82 research outputs found
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
The uncertainty in the radiative forcing caused by aerosols and its effect on climate change calls for research to improve knowledge of the aerosol
particle formation and growth processes. While experimental research has
provided a large amount of high-quality data on aerosols over the last 2Â decades, the inference of the process rates is still inadequate, mainly due to
limitations in the analysis of data. This paper focuses on developing
computational methods to infer aerosol process rates from size distribution
measurements. In the proposed approach, the temporal evolution of aerosol
size distributions is modeled with the general dynamic equation (GDE) equipped with
stochastic terms that account for the uncertainties of the process rates. The
time-dependent particle size distribution and the rates of the underlying
formation and growth processes are reconstructed based on time series of
particle analyzer data using Bayesian state estimation – which not only
provides (point) estimates for the process rates but also enables quantification of
their uncertainties. The feasibility of the proposed computational framework
is demonstrated by a set of numerical simulation studies.</p
Identification of new particle formation events with deep learning
New particle formation (NPF) in the atmosphere is globally an
important source of climate relevant aerosol particles. Occurrence of NPF
events is typically analyzed by researchers manually from particle size
distribution data day by day, which is time consuming and the classification
of event types may be inconsistent. To get more reliable and consistent
results, the NPF event analysis should be automatized. We have developed an
automatic analysis method based on deep learning, a subarea of machine
learning, for NPF event identification. To our knowledge, this is the first
time that a deep learning method, i.e., transfer learning of a convolutional
neural network (CNN), has successfully been used to automatically classify
NPF events into different classes directly from particle size distribution
images, similarly to how the researchers carry out the manual classification. The
developed method is based on image analysis of particle size distributions
using a pretrained deep CNN, named AlexNet, which was transfer learned to
recognize NPF event classes (six different types). In transfer learning, a
partial set of particle size distribution images was used in the training
stage of the CNN and the rest of the images for testing the success of the
training. The method was utilized for a 15-year-long dataset measured at San
Pietro Capofiume (SPC) in Italy. We studied the performance of the training
with different training and testing of image number ratios as well as with
different regions of interest in the images. The results show that clear
event (i.e., classes 1 and 2) and nonevent days can be identified with an
accuracy of ca. 80 %, when the CNN classification is compared with that
of an expert, which is a good first result for automatic NPF event analysis.
In the event classification, the choice between different event classes is
not an easy task even for trained researchers, and thus overlapping or confusion
between different classes occurs. Hence, we cross-validated the learning
results of CNN with the expert-made classification. The results show that the
overlapping occurs, typically between the adjacent or similar type of classes,
e.g., a manually classified Class 1 is categorized mainly into classes 1 and
2 by CNN, indicating that the manual and CNN classifications are very
consistent
for most of the days. The classification would be more consistent, by
both human and CNN, if only two different classes are used for event days
instead of three classes. Thus, we recommend that in the future analysis,
event days should be categorized into classes of quantifiable (i.e., clear
events, classes 1 and 2) and nonquantifiable (i.e., weak events, ClassÂ
3). This would better describe the difference of those classes: both
formation and growth rates can be determined for quantifiable days but not
both for nonquantifiable days. Furthermore, we investigated more deeply the
days that are classified as clear events by experts and recognized as
nonevents by the CNN and vice versa. Clear misclassifications seem to occur
more commonly in manual analysis than in the CNN categorization, which is
mostly due to the inconsistency in the human-made classification or errors in
the booking of the event class. In general, the automatic CNN classifier has
a better reliability and repeatability in NPF event classification than
human-made classification and, thus, the transfer-learned pretrained CNNs
are powerful tools to analyze long-term datasets. The developed NPF event
classifier can be easily utilized to analyze any long-term datasets more
accurately and consistently, which helps us to understand in detail
aerosol–climate interactions and the long-term effects of climate change on
NPF in the atmosphere. We encourage researchers to use the model in other
sites. However, we suggest that the CNN should be transfer learned again for
new site data with a minimum of ca. 150 figures per class to obtain good
enough classification results, especially if the size distribution evolution
differs from training data. In the future, we will utilize the method for
data from other sites, develop it to analyze more parameters and evaluate how
successfully CNN could be trained with synthetic NPF event data.</p
Munchausen by proxy syndrome mimicking systemic autoinflammatory disease: case report and review of the literature
Role of T198 Modification in the Regulation of p27Kip1 Protein Stability and Function
The tumor suppressor gene p27Kip1 plays a fundamental role in human cancer progression. Its expression and/or functions are altered in almost all the different tumor histotype analyzed so far. Recently, it has been demonstrated that the tumor suppression function of p27 resides not only in the ability to inhibit Cyclins/CDKs complexes through its N-terminal domain but also in the capacity to modulate cell motility through its C-terminal portion. Particular interest has been raised by the last amino-acid, (Threonine 198) in the regulation of both protein stability and cell motility
LeishVet update and recommendations on feline leishmaniosis
Limited data is available on feline leishmaniosis (FeL) caused by Leishmania infantum worldwide. The LeishVet group presents in this report a review of the current knowledge on FeL, the epidemiological role of the cat in L. infantum infection, clinical manifestations, and recommendations on diagnosis, treatment and monitoring, prognosis and prevention of infection, in order to standardize the management of this disease in cats. The consensus of opinions and recommendations was formulated by combining a comprehensive review of evidence-based studies and case reports, clinical experience and critical consensus discussions. While subclinical feline infections are common in areas endemic for canine leishmaniosis, clinical illness due to L. infantum in cats is rare. The prevalence rates of feline infection with L. infantum in serological or molecular-based surveys range from 0 % to more than 60 %. Cats are able to infect sand flies and, therefore, they may act as a secondary reservoir, with dogs being the primary natural reservoir. The most common clinical signs and clinicopathological abnormalities compatible with FeL include lymph node enlargement and skin lesions such as ulcerative, exfoliative, crusting or nodular dermatitis (mainly on the head or distal limbs), ocular lesions (mainly uveitis), feline chronic gingivostomatitis syndrome, mucocutaneous ulcerative or nodular lesions, hypergammaglobulinaemia and mild normocytic normochromic anaemia. Clinical illness is frequently associated with impaired immunocompetence, as in case of retroviral coinfections or immunosuppressive therapy. Diagnosis is based on serology, polymerase chain reaction (PCR), cytology, histology, immunohistochemistry (IHC) or culture. If serological testing is negative or low positive in a cat with clinical signs compatible with FeL, the diagnosis of leishmaniosis should not be excluded and additional diagnostic methods (cytology, histology with IHC, PCR, culture) should be employed. The most common treatment used is allopurinol. Meglumine antimoniate has been administered in very few reported cases. Both drugs are administered alone and most cats recover clinically after therapy. Follow-up of treated cats with routine laboratory tests, serology and PCR is essential for prevention of clinical relapses. Specific preventative measures for this infection in cats are currently not available
Long-term medical treatment in congenital hyperinsulinism: a descriptive analysis in a large cohort of patients from different clinical centers
Mos is not required for the initiation of meiotic maturation in Xenopus oocytes
International audienc
Assessment of the pathogen microorganisms and resistance patterns in patient with device-associated infections in the ICU
MPF amplification in Xenopus oocyte extracts depends on a two-step activation of Cdc25 phosphatase
International audienc
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