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ردیف | عنوان | نوع |
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61 |
Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images
شناسایی اشرشیا کولی تولید کننده سم غیر شیتا Sh157 با استفاده از روشهای یادگیری عمیق با تصاویر میکروسکوپ فوق قطبی-2020 Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121
and O145 often cause illness to people in the United States and the conventional identification of these
“Big-Six” are complex. The label-free hyperspectral microscope imaging (HMI) method, which provides
spectral “fingerprints” information of bacterial cells, was employed to classify serogroups at the cellular
level. In spectral analysis, principal component analysis (PCA) method and stacked auto-encoder (SAE)
method were conducted to extract principal spectral features for classification task. Based on these
features, multiple classifiers including linear discriminant analysis (LDA), support vector machine (SVM)
and soft-max regression (SR) methods were evaluated. Different sizes of datasets were also tested in
search for the suitable classification models. Among the results, SAE-based classification models performed
better than PCA-based models, achieving classification accuracy of SAE-LDA (93.5%), SAE-SVM
(94.9%) and SAE-SR (94.6%), respectively. In contrast, classification results of PCA-based methods such as
PCA-LDA, PCA-SVM and PCA-SR were only 75.5%, 85.7% and 77.1%, respectively. The results also suggested
the increasing number of training samples have positive effects on classification models. Taking
advantage of increasing dataset, the SAE-SR classification model finally performed better than others
with average accuracy of 94.9% in classifying STEC serogroups. Specifically, O103 serogroup was classified
with the highest accuracy of 97.4%, followed by O111 (96.5%), O26 (95.3%), O121 (95%), O145 (92.9%) and
O45 (92.4%), respectively. Thus, the HMI technology coupled with SAE-SR classification model has the
potential for “Big-Six” identification. Keywords: Foodborne bacteria | Classification | Food safety | Machine learning | Stacked auto-encoder | Optical method |
مقاله انگلیسی |
62 |
comSensitivity analysis of nondestructive magnetictechniques for the restoration of stamped markson low carbon steel
تجزیه و تحلیل حساسیت تکنیکهای مغناطیسی غیر مخرب برای بازسازی مهر فولادی کم کربن مارکسون-2020 Restoration of obliterated stamped marks is a common investigative technique used incriminal investigation. Considering the evolution of the obliteration techniques used bycriminals, a study of the feasibility of sensitive, effective, and nondestructive techniques isneeded. This study aims to evaluate the performance and sensibility of two nondestructivetechniques: magnetic particle restoration and magneto-optical imaging. To this end, steelsamples from automobile chassis were analyzed. The samples were characterized in orderto obtain information regarding material microstructure, magnetic characteristic, and hard-ness, followed by the restoration of stamp marks. Both magnetic particle restoration andmagneto-optical imaging were capable of partially restoring the identification code obliter-ated by overstamping. In addition, nondestructive techniques showed a restoration depthlimit of 260 m below the cavity. Keywords:Restoration | Sensibility | Obliterated marks | Magneto-optic imaging | Magnetic particle |
مقاله انگلیسی |
63 |
Clinical Decision Support: The Law, the Future, and the Role for Radiologists
پشتیبانی از تصمیم گیری بالینی: قانون ، آینده و نقش رادیولوژیست-2020 Clinical Decision Support (CDS) was designed as an interactive, electronic tool for use by clinicians that communicates Appropriate Use Criteria (AUC) information to the user and assists them in making the most appropriate treatment decision for a patient’s specific clinical condition. Policymakers recognized AUC as a potential solution to control inappropriate utilization of imaging and made CDS mandatory in the Protecting Access to Medicare Act of 2014. In the years since Protecting Access to Medicare Act, data on the potential impact of CDS has been mixed and much of the physician community has expressed concern about the logistics of the program. This article aims to review the legislation behind the AUC program, the events that have transpired since, and some of the challenges and opportunities facing radiologists in the current environment.br> |
مقاله انگلیسی |
64 |
Visualizing Mitochondrial Form and Function within the Cell
تجسم فرم و عملکرد میتوکندری در سلول-2020 The specific cellular role of mitochondria is influenced by the surrounding environment because
effective mitochondrial function requires the delivery of inputs (e.g., oxygen) and export of
products (e.g., signaling molecules) to and from other cellular components, respectively. Recent
technological developments in mitochondrial imaging have led to a more precise and comprehensive
understanding of the spatial relationships governing the function of this complex organelle,
opening a new era of mitochondrial research. Here, I highlight current imaging approaches
for visualizing mitochondrial form and function within complex cellular environments. Increasing
clarity of mitochondrial behavior within cells will continue to lend mechanistic insights into the
role of mitochondria under normal and pathological conditions and point to spatially regulated
processes that can be targeted to improve cellular function |
مقاله انگلیسی |
65 |
Emergence and interpretation of oscillatory behaviour similar to brain waves and rhythms
ظهور و تفسیر رفتار نوسانی شبیه به امواج مغزی و ریتم-2020 Electroencephalography (EEG) monitors —by either intrusive or noninvasive electrodes—time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms , which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behav- ior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by numerical simulating and studying a networked model of excitatory and inhibitory neurons which generates a vari- ety of comparable waves. In fact, we thus numerically reproduce oscillatory behavior sim- ilar to α, β, γand other rhythms as observed by EEG recordings, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual brain oscillatory activity in normal and pathological situations, and we also describe kind of stochastic resonance phenomena which could be useful to locate main qualitative changes of brain activity in (e.g.) humans. Keywords: EEG numerical simulation | Brain phase transitions | Brain activity stochastic resonance |
مقاله انگلیسی |
66 |
Taking up residence: A review of outcome studies examining residential treatment for youth with serious emotional and behavioural disorders
رفتن به محل اقامت : مروری بر نتایج مطالعاتی در مورد معالجه مسکونی جوانان با اختلالات عاطفی و رفتاری جدی -2020 This review focuses on studies that examine factors influencing the long-term outcome of youth after discharge
from residential treatment centres. We have identified 33 new publications since the last review was published
necessitating the current review. These outcome studies published between 2008 and 2018 described outcomes
at a minimum of thirty days after discharge. Pre-admission factors and intervention characteristics that influence
behavioural outcomes, placement outcomes, family outcomes, treatment adherence as well as criminality were
identified. Lack of randomised controlled studies makes it difficult to draw strong conclusions about efficacy of
the residential treatment. We identified other gaps in the extant research design and outcome measures. Much of
the research to-date has been informed by psychosocial models, without considering the fast growing stream of
neurobiological data from genetic and imaging studies. A broader model encompassing psychosocial and neurobiological
measures may improve our understanding of factors that influence outcomes after discharge. Over
time this promises deeper insights and more tailored interventions resulting in improved quality of care and
better outcomes. Keywords: Outcomes | Long-term | Residential treatment | Youth | Mental health | Post-discharge |
مقاله انگلیسی |
67 |
Long-term remote tracking the dynamics of surface water turbidity using a density peaks-based classification: A case study in the Three Gorges Reservoir, China
Long-term remote tracking the dynamics of surface water turbidity using a density peaks-based classification: A case study in the Three Gorges Reservoir, China-2020 Surface water turbidity (SWT), as a low-cost proxy of surface suspended sediment, is important for characterizing
the hydro-ecological process and light availability in the lake or reservoir ecosystem. In this study, we
proposed the combined use of HJ-1 charge-coupled device imaging and field observation to track the long-term
SWT dynamics with environmental changes in Lakes Gaoyang, Hanfeng, and Changshou of the Three Gorges
Reservoir, China. In situ remote sensing reflectance spectra were utilized to develop the characteristic spectral
indexes for the SWT estimation in different water optical classes separated by a density peaks-based classification.
Significant correlations were found between the red-, four-band, band ratio spectral indexes and SWT
(determination coefficient>0.71 and root-mean-square error<8.32 nephelometric turbidity unit), suggesting
a crucial role of the class-specific retrieval models for the SWT estimation in optically complex waters. The
proposed method was further used to monitor the spatio-temporal SWT dynamics over the three lakes from 2008
to 2019, demonstrating that the significant SWT decline in Lakes Gaoyang and Hanfeng and the relatively stable
trend in Lake Changshou during the 11-year period. Specifically, the SWT decreasing trends may be attributed to
the water level linkage mechanism of Three Gorges and Wuyang Dams. In addition, analyses with simultaneous
environmental factors showed that the seasonal and inter-annual variations of SWT appear to be closely correlated
with water level and rainfall. Long-term remote tracking of the SWT dynamics presented in this study
could provide new insight and reference for reservoir management in the post-Three Gorges Project Era. Keywords: Surface water turbidity | Remote sensing | Density peaks | Water optical classification | Long-term trend | Three Gorges Reservoir |
مقاله انگلیسی |
68 |
Mega-Archive and the EURONEAR tools for data mining world astronomical images
Mega-Archive و ابزارهای EURONEAR برای داده نویسی تصاویر نجومی جهان-2020 The world astronomical image archives offer huge opportunities to time-domain astronomy sciences
and other hot topics such as space defense, and astronomical observatories should improve this wealth
and make it more accessible in the big data era. In 2010 we introduced the Mega-Archive database and
the Mega-Precovery server for data mining images serendipitously containing Solar system bodies, with
focus on near Earth asteroids (NEAs). This paper presents the improvements and introduces some
new related data mining tools developed during the last years. Currently, Mega-Archive indexed 15
million images available from six major collections and other instrument archives and surveys. This
meta-data index collection is daily updated by a crawler which performs automated query of five
major collections. Since 2016, these data mining tools are installed on the new dedicated EURONEAR
server, and the database migrated to SQL which supports robust and fast queries. To constrain the
area to search for moving or fixed objects in images taken by large mosaic cameras, we built the
graphical tools FindCCD and FindCCD for Fixed Objects which overlay the targets across one of seven
mosaic cameras, plotting the uncertainty ellipse for poorly observed NEAs. In 2017 we improved
Mega-Precovery, which offers now two options for the ephemerides and three options for the input
(objects defined by designation, orbit or observations). Additionally, we developed Mega-Archive for
Fixed Objects (MASFO) and Mega-Archive Search for Double Stars (MASDS). We include a few use case
scenarios and we compare our data mining tools with other few similar services. The huge potential
of science imaging archives is still insufficiently exploited. Their use could be strongly enhanced by
defining a standard format needed to index the image archives. We recommend to the IAU to define
such a standard, asking the observatories to index their image archives in a homogeneous manner. Keywords: Data mining | Asteroids | Near Earth asteroids (NEAs) | Image archives | Mega-Archive | Mega-Precovery |
مقاله انگلیسی |
69 |
Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering
یادگیری عمیق و روش های هوش مصنوعی برای پراکندگی رامان و سطح رو به افزایش رامان-2020 Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides
an unprecedented opportunity to extract information from complex or big datasets in chromatography,
mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and
surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical
mixtures are acquired as large datasets for the analysis or imaging of chemical systems. The classical
linear methods of processing the information no longer suffice and thus machine learning methods for
extracting the chemical information from Raman and SERS experiments have been implemented
recently. In this review, we will provide a brief overview of the most common machine learning techniques
employed in Raman, a guideline for new users to implement machine learning in their data
analysis process, and an overview of modern applications of machine learning in Raman and SERS. Keywords: Deep learning | Machine learning | Artificial intelligence | Artificial neural network | Raman | Surface enhanced Raman scattering | SERS | Sensors |
مقاله انگلیسی |
70 |
Synchrotron-based techniques for studying the environmental health effects of heavy metals: Current status and future perspectives
تکنیک های مبتنی بر سنکروترون برای بررسی اثرات بهداشتی محیط زیست فلزات سنگین: وضعیت فعلی و دیدگاه های آینده-2020 This review summarizes the state-of-the-art synchrotron-based techniques for studying the environmental
health effects of heavy metals exposure. Synchrotron radiation based X-ray fluorescence (SRXRF)
is widely applied in quantification of metals in different biological and environmental samples. X-ray
absorption spectrometry (XAS) is used for speciation of heavy metals. With high energy resolution
fluorescence detected (HERFD) XAS, it is possible to study heavy metals in biological samples at realistic
concentrations. The focused synchrotron-based X-ray is applied to image metals down to nm resolution
at 2- or 3- dimension (2D or 3D). The combination of XAS with SRXRF can realize 2D or 3D spatial
speciation, along with other techniques like scanning transmission X-ray microscopy (STXM) and full
field XAS. The structure of metal-binding biomolecules can be characterized by Protein X-ray Diffraction
(PX) and/or XAS, together with neutron scattering. The future aspects of multimode detection, new
imaging methods, fast detector technologies and big data strategies in synchrotron-based techniques
were also discussed. Keywords: Synchrotron radiation | Quantification | Speciation | Mapping | Spatial speciation | Structure characterization | Heavy meta |
مقاله انگلیسی |