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SummaryAmyotrophic lateral sclerosis (ALS) presents motoneuron (MN)-selective protein inclusions and axonal degeneration but the underlying mechanisms of such are unknown. Using induced pluripotent cells (iPSCs) from patients with mutation in the Cu/Zn superoxide dismutase (SOD1) gene, we show that spinal MNs, but rarely non-MNs, exhibited neurofilament (NF) aggregation followed by neurite degeneration when glia were not present. These changes were associated with decreased stability of NF-L mRNA and binding of its 3′ UTR by mutant SOD1 and thus altered protein proportion of NF subunits. Such MN-selective changes were mimicked by expression of a single copy of the mutant SOD1 in human embryonic stem cells and were prevented by genetic correction of the SOD1 mutation in patient’s iPSCs. Importantly, conditional expression of NF-L in the SOD1 iPSC-derived MNs corrected the NF subunit proportion, mitigating NF aggregation and neurite degeneration. Thus, NF misregulation underlies mutant SOD1-mediated NF aggregation and axonal degeneration in ALS MNs.

Dysfunction of basal forebrain cholinergic neurons (BFCNs) and γ-aminobutyric acid (GABA) interneurons, derived from medial ganglionic eminence (MGE), is implicated in disorders of learning and memory. Here we present a method for differentiating human embryonic stem cells (hESCs) to a nearly uniform population of NKX2.1(+) MGE-like progenitor cells. After transplantation into the hippocampus of mice in which BFCNs and some GABA neurons in the medial septum had been destroyed by mu P75-saporin, human MGE-like progenitors, but not ventral spinal progenitors, produced BFCNs that synaptically connected with endogenous neurons, whereas both progenitors generated similar populations of GABA neurons. Mice transplanted with MGE-like but not spinal progenitors showed improvements in learning and memory deficits. These results suggest that progeny of the MGE-like progenitors, particularly BFCNs, contributed to learning and memory. Our findings support the prospect of using human stem cell-derived MGE-like progenitors in developing therapies for neurological disorders of learning and memory.

The transcriptional regulation of neuroectoderm (NE) specification is unknown. Here we show that Pax6 is uniformly expressed in early NE cells of human fetuses and those differentiated from human embryonic stem cells (hESCs). This is in contrast to the later expression of Pax6 in restricted mouse brain regions. Knockdown of Pax6 blocks NE specification from hESCs. Overexpression of either Pax6a or Pax6b, but not Pax6triangle upPD, triggers hESC differentiation. However, only Pax6a converts hESCs to NE. In contrast, neither loss nor gain of function of Pax6 affects mouse NE specification. Both Pax6a and Pax6b bind to pluripotent gene promoters but only Pax6a binds to NE genes during human NE specification. These findings indicate that Pax6 is a transcriptional determinant of the human NE and suggest that Pax6a and Pax6b coordinate with each other in determining the transition from pluripotency to the NE fate in human by differentially targeting pluripotent and NE genes.

Pattern recognition applied to blood samples for diagnosing leukemia remains an extremely difficult task which frequently leads to misclassification errors due in large part to the inherent problem of data overlap. A novel artificial neural network (ANN) algorithm is proposed for optimizing the classification of multidimensional data, focusing on acute leukemia samples. The programming tool established around the ANN architecture focuses on the classification of normal vs. abnormal blood samples, namely acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). There were 220 blood samples considered with 60 abnormal samples and 160 normal samples. The algorithm produced very high sensitivity results that improved up to 96.67% in ALL classification with increased data set size. With this type of accuracy, this programming tool provides information to medical doctors in the form of diagnostic references for the specific disease states that are considered for this study. The results obtained prove that a neural network classifier can perform remarkably well for this type of flow-cytometry data. Even more significant is the fact that experimental evaluations in the testing phase reveal that as the ALL data considered is gradually increased from small to large data sets, the more accurate are the classification results.

This study is a comparative evaluation of nonlinear classification methods with a focus on nonlinear decision functions and the standard method of support vector machines for seizure detection. These nonlinear classification methods are used on key features that were extracted on subdural EEG data after a thorough evaluation of all the frequency bands from 1 to 44 Hz. The sensitivity, specificity, and accuracy of seizure detection reveal that the gamma frequencies (36-44 Hz) are most suitable for detecting seizure files using a unique 2D decisional plane. We evaluated 157 intracranial EEG files from 14 patients by calculating the spectral power using nonoverlapping 1-s windows on different frequency bands. A key finding is in establishing a 2D decision plane, where duration of the seizure is used as the first dimension (x coordinate) and the maximum of the gamma frequency components is used as the second dimension (y coordinate). Within this 2D plane, the best results were observed when the nonlinearity degree is three for the proposed nonlinear decision functions, with a sensitivity of 96.3%, a specificity of 96.8%, and accuracy of 96.7%.

INTRODUCTION Computer interface research has known respectable growth in the last decade, and the deployed assistive technology tools have enabled persons with disabilities to harness the power of computers and access the variety of resources available to all [1-3]. Despite recent advances, challenges still remain for extending access to users with severe motor disabilities. A number of human-computer interfaces (HCIs) have integrated eye-gaze tracking (EGT) systems as one possible way for users to interact with the computer through eye movement [4-6]. Other studies have integrated different modalities, such as eye gazing, gesture recognition, and speech recognition, to allow the user more flexible interactions with computers [7-8]. Unfortunately, the use of EGT systems as the primary mechanism for controlling the mouse pointer and the graphical user interface has been complicated by inaccuracies arising from saccadic eye movement. Such natural involuntary movement of the eye results in sporadic, discontinuous motion of the pointer, or "jitter," a term used herein to generally refer to any undesired motion of the pointer resulting from a user's attempts to focus on a target, regardless of the specific medical or other reason or source of the involuntary motion. Some attempts to increase the accuracy of mouse cursor control through eye-gazing activity involve the integration of a complementary technology such as electromyogram [9-11]. However, these approaches require the users to wear devices such as electrodes, which may be uncomfortable. To make matters worse, the jitter effect generally varies in degree as a function of inherent user characteristics, which vary from one user to another. The jitter effect across multiple users may be so varied that a single control scheme to address each user's jitter effect would likely require significant and complex processing requirements that would impose unrealistic constraints on the demand for real-time processing. As a result, the system would then be unable to control the mouse pointer position in real time and would add cost to such processing power. But without real-time control and processing, users would experience noticeable delays between eye movement and positioning of the pointer, which would be frustrating. Some studies attempt to resolve the jitter dilemma based on Fitt's law, which defines the time needed to move the pointer to a target area MT as a function of the distance to A (amplitude of the movement) and size of the target W, as in Equation 1. MT = a + b x [log.sub.2] (A/W + 1), (1) where a corresponds to the start/stop time of the device and b is the inherent speed of the device. This type of study facilitates selection of a target by enlarging the target size. For instance, Spakov and Miniotas suggest the use of dynamic target expansion for menu item selection, which would require developing specialized applications [12]. Also, Bates and Istance use a full-screen zoom-in technique to increase eye-based interaction performance [13]. However, one downside to this approach is the loss of contextual information, since the peripheral region of the zoomed area is lost. Another approach uses a fisheye lens to expand the target and the area surrounding it [14]. The approach has two stages, one to activate the fisheye lens and another to lock and click on the target. Each stage lasts in accordance with predefined dwelling times. Since this technique is based on the user fixating a target, a conflict may arise when the user fixates on some item solely to obtain information and the program interprets this fixation as an input command. This problem is known as the Midas touch. Furthermore, other studies use a combination of eye gazing and standard computer input devices, such as the keyboard, for selecting a target [15]. The objective of our research endeavor is to develop an eye-gaze-based HCI system that accommodates and adapts to different users through artificial neural network (ANN) design customization and configuration. …

The objective of this study was to design an adaptive, real-time assistive system as an alternate HCI that will give computer access to individuals with severe motor disabilities by means of eye gazing only. It focused on the implementation of an algorithm to smooth out abrupt and unwanted jerky behavior of the mouse cursor due to the saccadic nature of the eye movement via the configuration of an artificial neural network that minimizes the jitter effect based on user characteristics. These characteristics were extracted via the creation of user profiles through an embedded graphical interface.

The use of human embryonic stem cells (hESCs) as a research and therapeutic tool will be facilitated by conditional gene expression. Here, we report drug-induced transgene expression, both in vitro and in vivo, from a tet-on hESC line with >95% purity. Using green fluorescent protein as an indicator, we demonstrated that the tet-on system allowed a tight control of the gene expression in both undifferentiated hESCs and differentiated cells of the three germ layers. More importantly, after the cells were transplanted into animals, the gene expression remained to be regulated by an orally administered drug. These results provide a technical basis for regulation of gene expression in hESCs and derivatives in vitro and in vivo. Disclosure of potential conflicts of interest is found at the end of this article.

The algorithm developed in this study integrates a frequency analysis of key frequency bands (alpha, beta, delta, and theta) with an inverse solution using the principal component analysis (PCA) to validate brain functional mappings associated with the characterization effects of an auditory/comprehension task. The results are found to be consistent with earlier findings involving the Wernicke and Broca's brain areas in relation to language comprehension. These cortical areas were discovered in 1874 and 1861 respectively, to be associated with the language comprehension and production. In support of these findings, spectral arrays and topographic maps are used for both listening and answering phases. The areas most responsible for the language comprehension were reverse-detected by means of an innovative search algorithm that iteratively relocates electrodes based on the direction of increasing PCA outcomes. The inverse PCA reveals that eigenvectors associated with the largest eigenvalues produce an inter...

Distributed computing is used to solve computational complexity problems. This paper explores the suitability of the .NET platform and XML Web services for distributed computing applications. This study demonstrates the practical feasibility of a .NET Web-services application in distributed computing and it also exposes APIs on the Internet. Thus from the experimental results, the speed of the algorithm introduced by Web services are determined and the cluster performance is achieved by scheduling algorithm, which properly selects the size of the work slice for each client to assign.

This study adapted the CURRY program and assessed the relationship of the 3-D spike sources to focal lesions evident on MRI scans. The subgroup is selected as it represents an initial step in determining the merit of this technique in the presurgical evaluation of children. Further studies comparing reconstructed spike sources with intracranial electrode recording are planned. CURRY is a software package that provides a variety of methods for accurately localizing the source of electrical activity in the brain. This is achieved by combining EEG and magnetoencephalogram (MEG) signals with structural imaging modalities.

This study introduces a new algorithm to optimize the pattern recognition of different white blood cell types in flow cytometry. The behavior of parametric data clusters in a multidimensional space is analyzed using the learning system known as Support Vector Machines (SVM). Beckman-Coulter Corporation supplied flow cytometry data of numerous patients to be used as training and testing sets for the algorithm. Subsequently, the characteristics of the cells provided in these sets were used to train a SVM based classifier. The objective in developing this algorithm was to identify the category of a given blood sample and provide information to medical doctors in the form of diagnostic references for a specific disease state, lymphocytic leukemia. With the application of the hypothesis space, the learning bias and the learning algorithm, the SVM classifier was successfully trained to evaluate misclassification ratios in flow cytometry data in an effort to recognize abnormal blood cell patterns and address the ubiquitous problem of data overlap through the use of the maximal margin classifier.

This study integrates a spectral analysis of key frequency bands (Alpha, Beta, Delta, and Theta) with an eigensystem-based study in order to validate brain functional mappings associated with the characterization effects of an Auditory/Comprehension paradigm. This numerical characterization supported by topographic functional maps brings added insight in the involvement of the Wernicke and Broca's brain areas to language comprehension. A thorough examination of EEG recordings through the eigensystem reveals that eigenvectors associated with the largest eigenvalues produce an interesting activity pattern located in the frontal area of the brain directly attributable to those characteristic behaviors found in the Alpha, Beta, Delta, and Theta frequency bands. An evaluation of spectral arrays is performed using topographic maps of the induced brain activities during both listening and answering phases. This evaluation is then augmented with quantifying measures using the eigensystem study while results are validated through integration of EEG activity and eigensystem modalities. Such a representation can provide insightful information on how different patients react during an auditory and response phases, and in the ability to detect the presence of potential neurological disorders by assessing similar/dissimilar behaviors with respects to all former patients already included in the database. The algorithm as developed in this study could be extended in its application to other brain functional mapping tasks given its simple but effective practical mathematical foundation.

The existing programming tools do not combine the attributes of easy to use and affordability. The existing procedures for spike detection consist mostly of sequences of tasks such as manual data preparation, followed by the use of multiple software packages (for example, from a commercial EEG recording program into MATLAB). The programming tool presented was developed to overcome these two major disadvantages. The system performs high-resolution external recordings of electroencephalographic (EEG) activity. The research goal was to propose an epileptiform activity (EFA) detection method that combines traditional task with energy criteria. The method uses EFA descriptors based on EEG recordings to produce formulas that can be applied for fast and automated EFA detection. An easy to use programming package was developed to demonstrate the proposed method.

Astrocytes are integral components of the homeostatic neural network as well as active participants in pathogenesis of and recovery from nearly all neurological conditions. Evolutionarily, compared with lower vertebrates and nonhuman primates, humans have an increased astrocyte-to-neuron ratio; however, a lack of effective models has hindered the study of the complex roles of human astrocytes in intact adult animals. Here, we demonstrated that after transplantation into the cervical spinal cords of adult mice with severe combined immunodeficiency (SCID), human pluripotent stem cell–derived (PSC-derived) neural progenitors migrate a long distance and differentiate to astrocytes that nearly replace their mouse counterparts over a 9-month period. The human PSC-derived astrocytes formed networks through their processes, encircled endogenous neurons, and extended end feet that wrapped around blood vessels without altering locomotion behaviors, suggesting structural, and potentially functional, integration into the adult mouse spinal cord. Furthermore, in SCID mice transplanted with neural progenitors derived from induced PSCs from patients with ALS, astrocytes were generated and distributed to a similar degree as that seen in mice transplanted with healthy progenitors; however, these mice exhibited motor deficit, highlighting functional integration of the human-derived astrocytes. Together, these results indicate that this chimeric animal model has potential for further investigating the roles of human astrocytes in disease pathogenesis and repair.

Serotonin neurons located in the raphe nucleus of the hindbrain have crucial roles in regulating brain functions and have been implicated in various psychiatric disorders. Yet functional human serotonin neurons are not available for in vitro studies. Through manipulation of the WNT pathway, we demonstrate efficient differentiation of human pluripotent stem cells (hPSCs) to cells resembling central serotonin neurons, primarily those located in the rhombomeric segments 2-3 of the rostral raphe, which participate in high-order brain functions. The serotonin neurons express a series of molecules essential for serotonergic development, including tryptophan hydroxylase 2, exhibit typical electrophysiological properties and release serotonin in an activity-dependent manner. When treated with the FDA-approved drugs tramadol and escitalopram oxalate, they release or uptake serotonin in a dose- and time-dependent manner, suggesting the utility of these cells for the evaluation of drug candidates.

Electric substations are facilities in charge of transform the voltage into safe and effective energy for the final consumers. This operation has to be carried out with enough quality assurance and without damaging the equipment. The associated cost to ensure this quality and security is high. Automatic mechanisms are used, however, they mostly operate under an individual's control and use the protection logic related with the equipment itself. They do not consider the state of the whole substation at a given moment. It is possible to control the state of circuit breakers (CB) in a substation using a knowledge base even when all the magnitudes to be controlled cannot be included in the analysis. It will be shown that it is possible to control the desired state while supervising some important magnitudes such as the voltage, power factor and harmonic distortion as well as the present state. At the same time, the programming tool developed for this purpose is discussed.

This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.

To study the neural networks reorganization in pediatric epilepsy, a consortium of imaging centers was established to collect functional imaging data. Common paradigms and similar acquisition parameters were used. We studied 122 children (64 control and 58 LRE patients) across five sites using EPI BOLD fMRI and an auditory description decision task. After normalization to the MNI atlas, activation maps generated by FSL were separated into three sub-groups using a distance method in the principal component analysis (PCA)-based decisional space. Three activation patterns were identified: (1) the typical distributed network expected for task in left inferior frontal gyrus (Broca's) and along left superior temporal gyrus (Wernicke's) (60 controls, 35 patients); (2) a variant left dominant pattern with greater activation in IFG, mesial left frontal lobe, and right cerebellum (three controls, 15 patients); and (3) activation in the right counterparts of the first pattern in Broca's area (one control, eight patients). Patients were over represented in Groups 2 and 3 (P < 0.0004). There were no scanner (P = 0.4) or site effects (P = 0.6). Our data-driven method for fMRI activation pattern separation is independent of a priori notions and bias inherent in region of interest and visual analyses. In addition to the anticipated atypical right dominant activation pattern, a sub-pattern was identified that involved intensity and extent differences of activation within the distributed left hemisphere language processing network. These findings suggest a different, perhaps less efficient, cognitive strategy for LRE group to perform the task. Hum Brain Mapp, 2011. © 2010 Wiley-Liss, Inc.

This study describes a new method for offline seizure detection using intracranial EEG (iEEG). The proposed method integrated two interrelated steps: (1) establishing a decisional space on the basis of the interelectrode mean of the spectral power in the gamma frequencies after a thorough evaluation of temporal and frequency-based features and (2) constructing an artificial neural network that operated on this decisional space to delineate EEG files that contained seizures from those that did not. The data were obtained from 14 patients who underwent two-stage epilepsy surgery with subdural recordings. Of the total 157 files considered, 35 (21 interictal and 14 ictal) iEEG data files or 22% were selected randomly and used initially in a training phase. The remaining 122 iEEG data files or 78% were then used in the testing phase to assess the merits in selecting gamma power as means to detect a seizure. The results obtained exhibited an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. Although this method had to contend with the complex nature of iEEG and the inherent heavy computational load, the constructed artificial neural networks together with the chosen decisional space yielded the best possible outcome. The proposed method was based on aggregating the power in the 36 to 44-Hz frequency range and analyzing its behavior in time, looking for patterns indicative of seizure evolution. It was shown that the power measurement in the gamma range contains the information needed to discriminate seizure files from nonseizure files. The algorithm consisted in establishing a decision space most suitable for iEEG data classification by relying on the power spectra in the gamma frequencies and constructing and implementing an artificial neural network that generates the highest classification accuracy possible. It was noted that although only 29% (35/122) of the files were used randomly for training the detector, high measures in sensitivity, specificity, and accuracy were still achieved in the remaining files, which were subsequently used in the testing phase. Seizures are known to occur intermittently and unpredictably, and massive amounts of EEG or iEEG data need to be analyzed offline to detect seizures. This is a challenge that can only be met through reliable and time-efficient seizure-detection paradigms, an affirmation this study attempted to prove.

Objective: This study proposes a new approach for offline seizure detection in intracranial (subdural) electroencephalogram recordings using nonlinear decision functions. It implements well-established features that are designed to deal with complex signals, such as brain recordings, and proposes a two-dimensional (2D) domain of analysis that overcomes the dilemma faced with the selection of empirical thresholds often used to delineate epileptic events. This unifying approach makes it possible for researchers in epilepsy to establish other performance evaluation criteria on the basis of the proposed nonlinear decision functions as well as introduce additional dimensions toward multidimensional analysis because the mathematics of these decision functions allows for any number of dimensions and any degree of complexity. Furthermore, because the features considered assume both time and frequency domains, the analysis is performed both temporally and as a function of different frequency ranges to ascertain those measures that are most suitable for seizure detection. In retrospect, by using nonlinear decision functions and by establishing a unified 2D domain of analysis, this study establishes a generalized approach to seizure detection that works across several features and across patients. Methods: Clinical experiments involved 14 patients with intractable seizures that were evaluated for potential surgical interventions. Of the total 157 files considered, 35 (21 interictal and 14 ictal) intracranial electroencephalogram data files or 22% were used initially in a training phase to ascertain the reliability of the formulated features that were implemented in the seizure detection process. The remaining 122 intracranial electroencephalogram data files or 78% were then used in the testing phase to assess the merits of each feature considered as means to detect a seizure. Results: The testing phase using the remaining 122 intracranial electroencephalogram data files revealed that the gamma power in the frequency domain is the feature that performed best across all patients with a sensitivity of 96.296%, an accuracy of 96.721%, and a specificity of 96.842%. The second best feature in the time domain was the mobility with a sensitivity of 81.481% an accuracy of 90.169%, and a specificity of 92.632%. In the frequency domain, all of the five other spectral bands lesser than 36 Hz revealed mixed results in terms of low sensitivity in some frequency bands and low accuracy in other frequency bands, which is expected given that the dominant frequencies during an ictal state are those higher than 30 Hz. In the time domain, other features, including complexity and correlation sum, revealed mixed success. Conclusions: All the features that are based on the time domain performed well, with mobility being the optimal feature for seizure detection. In the frequency domain, the gamma power outperformed the other frequency bands. Within this 2D plane, the best results were also observed when the degree of complexity is 3 or 4 in the implementation of the proposed nonlinear decision functions. Significance: A singular contribution of this study is in creating a common 2D space for analysis through the use of nonlinear decision functions for delineating data clusters of ictal files from data clusters of interictal files. This is critically important in establishing unifying measures that work across different features as expressed by the weight vector of the decision functions for a standardized assessment. The mathematical foundation is consequently established in support of a generalized seizure detection algorithm that works across patients, and in which all type of features that have been amply tested in the literature could be assessed within the realm of nonlinear decision functions.

To circumvent the silencing effect of transgene expression in human embryonic stem cells (hESCs), we employed the Cre recombination-mediated cassette exchange strategy to target the silencing-resistant site in the genome. We have identified new loci that sustain transgene expression during stem cell expansion and differentiation to cells representing the three germ layers in vitro and in vivo. The built-in double loxP cassette in the established master hESC lines was specifically replaced by a targeting vector containing the same loxP sites, using the cell-permeable Cre protein transduction method, resulting in successful generation of new hESC lines with constitutive functional gene expression, inducible transgene expression, and lineage-specific reporter gene expression. This strategy and the master cell lines allow for rapid production of transgenic hESC lines in ordinary laboratories. Disclosure of potential conflicts of interest is found at the end of this article.

Human embryonic stem cells (hESCs) offer a platform to bridge what we have learned from animal studies to human biology. Using oligodendrocyte differentiation as a model system, we show that sonic hedgehog (SHH)-dependent sequential activation of the transcription factors OLIG2, NKX2.2 and SOX10 is required for sequential specification of ventral spinal OLIG2-expressing progenitors, pre-oligodendrocyte precursor cells (pre-OPCs) and OPCs from hESC-derived neuroepithelia, indicating that a conserved transcriptional network underlies OPC specification in human as in other vertebrates. However,the transition from pre-OPCs to OPCs is protracted. FGF2, which promotes mouse OPC generation, inhibits the transition of pre-OPCs to OPCs by repressing SHH-dependent co-expression of OLIG2 and NKX2.2. Thus, despite the conservation of a similar transcriptional network across vertebrates, human stem/progenitor cells may respond differently to those of other vertebrates to certain extrinsic factors.

This study provides a performance evaluation of the correlation sum in terms of accuracy, sensitivity, and specificity in its ability to classify seizure files from non-seizure files. The main thrust of the study is whether computable properties (''metrics'') of EEG tracings over time allow a seizure to be detected. This study evaluates raw intracranial EEG (iEEG) recordings with the intent to detect a seizure and classify different EEG epoch files. One hundred twenty-six iEEG files from eleven sequential patients are processed and the correlation sum is extracted from non-overlapping scrolling windows of 1-s duration. The novelty of this research is in defining a generalized nonlinear approach to classify EEG seizure segments by introducing nonlinear decision functions with the flexibility in choosing any degree of complexity and with any number of dimensions, lending resiliency to data overlap and opportunity for multidimensional data analysis. A singular contribution of this work is in determining a 2-D decision plane, in this case, where duration is one dimension and window-based minima of the correlation sum is the second dimension. Also, experimental observations clearly indicate that a significant drop in the magnitude of the correlation sum signal actually coincides with the clinical seizure onset more so than the electrographic seizure onset as provided by the medical experts. The method with k-fold cross validation performed with an accuracy of 91.84%, sensitivity of 92.31%, and specificity of 91.67%, which makes this classification method most suitable for offline seizure detection applications.

We show that human embryonic stem cell-derived dopaminergic neurons survived transplantation to the neurotoxin 6-hydroxydopamine-lesioned rat striatum and, in combination with the cells newly differentiated from their progenitors, contributed to locomotive function recovery at 5 months. The animal behavioral improvement was correlated with the dopamine neurons present in the graft. Although the donor cells contained forebrain and midbrain dopamine neurons, the dopamine neurons present in the graft mainly exhibited a midbrain, or nigra, phenotype, suggesting the importance of midbrain dopamine neurons in functional repair. Furthermore, progenies of grafted cells were neurons and glia with greatly diminished mitotic activity by 5 months. Thus, the in vitro-produced human dopamine neurons can functionally engraft in the brain. Disclosure of potential conflicts of interest is found at the end of this article.

This study introduces an algorithm for a new application dedicated at discriminating between electrodes leading to a seizure onset and those that do not lead to seizure using interictal subdural EEG data. The significance of this study is in determining among all of these channels, all containing interictal spikes that are asynchronously, independent of region and time, which are selected randomly (these EEG portions may or may not contain spikes), and yet through the developed algorithm, we are able to classify those channels that lead to seizure and those that do not. The main zones of ictal activity are supposed to evolve from the tissue located at the channels that present interictal activity, but sometimes this is no the case. The purpose is to gain a better understanding of the dynamics of the human brain through a study of subdural EEG, with an emphasis on attempting to characterize the common behaviors of interictal EEG channels prior to an ictal activity. The study will try to correlate the clinical features with the EEG findings and to determine whether the patient has a consistent source of ictal activity, which is coming from the location of the group of channels that present interictal activity. If a method was found to detect the electrodes that present interictal activity, with the most potential to lead to an pileptic seizure, then the epilepsy focus could be located with a higher degree of certainty. This analysis allows for the detection of neurological disorders due to epileptic activity in the brain, and rings out how different patients react prior to a seizure.

Accurate classification of human blood cells plays a decisive role in the diagnosis and treatment of diseases. Artificial Neural Networks (ANN) have been consistently used as a trusted classification tool for this type of analysis. In this study, a new Artificial Neural Network approach is proposed for the multidimensional classification of two of the most common forms of leukemia: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML), also sometimes called Acute Myelogenous Leukemia. Beckman-Coulter Corporation supplied flow cytometry data of 120 patients that were used in the training and testing phases. The ANN algorithm was thus developed to exploit the different features of the different blood cells provided in an optimized fashion. The goal was to establish a programming tool, supported through this new ANN development, for the identification of normal and abnormal blood samples and provide information to medical doctors in the form of diagnostic references for the specific disease state that is considered for this study. The application of the ANN algorithm produced remarkable classification accuracy results that show a 95% classification accuracy for the normal blood samples and 90% classification accuracy for the abnormal samples even under the ubiquitous problem of overlap.

This study introduces an integrated algorithm based on the Walsh transform to detect interictal spikes and artifactual data in epileptic patients using recorded EEG data. The algorithm proposes a unique mathematical use of Walsh-transformed EEG signals to identify those criteria that best define the morphologic characteristics of interictal spikes. EEG recordings were accomplished using the 10-20 system interfaced with the Electrical Source Imaging System with 256 channels (ESI-256) for enhanced preprocessing and on-line monitoring and visualization. The merits of the algorithm are: (1) its computational simplicity; (2) its integrated design that identifies and localizes interictal spikes while automatically removing or discarding the presence of different artifacts such as electromyography, electrocardiography, and eye blinks; and (3) its potential implication to other types of EEG analysis, given the mathematical basis of this algorithm, which can be patterned or generalized to other brain dysfunctions. The mathematics that were applied here assumed a dual role, that of transforming EEG signals into mutually independent bases and in ascertaining quantitative measures for those morphologic characteristics deemed important in the identification process of interictal spikes. Clinical experiments involved 31 patients with focal epilepsy. EEG data collected from 10 of these patients were used initially in a training phase to ascertain the reliability of the observable and formulated features that were used in the spike detection process. Three EEG experts annotated spikes independently. On evaluation of the algorithm using the 21 remaining patients in the testing phase revealed a precision (positive predictive value) of 92% and a sensitivity of 82%. Based on the 20- to 30-minute epochs of continuous EEG recording per subject, the false detection rate is estimated at 1.8 per hour of continuous EEG. These are positive results that support further development of this algorithm for prolonged EEG recordings on ambulatory subjects and to serve as a support mechanism to the decisions made by EEG experts.

Accurate epileptic focus localization using single photon emission computed tomography (SPECT) images has proven to be a challenging endeavor. First, commonly used radiopharmaceuticals such as hexamethylpropylene amine oxime (HMPAO) quantitatively underestimate large blood flows, leading to subtracted SPECT images that do not reflect the true cerebral physiological conditions, and often display non-distinct epileptic foci. The proposed relative change subtraction method of SPECT image analysis helps alleviate this quantitative burden. Second, the image analysis process traditionally performed by physicians is time consuming and prone to error. Toward this end, an automated algorithm was designed to analyze SPECT images and provide feedback to users through a visual interface.

This study introduces a simplified approach for the implementation of artificial neural networks (ANN) for the recognition of epileptic data in electroencephalograph (EEG) recordings. The training set construction is based on a trend-adaptive polygon which simplifies the search process as it reduces the size of the training set. This data reduction, at a sampling rate of 200 Hz, yielded a reduction ratio of 34% as a minimum to an 81% in the best case scenario. With a higher sampling rate of 500 Hz, a reduction ratio of 73% as a minimum to an impressive 92% in the best case scenario was achieved. The outcome is thus a computationally attractive classifier with a simpler design implementation and with higher prospects for accurate diagnosis. The algorithm was trained and tested with EEG data from four epileptic patients using the k-fold cross-validation technique.

This study focuses on the design of orthogonal operators based on unique Electroencephalograph (EEG) signal decompositions in order to detect interictal spikes that characterize epileptic seizures in EEG data. The merits of the algorithm are: (a) in elaborating a unique analysis scheme that scrutinizes EEG data through orthogonal operators designed to extract features that best characterize spikes in epileptogenic EEG data; and (b) in establishing mathematical derivations that provide quantitative measures through the designed operators, and characterize and locate the event of an interictal spike. The uniqueness of this algorithm is in its good performance and simplicity of implementation. Clinical experiments involved 31 patients with focal epilepsy. EEG data collected from 10 of these patients were used initially in a training phase to ascertain the reliability of the observable and formulated features that were used in the spike detection process. Spikes were annotated independently by three EEG experts. On evaluation of the algorithm using the 21 remaining patients in the testing phase revealed a Precision (Positive Predictive Value) of 92% and a Sensitivity of 82%. Based on the 20 to 30-minute epochs of continuous EEG recording per subject, the false detection (FD) rate is estimated at 1.8 FD per hour of recorded EEG. These are good results that support further development of this algorithm for EEG diagnosis.

The objective of this study was to evaluate the feasibility of using the Walsh transformation to detect interictal spikes in electroencephalogram (EEG) data. Walsh operators were designed to formulate characteristics drawn from experimental observation, as provided by medical experts. The merits of the algorithm are: 1) in decorrelating the data to form an orthogonal basis and 2) simplicity of implementation. EEG recordings were obtained at a sampling frequency of 500 Hz using standard 10-20 electrode placements. Independent sets of EEG data recorded on 18 patients with focal epilepsy were used to train and test the algorithm. Twenty to thirty minutes of recordings were obtained with each subject awake, supine, and at rest. Spikes were annotated independently by two EEG experts. On evaluation, the algorithm identified 110 out of 139 spikes identified by either expert (True Positives = 79%) and missed 29 spikes (False Negatives = 21%). Evaluation of the algorithm revealed a Precision (Positive Predictive Value) of 85% and a Sensitivity of 79%. The encouraging preliminary results support its further development for prolonged EEG recordings in ambulatory subjects. With these results, the false detection (FD) rate is estimated at 7.2 FD per hour of continuous EEG recording.

Bone morphogenetic protein-1 (BMP-1)/Tolloid-like metalloproteinases play key roles in formation of mammalian extracellular matrix (ECM), through the biosynthetic conversion of precursor proteins into their mature functional forms. These proteinases probably play a further role in formation of bone through activation of transforming growth factor beta-like BMPs. Dentin matrix protein-1 (DMP1), deposited into the ECM during assembly and involved in initiating mineralization of bones and teeth, is thought to undergo proteolysis in vivo to generate functional cleavage fragments found in extracts of mineralized tissues. Here, we have generated recombinant DMP1 and demonstrate that it is cleaved, to varying extents, by all four mammalian BMP-1/Tolloid-like proteinases, to generate fragments similar in size to those previously isolated from bone. Consistent with possible roles for the BMP-1/Tolloid-like proteinases in the physiological processing of DMP1, NH2-terminal sequences of products generated by BMP-1 cleavage of DMP1 match those predicted from processing at the predicted DMP1 site that shows greatest cross-species conservation of sequences. Moreover, fibroblasts derived from mouse embryos homozygous null for genes encoding three of the four mammalian BMP-1/Tolloid-like proteinases appear to be deficient in processing of DMP1. Thus, a further role for BMP-1-Tolloid-like proteinases in formation of mineralized tissues is indicated, via proteolytic processing of DMP1.

Human embryonic stem cell-derived dopaminergic neurons reverse functional deficit in Parkinsonian rats