Identificação das bactérias envolvidas na sepse grave de fêmeas caninas com piometra submetidas a ovário-histerectomia terapêutica

Pyometra is a common reproductive disorder that affects female dogs. It can represent a serious clinical entity and progress to severe sepsis and septic shock. The premature antibiotic therapy is crucial for a better prognosis. The aim of this study was to identify the most frequent microorganisms involved in the context of pyometra and severe sepsis in animals undergoing ovariohysterectomy, by blood and uterine secretion culture and antibiogram. The tests were conducted in 33 female dogs with pyometra. The most frequent recovered bacteria were Escherichia coli in 57.57%. Staphylococcus sp. E. coli, followed by enrofloxacin, cephalexin and the combination of amoxicillin and clavulanic acid. Uterine secretions cultures were more sensitive than blood culture to identify the bacterial (p<0.0001). The bacterial identification followed by an antibiogram allows to choose a better therapy in the presented disease in dogs.A piometra é uma afecção reprodutiva comum que acomete fêmeas caninas, podendo se agravar e progredir para o quadro de sepse grave e choque séptico. A precocidade da instituição da antibioticoterapia é determinante para um melhor prognóstico. O objetivo deste estudo foi avaliar os principais microrganismos envolvidos nos casos de sepse grave em cadelas acometidas por piometra e submetidas à ovário-histerectomia terapêutica, por meio de realização de hemocultura e cultura da secreção uterina e antibiograma. Foram avaliadas 33 fêmeas caninas e o principal agente envolvido com a sepse grave secundária à piometra foi a Escherichia coli, identificada em 57,57% dos casos. Também foram identificados Staphylococcus sp., com incidência de 9,09%, Citrobacter koseri, Enterobacter cloacae, Enterobacter faecalis, Eduardsiella sp., Klebsiella pneumoniae e Streptococcus sp., com 3,03% de frequência cada. Após a realização do antibiograma pelo método de difusão, os antimicrobianos que apresentaram maior eficácia contra as cepas de Escherichia coli foram a gentamicina, a enrofloxacina, a cefalexina e a associação de amoxicilina com ácido clavulânico, nesta ordem. A cultura da secreção uterina foi mais sensível que a hemocultura para identificação do agente microbiano (p<0,0001). A identificação bacteriana é útil para direcionar a antibioticoterapia empírica mais específica, de acordo com o perfil de sensibilidade, minimizando assim o desenvolvimento de resistência, o custo do tratamento e o risco de reações adversas aos antimicrobianos utilizados

A new spectroscopic and interferometric study of the young stellar object V645 Cyg

We present the results of high-resolution optical spectroscopy, low-resolution near-IR spectroscopy and near-infrared speckle interferometry of the massive young stellar object candidate V645 Cyg, acquired to refine its fundamental parameters and the properties of its circumstellar envelope. Speckle interferometry in the $H$- and $K$-bands and an optical spectrum in the range 5200--6680 \AA with a spectral resolving power of $R$ = 60 000 were obtained at the 6-m telescope of the Russian Academy of Sciences. Another optical spectrum in the range 4300--10500 \AA with $R$ = 79 000 was obtained at the 3.6-m CFHT. Low-resolution spectra in the ranges 0.46--1.4 $\mu$m and 1.4--2.5 $\mu$m with $R \sim$ 800 and $\sim$ 700, respectively, were obtained at the 3-m Shane telescope of the Lick Observatory. Using a novel kinematical method based on the non-linear modeling of the neutral hydrogen density profile in the direction toward the object, we propose a distance of $D = 4.2\pm$0.2 kpc. We also suggest a revised estimate of the star's effective temperature, T$_{\rm eff} \sim$25 000 K. We resolved the object in both $H$- and $K$-bands. We conclude that V645 Cyg is a young, massive, main-sequence star, which recently emerged from its cocoon and has already experienced its protostellar accretion stage. The presence of accretion is not necessary to account for the high observed luminosity of (2--6)$\times 10^4$ M$_{\odot}$ yr$^{-1}$. The receding part of a strong, mostly uniform outflow with a terminal velocity of $\sim$800 km s$^{-1}$ is only blocked from view far from the star, where forbidden lines form.Comment: 14 pages, 10 figure

Multiwavelength variability and correlation studies of Mrk 421 during historically low X-ray and γ-ray activity in 2015-2016

We report a characterization of the multiband flux variability and correlations of the nearby (z = 0.031) blazar Markarian 421 (Mrk 421) using data from Metsahovi, Swift, Fermi-LAT, MAGIC, FACT, and other collaborations and instruments from 2014 November till 2016 June. Mrk 421 did not show any prominent flaring activity, but exhibited periods of historically low activity above 1 TeV (F->1 TeV 0.1 TeV) gamma-rays, which, despite the low activity, show a significant positive correlation with no time lag. The HRkeV and HRTeV show the harder-when-brighter trend observed in many blazars, but the trend flattens at the highest fluxes, which suggests a change in the processes dominating the blazar variability. Enlarging our data set with data from years 2007 to 2014, we measured a positive correlation between the optical and the GeV emission over a range of about 60 d centred at time lag zero, and a positive correlation between the optical/GeV and the radio emission over a range of about 60 d centred at a time lag of 43(-6)(+9) d. This observation is consistent with the radio-bright zone being located about 0.2 parsec downstream from the optical/GeV emission regions of the jet. The flux distributions are better described with a lognormal function in most of the energy bands probed, indicating that the variability in Mrk 421 is likely produced by a multiplicative process

Object Re-identification in Multiple Object Tracking

U ovom radu ćemo pokušati zamijeniti konvolucijsku mrežu sa transformerom sa ciljem ostvarivanja bržih i preciznijih rezultata na MOT zadatku. MOT zadatak ima niz izazova poput detekcije objekta, predviđanja pokreta objekta, izrada modela koji daje diskriminativne mape značajki za objekte u svrhu očuvanja identiteta kroz sekvence. Usprkos uspješnom treniranju snažnijeg modela za reidentifikaciju objekta, rezultati metrika praćenja više pokretnih objekata ostaju isti, što nas dovodi do dva moguća zaključka; ili Transformeri još nisu dorasli ovakvom zdatku ili postoji određena granica koliko se može postići samim modelom koji na temelju izgleda reidentificira objekte.In this thesis, we will try to replace a convolution neural network with a transformer in order to achieve faster and more precise results on an MOT task. MOT task has a row of challenges like object detection, object movement prediction, developing a model that provides discriminant feature maps for objects for the purpose of preserving the identity through sequences. Despite successfully training a stronger model for reidentification of an object, metric results for following multiple moving objects stayed the same as before the replacement, which leads us to two possible conclusions; one is that the Transformers are not yet up to such a task, and the second option is that there is a certain limit to what can be achieved by a model that reidentifies objects based on appearance

Human Body Segmentation

Kako bi se lakše razumio problem pa tako i njegovo rješavanje navedene su osnovne razlike između raznih problema računalnog vida kako bismo naglasili složenost i položaj problema klasifikacije i lokalizacije segmentacije. Prikazujemo problem semantičke segmentacije kako bi čitatelj razumio što pokušavamo postići i dobiti iz danih informacija, kako iz običnih slika dobiti vjerojatnosti pripadnosti svakog piksela po jednoj klasi te kako prilagoditi naše podatke kategorizacijom za takve mreže. Analizom konvolucijskih mreža i njihovih operacija dolazimo do konačnog modela kojeg treniramo korištenjem objašnjene funkcije pogreške i evaluiramo korištenjem raznih metrika kao što su IoU i kategorička točnost te demonstriramo još jasniji prikaz rezultata pomoću konfuzijske matrice. Kroz svaki korak je pokazano korištenje biblioteke Keras i javnih python biblioteka za lako praćenje izvedbe. Pokazani su uspješni rezultati i ostavljeno je mjesta za napredak i dublje ulaženje u ovu temu koja u ovom radu čini dobar početak za ulazak u polje računalnog vida računalne znanosti.In order to understand the issue at hand and therefor make it easier to solve we first discus differences and similarities within different computer vision problems so we can make it easier to place the complexity of our semantic segmentation problem that covers classification and localization within this field. We proceed to properly show and visualize our problem so we can understand what exactly are we trying to achieve with given information, how do we get the model to output probabilities for each class for every pixel on our image, and how do we make our data compatible for those nets with categorical shape. By analyzing different convolutional nets, we choose a single architecture that fits our needs and proceed to train it with explained loss function and then evaluate it with different metrics like IoU and categorical accuracy. A simple demonstration of Confusion matrix is also shown so the results are perfectly clear. On every step of the way a Keras and python methods usage is shown so it is easier to follow along. Results are shown with room left to improve and explore. This paper makes a good start for all those interested in the field of computer vision

Object Re-identification in Multiple Object Tracking

U ovom radu ćemo pokušati zamijeniti konvolucijsku mrežu sa transformerom sa ciljem ostvarivanja bržih i preciznijih rezultata na MOT zadatku. MOT zadatak ima niz izazova poput detekcije objekta, predviđanja pokreta objekta, izrada modela koji daje diskriminativne mape značajki za objekte u svrhu očuvanja identiteta kroz sekvence. Usprkos uspješnom treniranju snažnijeg modela za reidentifikaciju objekta, rezultati metrika praćenja više pokretnih objekata ostaju isti, što nas dovodi do dva moguća zaključka; ili Transformeri još nisu dorasli ovakvom zdatku ili postoji određena granica koliko se može postići samim modelom koji na temelju izgleda reidentificira objekte.In this thesis, we will try to replace a convolution neural network with a transformer in order to achieve faster and more precise results on an MOT task. MOT task has a row of challenges like object detection, object movement prediction, developing a model that provides discriminant feature maps for objects for the purpose of preserving the identity through sequences. Despite successfully training a stronger model for reidentification of an object, metric results for following multiple moving objects stayed the same as before the replacement, which leads us to two possible conclusions; one is that the Transformers are not yet up to such a task, and the second option is that there is a certain limit to what can be achieved by a model that reidentifies objects based on appearance

Human Body Segmentation

Kako bi se lakše razumio problem pa tako i njegovo rješavanje navedene su osnovne razlike između raznih problema računalnog vida kako bismo naglasili složenost i položaj problema klasifikacije i lokalizacije segmentacije. Prikazujemo problem semantičke segmentacije kako bi čitatelj razumio što pokušavamo postići i dobiti iz danih informacija, kako iz običnih slika dobiti vjerojatnosti pripadnosti svakog piksela po jednoj klasi te kako prilagoditi naše podatke kategorizacijom za takve mreže. Analizom konvolucijskih mreža i njihovih operacija dolazimo do konačnog modela kojeg treniramo korištenjem objašnjene funkcije pogreške i evaluiramo korištenjem raznih metrika kao što su IoU i kategorička točnost te demonstriramo još jasniji prikaz rezultata pomoću konfuzijske matrice. Kroz svaki korak je pokazano korištenje biblioteke Keras i javnih python biblioteka za lako praćenje izvedbe. Pokazani su uspješni rezultati i ostavljeno je mjesta za napredak i dublje ulaženje u ovu temu koja u ovom radu čini dobar početak za ulazak u polje računalnog vida računalne znanosti.In order to understand the issue at hand and therefor make it easier to solve we first discus differences and similarities within different computer vision problems so we can make it easier to place the complexity of our semantic segmentation problem that covers classification and localization within this field. We proceed to properly show and visualize our problem so we can understand what exactly are we trying to achieve with given information, how do we get the model to output probabilities for each class for every pixel on our image, and how do we make our data compatible for those nets with categorical shape. By analyzing different convolutional nets, we choose a single architecture that fits our needs and proceed to train it with explained loss function and then evaluate it with different metrics like IoU and categorical accuracy. A simple demonstration of Confusion matrix is also shown so the results are perfectly clear. On every step of the way a Keras and python methods usage is shown so it is easier to follow along. Results are shown with room left to improve and explore. This paper makes a good start for all those interested in the field of computer vision