Treatment algorithm for infants diagnosed with spinal muscular atrophy through newborn screening

Spinal muscular atrophy (SMA) is an autosomal recessive disease characterized by the degeneration of alpha motor neurons in the spinal cord, leading to muscular atrophy. SMA is caused by deletions or mutations in the survival motor neuron 1 gene (SMN1). In humans, a nearly identical copy gene, SMN2, is present. Because SMN2 has been shown to decrease disease severity in a dose-dependent manner, SMN2 copy number is predictive of disease severity. To develop a treatment algorithm for SMA-positive infants identified through newborn screening based upon SMN2 copy number. A working group comprised of 15 SMA experts participated in a modified Delphi process, moderated by a neutral third-party expert, to develop treatment guidelines. The overarching recommendation is that all infants with two or three copies of SMN2 should receive immediate treatment (n = 13). For those infants in which immediate treatment is not recommended, guidelines were developed that outline the timing and appropriate screens and tests to be used to determine the timing of treatment initiation. The identification SMA affected infants via newborn screening presents an unprecedented opportunity for achievement of maximal therapeutic benefit through the administration of treatment pre-symptomatically. The recommendations provided here are intended to help formulate treatment guidelines for infants who test positive during the newborn screening process

Artificial Neural Networks applied to improve low-cost air quality monitoring precision

It is a fact that air pollution is a major environmental health problem that affects everyone, especially in urban areas. Furthermore, the cost of high-end air pollution monitoring sensors is considerably high, so public administrations are unable to afford to place an elevated number of measuring stations, leading to the loss of information that could be very helpful. Over the last few years, a large number of low-cost sensors have been released, but its use is often problematic, due to their selectivity and precision problems. A calibration process is needed in order to solve an issue with many parameters with no clear relationship among them, which is a field of application of Machine Learning. The objectives of this project are first, integrating three low-cost air quality sensors into a Raspberry Pi and then, training an Artificial Neural Network model that improves precision in the readings made by the sensors.Es un hecho que la contaminación del aire es un gran problema para la salud a nivel mundial, especialmente en zonas urbanas. Además, el coste de los sensores de contaminación de gama alta es considerablemente alto, por lo que los organismos públicos no pueden permitirse emplazar un gran número de estaciones de medida, perdiendo información que podría ser muy útil. A lo largo de los últimos años, han surgido muchos sensores de contaminación de bajo coste, pero su uso suele ser complicado, ya que tienen problemas de selectividad y precisión. Los objetivos de este proyecto son primero integrar tres sensores de contaminación de bajo coste en una Raspberry Pi y sobre todo, entrenar un modelo basado en una red neuronal artificial que mejore la precisión de las lecturas realizadas por los sensores.Està demostrat que la contaminació de l'aire és un gran problema per a la salut a nivell mundial, especialment en zones urbanes. A més, el cost dels sensors de contaminació de gama alta és considerablement alt, motiu pel qual els organismes públics no es poden permetre emplaçar una gran quantitat d'estacions de mesura, perdent informació que podria resultar molt útil. Al llarg dels últims anys, han sorgit molts sensors de contaminació de baix cost, però el seu ús és sovint complicat, ja que tenen problemes de selectivitat i precisió. Els objectius d'aquest projecte són primer de tot integrar tres sensors de contaminació de baix cost en una Raspberry Pi i sobretot, entrenar un model basat en una xarxa neuronal artificial que millori la precisió de les lectures realitzades pels sensors

How visual confidence on global motion is affected by local motion ambiguity and type of motion noise, and its correlation with autistic trait tendency?

Perceptual confidence has been found to correlate with task performance in general, and is believed to be independent of stimulus features. However, certain stimulus feature could induce a subjective sense of uncertainty, which could potentially influence confidence judgments beyond task performance. The present studies aimed at assessing the effects of the ambiguity of local motion signals on perceptual confidence on a global-motion task. Participants first discriminated the global motion directions of two multiple-aperture, global-motion patterns, one generated using multiple Gabor elements and the other using multiple Plaid elements. They then performed a two-interval, forced-choice confidence task by choosing which of the two perceptual responses they were more confident in being correct. In Experiment 1, when perceptual performance was controlled by varying coherence, we found that participants chose plaids more often than Gabors, even with perceptual performance matched between the two patterns. In Experiment 2, when perceptual performance was controlled by varying luminance contrast of noisy pixels in every motion frame, such “plaid preference” in confidence bias was significantly weakened. Besides, there has been numerous studies on visual perception of autistic individuals. But not many of them has looked into the relationship between their metacognition and perceptual judgement. This study aimed at assessing the relationship between the autistic trait tendency and metacognitive process about one’s perceptual performance. Our results show that, at the same level of objective task performance, subject perceptual confidence depends on both the ambiguity of local motion signals and the type of noise. Our results also shows that there is an association between the subject perceptual confidence and the autistic trait tendency

Network Properties Revealed during Multi-Scale Calcium Imaging of Seizure Activity in Zebrafish.

Seizures are characterized by hypersynchronization of neuronal networks. Understanding these networks could provide a critical window for therapeutic control of recurrent seizure activity, i.e., epilepsy. However, imaging seizure networks has largely been limited to microcircuits in vitro or small "windows" in vivo. Here, we combine fast confocal imaging of genetically encoded calcium indicator (GCaMP)-expressing larval zebrafish with local field potential (LFP) recordings to study epileptiform events at whole-brain and single-neuron levels in vivo. Using an acute seizure model (pentylenetetrazole, PTZ), we reliably observed recurrent electrographic ictal-like events associated with generalized activation of all major brain regions and uncovered a well-preserved anterior-to-posterior seizure propagation pattern. We also examined brain-wide network synchronization and spatiotemporal patterns of neuronal activity in the optic tectum microcircuit. Brain-wide and single-neuronal level analysis of PTZ-exposed and 4-aminopyridine (4-AP)-exposed zebrafish revealed distinct network dynamics associated with seizure and non-seizure hyperexcitable states, respectively. Neuronal ensembles, comprised of coactive neurons, were also uncovered during interictal-like periods. Taken together, these results demonstrate that macro- and micro-network calcium motifs in zebrafish may provide a greater understanding of epilepsy

Small-variance asymptotics for Bayesian neural networks

Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data. In this thesis, we explore the use of small-variance asymptotics-an approach to yielding fast algorithms from probabilistic models-on various Bayesian neural network models. We first demonstrate how small-variance asymptotics shows precise connections between standard neural networks and BNNs; for example, particular sampling algorithms for BNNs reduce to standard backpropagation in the small-variance limit. We then explore a more complex BNN where the number of hidden units is additionally treated as a random variable in the model. While standard sampling schemes would be too slow to be practical, our asymptotic approach yields a simple method for extending standard backpropagation to the case where the number of hidden units is not fixed. We show on several data sets that the resulting algorithm has benefits over backpropagation on networks with a fixed architecture.2019-01-02T00:00:00

Analysis of quantification methods used for cell viability, cell morphology, and synaptic formation in modeling HIV associated dementia in primary neuronal cultures.

Change is inevitable, changes in neuronal function occur in physiologic and pathologic processes. The ability to reliably analyze and quantify those changes in neuronal morphology and function has been an important part of technical developments in Neuroscience. A key innovation in the Neuroscience was the development of primary neuronal cultures. Primary neuronal cultures allow neurons to be dissociated and studied as individual components. The study of specific pathologic processes associated with neurodegeneration have benefited greatly from the development and characterization of dissociated primary neuronal cultures. Human Immunodeficiency Virus can lead to a neurodegenerative process. Establishing a consistent model for studying the effects of HIV infection in the brain has provided a unique challenge. The use of analysis of quantification of neuronal changes in dissociated primary neurons modeling HIV dementia has proven useful. As the study of this disorder continues the characterization of the model system will become increasing important. This review will focus on analysis of specific techniques used to quantify specific changes in neurons in this model system. As this field moves forward it will be important to specifically focus on techniques involved in cell viability, morphologic changes, and synaptic formatio

New acceleration technique for the backpropagation algorithm

Artificial neural networks have been studied for many years in the hope of achieving human like performance in the area of pattern recognition, speech synthesis and higher level of cognitive process. In the connectionist model there are several interconnected processing elements called the neurons that have limited processing capability. Even though the rate of information transmitted between these elements is limited, the complex interconnection and the cooperative interaction between these elements results in a vastly increased computing power; The neural network models are specified by an organized network topology of interconnected neurons. These networks have to be trained in order them to be used for a specific purpose. Backpropagation is one of the popular methods of training the neural networks. There has been a lot of improvement over the speed of convergence of standard backpropagation algorithm in the recent past. Herein we have presented a new technique for accelerating the existing backpropagation without modifying it. We have used the fourth order interpolation method for the dominant eigen values, by using these we change the slope of the activation function. And by doing so we increase the speed of convergence of the backpropagation algorithm; Our experiments have shown significant improvement in the convergence time for problems widely used in benchmarKing Three to ten fold decrease in convergence time is achieved. Convergence time decreases as the complexity of the problem increases. The technique adjusts the energy state of the system so as to escape from local minima

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.Comment: 14 pages, 2 figures, 4 listing

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

End-of-life priorities of older adults with terminal illness and caregivers: A qualitative consultation

Abstract Background As older adults approach the end‐of‐life (EOL), many are faced with complex decisions including whether to use medical advances to prolong life. Limited information exists on the priorities of older adults at the EOL. Objective This study aimed to explore patient and family experiences and identify factors deemed important to quality EOL care. Method A descriptive qualitative study involving three focus group discussions (n = 18) and six in‐depth interviews with older adults suffering from either a terminal condition and/or caregivers were conducted in NSW, Australia. Data were analysed thematically. Results Seven major themes were identified as follows: quality as a priority, sense of control, life on hold, need for health system support, being at home, talking about death and competent and caring health professionals. An underpinning priority throughout the seven themes was knowing and adhering to patient's wishes. Conclusion Our study highlights that to better adhere to EOL patient's wishes a reorganization of care needs is required. The readiness of the health system to cater for this expectation is questionable as real choices may not be available in acute hospital settings. With an ageing population, a reorganization of care which influences the way we manage terminal patients is required