ApiAP2 factors as candidate regulators of stochastic commitment to merozoite production in Theileria annulata

The ability of vector-borne Apicomplexan parasites (Babesia, Plasmodium and Theileria) to change from one life-cycle stage to the next is critical for establishment of infection and transmission to new hosts. Stage differentiation steps of both Plasmodium and Theileria are known to involve stochastic transition through an intermediate form to a point that commits the cell to generate the next stage in the life-cycle. In this study we have identified genes encoding ApiAP2 DNA binding proteins in Theileria annulata that are differentially expressed during differentiation from the macroschizont stage, through merozoite production (merogony) to the piroplasm stage. The results provide evidence that the ApiAp2 factor in Theileria that possesses the orthologue of the Plasmodium AP2-G domain may also operate to regulate gametocytogenesis, and that progression to merogony is promoted by the ability of a merozoite DNA binding protein to preferentially up-regulate its own production. In addition, identification of multiple ApiAP2 DNA binding domains that bind related motifs within and across vector-borne Apicomplexan genera lead to the proposal that the mechanisms that promote the transition from asexual to sexual replication will show a degree of conservation

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning:a retrospective observational study

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].</p

Object tracking with spectral imagery

Niniejsza praca poświęcona jest analizie skuteczności śledzenia obiektów przy pomocy obrazowania spektralnego wykonywanego za pomocą 16-kanałowej kamery spektralnej rejestrującej dane w trybie wideo w zakresie 400-1000 nm. Wykorzystano algorytm Lucas-Kanade, wyznaczający przepływ optyczny w charakterystycznych punktach obrazu, określonych metodą Shi-Tomasi. Śledzenie inicjowane jest ręcznie poprzez wskazanie prostokątnego okna zawierającego obiekt. Do przetwarzania wybierany jest monochromatyczny obraz odpowiadający długości fali, dla której liczba punktów leżących w tym oknie jest największa. Zastosowano reprezentację obrazu w formie piramidy, dzięki czemu zmniejszono zależności od zmian skali obserwowanego obiektu. Otrzymane w każdym kroku śledzenia nowe pozycje punktów charakterystycznych były analizowane w celu odrzucenia obserwacji odstających. Wykonano szereg eksperymentów polegających na próbie śledzenia makiety samochodu wojskowego w trudnych warunkach oświetlenia i przy niejednorodnym tle o kolorystyce zbliżonej do barw maskujących pojazdu. Otrzymane rezultaty potwierdziły zasadność stosowania obrazowania spektralnego do śledzenia obiektów.This paper is devoted to the analysis of the effectiveness of object tracking with spectral imagery performed with a 16-channel spectral video camera operating in the 400-1000 nm range. We used the Lucas-Kanade algorithm which computes the optical flow at characteristic points of the image which were determined by the Shi-Tomasi method. The tracking is initialized manually by pointing to a rectangular window containing the object. Monochrome image corresponding to the wavelength for which the number of points lying in this window is the greatest is selected for processing. We used a representation of an image in the form of a pyramid, so that dependence on scale changes of the observed object was reduced. New positions of characteristic points received in each step of tracking were analyzed in order to reject outliers. We performed a series of experiments that tries to track military vehicle model under difficult lighting conditions and heterogeneous background of a color similar to the vehicle masking colors. Obtained results confirmed the advisability of applying spectral imagery for object tracking

The use of spectral imaging for material identification in waste sorting

This study covers the application of multispectral imaging to identify materials for municipal waste sorting purposes. Three classes of objects were considered by differentiating waste fractions between paper, plastic and organic waste while objects of different classes may had very similar colors which makes their identification difficult with the use of conventional RGB cameras. The pre-processing was applied including the removal of dark frame, radiometric distortion correction and conversion to the reflectance. The classification was undertaken using the nearest neighbor method taking as the similarity measure the Euclidean distance between the normalized spectral signatures. The tests were performed using a 128-channel hyperspectral camera that captures the exact signatures but has a long acquisition time and 16-channel multispectral camera that allows for the processing online. The results obtained for the 128-channel camera were used to select the bands recorded by the multispectral camera. Two variants of band selection were examined.W artykule zastosowano obrazowanie multispektralne do identyfikacji materiałów na potrzeby sortowania odpadów komunalnych. Rozważono trzy klasy obiektów, różnicując frakcje odpadów na: papier, plastik i odpady organiczne, przy czym obiekty różnych klas mogły mieć bardzo zbliżone kolory, co utrudnia ich identyfikację z wykorzystaniem typowych kamer RGB. Zastosowano przetwarzanie wstępne obejmujące usuwanie ramki ciemnej, korektę zniekształceń radiometrycznych i konwersję do reflektancji. Klasyfikacji dokonano metodą najbliższego sąsiada przyjmując jako miarę podobieństwa odległość euklidesową pomiędzy znormalizowanymi sygnaturami spektralnymi. Testy wykonano z wykorzystaniem 128-kanałowej kamery hiperspektralnej, która rejestruje dokładne sygnatury, ale ma długi czas akwizycji oraz 16-kanałowej kamery multispektralnej pozwalającej na przetwarzanie w trybie online. Wyniki otrzymane dla kamery 128-kanałowej wykorzystano do wyboru pasm rejestrowanych przez kamerę multispektralną. Przebadano dwa warianty wyboru pasm

Tetrapterys crispa

Angiosperm