Neural Networks Based on Synthesized Training Data for the Automated Detection of Chemical Plumes in Passive Infrared Multispectral Images.

Automated detection of volatile organic compounds in the atmosphere can be achieved by applying pattern recognition analysis to passive infrared (IR) multispectral remote sensing data. However, obtaining analyte-active training data through field experiments is time-consuming and expensive. To address this issue, methodology has been developed for simulating radiance profiles acquired using a multispectral IR line-scanner mounted in a downward-looking position on a fixed-wing aircraft. The simulation strategy used Planck's radiation law and a radiometric model along with the laboratory spectrum of the target compound to compute the upwelling IR background radiance with the presence of the analyte within the instrumental field-of-view. By combining the simulated analyte-active radiances and field-collected analyte-inactive radiances, a synthetic training dataset was constructed. A backpropagation neural network was employed to build classifiers with the synthetic training dataset. Employing methanol as the target compound, the performance of the classifiers was evaluated with field-collected data from airborne surveys at two test fields.

Understanding barriers to and facilitators of clinician-patient conversations about brain health and cognitive concerns in primary care: a systematic review and practical considerations for the clinician.

BACKGROUND: Primary care clinicians (PCCs) are typically the first practitioners to detect cognitive impairment in their patients, including those with Alzheimer's disease or related dementias (ADRD). However, conversations around cognitive changes can be challenging for patients, family members, and clinicians to initiate, with all groups reporting barriers to open dialogue. With the expanding array of evidence-based interventions for ADRD, from multidomain care management to novel biotherapeutics for early-stage AD, incorporating conversations about brain health into routine healthcare should become a standard of care. We conducted a systematic review to identify barriers to and facilitators of brain health conversations in primary care settings. METHODS: We systematically searched PubMed, Scopus, Web of Science, and the Cochrane Library for qualitative or quantitative studies conducted in the US between January 2000 and October 2022 that evaluated perceptions of cognition and provider-patient brain health conversations prior to formal screening for, or diagnosis of, mild cognitive impairment or ADRD. We assessed the quality of the included studies using the Mixed Methods Appraisal Tool. RESULTS: In total, 5547 unique abstracts were screened and 22 articles describing 19 studies were included. The studies explored perceptions of cognition among laypersons or clinicians, or provider-patient interactions in the context of a patient's cognitive concerns. We identified 4 main themes: (1) PCCs are hesitant to discuss brain health and cognitive concerns; (2) patients are hesitant to raise cognitive concerns; (3) evidence to guide clinicians in developing treatment plans that address cognitive decline is often poorly communicated; and (4) social and cultural context influence perceptions of brain health and cognition, and therefore affect clinical engagement. CONCLUSIONS: Early conversations about brain health between PCCs and their patients are rare, and effective tools, processes, and strategies are needed to make these vital conversations routine.

Results and insights from a phase I clinical trial of Lomecel-B for Alzheimer's disease.

HYPOTHESIS: We hypothesized that Lomecel-B, an allogeneic medicinal signaling cell (MSC) therapeutic candidate for Alzheimer's disease (AD), is safe and potentially disease-modifying via pleiotropic mechanisms of action. KEY PREDICTIONS: We prospectively tested the predictions that Lomecel-B administration to mild AD patients is safe (primary endpoint) and would provide multiple exploratory indications of potential efficacy in clinical and biomarker domains (prespecified secondary/exploratory endpoints). STRATEGY AND KEY RESULTS: Mild AD patient received a single infusion of low- or high-dose Lomecel-B, or placebo, in a double-blind, randomized, phase I trial. The primary safety endpoint was met. Fluid-based and imaging biomarkers indicated significant improvement in the Lomecel-B arms versus placebo. The low-dose Lomecel-B arm showed significant improvements versus placebo on neurocognitive and other assessments. INTERPRETATION: Our results support the safety of Lomecel-B for AD, suggest clinical potential, and provide mechanistic insights. This early-stage study provides important exploratory information for larger efficacy-powered clinical trials.

Unmet needs in the diagnosis and treatment of Parkinson's disease psychosis and dementia-related psychosis.

Dementia due to Parkinson's disease and Alzheimer's disease are associated with behavioural and psychological symptoms, including psychosis. Long-term management presents a challenge for health care providers and caregivers. Symptoms of psychosis include hallucinations and delusions; if untreated, these can lead to institutionalisation, decreased quality of life, and significant patient and caregiver distress. A critical step in the effective management of dementia-related psychosis (DRP) is the identification and diagnosis of affected patients. The lack of a standardised diagnostic approach presents a barrier to treatment and there are no consensus guidelines for DRP. Furthermore, there are no approved therapies for the treatment of DRP. Antipsychotic medications are often prescribed off-label, even though some are associated with an increased risk of adverse events or mortality. We present currently available screening tools and guidelines for the diagnosis and treatment of Parkinson's disease psychosis and DRP in the context of what is needed for effective management of psychosis.KEY POINTSWe present currently available screening tools and guidelines for Parkinson's disease psychosis and dementia-related psychosis, and discuss the unmet need for simple clinical diagnostic tools and treatment guidelines.The identification of psychosis is variable across different settings and specialties, without a unified approach to screening, definition, or diagnosis.Currently used tools for defining and assessing psychosis in a research setting are usually too cumbersome for everyday clinical practice.The development of a standardised set of diagnostic criteria would provide clinicians the opportunity to improve the detection, treatment, and quality of life of patients and their caregivers.

Gamma-ray anomaly detector for airborne surveys based on a machine learning methodology.

Machine learning methods were used to develop an automated classification model for detecting gamma-ray spectra with anomalous (i.e., non-background) signatures collected during airborne surveys. Spectra were preprocessed with an altitude-based normalization procedure, followed by application of a digital filter to remove baseline and noise effects. Segments of the preprocessed spectra were then used as input patterns to piecewise linear discriminant analysis (PLDA). For use in building the classification model, a training set was constructed based on two data classes: (1) spectra containing various radioisotope signatures and (2) background spectra. Through the use of a piecewise linear discriminant based on seven separating boundaries, a general-purpose spectral anomaly detector was developed for use in the automated screening of large spectral datasets collected during airborne surveys. The intended application of the methodology is to aid first responders in locating lost or stolen radioactive sources or in managing other incidents in which radioactive material is released into the environment. In developing and testing the methodology, laboratory spectra, spectra collected during airborne surveys, and mathematically synthesized spectra were used. When applied to 17 airborne surveys in which known radioisotope sources were present, the anomaly detector was able to locate each source with high confidence. While false detections were observed at a rate of 3.7%, many of these were in the vicinity of the known source location but the radioisotope signature could not be visually observed in the spectrum. For false detections away from the source locations, elevated signatures from naturally occurring background components were typically observed.

Pattern recognition analysis of marine oil spills in airborne passive infrared multispectral remote sensing images.

Pattern recognition methodology was developed for the automated detection of marine oil spills in passive infrared multispectral remote sensing images. The images employed in this work were collected from the Deepwater Horizon oil spill accident in 2010. The imaging instrument for data collection was a downward-looking infrared line scanner equipped with eight optical bandpass filters in the spectral range of 8-12 µm on a fixed-wing aircraft. Oil slicks may show either positive or negative thermal contrast against the surrounding sea water, depending on the sun glint conditions or the oil thickness. Classifiers were developed separately to detect oil with different contrasts by the application of backpropagation neural networks to the preprocessed radiances. Preprocessing strategies included: (1) assembly of training data through k-means clustering analysis; (2) elimination of variation in radiance magnitudes by a customized temperature correction method; (3) removal of sun glint artifacts in images by polynomial correction; and (4) extraction of the most representative features as inputs for the neural networks by a subset selection approach. The classifiers designed to detect oil with positive and negative thermal contrast relative to water achieved overall classification accuracies of 88.7 and 92.2%, respectively. Composite classification images were generated by integrating classification scores produced by the two classifiers. The prediction performance of the classification system was demonstrated through its application to images not involved during the training of the networks.

Updates in the Management of Mild Cognitive Impairment and Alzheimer Disease.

LEARNING OBJECTIVES: At the end of the activity, participants will be able to:Implement evidence-based methods for cognitive impairment screening in primary care. Identify correct diagnostic criteria for mild cognitive impairment (MCI) and Alzheimer disease (AD) based on current guideline recommendations. Design appropriate and effective treatment plans for patients with MCI and AD and refer to a specialist when necessary. Describe advances in testing and treatment for AD that may impact dementia care.

Screening, Diagnosis, and Management of Parkinson's Disease Psychosis: Recommendations From an Expert Panel.

INTRODUCTION: Hallucinations and delusions present with psychosis are debilitating non-motor symptoms of Parkinson's disease, with a prevalence of up to 50-70% at some point during the course of the disease. Often patients and caregivers do not report the presence of hallucinations or delusions unless specifically questioned. A panel of experts in neurology and geriatric psychiatry convened to develop a simple screening tool and guidance on diagnosis and treatment of Parkinson's disease psychosis (PDP). METHODS: The working group reviewed literature for existing PDP guidelines on diagnosis and management and identified gaps in recommendations. The group discussed and developed a screening tool and treatment guidance that addressed the gaps in existing methodology based on their clinical experience. RESULTS: The proposed screening tool consists of two parts: (1) a brief pre-visit screening portion to be completed by the patient and caregiver, and (2) a clinician portion to be completed via clinical interview of the patient and caregiver. If psychotic symptoms are present, an appropriate treatment plan is developed for PDP based on evaluation. CONCLUSIONS: This simple screening tool and treatment guidance offers a practical clinical approach for clinicians in the diagnosis and management of PDP.

Symptoms relating to psychosis are debilitating, progressive, and often emerge in patients with Parkinson's disease. Symptoms of Parkinson's disease psychosis include illusions, a false sense of presence, and hallucinations or delusions or both. While there are established consensus criteria for the diagnosis of Parkinson's disease psychosis, there is currently a lack of simple and standardized criteria for the screening of Parkinson's disease psychosis. This can make it challenging to identify patients who may benefit from treatment for Parkinson's disease psychosis symptoms. A group of clinical experts met to discuss guidance for the screening, clinical diagnosis, and management of Parkinson's disease psychosis. The group identified a paucity of screening tools, weaknesses in existing criteria for diagnosing Parkinson's disease psychosis, and variability in treatment recommendations. The group proposed a screening tool that includes two parts: (1) a simple pre-visit screening to be completed by the patient and caregiver before an appointment, and (2) a clinician portion to be discussed with the patient and caregiver during the appointment. If a patient has hallucinations and/or delusions that require treatment, the proposed guidance includes potential interventions or medications, which were established by review of evidence-based literature and the US Food and Drug Administration guidelines. This provides a quick and relatively simple clinical tool for a patient and caregiver to report symptoms of Parkinson's disease psychosis, and for the clinician to formulate an accurate diagnosis and a decision tree to consider treatment options.

The Effect of Memory Training on Memory Control Beliefs in Older Adults with Subjective Memory Complaints.

Objective: To study whether memory control beliefs predict response to memory training, or change as a result of participating in memory training. Methods: Eighty community based participants with subjective memory complaints Community-based study at UCLA were randomized to one of three conditions: Memory Training, the program consisted of weekly 120-minute classes featuring instruction in three specific strategies: Method of Loci; Chunking Technique; and Face-Name Association, Health Education or Wait-List over seven weeks. All participants underwent pre- and 1-week post-intervention follow-up memory testing for recalling word lists (in serial order and any order) and face-name pairs. Memory control beliefs were assessed at baseline and follow-up using the Memory Controllability Inventory, which consists of four subscales; Present Ability; Potential Improvement; Effort Utility; and Inevitable Decrement. Results: Sixty-three participants (mean age [SD] 68.3 [6.7] years) were included in the analysis. ANCOVA revealed significant group differences in the Present Ability subscale, F2,58 = 4.93, p =.01. Participants in the Memory Training group significantly improved on the Present Ability subscale compared to the Health Education group (mean difference =.96, SE =.31, p =.003, effect size = 0.93). From regression analyses, baseline Memory Controllability Inventory subscales did not significantly predict memory performance after memory training. Conclusions: Baseline memory control beliefs did not predict memory performance following the intervention, but participating in memory training enhanced memory control beliefs about current memory function. These results suggest that participating in memory training can enhance confidence in one's memory ability.

Sleep quality, neurocognitive performance, and memory self-appraisal in middle-aged and older adults with memory complaints.

OBJECTIVE: Because of inconsistent findings regarding the relationship between sleep quality and cognitive function in people with age-related memory complaints, we examined how self-reports of sleep quality were related to multiple domains of both objective and subjective cognitive function in middle-aged and older adults. DESIGN: A cross-sectional study involving analysis of baseline data, collected as part of a clinical trial. MEASUREMENTS: Two hundred and three participants (mean age = 60.4 [6.5] years, 69.0% female) with mild memory complaints were asked to rate their sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and their memory performance using the Memory Functioning Questionnaire (MFQ), which measures self-awareness of memory ability. Neurocognitive performance was evaluated using the Continuous Performance Test (CPT), Trail Making Test, Buschke Selective Reminding Test, and the Brief Visuospatial Test - Revised (BVMT-R). RESULTS: Total PSQI scores were significantly associated with objective measures of sustained attention (CPT hit reaction time by block and standard error by block) and subjective memory loss (MFQ frequency and seriousness of forgetting). The PSQI components of (poorer) sleep quality and (greater) sleep disturbance were related to (worse) sustained attention scores while increased sleep latency and daytime sleepiness were associated with greater frequency and seriousness of forgetting. CONCLUSIONS: Sleep quality is related to both objective measures of sustained attention and self-awareness of memory decline. These findings suggest that interventions for improving sleep quality may contribute not only to improving the ability to focus on a particular task but also in reducing memory complaints in middle-aged and older adults.

Pomegranate Metabolites Impact Tryptophan Metabolism in Humans and Mice.

BACKGROUND: We showed that pomegranate juice (PomJ) can help to maintain memory in adults aged >50 y. The mechanism for this effect is unknown, but might involve Trp and its metabolites, which are important in brain function. OBJECTIVES: We aimed to test the hypothesis that PomJ and its metabolites ellagic acid (EA) and urolithin A (UA) affect Trp metabolism. METHODS: Stool and plasma from a cohort [11 PomJ, 9 placebo drink (PL)] of subjects enrolled in our double-blind, placebo-controlled trial (NCT02093130) were collected at baseline and after 1 y of PomJ or PL consumption. In a mouse study, cecum and serum were collected from DBA/2J mice receiving 8 wk of dietary 0.1% EA or UA supplementation. Trp metabolites and intestinal microbiota were analyzed by LC-MS and 16S rRNA gene sequencing, respectively. RESULTS: In the human study, the change in the plasma Trp metabolite indole propionate (IPA) over 1 y was significantly different between PomJ and PL groups (P = 0.03). In serum of experimental mice, we observed a 230% increase of IPA by EA but not UA, a 54% increase of indole sulfate by UA but not EA, and 43% and 34% decreases of kynurenine (KYN) by EA and UA, respectively. In cecum, there was a 32% decrease of Trp by UA but not EA, and an 86% decrease of KYN by EA but not UA (P < 0.05). The abundance of 2 genera, Shigella and Catenibacterium, was reduced by PomJ in humans as well as by UA in mice, and their abundance was negatively associated with blood IPA in humans and mice (P < 0.05). CONCLUSIONS: These results suggest a novel mechanism involving the regulation of host and microbial Trp metabolism that might contribute to the health benefits of ellagitannins and EA-enriched food, such as PomJ.

Managing the Burden of Dementia Related Delusions and Hallucinations.

Identify the burden experienced by patients with dementia-related delusions and hallucinations. Assess patients with dementia for the presence of delusions and hallucinations. Individualize treatment in patients with dementia-related delusions and hallucinations. Align treatment of patients with Parkinson's psychosis with current recommendations.

Brain health consequences of digital technology use
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Emerging scientific evidence indicates that frequent digital technology use has a significant impact-both negative and positive-on brain function and behavior. Potential harmful effects of extensive screen time and technology use include heightened attention-deficit symptoms, impaired emotional and social intelligence, technology addiction, social isolation, impaired brain development, and disrupted sleep. However, various apps, videogames, and other online tools may benefit brain health. Functional imaging scans show that internet-naive older adults who learn to search online show significant increases in brain neural activity during simulated internet searches. Certain computer programs and videogames may improve memory, multitasking skills, fluid intelligence, and other cognitive abilities. Some apps and digital tools offer mental health interventions providing self-management, monitoring, skills training, and other interventions that may improve mood and behavior. Additional research on the positive and negative brain health effects of technology is needed to elucidate mechanisms and underlying causal relationships.
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La evidencia científica que está surgiendo muestra que el empleo frecuente de la tecnología digital tiene un impacto significativo, tanto negativo como positivo, en la función cerebral y en el comportamiento. Los posibles efectos nocivos del tiempo prolongado frente a la pantalla y del empleo de la tecnología incluyen síntomas como marcado déficit de atención, deterioro de la inteligencia emocional y social, adicción a la tecnología, aislamiento social, deterioro del desarrollo cerebral y alteraciones del sueño. Sin embargo, hay varias aplicaciones, videojuegos y otras herramientas en línea que pueden beneficiar la salud del cerebro. En las imágenes cerebrales funcionales se ha observado que los adultos mayores vírgenes a internet que aprenden a buscar en línea, muestran aumentos significativos en la actividad neuronal cerebral durante las búsquedas simuladas en internet. Ciertos programas computacionales y videojuegos pueden mejorar la memoria, las destrezas en tareas múltiples, la fluidez de la inteligencia y otras habilidades cognitivas. Hay varias aplicaciones y herramientas digitales que ofrecen intervenciones en salud mental y que proporcionan automanejo, monitoreo, capacitación junto a otras intervenciones que pueden mejorar el estado de ánimo y el comportamiento. Se require de investigación adicional acerca de los efectos positivos y negativos de la tecnología sobre la salud del cerebro para dilucidar los mecanismos y las relaciones causales subyacentes.

D'après de nouvelles données scientifiques, l'usage fréquent des technologies numériques influe significativement sur le comportement et le fonctionnement cérébral, de façon aussi bien négative que positive. Une pratique excessive des écrans et des technologies numériques peut avoir des effets néfastes comme des symptômes de déficit d'attention, une intelligence émotionnelle et sociale altérée, une dépendance à la technologie, un isolement social, un développement cérébral dégradé et des troubles du sommeil. Cependant, certaines applications, jeux vidéo et autres outils en ligne peuvent avoir des effets bénéfiques sur le cerveau. L'imagerie fonctionnelle montre une activité neuronale significativement augmentée chez des personnes âgées jamais exposées à Internet et qui apprennent à faire des recherches en ligne. Certains programmes informatiques et jeux vidéo peuvent améliorer la mémoire, les compétences multitâches, l'agilité de l'intelligence et d'autres capacités cognitives. Dans le domaine de la santé mentale, différents outils et applications numériques permettant l'autogestion, le suivi, l'acquisition de compétences et d'autres techniques sont susceptibles d'améliorer l'humeur et le comportement du patient. Les effets positifs et négatifs de la technologie sur la santé cérébrale nécessitent d'être encore étudiés afin d'en mieux comprendre les mécanismes et les relations de cause à effet.

Committee classifier based on linear discriminant analysis for the detection of radioisotopes from airborne gamma-ray spectra.

A committee classifier was developed for use in the application of real-time pattern recognition to gamma-ray spectra collected from airborne surveys. This technique was designed to enhance detection performance relative to that of a single linear discriminant analysis model. The approach was based on utilizing multiple classifiers to check one another through a signal averaging method. This resulted in an ability to reject random false detections while maximizing detection sensitivity. Making use of spectral preprocessing algorithms previously studied, the committee classifiers were applied to the detection of cesium-137 and cobalt-60 in spectra collected in the field during airborne surveys. Applying a z-score methodology to the classification scores allowed classifiers developed with different processing parameters to operate in the same dataspace for the purpose of classifying the target spectra. The optimized classifiers were tested over 13 diverse locations, with nine of the sites containing the respective target isotopes. Results of the committee classifiers indicated an improvement in missed and false detection performance for both radioisotopes. In addition, work was performed to confirm that several suspected false detections were actually weak target signals only visible once co-added with other similar spectra. This result suggested the committee classifier performance may have exceeded the capabilities of the visual spectral inspection on which the performance statistics were based.

Randomized placebo-controlled study of the memory effects of pomegranate juice in middle-aged and older adults.

BACKGROUND: Antioxidant nutrients such as the polyphenols in pomegranate juice may prevent neuronal damage from the free radicals produced during normal metabolism. Previous research in animals and a short-term clinical trial in middle-aged and older adults support the potential memory benefits of pomegranate juice; however, the long-term effects of pomegranate juice consumption on cognition have not been studied. OBJECTIVE: In this study, we investigated the long-term effect of pomegranate juice on memory in nondemented middle-aged and older adults. METHODS: We performed a 12-month, randomized, double-blind, placebo-controlled trial of pomegranate juice in middle-aged and older adults. Two hundred and sixty-one subjects (aged 50-75 y) were randomly assigned to consume pomegranate juice [8 oz (236.5 mL) per day] or a placebo drink (8 oz, matched constituents of pomegranate juice except for pomegranate polyphenols). Memory measures [Brief Visuospatial Memory Test-Revised (BVMT-R) and Buschke Selective Reminding Test (SRT)] were assessed at 6 and 12 mo and analyzed using a mixed-effects general linear model. RESULTS: Twenty-eight subjects in the pomegranate juice group and 33 subjects in the placebo group dropped out before completing the study. Baseline variables in the 98 pomegranate juice and 102 placebo group subjects who completed the study did not differ significantly. Group by time interaction was statistically significant for BVMT-R Learning (F[2, 257]= 5.90, P = 0.003; between-group effect size [ES] = 0.45): the change within the pomegranate group was not significant (ES = 0.15), whereas the placebo group showed a significant decline (ES = -0.35). Changes in the other BVMT-R scores as well as the SRT measures were not significantly different between groups. CONCLUSIONS: Daily consumption of pomegranate juice may stabilize the ability to learn visual information over a 12-mo period. This trial was registered at clinicaltrials.gov as NCT02093130.

Temperature correction strategy for improving concentration predictions with near-infrared spectra of aqueous-based samples.

Concentration predictions from near-infrared spectra are used across a range of application areas. When aqueous samples are employed, the extreme temperature sensitivity of underlying water absorption bands can lead to significant errors in predicted analyte concentrations, even when efforts are made to control sample temperatures. To address this issue, a temperature-correction procedure was developed on the basis of modeling the systematic error that occurs in predicted concentrations as a function of variation in sample temperature. With this approach, a quantitative calibration model was developed for samples at a fixed temperature. This model was subsequently applied to the spectra of a second set of samples with known analyte concentrations collected under conditions of varying temperature. Using either measured temperatures or those estimated from a spectral temperature prediction model, a least-squares polynomial fit was performed between concentration residuals and temperature. Going forward, for a given sample temperature, the polynomial model was used to estimate the concentration residual at that temperature. The estimated residual was then used to correct the predicted concentration. For spectra collected in the 5000-4000 cm-1 near-infrared region, this methodology was tested for samples of glucose in buffer and mixture samples of glucose and lactate in buffer over the temperature range of 20.0-40.5 °C.