Sleep Thresholds in Depression

1962 ◽  
Vol 108 (455) ◽  
pp. 466-473 ◽  
Author(s):  
Irene Martin ◽  
Brian M. Davies
Keyword(s):  
Limited Success ◽  
Series Of Experiments ◽  
Biochemical Features

There are conflicting psychiatric opinions about the value of separating depressive illnesses into several syndromes on clinical grounds, and so far experimental attempts to reach a classification of depression by distinguishing physiological or biochemical features which characterize one group rather than another have had only limited success. One interesting series of experiments in this area has been that of Shagass and his colleagues on the sedation threshold (Shagass et al., 1956, Shagass and Naiman, 1956, Shagass, 1954); they report that neurotic and psychotic depressions can be differentiated by means of EEG and other reactions to sodium amytal (amylobarbitone sodium) neurotics requiring greater amounts of the drug to reach the sedation threshold.

COMPARATIVE EVALUATION OF NEW OSTEOPLASTIC MATERIALS BASED ON THE RESULTS OF ACUTE TOXICITY STUDIES

2021 ◽  
Vol Special issue (2) ◽  
pp. 29-33
Author(s):  
А.N. Akbarov ◽  
◽  
N.S. Ziyadullaeva
Keyword(s):  
Bioactive Glass ◽  
Intraperitoneal Injection ◽  
Comparative Evaluation ◽  
Laboratory Animals ◽  
Toxic Substances ◽  
Series Of Experiments ◽  
Osteoplastic Material ◽  
Rats And Mice ◽  
Lethal Doses

Three series of experiments were carried out and lethal doses of the new osteoplastic material 47.5 V were determined by the intraperitoneal and intragastric injection of the material to laboratory animals. A comparative evaluation with Bioactive glass BG-1D was also carried out. It was found that the LD50of 47,5B was 4274.51:4770.58 mg/kg for intragastric injection and 2358.31:2895.65 mg/kg for intraperitoneal injection to rats. In animals getting Bioactive glass BG-1D, these indicators changed slightly, amounting to 3439.04:3810.53 mg/kg and 1732.77:2730.93 mg/kg, respectively. Thus, according to the classification of substances according to the degree of toxicity, these materials can be attributed to practically non-toxic substances (according to the results of intraperitoneal injection of the material suspension to rats and mice) and low-toxic substances (according to the results of intragastric injection of the material suspension to rats)

scholarly journals SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

2019 ◽  
Vol 9 (9) ◽  
pp. 1779 ◽  
Author(s):  
Yaguang Zhu ◽  
Chaoyu Jia ◽  
Chao Ma ◽  
Qiong Liu
Keyword(s):  
Legged Robot ◽  
Terrain Classification ◽  
Walking Robot ◽  
Adaptive Locomotion ◽  
Speeded Up Robust Features ◽  
Pixel Image ◽  
Series Of Experiments ◽  
Multilegged Walking Robot ◽  
Local Image

In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion.

scholarly journals Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

2020 ◽  
Vol 12 (3) ◽  
pp. 582 ◽  
Author(s):  
Rui Li ◽  
Shunyi Zheng ◽  
Chenxi Duan ◽  
Yang Yang ◽  
Xiqi Wang
Keyword(s):  
Deep Learning ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Feature Maps ◽  
Spatial Features ◽  
Training Samples ◽  
Series Of Experiments

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

Dissociating Face Processing Skills: Decisions about Lip read Speech, Expression, and Identity

1996 ◽  
Vol 49 (2) ◽  
pp. 295-314 ◽  
Author(s):  
Ruth Campbell ◽  
Barbara Brooks ◽  
Edward de Haan ◽  
Tony Roberts
Keyword(s):  
Face Processing ◽  
Face Identification ◽  
Speech Sounds ◽  
Manual Responses ◽  
Lip Reading ◽  
Identity Matching ◽  
Unfamiliar Face ◽  
Identity Based ◽  
Series Of Experiments

The separability of different subcomponents of face processing has been regularly affirmed, but not always so clearly demonstrated. In particular, the ability to extract speech from faces (lip-reading) has been shown to dissociate doubly from face identification in neurological but not in other populations. In this series of experiments with undergraduates, the classification of speech sounds (lip-reading) from personally familiar and unfamiliar face photographs was explored using speeded manual responses. The independence of lip-reading from identity-based processing was confirmed. Furthermore, the established pattern of independence of expression-matching from, and dependence of identity-matching on, face familiarity was extended to personally familiar faces and “difficult”-emotion decisions. The implications of these findings are discussed.

scholarly journals Bone Degreasing – Finding a New Solution to an Old Problem

2018 ◽  
Vol 2 ◽  
pp. e26392
Author(s):  
David Stemmer ◽  
Odi Kehagias
Keyword(s):  
State Government ◽  
Glycol Ether ◽  
High Dilution ◽  
Glycol Ethers ◽  
Solubility Range ◽  
Limited Success ◽  
Replacement Process ◽  
History Of ◽  
Series Of Experiments ◽  
Ether Solvents

The South Australian Museum boasts the largest and most comprehensive cetacean collection in Australia, including various large cetacean skeletons. The preparation of these skeletons was done at various locations throughout the history of the Museum until the state government funded a purpose-built preparation facility which opened in 1983. The well-equipped centre was fitted with a large (2800 L) custom-built liquid-vapour degreaser that used trichloroethylene (TCE) as solvent. Many beautifully degreased skeletons, including a 22 m pygmy blue whale, were prepared during its 15-year operation. An accidental spill of TCE in 1999 led to the decommissioning of the unit. The decision to abandon the use of the toxic and dangerous TCE has led to a series of experiments to find a benign replacement process that will work either with the existing degreaser or heated maceration vats. Numerous chemicals and treatment methods have been trialled with limited success. However, one particular group of chemicals, glycol ether surfactant compounds, has shown promise and has been the main focus for our ongoing studies. Glycol ethers are broad-spectrum active solvents characterised by high dilution ratios, low evaporation rates and wide solubility range. Their unique solubility characteristics also allow them to be used as a coupling solvent in more complex situations containing both hydrophilic and hydrophobic components, and because of their compatibility with non-ionic surfactants, blended formulations with glycol ether solvents may provide a new solution to an old problem.

scholarly journals Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5283 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Muhammad Zulkifal Aziz
Keyword(s):  
Neural Network ◽  
Mental Imagery ◽  
Practical Applications ◽  
The Public ◽  
Series Of Experiments ◽  
Neural Network Classifiers ◽  
Public Datasets ◽  
Dependent Tasks ◽  
First Time

The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.

Morphometric variability in larvae of the Antarctic fly, Belgica antarctica (Diptera: Chironomidae)

1979 ◽  
Vol 57 (12) ◽  
pp. 2311-2318 ◽  
Author(s):  
William R. Atchley ◽  
Larry R. Hilburn
Keyword(s):  
Sexual Dimorphism ◽  
Head Capsule ◽  
Free Living ◽  
Holometabolous Insect ◽  
Limited Success ◽  
Belgica Antarctica ◽  
Ecological Parameters ◽  
Western Side ◽  
The Antarctic

Morphometric variability is examined in the larval head capsule of the midge Belgica antarctica Jacobs (Diptera: Chironomidae). This species is the southernmost free-living holometabolous insect and occurs over about a 650-km range on the western side of the Antarctic Peninsula. Highly significant differences were found between samples and between sexes for nine larval head capsule characters. No morphometric effect was found due to chromosomal inversion heterozygosity. The expression of sexual dimorphism was found to vary greatly between samples. Some samples exhibited no sexual dimorphism whereas others exhibited highly complex patterns of sexual dimorphism. Attempts to relate morphometric variability to geographic and ecological parameters met with only limited success. No correspondence was noted between the classification of samples based on inversion heterozygosity and that based on larval morphology.

scholarly journals Novel Techniques for Classification of Lung Nodules using Deep Learning Approach

2019 ◽  
Vol 13 (1) ◽  
pp. 120-126
Author(s):  
K. Bhavanishankar ◽  
M. V. Sudhamani
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Ground Truth ◽  
Lung Nodules ◽  
Learning Approach ◽  
Data Sets ◽  
Ground Truth Data ◽  
Series Of Experiments ◽  
Sensitivity Specificity

Objective: Lung cancer is proving to be one of the deadliest diseases that is haunting mankind in recent years. Timely detection of the lung nodules would surely enhance the survival rate. This paper focusses on the classification of candidate lung nodules into nodules/non-nodules in a CT scan of the patient. A deep learning approach –autoencoder is used for the classification. Investigation/Methodology: Candidate lung nodule patches obtained as the results of the lung segmentation are considered as input to the autoencoder model. The ground truth data from the LIDC repository is prepared and is submitted to the autoencoder training module. After a series of experiments, it is decided to use 4-stacked autoencoder. The model is trained for over 600 LIDC cases and the trained module is tested for remaining data sets. Results: The results of the classification are evaluated with respect to performance measures such as sensitivity, specificity, and accuracy. The results obtained are also compared with other related works and the proposed approach was found to be better by 6.2% with respect to accuracy. Conclusion: In this paper, a deep learning approach –autoencoder has been used for the classification of candidate lung nodules into nodules/non-nodules. The performance of the proposed approach was evaluated with respect to sensitivity, specificity, and accuracy and the obtained values are 82.6%, 91.3%, and 87.0%, respectively. This result is then compared with existing related works and an improvement of 6.2% with respect to accuracy has been observed.

scholarly journals Influence of Membership Functions on Classification of Multi-Dimensional Data

2011 ◽  
Vol 45 (1) ◽  
pp. 78-84
Author(s):  
Madara Gasparovica ◽  
Irena Tuleiko ◽  
Ludmila Aleksejeva
Keyword(s):  
Previous Experiment ◽  
Real Data ◽  
Data Sets ◽  
Membership Functions ◽  
Classification Result ◽  
Series Of Experiments ◽  
Filter Solution ◽  
Function Construction ◽  
Study Center

Influence of Membership Functions on Classification of Multi-Dimensional Data The aim of this study is to explore whether the number of intervals for each attribute influences the classification result and whether a larger number of intervals provide better classification accuracy using the Fuzzy PRISM algorithm. The feature selection has been carried out using Fast correlation-based filter solution, and then the decreased data sets have been applied in experiments with preferences used in the previous experiment series. The article also provides conclusions about the obtained classification results and analyzes criteria of certain experiments and their impact on the final result. Also a series of experiments was carried out to assess how and whether the classification result is influenced by categorization of continuous data, which is one of the membership function construction steps; Fuzzy unordered rule induction algorithm was used. The experiments have been carried out using four real data sets - Golub leukemia, Singh prostate, as well as Gastric cancer and leukemia donor data sets of the Latvian Biomedical Research and Study Center.

scholarly journals Derivative-free optimization adversarial attacks for graph convolutional networks

2021 ◽  
Vol 7 ◽  
pp. e693
Author(s):  
Runze Yang ◽  
Teng Long
Keyword(s):  
Search Space ◽  
Classification Performance ◽  
Convolutional Networks ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Adversarial Examples ◽  
Series Of Experiments ◽  
Adversarial Attack ◽  
Direct Attack

In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks. An attacker can maliciously modify edges or nodes of the graph to mislead the model’s classification of the target nodes, or even cause a degradation of the model’s overall classification performance. In this paper, we first propose a black-box adversarial attack framework based on derivative-free optimization (DFO) to generate graph adversarial examples without using gradient and apply advanced DFO algorithms conveniently. Second, we implement a direct attack algorithm (DFDA) using the Nevergrad library based on the framework. Additionally, we overcome the problem of large search space by redesigning the perturbation vector using constraint size. Finally, we conducted a series of experiments on different datasets and parameters. The results show that DFDA outperforms Nettack in most cases, and it can achieve an average attack success rate of more than 95% on the Cora dataset when perturbing at most eight edges. This demonstrates that our framework can fully exploit the potential of DFO methods in node classification adversarial attacks.

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