Additional file 3 of NKD2 is correlated with the occurrence, progression and prognosis of thyroid carcinoma

Additional file 3. The original results of western blot

Additional file 2 of NKD2 is correlated with the occurrence, progression and prognosis of thyroid carcinoma

Additional file 2: Table S2. GSEA result

Additional file 1 of NKD2 is correlated with the occurrence, progression and prognosis of thyroid carcinoma

Additional file 1: Table S1. Clinical features of thyroid cancer patients

Puma GPS movement points and attributes

This file includes GPS points collected for 16 pumas collected from 2011 through 2013 in the Santa Cruz Mountains of California. Refer to readme file for column heading descriptions

DataSheet1_A characterization method for equivalent elastic modulus of rock based on elastic strain energy.PDF

Energy is an internal variable during rock deformation and failure, and its dissipation and conversion law can reflect the rock’s internal damage and deterioration state. Analysis of rock deformation and failure process from the perspective of energy is helpful to deeply understand the mechanism of rock damage, fracture and instability failure, and has important theoretical and practical significance for the stability evaluation and support control of surrounding rock. In this study, through single cyclic loading and unloading (SCLU) experiments, cyclic triaxial loading and unloading (CTLU) experiments and conventional triaxial compression (CTC) experiments, the equivalent elastic modulus method based on elastic strain energy is proposed to analyze the energy conversion of rock. The results show that the error of the elastic strain energy calculated by the strain energy formula method is generally higher than 10% with the secant and tangent modulus of the loading and unloading curve as input parameters. Taking the equivalent elastic modulus proposed in this study as an input parameter, more accurate elastic strain energy can be obtained by the strain energy formula. During the rock failure process, the equivalent elastic modulus shows a three-stage characteristic of increase, steady and decrease. The equivalent elastic modulus can be estimated by the quadratic function between the equivalent elastic modulus and confining pressure and axial strain. Under the same deformation and deviatoric stress, the elastic strain energy stored in rock increases with increasing confining pressure. The local maximum energy dissipation rate corresponds to stress drop, and the peak energy dissipation rate appears near the peak strength. High energy dissipation mainly occurs in a short time after peak strength, and energy release and dissipation are more sudden and severe under high confining pressure.</p

Table1_A characterization method for equivalent elastic modulus of rock based on elastic strain energy.DOCX

Energy is an internal variable during rock deformation and failure, and its dissipation and conversion law can reflect the rock’s internal damage and deterioration state. Analysis of rock deformation and failure process from the perspective of energy is helpful to deeply understand the mechanism of rock damage, fracture and instability failure, and has important theoretical and practical significance for the stability evaluation and support control of surrounding rock. In this study, through single cyclic loading and unloading (SCLU) experiments, cyclic triaxial loading and unloading (CTLU) experiments and conventional triaxial compression (CTC) experiments, the equivalent elastic modulus method based on elastic strain energy is proposed to analyze the energy conversion of rock. The results show that the error of the elastic strain energy calculated by the strain energy formula method is generally higher than 10% with the secant and tangent modulus of the loading and unloading curve as input parameters. Taking the equivalent elastic modulus proposed in this study as an input parameter, more accurate elastic strain energy can be obtained by the strain energy formula. During the rock failure process, the equivalent elastic modulus shows a three-stage characteristic of increase, steady and decrease. The equivalent elastic modulus can be estimated by the quadratic function between the equivalent elastic modulus and confining pressure and axial strain. Under the same deformation and deviatoric stress, the elastic strain energy stored in rock increases with increasing confining pressure. The local maximum energy dissipation rate corresponds to stress drop, and the peak energy dissipation rate appears near the peak strength. High energy dissipation mainly occurs in a short time after peak strength, and energy release and dissipation are more sudden and severe under high confining pressure.</p

CitSci.org extensible metadata schema related to a simple field observation.

<p>An observer measures the turbidity of a stream using a Secchi disk. We capture the protocol used to measure turbidity (Secchi disk) in a metadata table (TBL_Metadata) and relate it to the observation (TBL_Observations). This extensible structure allows us to capture any type of metadata, and relate it to observations in addition to other entities, such as projects, individuals, media, etc.</p

Predator Sample by OTU

Sequence counts in individual scat samples matching reference sequences of extant carnivores in the Santa Cruz Mountains. Scat samples were collected by citizen scientists on trails in seven Midpeninsula Open Space preserves and were all less than one week old

Prey Sample by OTU

Sequence counts in individual scat samples matching reference sequences of extant vertebrates (potential prey species of mesocarnivores) in the Santa Cruz Mountains. Scat samples were collected by citizen scientists on trails in seven Midpeninsula Open Space preserves and were all less than one week old

DataSheet_1_Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers.docx

Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.</p