scholarly journals The burrowing origin of modern snakes

2015 ◽  
Vol 1 (10) ◽  
pp. e1500743 ◽  
Author(s):  
Hongyu Yi ◽  
Mark A. Norell
Keyword(s):  
Inner Ear ◽  
Predictive Models ◽  
Comparative Data ◽  
Data Set ◽  
X Ray ◽  
Virtual Models ◽  
Habitat Specialists ◽  
Substrate Vibrations ◽  
Hypothetical Ancestor ◽  
Habitat Generalists

Modern snakes probably originated as habitat specialists, but it controversial unclear whether they were ancestrally terrestrial burrowers or marine swimmers. We used x-ray virtual models of the inner ear to predict the habit ofDinilysia patagonica, a stem snake closely related to the origin of modern snakes. Previous work has shown that modern snakes perceive substrate vibrations via their inner ear. Our data show thatD. patagonicaand modern burrowing squamates share a unique spherical vestibule in the inner ear, as compared with swimmers and habitat generalists. We built predictive models for snake habit based on their vestibular shape, which estimatedD. patagonicaand the hypothetical ancestor of crown snakes as burrowers with high probabilities. This study provides an extensive comparative data set to test fossoriality quantitatively in stem snakes, and it shows that burrowing was predominant in the lineages leading to modern crown snakes.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

A synchrotron X-ray study of the electron density in C-type rare earth oxides

1996 ◽  
Vol 52 (3) ◽  
pp. 414-422 ◽  
Author(s):  
E. N. Maslen ◽  
V. A. Streltsov ◽  
N. Ishizawa
Keyword(s):  
Heavy Metal ◽  
Rare Earth ◽  
Electron Density ◽  
Approximate Symmetry ◽  
Data Set ◽  
X Ray ◽  
Metal Atoms ◽  
Structure Factors ◽  
Deformation Electron Density ◽  
Synthetic Crystals

Structure factors for small synthetic crystals of the C-type rare earth (RE) sesquioxides Y2O3, Dy2O3 and Ho2O3 were measured with focused λ = 0.7000 (2) Å, synchrotron X-radiation, and for Ho2O3 were re-measured with an MoKα (λ = 0.71073 Å) source. Approximate symmetry in the deformation electron density (Δρ) around a RE atom with pseudo-octahedral O coordination matches the cation geometry. Interactions between heavy metal atoms have a pronounced effect on the Δρ map. The electron-density symmetry around a second RE atom is also perturbed significantly by cation–anion interactions. The compounds magnetic properties reflect this complexity. Space group Ia{\bar 3}, cubic, Z = 16, T = 293 K: Y2O3, Mr = 225.82, a = 10.5981 (7) Å, V = 1190.4 (2) Å3, Dx = 5.040 Mg m−3, μ 0.7 = 37.01 mm−1, F(000) = 1632, R = 0.067, wR = 0.067, S = 9.0 (2) for 1098 unique reflections; Dy2O3, Mr = 373.00, a = 10.6706 (7) Å, V = 1215.0 (2) Å3, Dx = 8.156 Mg m−3, μ 0.7 = 44.84 mm−1, F(000) = 2496, R = 0.056, wR = 0.051, S = 7.5 (2) for 1113 unique reflections; Ho2O3, Mr = 377.86, a = 10.606 (2) Å, V = 1193.0 (7) Å3, Dx = 8.415 Mg m−3, μ 0.7 = 48.51 mm−1 F(000) = 2528, R = 0.072, wR = 0.045, S = 9.2 (2) for 1098 unique reflections of the synchrotron data set.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

Design of a novel Peltier-based cooling device and its use in neutron diffraction data collection of perdeuterated yeast pyrophosphatase

2010 ◽  
Vol 43 (5) ◽  
pp. 1113-1120 ◽  
Author(s):  
Esko Oksanen ◽  
François Dauvergne ◽  
Adrian Goldman ◽  
Monika Budayova-Spano
Keyword(s):  
Data Collection ◽  
Neutron Diffraction ◽  
Diffraction Data ◽  
Catalytic Mechanism ◽  
Inorganic Pyrophosphatase ◽  
Neutron Diffraction Data ◽  
Cooling Device ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography

H atoms play a central role in enzymatic mechanisms, but H-atom positions cannot generally be determined by X-ray crystallography. Neutron crystallography, on the other hand, can be used to determine H-atom positions but it is experimentally very challenging. Yeast inorganic pyrophosphatase (PPase) is an essential enzyme that has been studied extensively by X-ray crystallography, yet the details of the catalytic mechanism remain incompletely understood. The temperature instability of PPase crystals has in the past prevented the collection of a neutron diffraction data set. This paper reports how the crystal growth has been optimized in temperature-controlled conditions. To stabilize the crystals during neutron data collection a Peltier cooling device that minimizes the temperature gradient along the capillary has been developed. This device allowed the collection of a full neutron diffraction data set.

X-ray microanalysis of inner ear fluids in the embryonic and newborn guinea pig

1982 ◽  
Vol 234 (2) ◽  
pp. 125-130 ◽  
Author(s):  
Matti Anniko ◽  
Aron Sobin ◽  
Romuald Wr�blewski
Keyword(s):  
Guinea Pig ◽  
Inner Ear ◽  
X Ray ◽  
Inner Ear Fluids

scholarly journals Purification, crystallization and preliminary X-ray diffraction analysis of the effector protein PevD1 fromVerticillium dahliae

2012 ◽  
Vol 68 (7) ◽  
pp. 802-805 ◽  
Author(s):  
Lei Han ◽  
Zheng Liu ◽  
Xinqi Liu ◽  
Dewen Qiu
Keyword(s):  
Pathogenic Fungus ◽  
Sodium Formate ◽  
Diffusion Method ◽  
Effector Protein ◽  
X Ray Diffraction ◽  
Hanging Drop ◽  
Dispersion Method ◽  
Data Set ◽  
X Ray ◽  
Anomalous Signal

The effector protein PevD1 from the pathogenic fungusVerticillium dahliaewas purified and crystallized using the hanging-drop vapour-diffusion method. Native crystals appeared in a solution consisting of 4.0 Msodium formate. A native data set was collected at 1.9 Å resolution at 100 K using an in-house X-ray source. Because of the absence of useful methinione in the protein sequence, derivative crystals that contained iodine were obtained by soaking in 1.25 Mpotassium iodide, and a data set that contained anomalous signal was collected using the same X-ray facility at a wavelength of 1.54 Å. The single-wavelength anomalous dispersion method was used to successfully solve the structure based on the anomalous signal generated from iodine.

A batch-wise non-linear fitting and analysis tool for treating large X-ray diffraction data sets

2006 ◽  
Vol 39 (2) ◽  
pp. 262-266 ◽  
Author(s):  
R. J. Davies
Keyword(s):  
Diffraction Data ◽  
Operation Mode ◽  
Large Data ◽  
Scattering Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Data Set ◽  
X Ray ◽  
Linear Fitting ◽  
Non Linear

Synchrotron sources offer high-brilliance X-ray beams which are ideal for spatially and time-resolved studies. Large amounts of wide- and small-angle X-ray scattering data can now be generated rapidly, for example, during routine scanning experiments. Consequently, the analysis of the large data sets produced has become a complex and pressing issue. Even relatively simple analyses become difficult when a single data set can contain many thousands of individual diffraction patterns. This article reports on a new software application for the automated analysis of scattering intensity profiles. It is capable of batch-processing thousands of individual data files without user intervention. Diffraction data can be fitted using a combination of background functions and non-linear peak functions. To compliment the batch-wise operation mode, the software includes several specialist algorithms to ensure that the results obtained are reliable. These include peak-tracking, artefact removal, function elimination and spread-estimate fitting. Furthermore, as well as non-linear fitting, the software can calculate integrated intensities and selected orientation parameters.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

scholarly journals Expression, purification, crystallization and preliminary X-ray analysis of tannase fromLactobacillus plantarum

2013 ◽  
Vol 69 (4) ◽  
pp. 456-459 ◽  
Author(s):  
Mingbo Wu ◽  
Xiaohong Peng ◽  
Hua Wen ◽  
Qin Wang ◽  
Qianming Chen ◽  
...  
Keyword(s):  
Synchrotron Radiation ◽  
Gallic Acid ◽  
Diffusion Method ◽  
Ester Bond ◽  
Asymmetric Unit ◽  
Data Set ◽  
X Ray ◽  
Vapour Diffusion ◽  
Serine Esterases ◽  
Hydrolysis Of

Tannase catalyses the hydrolysis of the galloyl ester bond of tannins to release gallic acid. It belongs to the serine esterases and has wide applications in the food, feed, beverage, pharmaceutical and chemical industries. The tannase fromLactobacillus plantarumwas cloned, expressed and purified. The protein was crystallized by the sitting-drop vapour-diffusion method with microseeding. The crystals belonged to space groupP1, with unit-cell paramtersa= 46.5,b= 62.8,c= 83.8 Å, α = 70.4, β = 86.0, γ = 79.4°. Although the enzyme exists mainly as a monomer in solution, it forms a dimer in the asymmetric unit of the crystal. The crystals diffracted to beyond 1.60 Å resolution using synchrotron radiation and a complete data set was collected to 1.65 Å resolution.

scholarly journals Crystallization and preliminary X-ray diffraction analysis of UspE fromEscherichia coli

2014 ◽  
Vol 70 (12) ◽  
pp. 1640-1642 ◽  
Author(s):  
Yongbin Xu ◽  
Chun-Shan Quan ◽  
Xuanzhen Jin ◽  
Xiaoling Jin ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Stress Proteins ◽  
Gel Filtration ◽  
X Ray Diffraction ◽  
Unit Cell Parameters ◽  
Data Set ◽  
X Ray ◽  
Cell Parameters ◽  
E Coli ◽  
Vapour Diffusion ◽  
Induced Genes

Universal stress proteins (Usps) are among the most highly induced genes when bacteria are subjected to several stress conditions such as heat shock, nutrient starvation or the presence of oxidants or other stress agents.Escherichia colihas five small Usps and one tandem-type Usp. UspE (or YdaA) is the tandem-type Usp and consists of two Usp domains arranged in tandem. To date, the structure of UspE remains to be elucidated. To contribute to the molecular understanding of the function of the tandem-type UspE, UspE fromE. coliwas overexpressed and the recombinant protein was purified using Ni–NTA affinity, Q anion-exchange and gel-filtration chromatography. Crystals of UspE were obtained by sitting-drop vapour diffusion. A diffraction data set was collected to a resolution of 3.2 Å from flash-cooled crystals. The crystals belonged to the tetragonal space groupI4122 orI4322, with unit-cell parametersa=b= 121.1,c = 241.7 Å.

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