论文信息 - CAMDEX: A Standardised Instrument for the Diagnosis of Mental Disorder in the Elderly with Special Reference to the Early Detection of Dementia

2019 - 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Design of a Robotic Crib Mobile to Support Studies in the Early Detection of Cerebral Palsy: A Pilot Study

According to data from the Centers for Disease Control and Prevention, developmental disorders such as Autism Spectrum Disorder (ASD) and Cerebral Palsy (CP) affect nearly one in six children between the ages of 3 to 17 in the United States alone. In order to improve the quality of life for these individuals, there is increased emphasis on providing early intervention at infancy, when key developmental milestones are being achieved. This however requires accurate early detection of motor development delays in at-risk infants. Our research focuses on enabling early detection through the design of a robotic crib mobile that affects infant behavior. Stimuli integrated into the robotic mobile can be used to encourage certain motions such as kicking among infants in order to study infant motor development and identify at-risk populations. In this paper, we propose the design of such a robotic crib mobile and discuss preliminary results from deploying the mobile in the infants’ home environment during a pilot study.

2015 - Decis. Support Syst.

Early detection of network element outages based on customer trouble calls

This paper deals with the issue of early detection of network element outages. Timeliness of outage detection as well as accuracy in finding outages on equipment in a telecommunication network depend on the monitoring system used and its performance. The intent of this paper is to investigate and propose a complementary solution to improve the performance of the existing systems in detecting faults earlier than it was able to do before. In developing our approach two constraints are given. The existing operational environment cannot be changed; threshold tuning and parameter changing cannot be done; furthermore no additional infrastructure investment has been planned. Hence, our approach relies on an alternative method based on a two-stage hybrid statistical and diagnostic detector which we designed in a way that exploits additional available data and avoids alarm monitoring system imperfections. The role of this detector is twofold: early detection of network element outages based on customer trouble calls and rule-based decision making for faulty-element isolation based on knowledge derived from fault and network management data. In this paper we present results of statistical analysis of trouble-reporting data. The analysis showed that the timing of customers' trouble reports and their content have information potential that can be utilized for early detection of outages. The detector is explained in detail and its accuracy and reduction delay is evaluated. The method presented can reduce the outage detection delay time by 2.33h on average observed in relation to the performance of an existing fault management process which was designed to detect outages solely on the basis of an alarm monitoring system, for the "difficulties in work" type of malfunction. We attained an overall probability of correct detection of 95.3%. Out of the total number of outages that hypothetically could be detected, by using this method we were able to detect 77.5% of cases 1h before the alarm was raised in the existing alarm system, while 23% of cases were detected 4h before the actual alarm. The approach has been tested on real telecommunication network data over the period of one year. The sequence of reported failures has information potential for outage detection.We get the empirical distributions of how quickly customers report a failure.We proposed a hybrid model of detection adapted to the specific environment.A two-stage detector, for outage detection and element isolation, is designed.The method presented can reduce the outage detection delay time by 2.33h.

2011 - 2011 15th International Conference on Information Visualisation

Preprocessing for Automating Early Detection of Cervical Cancer

Uterine Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, colposcopic images or cervigrams are acquired in raw form. They contain specular reflections which appear as bright spots heavily saturated with white light and occur due to the presence of moisture on the uneven cervix surface and. The cervix region occupies about half of the raw cervigram image. Other parts of the image contain irrelevant information, such as equipment, frames, text and non-cervix tissues. This irrelevant information can confuse automatic identification of the tissues within the cervix. Therefore we focus on the cervical borders, so that we have a geometric boundary on the relevant image area. Our novel technique eliminates the SR, identifies the region of interest and makes the cervigram ready for segmentation algorithms.

2000

Developing Technologies for Early Detection of Breast Cancer

B reast cancer takes a tremendous toll in the United States. Each year, over 180,000 new cases of invasive breast cancer are diagnosed, and more than 40,000 women die from the disease. Until research uncovers a way to prevent breast cancer or to cure all women regardless of when their tumors are found, early detection will be looked upon as the best hope for reducing the burden of this disease. The hope is that early detection of breast cancer by screening could be as effective in saving lives as the Papanicolau smear used for cervical cancer screening. X-ray mammography has been shown in clinical trials to reduce breast cancer deaths. However, mammography is not perfect. Routine screening in clinical trials resulted in a 25–30% decrease in breast cancer mortality among women between the ages of 50 and 70. A lesser benefit was seen among women aged 40–49. The benefit of screening mammography for women over 70 was more difficult to as sess due to a lack of data. Screening mammography cannot eliminate all breast cancer deaths because it does not detect all cancers. Some tumors may also develop too quickly to be identified at the most treatable stage using the standard screening intervals. Fur thermore, it is technically difficult to consistently produce mammograms of high quality and interpretation is subjective. Mammography can also have deleterious effects for some women, in the form of false positive results and overdiagnosis and overtreatment. As many as threequarters of all breast lesions biopsied because of suspicious findings on a mam mogram turn out to be benign, i.e., the mammographic findings were falsely posi tive. “Overdiagnosis” is the labeling of small lesions as cancer or precancer when in fact the lesions may never have progressed to a life-threatening disease if left undetected and untreated. In such cases, some of the “cures” following early de tection may not be real, and thus such women are unnecessarily “overtreated.” Technical improvements in breast imaging techniques have led to an increase in the identification of these small abnormalities, such as carcinoma in situ, whose biology is not well understood. Currently, methods for classifying such lesions detected by mammography are based on tissue structure, and the ability to determine the lethal potential of breast abnormalities is crude at best. The immense burden of breast cancer and the inherent limitations of mam mography have been the driving forces behind efforts to develop technologies for the early detection of breast cancer. The Institute of Medicine (IOM), an arm of the National Academies, released a report called Mammography and Beyond in Breast cancer screen ing by X-ray mammog raphy has been shown in clinical trials to re duce breast cancer deaths.

2009 - ICONIP

Early Detection of the Alzheimer Disease Combining Feature Selection and Kernel Machines

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. However, currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on feature selection and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach attaining a 90% AD diagnosis accuracy.

2020 - Frontiers in Neuroscience

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.

2004 - Indoor and Built Environment

Electronic Nose for the Early Detection of Moulds in Libraries and Archives

The development of sensor technology has stimulated interest in the use of characteristic volatile and odorous compounds produced by fungi as early indicators of deterioration in materials. Sensor arrays to measure traces of volatile chemicals could detect early fungal growth in libraries and archives. In this study we tested an electronic nose to detect fungi actively growing on paper samples. The main aim was to verify whether or not a device, currently used to detect fungal activity in stored grain, might be suitable for detecting mould activity on paper. The findings indicate that it is possible to discriminate “in vitro” between paper samples affected by moulds and those unaffected, both at 100% RH (relative humidity), and at 75% RH, simply by measuring their odour fingerprint with an electronic nose. The sensors used in this study discriminated for each paper type three different species of actively growing fungi. Cluster analysis (CA) showed that it was possible to differentiate between specific species. Different paper types influenced the emission of odorous signals by moulds. When considering data from all the paper types, principal component analysis (PCA) indicated that only samples analysed at 100% RH could be separated. This study suggests that, before electronic-nose technology can be applied to the early detection of mould growth in libraries, archives, museums or in display cases, more information will be required on the influence of substrata and of other environmental parameters in the production of volatile chemicals by fungi.

2019 - Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53

Future Directions for Early Childhood Prevention of Mental Disorders: A Road Map to Mental Health, Earlier

Mental disorders are the predominant chronic diseases of youth, with substantial life span morbidity and mortality. A wealth of evidence demonstrates that the neurodevelopmental roots of common mental health problems are present in early childhood. Unfortunately, this has not been translated to systematic strategies for improving population-level mental health at this most malleable neurodevelopmental period. We lay out a translational Mental Health, Earlier road map as a key future direction for prevention of mental disorder. This paradigm shift aims to reduce population attributable risk of mental disorder emanating from early life, by preventing, attenuating, or delaying onset/course of chronic psychopathology via the promotion of self-regulation in early childhood within large-scale health care delivery systems. The Earlier Pillar rests on a “science of when to worry” that (a) optimizes clinical assessment methods for characterizing probabilistic clinical risk beginning in infancy via deliberate incorporation of neurodevelopmental heterogeneity, and (b) universal primary-care-based screening targeting patterns of dysregulated irritability as a robust transdiagnostic marker of vulnerability to life span mental health problems. The core of the Healthier Pillar is provision of low-intensity selective intervention promoting self-regulation for young children with developmentally atypical patterns of irritability within an implementation science framework in pediatric primary care to ensure highest population impact and sustainability. These Mental Health, Earlier strategies hold much promise for transforming clinical outlooks and ensuring young children’s mental health and well-being in a manner that reverberates throughout the life span.

2010 - International Journal of Alzheimer's Disease

Early Detection of Brain Pathology Suggestive of Early AD Using Objective Evaluation of FDG-PET Scans

The need for early detection of AD becomes critical as disease-modifying agents near the marketplace. Here, we present results from a study focused on improvement in detection of metabolic deficits related to neurodegenerative changes consistent with possible early AD with statistical evaluation of FDG-PET brain images. We followed 31 subjects at high risk or diagnosed with MCI/AD for 3 years. 15 met criteria for diagnosis of MCI, and five met criteria for AD. FDG-PET scans were completed at initiation and termination of the study. PET scans were read clinically and also evaluated objectively using Statistical Parametric Mapping (SPM). Using standard clinical evaluation of the FDG-PET scans, 11 subjects were detected, while 18 were detected using SPM evaluation. These preliminary results indicate that objective analyses may improve detection; however, early detection in at-risk normal subjects remains tentative. Several FDA-approved software packages are available that use objective analyses, thus the capacity exists for wider use of this method for MCI/AD.

2008 - Neuropsychopharmacology

Mapping Callosal Morphology in Early- and Late-Onset Elderly Depression: An Index of Distinct Changes in Cortical Connectivity

There is some evidence of corpus callosum abnormalities in elderly depression, but it is not known whether these deficits are region-specific or differ based on age at onset of depression. Twenty-four patients with early-onset depression (mean age=68.00, SD±5.83), 22 patients with late-onset depression (mean age=74.50, SD±8.09) and 34 elderly control subjects (mean age=72.38; SD±6.93) were studied. Using 3D MRI data, novel mesh-based geometrical modeling methods were applied to compare the midsagittal thickness of the corpus callosum at high spatial resolution between groups. Neuropsychological correlates of midsagittal callosal area differences were additionally investigated in a subsample of subjects. Depressed patients exhibited significant callosal thinning in the genu and splenium compared to controls. Significant callosal thinning was restricted to the genu in early-onset patients, but patients with late-onset depression exhibited significant callosal thinning in both the genu and splenium relative to controls. The splenium of the corpus callosum was also significantly thinner in subjects with late- vs early-onset depression. Genu and splenium midsagittal areas significantly correlated with memory and attention functioning among late-onset depressed patients, but not early-onset depressed patients or controls. Circumscribed structural alterations in callosal morphology may distinguish late- from early-onset depression in the elderly. These findings suggest distinct abnormalities of cortical connectivity in late- and early-onset elderly depression with possible influence on the course of illness. Patients with a late onset of depression may be at higher risk of illness progression and eventually dementia conversion than early-onset depression, with potentially important implications for research and therapy.

2014 - Int. J. Appl. Earth Obs. Geoinformation

Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion

Abstract Early detection of crop injury from herbicide glyphosate is of significant importance in crop management. In this paper, we attempt to detect glyphosate-induced crop injury by PROSPECT (leaf optical PROperty SPECTra model) inversion through leaf hyperspectral reflectance measurements for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton leaves. The PROSPECT model was inverted to retrieve chlorophyll content ( C a+b ), equivalent water thickness ( C w ), and leaf mass per area ( C m ) from leaf hyperspectral reflectance spectra. The leaf stress conditions were then evaluated by examining the temporal variations of these biochemical constituents after glyphosate treatment. The approach was validated with greenhouse-measured datasets. Results indicated that the leaf injury caused by glyphosate treatments could be detected shortly after the spraying for both soybean and cotton by PROSPECT inversion, with C a+b of the leaves treated with high dose solution decreasing more rapidly compared with leaves left untreated, whereas the C w and C m showed no obvious difference between treated and untreated leaves. For both non-GR soybean and non-GR cotton, the retrieved C a+b values of the glyphosate treated plants from leaf hyperspectral data could be distinguished from that of the untreated plants within 48 h after the treatment, which could be employed as a useful indicator for glyphosate injury detection. These findings demonstrate the feasibility of applying the PROSPECT inversion technique for the early detection of leaf injury from glyphosate and its potential for agricultural plant status monitoring.

2015 - Canada communicable disease report = Releve des maladies transmissibles au Canada

Big Data and the Global Public Health Intelligence Network (GPHIN).

Background Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events. Objective To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN. Findings Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. Conclusion The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.

2017

Gravimetric Viral Diagnostics: QCM Based Biosensors for Early Detection of Viruses

Viruses are pathogenic microorganisms that can inhabit and replicate in human bodies causing a number of widespread infectious diseases such as influenza, gastroenteritis, hepatitis, meningitis, pneumonia, acquired immune deficiency syndrome (AIDS) etc. A majority of these viral diseases are contagious and can spread from infected to healthy human beings. The most important step in the treatment of these contagious diseases and to prevent their unwanted spread is to timely detect the disease-causing viruses. Gravimetric viral diagnostics based on quartz crystal microbalance (QCM) transducers and natural or synthetic receptors are miniaturized sensing platforms that can selectively recognize and quantify harmful virus species. Herein, a review of the label-free QCM virus sensors for clinical diagnostics and point of care (POC) applications is presented with major emphasis on the nature and performance of different receptors ranging from the natural or synthetic antibodies to selective macromolecular materials such as DNA and aptamers. A performance comparison of different receptors is provided and their limitations are discussed.

2016 - Comput. Electron. Agric.

Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging

Early detection of fungal infection in citrus fruit is one of the major problems.Hyperspectral information visualization was first used to detect decayed citrus.Mean normalization is proposed to reduce spectral variability due to spherical fruit.Four spectral images were selected for development of multispectral algorithm.The total success rate is 98.6% for test set with no false negatives. Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest phase. The automation of this task is still a challenge for the citrus industry. In this study, the potential application of hyperspectral imaging, which combines conventional imaging and spectroscopy to simultaneously acquire both spatial and spectral information from an object, was evaluated for automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Hyperspectral images of sound and decayed navel oranges were acquired in the wavelength range of 325-1100nm. Principal component analysis (PCA) was applied to a dataset comparing of average spectra from decayed and sound tissue to reduce the dimensionality of data and to observe the ability of visible-near infrared (Vis-NIR) hyper-spectra to discriminate data from two classes. And, a mean normalization step is applied prior to PCA to reduce the effect of sample curvature on spectral profiles. In this case it was observed that sound and decayed spectra were separable along the resultant first principal component (PC1) axis, then, four wavelength images centered at 575, 698, 810 and 969nm were selected as the characteristic wavelength images by analyzing the weight coefficients of PC1 in order to develop a fast classification method for establishing an on-line multispectral imaging system. Subsequently, a combination image, which obtained by multiplying the characteristic weight coefficients by corresponding to mean-normalized characteristic wavelength images of each orange sample, was calculated for determination of decayed fruits. Based on the obtained multispectral combination image, the technique of intensity slicing as one of the pseudo-color image processing methods is used to transform the combination image into a 2-D visual classification image that would enhance the contrast between sound and decayed classes. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudo-color image coupled with a simple thresholding method. For the investigated 210 naval orange samples including 80 sound fruits and 130 infected fruits, the total success rate is 100% for training set and 98.6% for test set with no false negatives, respectively, indicating that the proposed multispectral algorithm here is capable of detecting decay caused by penicillium digitatum in naval orange fruit using only four key wavelength images. The results from this study could be used for development of a non-destructive monitoring system for rapid detection of decayed citrus on the processing line. The idea behind the proposed algorithm can be extended to detect the non-visible damages of other fruit, such as slight bruise and chilling injury in apples.

2015 - IEEE Journal of Translational Engineering in Health and Medicine

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors' subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.

2012 - ISSTA 2012

Undangle: early detection of dangling pointers in use-after-free and double-free vulnerabilities

Use-after-free vulnerabilities are rapidly growing in popularity, especially for exploiting web browsers. Use-after-free (and double-free) vulnerabilities are caused by a program operating on a dangling pointer. In this work we propose early detection, a novel runtime approach for finding and diagnosing use-after-free and double-free vulnerabilities. While previous work focuses on the creation of the vulnerability (i.e., the use of a dangling pointer), early detection shifts the focus to the creation of the dangling pointer(s) at the root of the vulnerability. Early detection increases the effectiveness of testing by identifying unsafe dangling pointers in executions where they are created but not used. It also accelerates vulnerability analysis and minimizes the risk of incomplete fixes, by automatically collecting information about all dangling pointers involved in the vulnerability. We implement our early detection technique in a tool called Undangle. We evaluate Undangle for vulnerability analysis on 8 real-world vulnerabilities. The analysis uncovers that two separate vulnerabilities in Firefox had a common root cause and that their patches did not completely fix the underlying bug. We also evaluate Undangle for testing on the Firefox web browser identifying a potential vulnerability.

2011 - IEEE Transactions on Biomedical Engineering

A Successive Clutter-Rejection-Based Approach for Early Detection of Diabetic Retinopathy

The presence of microaneurysms (MAs) is usually an early sign of diabetic retinopathy and their automatic detection from color retinal images is of clinical interest. In this paper, we present a new approach for automatic MA detection from digital color fundus images. We formulate MA detection as a problem of target detection from clutter, where the probability of occurrence of target is considerably smaller compared to the clutter. A successive rejection-based strategy is proposed to progressively lower the number of clutter responses. The processing stages are designed to reject specific classes of clutter while passing majority of true MAs, using a set of specialized features. The true positives that remain after the final rejector are assigned a score which is based on its similarity to a true MA. Results of extensive evaluation of the proposed approach on three different retinal image datasets are reported, and used to highlight the promise in the presented strategy.

2011 - Gut

Colorectal cancer screening with odour material by canine scent detection

Objective Early detection and early treatment are of vital importance to the successful treatment of various cancers. The development of a novel screening method that is as economical and non-invasive as the faecal occult blood test (FOBT) for early detection of colorectal cancer (CRC) is needed. A study was undertaken using canine scent detection to determine whether odour material can become an effective tool in CRC screening. Design Exhaled breath and watery stool samples were obtained from patients with CRC and from healthy controls prior to colonoscopy. Each test group consisted of one sample from a patient with CRC and four control samples from volunteers without cancer. These five samples were randomly and separately placed into five boxes. A Labrador retriever specially trained in scent detection of cancer and a handler cooperated in the tests. The dog first smelled a standard breath sample from a patient with CRC, then smelled each sample station and sat down in front of the station in which a cancer scent was detected. Results 33 and 37 groups of breath and watery stool samples, respectively, were tested. Among patients with CRC and controls, the sensitivity of canine scent detection of breath samples compared with conventional diagnosis by colonoscopy was 0.91 and the specificity was 0.99. The sensitivity of canine scent detection of stool samples was 0.97 and the specificity was 0.99. The accuracy of canine scent detection was high even for early cancer. Canine scent detection was not confounded by current smoking, benign colorectal disease or inflammatory disease. Conclusions This study shows that a specific cancer scent does indeed exist and that cancer-specific chemical compounds may be circulating throughout the body. These odour materials may become effective tools in CRC screening. In the future, studies designed to identify cancer-specific volatile organic compounds will be important for the development of new methods for early detection of CRC.

1996 - Schizophrenia bulletin

Early detection and intervention with schizophrenia: rationale.

This article explores the rationale for early detection and intervention in schizophrenia. The most compelling reason is the disorder's severity and chronicity and our knowledge that, while many treatments for schizophrenia are effective, they are also limited and palliative. This state of affairs suggests that researchers pay closer attention to schizophrenia's premorbid and onset phases, when the vulnerability to psychosis becomes expressed and the neurobiological deficit processes driving symptom formation appear to be the most active. We review the evidence that brain plasticity can be retained or reversed despite deficit processes. This evidence includes the putative attenuation of the severity of schizophrenia throughout the 20th century, retrospective and prospective linkage of earlier neuroleptic treatment and better long-term outcome, and data from a program designed to intervene in the prodromal phase of disorder. While the evidence to date does not demonstrate that early intervention with known treatments can change the natural history of schizophrenia, it is suggestive enough (for both biological and psychosocial treatment) to support further investigation. Focusing on the early course of schizophrenia also offers the possibility of identifying potential patients long before onset using vulnerability markers and of making more feasible primary prevention efforts. Finally, studies of untreated psychosis in first-episode cases have revealed that patients are often actively psychotic for a very long time before they get help. Bringing treatment more rapidly to a person who has been psychotic is in itself enough to justify early detection efforts.

1997 - SIGCOMM '97

Dynamics of random early detection

In this paper we evaluate the effectiveness of Random Early Detection (RED) over traffic types categorized as non-adaptive, fragile and robust, according to their responses to congestion. We point out that RED allows unfair bandwidth sharing when a mixture of the three traffic types shares a link. This unfairness is caused by the fact that at any given time RED imposes the same loss rate on all flows, regardless of their bandwidths.We propose Fair Random Early Drop (FRED), a modified version of RED. FRED uses per-active-flow accounting to impose on each flow a loss rate that depends on the flow's buffer use.We show that FRED provides better protection than RED for adaptive (fragile and robust) flows. In addition, FRED is able to isolate non-adaptive greedy traffic more effectively. Finally, we present a "two-packet-buffer" gateway mechanism to support a large number of flows without incurring additional queueing delays inside the network. These improvements are demonstrated by simulations of TCP and UDP traffic.FRED does not make any assumptions about queueing architecture; it will work with a FIFO gateway. FRED's per-active-flow accounting uses memory in proportion to the total number of buffers used: a FRED gateway maintains state only for flows for which it has packets buffered, not for all flows that traverse the gateway.

CAMDEX: A Standardised Instrument for the Diagnosis of Mental Disorder in the Elderly with Special Reference to the Early Detection of Dementia

The structure of wandering in dementia

Harmonization of Cognitive Measures in Individual Participant Data and Aggregate Data Meta-Analysis

morphometry impairment and dementia using voxel-based with Parkinson ’ s disease with mild cognitive A magnetic resonance imaging study of patients

Examination of the current top candidate genes for AD in a genome-wide association study

Altered tract-specific white matter microstructure is related to poorer cognitive performance: The Rotterdam Study

Diffusion tensor imaging in Alzheimer's disease and dementia with Lewy bodies

Comparing gray matter loss profiles between dementia with Lewy bodies and Alzheimer's disease using cortical pattern matching: diagnosis and gender effects

Risk factors for Alzheimer's disease and dementia

Bilateral high‐frequency stimulation of the internal globus pallidus in advanced Parkinson's disease

Quantitative EEG analysis in early onset Alzheimer's disease: correlations with severity, clinical characteristics, visual EEG and CCT.

Quantitative EEG analysis in early onset Alzheimer's disease: a controlled study.

Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer's disease: an EEG study

Subclinical hyperthyroidism and the risk of dementia. The Rotterdam study

Longitudinal design considerations to optimize power to detect variances and covariances among rates of change: simulation results based on actual longitudinal studies.

Retrieval and encoding of episodic memory in normal aging and patients with mild cognitive impairment

Brain tissue volumes in relation to cognitive function and risk of dementia

Human-computer interaction in the administration and analysis of neuropsychological tests

Endogenous estradiol and risk of dementia in women and men: The Rotterdam Study

A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry

Vascular dementia Cognitive , functional and behavioral assessment