با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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41 |
Discrimination of wet or dried arterial and venous blood for forensic applications via eosin fluorescence lifetime
تبعیض شریانی مرطوب یا خشک شده خون وریدی برای کاربردهای پزشکی قانونی از طریق طول عمر فلورسانس ائوزین-2020 The arteriovenous oxygen content difference disappears after blood is exposed to air. The blood oxygen leveldependent
imaging method is unable to differentiate oxygen-saturated arteriovenous bloods, and no other
method exists for determining them. To overcome this limitation, we first proposed that the fluorescence lifetime
of eosin added to wet whole blood (WB) can distinguish air-exposed wet arterial blood (AB) from venous blood
(VB). We then demonstrated that the urea level in wet WB can be employed as an indicator for further confirmation
of wet arteriovenous blood based on the change in the eosin lifetime in the urease-urea reaction.
Because arterial clots mostly comprise aggregated platelets in dried blood whereas venous clots contain higher
amounts of fibrin. The eosin-stained platelets and fibrin have different eosin lifetimes, and this difference between
platelets and fibrin during clotting when the blood dries explains why the eosin lifetime can be used to
differentiate air-exposed wet and dried blood. Moreover, we found that the eosin short lifetime (τ1), mean
lifetime (τm), and lifetime-based scatter parameters can be applied to discriminate dried AB and VB clots.
Indeed, fluorescence lifetime imaging microscopy (FLIM) could be a potential method to support bloodstain
pattern analysis in criminal cases. Keywords: Oxygen content | Arteriovenous blood | Urease-urea reaction | Fluorescence lifetime | Criminal cases |
مقاله انگلیسی |
42 |
Oocyte and embryo evaluation by AI and multi-spectral autofluorescence imaging: Livestock embryology needs to catch-up to clinical practice
ارزیابی تخمک و جنین توسط هوش مصنوعی و تصویربرداری خودکار فلورسانس چند طیفی: جنین شناسی دام باید به مراحل بالینی برسد-2020 A highly accurate ‘non-invasive quantitative embryo assessment for pregnancy’ (NQEAP) technique that
determines embryo quality has been an elusive goal. If developed, NQEAP would transform the selection
of embryos from both Multiple Ovulation and Embryo Transfer (MOET), and even more so, in vitro
produced (IVP) embryos for livestock breeding. The area where this concept is already having impact is in
the field of clinical embryology, where great strides have been taken in the application of morphokinetics
and artificial intelligence (AI); while both are already in practice, rigorous and robust evidence of efficacy
is still required. Even the translation of advances in the qualitative scoring of human IVF embryos have
yet to be translated to the livestock IVP industry, which remains dependent on the MOET-standardised 3-
point scoring system. Furthermore, there are new ways to interrogate the biochemistry of individual
embryonic cells by using new, light-based methodologies, such as FLIM and hyperspectral microscopy.
Combinations of these technologies, in particular combining new imaging systems with AI, will lead to
very accurate NQEAP predictive tools, improving embryo selection and recipient pregnancy success. Keywords: Embryo selection | Machine learning | Pregnancy establishment | Embryo metabolism | Morphokinetics |
مقاله انگلیسی |
43 |
Phase volume quantification of agarose-ghee gels using 3D confocal laser scanning microscopy and blending law analysis: A comparison
اندازه گیری حجم فاز ژل های agarose-ghee با استفاده از میکروسکوپ اسکن لیزر کانفوکال 3D و تجزیه و تحلیل قانون: یک مقایسه -2020 A thorough understanding of the phase behaviour of biomaterial composites is imperative for manipulating the
structural and textural properties in novel food products. This study probed the phase behaviour of a model
system comprising agarose and a varying concentration of ghee. Results obtained from scanning electron microscopy
(SEM), micro differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR)
and dynamic oscillation in-shear revealed discontinuous and hard inclusions of ghee reinforcing the continuous,
weaker agarose matrix with increasing concentrations of the former. Phase behaviour of the system was
quantified in parallel with a novel method combining 3D confocal laser scanning microscopy (CLSM) imaging
and image analysis software - FIJI and Imaris - in an effort to substantiate the efficacy of the microscopic
protocol in quantifying phase behaviour. Phase volumes recorded with the microscopic protocol were in close
agreement to those modelled with the Lewis-Nielsen blending law using small-deformation dynamic oscillation.
However, results indicated that the inner filtering effect or ‘self-shadowing’ observed commonly in CLSM images
may pose a limitation to the application of this technique, necessitating further development before it can be
applied to more complex, industrially relevant systems. Keywords: Lewis-Nielsen blending law | Phase behaviour | Confocal laser scanning microscopy | 3D imaging | Image analysis |
مقاله انگلیسی |
44 |
Towards the interpretation of complex visual hallucinations in termsof self-reorganization of neural networks
به سمت تفسیر توهمات پیچیده بصری از نظر خود سازماندهی مجدد شبکه های عصبی-2020 Patients suffering from dementia with Lewy body (DLB) often see complex visual hallucinations (CVH).Despite many pathological, clinical, and neuroimaging studies, the mechanism of CVH remains unknown.One possible scenario is that top-down information is being used to compensate for the lack of bottom-up information. To investigate this possibility and understand the underlying mathematical structureof the CVH mechanism, we propose a simple computational model of synaptic plasticity with particu-lar focus on the effect of selective damage to the bottom-up network on self-reorganization. We showneurons that undergo a change in activity from a bottom-up to a top-down network framework duringthe reorganization process, which can be understood in terms of state transitions. Assuming that thepre-reorganization representation of this neuron remains after reorganization, it is possible to interpretneural response induced by top-down information as the sensation of bottom-up information. This sit-uation might correspond to a hallucinatory situation in DLB patients. Our results agree with existingexperimental evidence and provide new insights into data that have hitherto not been experimentallyvalidated on patients with DLB. Keywords : Network self-reorganization | Complex visual hallucinations| Synaptic plasticity | State transition | Oscillology |
مقاله انگلیسی |
45 |
STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method
تصویربرداری گرادیان اکو (STAGE) استراتژیک ، بخش سوم: پیشرفت های فنی و برنامه های بالینی از یک روش تصویربرداری سریع مغزی چند پارامتری سریع با کنتراست-2020 One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative
multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of
artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such
method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE
provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted
images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the
form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter
and cerebrospinal fluid. STAGE uses vendors product gradient echo sequences. It can be applied from 0.35 T to
7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we
discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a
large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a
new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion
recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1
mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given
individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment
of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and
white matter than conventional T1W images in both patients with Parkinsons disease and healthy controls. We
also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a
fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous
qualitative and quantitative images. Keywords: Quantitative magnetic resonance imaging | Susceptibility weighted imaging | T1 mapping | Quantitative susceptibility mapping | Multi-parametric magnetic resonance imaging | Strategically acquired gradient echo |
مقاله انگلیسی |
46 |
French magistrates perception of the introduction of neuroscientific data in expert reports: Effects on the assessment of the expert’s report and criminal case
تصور دادرسان فرانسوی از معرفی داده های علوم اعصاب در گزارش های کارشناسی : تأثیرات ارزیابی گزارش کارشناسی و پرونده جنایی-2020 Objective. – To analyze whether the judge’s perception of the quality, and scientific basis of a psychiatric
expert report of a criminal defendant can vary according to whether or not this evaluation includes
neuroscientific data (a written description of a structural neuroimaging MRI scan) and their effects on
the decisions made by judges. Experimental psychology has demonstrated a number of cognitive effects arising from exposure to neuroscientific explanations and/or neuroimaging data and which may bias
judgments and lead to (mis)interpretations that can affect decisions. This research suggests that
including neuroscience evidence in an expert report may impact they way the report is assessed by nonspecialists,
such as judges, whose work requires them to take into account such reports.
Method. – We conducted a study on 41 French judges in order to determine whether their perceptions
of the expert report (objectivity, reliability, scientific basis, quality, relevance, credibility, and
persuasiveness) and their assessment of risk of recidivism, link between the disorder and offense
and the influence of expert report on their decision-making, vary according to whether or not the
evaluation includes neuroscientific data. The magistrates had to read a clinical case, summarizing an
expertise, with or without neuroscientific data, and then answer various closed (criteria were evaluated
using 7-point Likert-scales) and open-ended questions (asking respondents to indicate the reasons
underlying their Likert-scale responses). Half of the magistrates received report containing
neuroscientific data and the other half a similar report, without this type of data. Quantitative analyses
were carried out to assess the effect of the sample’s characteristics on the responses given and to
compare the results between the two conditions (correlation analyses and Student T). Qualitative
analyses, terminological and thematic, were also carried out.
Results. – Quantitative and qualitative results show that the presence of neuroscience data in an expert
report affects judges’ perceptions of the report and the magistrates’ perceptions of the link between
disorder and offense. The judges considered the expert report including neuroscientific data to be more
relevant, more objective, better quality, and more reliable than the report without such data.
Furthermore, they found the expert’s arguments to be more persuasive and that these arguments had a
greater scientific basis when the report included neuroscientific data than when such data was absent.
Moreover, this phenomenon was stronger in more experienced magistrates than in less experienced
magistrates. The qualitative finding shows a greater ability to recognize shortcomings in expert reports
when they do not contain neuroscience data. The Expert reports including neuroscience data are
perceived as more scientific and objective.
Conclusion. – The presence of neuroscience data in an expert report affects judges’ perceptions of that
report. These effects may be related to cognitive biases described in the literature, in particular the
perceived scientific nature of neuroscience data. If judges are aware of their limits when it comes to
assessing technical data, they appear relatively unaware that scientific data can induce cognitive biases
and thereby affect their perceptions of expert reports. Keywords: Criminal liability | Evaluation | Justice decision | Magistrate | Neuroscience | Psychiatric expertise |
مقاله انگلیسی |
47 |
Current limits of neurolaw: A brief overview
محدودیت های فعلی حقوق عصبشناختی : مروری کوتاه-2020 Nowadays, we are witnessing the increasing scholarly attention to neuroscience achievements in thecontext of the law. However, the complexity of the brain cognitive functions and the performance limitsof current neuroscience techniques for discovering the secrets of the brain on one hand, and the needfor neuro-litigation development with ethical-legal constraints, on the other hand, caused some limitsin ‘law and neuroscience’. What credit can be given to neuroscience today in full development? Canfunctional brain imaging have a place in criminal procedures to assess liability and dangerousness, forexample? We will consider the limits of the techniques as well as the possible progress of the legislationin this field. These questions justify the creation of a new interdisciplinary concept: Neurolaw or Lawapplied to neuroscience. Keywords: Dangerousness | Law and neuroscience | Neurolaw limits | Functional magnetic resonance imaging | (fMRI) |
مقاله انگلیسی |
48 |
Detection and classification of bruises of pears based on thermal images
تشخیص و طبقه بندی کبودی گلابی بر اساس تصاویر حرارتی-2020 The detection and classification of bruises of pears based on thermal images have been investigated. A simple
thermal imaging system in the long-wavelength ranges (8–14 μm) assembledμwith hot air equipment was
constructed to capture cleaner images. Higher velocity and temperature of the air reduced the time required to
obtain a clean image, but the images were not sufficient able to discriminate the slight and invisible variation of
bruises over consecutive days. The grey-level co-occurrence matrix of the thermal images were analysed, and the
slight differences in the pears over consecutive days were presented in the form of a line chart. A traditional deep
learning algorithm commonly used in classification of big data sets was modified to one suitable for classification
of a small sample data set of thermasl images (3246 samples were used as the training data set and 1125 were
used as a test data set) collected from 300 pears over 10 days. The best test prediction accuracy obtained was
99.25%. Keywords: Detection and classification | Thermal images | Grey-level co-occurrence matrix | Deep learning |
مقاله انگلیسی |
49 |
Two dimensional joint inversion of direct current resistivity and radiomagnetotelluric data based on unstructured mesh
وارونگی مشترک دو بعدی مقاومت جریان مستقیم و داده های رادیوماگنتوتلوژنی بر اساس مش بدون ساختار-2020 Using the unstructured mesh, a new two-dimensional joint inversion algorithm has been developed for
Radiomagnetotelluric and Direct current resistivity data. The unstructured mesh is generated with triangular
cells, whose vertical and lateral lengths increase towards the depths. The Finite Element Method (FEM) has
been used in the forward modelling part of the developed joint inversion algorithm. In the previous studies,
structured grid-based joint inversion algorithms have been developed using the Finite Difference Method
(FDM). In the structured grid-based algorithms, when the mesh is being generated with rectangular cells, the
vertical lengths of the cells get bigger towards the depths while the lateral lengths remain constant. With the
structured mesh, the undulated surface topography cannot be represented well enough. Also, because of the incompatible
aspect ratio ofmodel cell sizes in deepermodel sections, the resolution of themodel parameters will
get smaller and cannot be resolved well with the structured grids. Imaging of surface topography and underground
resistivity structures by the new algorithm requires fewer elements than those using structured grids.
Therefore, the developed algorithm is faster than traditional 2D inversion algorithms. Furthermore, the resolution
of the deeper model parameters has been increased by using the definition of the unstructured grid. A regularized
inversion scheme with a smoothness-constrained stabilizer has been employed to invert the data. First,
we have tested the developed joint inversion algorithm using synthetic data simplified from archaeological and
mine site scenario and the results have been compared with the conventional algorithms using structured grids.
We have also tested our algorithmwith the real data which were collected frommineral investigation site at approximately
10 kmeast of the Elbistan district of Kahramanmaraş province, in thewest of the TaurusMountains,
Turkey. The results show that the developed joint inversion algorithm is a powerful tool to detect both resistive
and conductive targets. Keywords: Direct current resistivity | Radiomagnetotelluric | Joint Inversion | Mineral Exploration | Unstructured mesh | Modelling |
مقاله انگلیسی |
50 |
AI-enabled Microscopic Blood Analysis for Microfluidic COVID-19 Hematology
آنالیز خون میکروسکوپی مجهز به هوش مصنوعی برای هماتولوژی میکروسیال COVID-19-2020 Microscopic blood cell analysis is an important
methodology for medical diagnosis, and complete blood cell
counts (CBCs) are one of the routine tests operated in hospitals.
Results of the CBCs include amounts of red blood cells, white
blood cells and platelets in a unit blood sample. It is possible to
diagnose diseases such as anemia when the numbers or shapes
of red blood cells become abnormal. The percentage of white
blood cells is one of the important indicators of many severe
illnesses such as infection and cancer. The amounts of platelets
are decreased when the patient suffers hemophilia. Doctors
often use these as criteria to monitor the general health
conditions and recovery stages of the patients in the hospital.
However, many hospitals are relying on expensive hematology
analyzers to perform these tests, and these procedures are often
time consuming. There is a huge demand for an automated, fast
and easily used CBCs method in order to avoid redundant
procedures and minimize patients’ burden on costs of
healthcare. In this research, we investigate a new CBC detection
method by using deep neural networks, and discuss state of the
art machine learning methods in order to meet the medical
usage requirements. The approach we applied in this work is
based on YOLOv3 algorithm, and our experimental results
show the applied deep learning algorithms have a great
potential for CBCs tests, promising for deployment of deep
learning methods into microfluidic point-of-care medical
devices. As a case of study, we applied our blood cell detector to
the blood samples of COVID-19 patients, where blood cell clots
are a typical symptom of COVID-19. Keywords : microfluidic device | microscopic imaging | blood analysis haematology | COVID-19 | deep learning at edge |
مقاله انگلیسی |