A simple and accurate matrix for model based photoacoustic imaging

Accurate model-based methods in Photo-Acoustic Tomography (PAT) can reconstruct the image from insufficient and inaccurate measurements. Most of the models either make the simplified assumption of spherical averaging or use accurate models that have computationally burdensome implementations. We present a simple and accurate measurement matrix that is derived from the pseudo-spectral PAT model. The accuracy of the measurement matrix is first validated against the experimental PAT signal. We also compare the model against the standard k-wave measurement model and the spherical averaging model. We then highlight several reconstruction strategies based on the nature of the region of interest to further demonstrate the accuracy of the proposed measurement matrix

Machine learning techniques for the diagnosis of alzheimer's disease: A review

© 2020 ACM. Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions

Systematic Literature Review of the Global Incidence and Prevalence of Myelodysplastic Syndrome and Acute Myeloid Leukemia

Abstract Introduction: Acute myeloid leukemia (AML) consists of a group of relatively well-defined hematopoietic neoplasms, while myelodysplastic syndrome (MDS) comprises a heterogeneous group of hematopoietic stem cell disorders. These disorders may occur de novo or may arise secondary to prior malignancies for which patients received cytotoxic chemotherapy and/or radiation therapy. While AML and MDS are reported in a number of national cancer registries, competing classifications prior to 2001 often preclude comparison of results globally. We conducted a systematic literature review in order to identify global rates of the incidence and prevalence of AML and MDS. Methods: We searched Embase and MEDLINE for published studies and recent conference abstracts on the incidence and/or prevalence of AML and MDS for data collected from 2001-2016. This time period parallels the introduction of the World Health Organization (WHO) classification of hematopoietic neoplasms. Two reviewers independently screened all abstracts and selected articles, and subsequently abstracted multiple data elements including: AML or MDS, geographic region, standardized incidence and/or prevalence rates, years of data collection, disease classification criteria (e.g. WHO, French American British (FAB), ICD-O-3, ICD-9/10), if condition was diagnosed de novo or treatment associated, age category, and gender. Incidence rates and prevalence by sub-disease classification were also abstracted when available. Results: Literature Review: Our search strategy yielded 874 abstracts initially reviewed for applicability. Of these, 84 were selected for article screening; 44 articles were excluded after screening and 40 articles proceeded to data abstraction and were included in analyses. 25 studies reported on MDS incidence and 2 on MDS prevalence; 17 studies reported on AML incidence and 4 on AML prevalence (some studies report both AML and MDS and incidence and prevalence). While the majority of studies are based on existing regional disease registries, a number describe analyses based on administrative claims data, or patient charts. Nine (9) studies reported on therapy associated incidence. Classification of AML or MDS was based on FAB (9 studies), WHO (10 studies), ICD-O-3 (11 studies) or ICD-9/10 (7 studies) criteria. Three studies were classified on the basis of chart review and/or clinical diagnosis by a physician. Regional distribution was: North America (10 studies), Europe (17 studies), Australia (4 studies), and the Rest of World (including 3 studies in Asia, 2 studies in Africa, 2 in South America and 2 in the Middle East). Analysis: Incidence rates are reported in Figure 1 by region for those studies reporting overall rates across clinical factors, age and gender (27 articles). Incidence of AML as a result of treatment for another cancer ranged from 0.06-2.6 per 100,000 and for MDS from 0.06-0.26 per 100,000. In general, incidence rates increased with increasing age in studies that reported results by age group. Prevalence of AML ranged from 0.6-11.0 per 100,000 and for MDS ranged from 0.22-13.2 per 100,000 for all age categories, genders and ethnicities. Discussion:This is the first study to report on the global incidence of AML and MDS and whether or not the disease occurs as a result of treatment for another oncologic condition or occurs de novo. Variation in study designs and heterogeneous population characteristics make interpretation of results challenging. Figure 1 Overall Incidence Rates for AML and MDS by Geographic Region Figure 1. Overall Incidence Rates for AML and MDS by Geographic Region Disclosures Lubeck: Outcomes Insights: Employment. Danese:Outcomes Insights: Employment. Jennifer:Outcomes Insights: Employment. Miller:Outcomes Insights: Consultancy. Richhariya:Seattle Genetics, Inc.: Employment. Garfin:Seattle Genetics, Inc.: Employment. </jats:sec

Circadian rhythms are more resilient to pacemaker neuron disruption in female Drosophila.

The circadian system regulates the timing of multiple molecular, physiological, metabolic, and behavioral phenomena. In Drosophila, as in other species, most of the research on how the timekeeping system in the brain controls the timing of behavioral outputs has been conducted in males, or sex has not been included as a biological variable. A critical set of circadian pacemaker neurons in Drosophila release the neuropeptide pigment-dispersing factor (PDF), which functions as a key output factor in the network with complex effects on other clock neurons. Lack of Pdf or its receptor, PdfR, results in most flies displaying arrhythmicity in activity-rest cycles under constant conditions. However, our results show that female circadian rhythms are less affected by mutations in both Pdf and PdfR. CRISPR-Cas9-mediated mutagenesis of Pdf, specifically in ventral lateral neurons (LNvs), also has a greater effect on male rhythms. We tested the influence of M-cells on the circadian network and showed that speeding up the molecular clock specifically in M-cells led to sexually dimorphic phenotypes, with a more pronounced effect on male rhythmic behavior. Our results suggest that the female circadian system is more resilient to manipulations of M-cells and the PDF pathway, suggesting that circadian timekeeping is more distributed across the clock neuron network in females

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.</jats:p