argonne Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.
文摘Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications,including superconductivity,catalysis,and thermoelectricity.Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data.For such applications,reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed.Here,we present a general,data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis.We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values.By extracting the most influential material properties from this model,we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.
基金This work received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement No.951786(NOMAD CoE)the ERC Advanced Grant TEC1P(No.740233)+1 种基金the German Research Foundation(DFG)through the NFDI consortium“FAIRmat”,project 460197019Open Access funding enabled and organized by Projekt DEAL.
文摘We present the Novel-Materials-Discovery(NOMAD)Artificial-Intelligence(AI)Toolkit,a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable,accessible,interoperable,and reusable(FAIR)data.The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive,the largest database of materials-science data worldwide,as well as locally stored,users’owned data.The NOMAD Oasis,a local,stand-alone server can be also used to run the AI Toolkit.By using Jupyter notebooks that run in a web-browser,the NOMAD data can be queried and accessed;data mining,machine learning,and other AI techniques can be then applied to analyze them.This infrastructure brings the concept of reproducibility in materials science to the next level,by allowing researchers to share not only the data contributing to their scientific publications,but also all the developed methods and analytics tools.Besides reproducing published results,users of the NOMAD AI toolkit can modify the Jupyter notebooks toward their own research work.
基金Work at CMU was supported by the National Science Foundation(NSF)Division of Materials Research through grant DMR-2021803This research used resources of the Argonne Leadership Computing Facility(ALCF),which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357of the National Energy Research Scientific Computing Center(NERSC),a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy,under Contract DE-AC02-05CH11231.
文摘Singlet fission(SF),the conversion of one singlet exciton into two triplet excitons,could significantly enhance solar cell efficiency.Molecular crystals that undergo SF are scarce.Computational exploration may accelerate the discovery of SF materials.However,many-body perturbation theory(MBPT)calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening.We use the sure-independence-screening-and-sparsifying-operator(SISSO)machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons(PAH101).SISSO generates models by iteratively combining physical primary features.The best models are selected by linear regression with cross-validation.The SISSO models successfully predict the SF driving force with errors below 0.2 eV.Based on the cost,accuracy,and classification performance of SISSO models,we propose a hierarchical materials screening workflow.Three potential SF candidates are found in the PAH101 set.
基金L.M.G.acknowledges funding from the European Union’s Horizon 2020 research and innovation program,under grant agreements No.951786(NOMAD CoE)and No.740233(TEC1p)Furthermore,the authors acknowledge the Max Planck Computing and Data facility(MPCDF)for computational resources and support,which enabled neural-network training on 1 GPU(Tesla Volta V10032GB)on the Talos machine learning clusterB.C.Y.acknowledges funding from the National Research Foundation(NRF)of Korea under Project Number 2021M3A7C2090586.
文摘Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision.Here,we present AI-STEM,an automatic,artificial-intelligence based method,for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy(STEM)images of polycrystalline materials.The method is based on a Bayesian convolutional neural network(BNN)that is trained only on simulated images.AI-STEM automatically and accurately identifies crystal structure,lattice orientation,and location of interface regions in synthetic and experimental images.The model is trained on cubic and hexagonal crystal structures,yielding classifications and uncertainty estimates,while no explicit information on structural patterns at the interfaces is included during training.This work combines principles from probabilistic modeling,deep learning,and information theory,enabling automatic analysis of experimental,atomic-resolution images.
基金the National Science Foundation Dynamics of Coupled Natural and Human Systems(CNH)Program(award BCS-1011801 entitled Does Community-Based Rangeland Ecosystem Management Increase Coupled Systems'Resilience to Climate Change in Mongolia?).
文摘In semi-arid regions,air temperatures have increased in the last decades more than in many other parts of the world.Mongolia has an arid/semi-arid climate and much of the population are herders whose livelihoods depend upon limited water resources that fluctuate with a variable climate.Herders were surveyed to identify their observations of changes in climate extremes for two soums of central Mongolia,Ikh-Tamir in the forest steppe north of the Khangai Mountains and Jinst in the desert steppe south of the mountains.The herders’indigenous knowledge of changes in climate extremes mostly aligned with the station-based analyses of change.Temperatures were warming with more warm days and nights at all stations.There were fewer cool days and nights observed at the mountain stations both in the summer and winter,yet more cool days and nights were observed in the winter at the desert steppe station.The number of summer days is increasing while the number of frost days is decreasing at all stations.The results of this study support further use of local knowledge and meteorological observations to provide more holistic analysis of climate change in different regions of the world.
