Performance-Based Measures in Autism: Implications for Diagnosis, Early Detection, and Identification of Cognitive Profiles

2000 ◽  
Vol 29 (4) ◽  
pp. 479-492 ◽  
Author(s):  
Laura Grofer Klinger ◽  
Peggy Renner
Keyword(s):  
Early Detection ◽  
Cognitive Profiles ◽  
Detection And Identification

scholarly journals Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3616
Author(s):  
Jan Ubbo van Baardewijk ◽  
Sarthak Agarwal ◽  
Alex S. Cornelissen ◽  
Marloes J. A. Joosen ◽  
Jiska Kentrop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Chemical Agent ◽  
Counter Measures ◽  
Detection And Identification ◽  
Life Saving ◽  
Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

scholarly journals A method for early detection and identification of fungal contamination of building materials using e-nose

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215179 ◽  
Author(s):  
Zbigniew Suchorab ◽  
Magdalena Frąc ◽  
Łukasz Guz ◽  
Karolina Oszust ◽  
Grzegorz Łagód ◽  
...  
Keyword(s):  
Early Detection ◽  
Building Materials ◽  
Fungal Contamination ◽  
Detection And Identification

A Novel Approach for the Early Detection and Identification of Botnets

2011 ◽  
Vol 403-408 ◽  
pp. 4469-4475
Author(s):  
S. Benson Edwin Raj ◽  
V.S. Jayanthi ◽  
R. Shalini
Keyword(s):  
Early Detection ◽  
Optimization Technique ◽  
Machine Learning Algorithms ◽  
Ip Address ◽  
Detection Techniques ◽  
Detection And Identification ◽  
Novel Approach ◽  
Size Number ◽  
Ip Spoofing ◽  
The Right

Botnets are growing in size, number and impact. It continues to be one of the top three web threats that mankind has ever known. The botnets are the souped-up cyber engines driving nearly all criminal commerce on the Internet and are seen as relaying millions of pieces of junk e-mail, or spam. Thus, the need of the hour is the early detection and identification of the heart of network packet flooding or the C&C centre. Most of the botmasters perform DDos attacks on a target server by spoofing the source IP address. The existing botnet detection techniques rely on machine learning algorithms and do not expound the IP spoofing issue. These approaches are also found to be unsuccessful in the meticulous identification of the botmasters. Here we propose an architecture that depend on the PSO-based IP tracebacking. Our architecture also introduces the IP spoofing detector unit so as to ensure that the Traceback moves in the right direction. The approach also detects the zombies and utilizes the PSO optimization technique that aid in the identification of the C&C node. The experimental results show that our approach is successful in prompt detection of the bots.

scholarly journals Early Detection and Identification of Commonly Encountered Candida Species from Simulated Blood Cultures by Using a Real-Time PCR-Based Assay

2004 ◽  
Vol 6 (2) ◽  
pp. 108-114 ◽  
Author(s):  
Younes Maaroufi ◽  
Jean-Marc De Bruyne ◽  
Valérie Duchateau ◽  
Aspasia Georgala ◽  
Françoise Crokaert
Keyword(s):  
Early Detection ◽  
Real Time ◽  
Real Time Pcr ◽  
Candida Species ◽  
Blood Cultures ◽  
Detection And Identification

scholarly journals Deterioro Cognitivo Leve y Enfermedad de Alzheimer: Revisión de conceptos

2017 ◽  
Vol 5 (2) ◽  
pp. 53-82
Author(s):  
IPC USMA
Keyword(s):  
Cognitive Impairment ◽  
Early Detection ◽  
Clinical Manifestations ◽  
The Elderly ◽  
Long Term Memory ◽  
Cognitive Profiles ◽  
Aging Research ◽  
Visuospatial Abilities ◽  
Para Determinar ◽  
Sistemas Sanitarios

La enfermedad de Alzheimer (EA) es una enfermedad neurológica degenerativa que afecta a más de 46 millones de personas alrededor del mundo. Representa el tipo de demencia más común en los adultos mayores y cursa con una alteración grave en la memoria y en la funcionalidad de la persona. La EA impacta al individuo, a su familia y/o cuidador y a la sociedad, generando grandes cargas para los sistemas sanitarios, sociales y económicos. La detección temprana de la EA se ha vuelto el foco de estudio en el área del envejecimiento en los últimos años. Diagnosticar la EA en etapas prodrómicas, cuando hay cambios cerebrales subyacentes a EA pero aún no se ha desarrollado la demencia pudiera incidir en mejorías en la intervención y en retrasar la aparición de los síntomas demenciales. Por ende es crucial estudiar el deterioro cognitivo leve (DCL), fase que precede a EA. Delimitar sus manifestaciones, criterios diagnósticos y su relación con EA es fundamental para identificar a aquellos sujetos que tienen mayor riesgo de progresar a EA. El estudio de las alteraciones cognitivas y biomarcadores de DCL y EA es la base para realizar diagnósticos diferenciales oportunos. La evaluación neuropsicológica es fundamental para determinar perfiles cognitivos y evaluar la progresión de la enfermedad. Una memoria episódica deficiente es la primera manifestación en DCL amnésico. Si la persona progresa a EA, este déficit se vuelve más severo inhabilitando la recuperación de la información. Otras funciones como la atención, el lenguaje, las capacidades visuoespaciales, razonamiento, y la flexibilidad mental pueden también estar afectadas en DCL, deteriorándose progresivamente en EA hasta deteriorar severamente la autonomía de la persona. El estudio de los biomarcadores en líquido cefalorraquídeo (LCR), estudios con neuroimagen y biomarcadores en sangre ha permitido establecer los procesos patológicos subyacentes en DCL y EA y junto con la evaluación neuropsicológica constituyen el enfoque más eficaz para el diagnóstico precoz.   Abstract Alzheimer´s Disease (AD) is a neurological degenerative condition that affects over 46 million people around the world. It is the most common cause of dementia in the elderly and is characterized by a major memory impairment affecting a person’s ability to perform everyday activities. AD impacts the person, their family/caregiver and society causing a great burden on health, social and economic systems. In recent years, early detection of AD has become the main focus in aging research. Diagnosing AD in its prodromal stage, where brain pathology is present but dementia still has not appeared, is key to improving intervention mechanisms and to delay the expression of symptoms.As a result, it is crucial to study Mild Cognitive Impairment (MCI), the symptomatic pre-dementia phase. Defining MCI´s clinical manifestations, diagnostic criteria and its relation with AD is critical to the development of methods that aid in identifying individuals who are at risk of developing dementia. The study of cognitive impairment and biomarkers allows early and differential diagnosis of AD.  Neuropsychological evaluation is essential to determine different cognitive profiles and to assess the progression of MCI to AD. Impairment in episodic memory, the first neuropsychological symptom of amnestic MCI, deteriorates severely if the person develops AD, affecting long term memory. Other cognitive functions such as attention, language, visuospatial abilities, reasoning and mental flexibility can be affected in MCI and deteriorate even further in AD interfering with the person´s independence and functional integrity.Likewise, the study of biomarkers in cerebrospinal fluid (CSF), neuroimaging and blood biomarkers has permitted the identification of neuropathological signs of the disease. Together with neuropsychological assessment, biomarkers constitute the most effective diagnostic approach for early detection of AD.

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