Abstract: In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.
Abstract: In this study we present the effect of elevated
temperatures from 300K to 400K on the electrical properties of
copper Phthalocyanine (CuPc) based organic field effect transistors
(OFET). Thin films of organic semiconductor CuPc (40nm) and
semitransparent Al (20nm) were deposited in sequence, by vacuum
evaporation on a glass substrate with previously deposited Ag source
and drain electrodes with a gap of 40 μm. Under resistive mode of
operation, where gate was suspended it was observed that drain
current of this organic field effect transistor (OFET) show an
increase with temperature. While in grounded gate condition metal
(aluminum) – semiconductor (Copper Phthalocyanine) Schottky
junction dominated the output characteristics and device showed
switching effect from low to high conduction states like Zener diode
at higher bias voltages. This threshold voltage for switching effect
has been found to be inversely proportional to temperature and shows
an abrupt decrease after knee temperature of 360K. Change in
dynamic resistance (Rd = dV/dI) with respect to temperature was
observed to be -1%/K.
Abstract: Integrins are a large family of multidomain α/β cell
signaling receptors. Some integrins contain an additional inserted I
domain, whose earliest expression appears to be with the chordates,
since they are observed in the urochordates Ciona intestinalis (vase
tunicate) and Halocynthia roretzi (sea pineapple), but not in integrins
of earlier diverging species. The domain-s presence is viewed as a
hallmark of integrins of higher metazoans, however in vertebrates,
there are clearly three structurally-different classes: integrins without
I domains, and two groups of integrins with I domains but separable
by the presence or absence of an additional αC helix. For example,
the αI domains in collagen-binding integrins from Osteichthyes
(bony fish) and all higher vertebrates contain the specific αC helix,
whereas the αI domains in non-collagen binding integrins from
vertebrates and the αI domains from earlier diverging urochordate
integrins, i.e. tunicates, do not. Unfortunately, within the early
chordates, there is an evolutionary gap due to extinctions between the
tunicates and cartilaginous fish. This, coupled with a knowledge gap
due to the lack of complete genomic data from surviving species,
means that the origin of collagen-binding αC-containing αI domains
remains unknown. Here, we analyzed two available genomes from
Callorhinchus milii (ghost shark/elephant shark; Chondrichthyes –
cartilaginous fish) and Petromyzon marinus (sea lamprey;
Agnathostomata), and several available Expression Sequence Tags
from two Chondrichthyes species: Raja erinacea (little skate) and
Squalus acanthias (dogfish shark); and Eptatretus burgeri (inshore
hagfish; Agnathostomata), which evolutionary reside between the
urochordates and osteichthyes. In P. marinus, we observed several
fragments coding for the αC-containing αI domain, allowing us to
shed more light on the evolution of the collagen-binding integrins.
Abstract: A technique proposed for the automatic detection
of spikes in electroencephalograms (EEG). A multi-resolution
approach and a non-linear energy operator are exploited. The
signal on each EEG channel is decomposed into three sub bands
using a non-decimated wavelet transform (WT). The WT is a
powerful tool for multi-resolution analysis of non-stationary signal
as well as for signal compression, recognition and restoration.
Each sub band is analyzed by using a non-linear energy operator,
in order to detect spikes. A decision rule detects the presence of
spikes in the EEG, relying upon the energy of the three sub-bands.
The effectiveness of the proposed technique was confirmed by
analyzing both test signals and EEG layouts.
Abstract: Analysis of heart rate variability (HRV) has become a
popular non-invasive tool for assessing the activities of autonomic
nervous system. Most of the methods were hired from techniques
used for time series analysis. Currently used methods are time
domain, frequency domain, geometrical and fractal methods. A new
technique, which searches for pattern repeatability in a time series, is
proposed for quantifying heart rate (HR) time series. These set of
indices, which are termed as pattern repeatability measure and
pattern repeatability ratio are able to distinguish HR data clearly
from noise and electroencephalogram (EEG). The results of analysis
using these measures give an insight into the fundamental difference
between the composition of HR time series with respect to EEG and
noise.
Abstract: Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.
Abstract: Determining depth of anesthesia is a challenging problem
in the context of biomedical signal processing. Various methods
have been suggested to determine a quantitative index as depth of
anesthesia, but most of these methods suffer from high sensitivity
during the surgery. A novel method based on energy scattering of
samples in the wavelet domain is suggested to represent the basic
content of electroencephalogram (EEG) signal. In this method, first
EEG signal is decomposed into different sub-bands, then samples
are squared and energy of samples sequence is constructed through
each scale and time, which is normalized and finally entropy of the
resulted sequences is suggested as a reliable index. Empirical Results
showed that applying the proposed method to the EEG signals can
classify the awake, moderate and deep anesthesia states similar to
BIS.
Abstract: Antifungal activities of ether and methanolic extracts of volatiles oils of Nigella Sativa seeds were tested against pathogenic bacterias and fungies strains.The volatile oil were found to have significant antifungal and antibacterial activities compare to tetracycline, cefuroxime and ciprofloxacin positive controls.The ether and methanolic esxtracts were compared to each other for antifungal and antibacterial activities and ether extracts showed stonger activity than methanolic one.
Abstract: This study focuses on emission of black carbon (BC)
from field open burning of corn residues. Real-time BC
concentration was measured by Micro Aethalometer from field
burning and simulated open burning in a chamber (SOC)
experiments. The average concentration of BC was 1.18±0.47 mg/m3
in the field and 0.89±0.63 mg/m3 in the SOC. The deduced emission
factor from field experiments was 0.50±0.20 gBC/kgdm, and 0.56±0.33
gBC/kgdm from SOC experiment, which are in good agreement with
other studies. In 2007, the total burned area of corn crop was 8,000
ha, resulting in an emission load of BC 20 ton corresponding to 44.5
million kg CO2 equivalent. Therefore, the control of open burning in
corn field represents a significant global warming reduction option.
Abstract: Pyritisation halos are identified in weathering crusts and unconsolidated formations at five locations within large fault structure of the Urals’ eastern slope. Electron microscopy reveals the presence of inclusions and growths on pyrite faces – normally on cubic pyrite with striations, or combinations of cubes and other forms. Following neogenesis types are established: native elements and intermetallic compounds (including gold and silver), halogenides, sulphides, sulfosalts, tellurides, sulphotellurides,
selenides, tungstates, sulphates, phosphates, carbon-based substances. Direct relationship is noted between amount and diversity of such mineral phases, and proximity to and scale of ore-grade mineralization. Gold and silver, both in native form and within tellurides, presence of lead (galena, native lead), native tungsten, and, possibly, molybdenite and sulfosalts can indicate gold-bearing formations. First find of native tungsten in the Urals is for the first time – in crystallised and druse-like form. Link is suggested between unusual mineralization and “reducing” hydrothermal fluids from deep-seated faults at later stages of Urals’ reactivation.
Abstract: Noise causes significant sensibility changes on a human. This study investigated the effect of five different noises on electroencephalogram (EEG) and subjective evaluation. Six human subjects were exposed to classic piano, ocean wave, alarm in army, ambulance, mosquito noise and EEG data were collected during the experimental session. Alpha band activity in the mosquito noise was smaller than that in the classic piano. Alpha band activity decreased 43.4 ± 8.2 % in the mosquito noise. On the other hand, Beta band activity in the mosquito noise was greater than that in the classic piano. Beta band activity increased 60.1 ± 10.7 % in the mosquito noise. The advances from this study may aid the product design process with human sensibility engineering. This result may provide useful information in designing a human-oriented product to avoid the stress.
Abstract: The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.
Abstract: In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
Abstract: A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
Abstract: The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.
Abstract: The direct sewage sludge application is a relative
cheap method for their liquidation. In the past heavy metal contents
increase in soils treated with sewage sludge was observed. In 2003
there was acceptance on act n.188/2003 about sewage sludge
application on soils. The basic philosophy of act is a safety of the
environmental proof of sludge application on soils. The samples of
soils from wastewater treatment plant (WTP) Poprad (35) and WTP
Michalovce (33 samples) were analyzed which were chosen for
sludge application on soils. According to the results only 14 areas for
Poprad and 25 areas for Michalovce are suitable for sludge
application according to act No. 188/2003. The application dose of
sludge was calculated 50 t.ha-1 or 75 t. ha-1 once in 5 years to ensure
that heavy metal contents in treated soils will be kept.
Abstract: Periphyton development and composition were
studied in three different treatments: (i) two fishpond units of
wetland-type wastewater treatment pond systems, (ii) two fishponds
in combined intensive-extensive fish farming systems and (iii) three
traditional polyculture fishponds. Results showed that amounts of
periphyton developed in traditional polyculture fishponds (iii) were
different compared to the other treatments (i and ii), where the main
function of ponds was stated wastewater treatment. Negative
correlation was also observable between water quality parameters
and periphyton production. The lower trophity, halobity and
saprobity level of ponds indicated higher amount of periphyton. The
dry matter content of periphyton was significantly higher in the
samples, which were developed in traditional polyculture fishponds
(2.84±3.02 g m-2 day-1, whereby the ash content in dry matter 74%),
than samples taken from (i) (1.60±2.32 g m-2 day-1, 61%) and (ii)
fishponds (0.65±0.45 g m-2 day-1, 81%).
Abstract: Trihalogenmethanes are the most significant byproducts of the reaction of disinfection agent with organic precursors naturally present in ground and surface waters.Their incidence negatively affects the quality of drinking water in relation to their nephrotoxic, hepatotoxic and genotoxic effects on human health. Taking into consideration the considerable volatility of monitored contaminants it could be assumed that their incidence in drinking water would depend on the distance of sampling from the area of disinfection. Based on the concentration of trihalogenmethanes determined with the help of gas chromatography with mass detector and the analysis of variance (ANOVA) such dependence has been proved as statistically significant. The acquired outcomes will be used for assessing the non-carcinogenic and genotoxic risks to consumers.
Abstract: In order to study of hydropriming and halopriming on
germination and early growth stage of wheat (Triticum aestivum) an
experiment was carried out in laboratory of the Department of
Agronomy and Plant breeding, Shahrood University of Technology.
Seed treatments consisted of T1: control (untreated seeds), T2:
soaking in distilled water for 18 h (hydropriming). T3: soaking in -
1.2 MPa solution of CaSO4 for 36 h (halopriming). Germination and
early seedling growth were studied using distilled water (control) and
under osmotic potentials of -0.4, -0.8 and -1.2 MPa for NaCl and
polyethylene glycol (PEG 6000), respectively. Results showed that
Hydroprimed seeds achieved maximum germination seedling dry
weight, especially during the higher osmotic potentials. Minimum
germination was recorded at untreated seeds (control) followed by
osmopriming. Under high osmotic potentials, hydroprimed seeds had
higher GI (germination index) as compared to haloprimed or
untreated seeds. Interaction effect of seed treatment and osmotic
potential significantly affected the seedling vigour index (SVI).
Abstract: The high world interest given to the researches concerning the study of moderately halophilic solvent-tolerant bacteria isolated from marine polluted environments is due to their high biotechnological potential, and also to the perspective of their application in different remediation technologies. Using enrichment procedures, I isolated two moderately halophilic Gram-negative bacterial strains from seawater sample, which are tolerant to organic solvents. Cell tolerance, adhesion and cells viability of Aeromonas salmonicida IBBCt2 and Pseudomonas aeruginosa IBBCt3 in the presence of organic solvents depends not only on its physicochemical properties and its concentration, but also on the specific response of the cells, and the cellular response is not the same for these bacterial strains. n-hexane, n-heptane, propylbenzene, with log POW between 3.69 and 4.39, were less toxic for Aeromonas salmonicida IBBCt2 and Pseudomonas aeruginosa IBBCt3, compared with toluene, styrene, xylene isomers and ethylbenzene, with log POW between 2.64 and 3.17. The results indicated that Aeromonas salmonicida IBBCt2 is more susceptible to organic solvents than Pseudomonas aeruginosa IBBCt3. The mechanisms underlying solvent tolerance (e.g., the existance of the efflux pumps) in Aeromonas salmonicida IBBCt2 and Pseudomonas aeruginosa IBBCt3 it was also studied.