Burnhamgregory5892

Kidney biopsies are currently performed using preoperative imaging to identify the lesion of interest and intraoperative imaging used to guide the biopsy needle to the tissue of interest. Often, these are not the same modalities forcing the physician to perform a mental cross-modality fusion of the preoperative and intraoperative scans. This limits the accuracy and reproducibility of the biopsy procedure. In this study, we developed an augmented reality system to display holographic representations of lesions superimposed on a phantom. This system allows the integration of preoperative CT scans with intraoperative ultrasound scans to better determine the lesion's real-time location. An automated deformable registration algorithm was used to increase the accuracy of the holographic lesion locations, and a magnetic tracking system was developed to provide guidance for the biopsy procedure. Our method achieved a targeting accuracy of 2.9 ± 1.5 mm in a renal phantom study.Pelvic trauma surgical procedures rely heavily on guidance with 2D fluoroscopy views for navigation in complex bone corridors. This "fluoro-hunting" paradigm results in extended radiation exposure and possible suboptimal guidewire placement from limited visualization of the fractures site with overlapped anatomy in 2D fluoroscopy. A novel computer vision-based navigation system for freehand guidewire insertion is proposed. The navigation framework is compatible with the rapid workflow in trauma surgery and bridges the gap between intraoperative fluoroscopy and preoperative CT images. The system uses a drill-mounted camera to detect and track poses of simple multimodality (optical/radiographic) markers for registration of the drill axis to fluoroscopy and, in turn, to CT. Surgical navigation is achieved with real-time display of the drill axis position on fluoroscopy views and, optionally, in 3D on the preoperative CT. The camera was corrected for lens distortion effects and calibrated for 3D pose estimation. Custom marker jigs were constructed to calibrate the drill axis and tooltip with respect to the camera frame. A testing platform for evaluation of the navigation system was developed, including a robotic arm for precise, repeatable, placement of the drill. Experiments were conducted for hand-eye calibration between the drill-mounted camera and the robot using the Park and Martin solver. Experiments using checkerboard calibration demonstrated subpixel accuracy [-0.01 ± 0.23 px] for camera distortion correction. The drill axis was calibrated using a cylindrical model and demonstrated sub-mm accuracy [0.14 ± 0.70 mm] and sub-degree angular deviation.Segmentation of the uterine cavity and placenta in fetal magnetic resonance (MR) imaging is useful for the detection of abnormalities that affect maternal and fetal health. In this study, we used a fully convolutional neural network for 3D segmentation of the uterine cavity and placenta while a minimal operator interaction was incorporated for training and testing the network. The user interaction guided the network to localize the placenta more accurately. We trained the network with 70 training and 10 validation MRI cases and evaluated the algorithm segmentation performance using 20 cases. The average Dice similarity coefficient was 92% and 82% for the uterine cavity and placenta, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of 2% and 9%, respectively. The results demonstrate that the deep learning-based segmentation and volume estimation is possible and can potentially be useful for clinical applications of human placental imaging.Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance. We added a set of sparse surface points as an additional input to supervise the CNNs for more accurate image segmentation. We tested our technique by applying minimal interactions to supervise the networks for segmentation of the prostate on magnetic resonance images. We used U-Net and a new network architecture that was based on U-Net (dual-input path [DIP] U-Net), and showed that our supervising technique could significantly increase the segmentation accuracy of both networks as compared to fully automatic segmentation using U-Net. see more We also showed DIP U-Net outperformed U-Net for supervised image segmentation. We compared our results to the measured inter-expert observer difference in manual segmentation. This comparison suggests that applying about 15 to 20 selected surface points can achieve a performance comparable to manual segmentation.Sila-Peterson type reactions of the 1,4,4-tris(trimethylsilyl)-1-metallooctamethylcyclohexasilanes (Me3Si)2Si6Me8(SiMe3)M (2a, M = Li; 2b, M = K) with various ketones were investigated. The obtained products strongly depend on the nature of the ketone component. With 2-adamantanone 2a,b afforded the moderately stable silene 3. 3 is the first example of an Apeloig-Ishikawa-Oehme-type silene with the tricoordinate silicon atom incorporated into a cyclopolysilane framework and could be characterized by NMR and UV spectroscopy as well as by trapping reactions with water, methanol, and MeLi. The reaction of 2b with aromatic ketones also follows a sila-Peterson type mechanism with formation of carbanionic species. With 1,2-diphenylcyclopropenone 2b reacted by conjugate 1,4-addition to give a spirocyclic carbanion. In most cases the underlying reaction mechanism could be elucidated by the isolation and characterization of unstable intermediates and final products after proper derivatization.Research indicates that individuals of different races, ethnic backgrounds, and class origins differ in their unemployment rates. We know less, however, about whether these differences result from the differing groups' unequal hazards of entering or exiting unemployment and even less about how economic fluctuations moderate the ethnoracial and class-origin gaps in the long-term risks of transitioning into and out of unemployment. Using Rounds 1-17 of the National Longitudinal Survey of Youth 1997 and event history models, we show that non-Hispanic blacks become more similar to non-Hispanic whites in their paces of entering unemployment as their local unemployment rate rises, perhaps because jobs largely closed to the former are eliminated in a greater proportion during recessions. Nonetheless, blacks' relatively slow pace of transitioning from unemployment to having a job decelerates further with economic downturns. By contrast, Hispanics' paces of entering and exiting unemployment relative to non-Hispanic whites hardly change with local unemployment rates, despite unemployed Hispanics' slower rate of transitioning to having a job. With respect to class origin, we find that the advantages in both unemployment entry and recovery of young men with relatively educated parents diminish with economic deterioration. Based on these results, we suggest that facing economic pressure, employers' preference for workers of a higher class origin is more malleable than their preference for whites over blacks, making unemployed blacks especially disadvantaged in uncertain economic times.Farm mechanization among smallholder farming systems in developing countries is emerging as a viable option to off-set the effects of labor out-migration and shortages that undermine agricultural productivity. However, there is limited empirical literature on gender and farm mechanization. link2 This study assesses the impacts of the gender of household heads on mini-tiller adoption in the hills of Nepal, using an exogenous switching treatment regression model. Our findings reveal that there is a significant gender gap in mini-tiller adoption between male-headed households (MH-HHs) and female-headed households (FH-HHs). Compared to MH-HHs, the mini-tiller adoption rate is significantly lower among the FH-HHs, and a large amount of unobserved heterogeneity is deriving this difference. Moreover, when MH-HHs and FH-HHs have similar observed attributes, the mini-tiller adoption rate among the food insecure FH-HHs is higher than in the food secure group. link3 The gender-differentiated mini-tiller adoption rate can be minimized primarily by enhancing market access. Findings suggest that farm mechanization policies and programs targeted to the FH-HHs can reduce the gender-differentiated adoption gap in Nepal and similar hill production agro-ecologies in South Asia, which will enhance the farm yield and profitability.Background Candida auris is an emerging yeast frequently reported as resistant to multiple antifungal drugs commonly used to treat Candida infections. This specie can colonize the patient's skin and has great ability for producing outbreaks in hospitals. C. auris is phylogenetically related to other Candida species, can be misidentified using conventional biochemical or commercial methods and requires specific technology for its identification. Case report We report the first isolate of C. auris in Cali, Colombia, from a central venous catheter in a 37-year-old patient with rheumatoid arthritis and endocarditis who did not have symptoms of sepsis. The yeast was initially misidentified as C. haemulonii using the Phoenix system and subsequently identified as C. auris by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). The broth microdilution method was used to determine the minimum inhibitory concentration; the isolate was susceptible to fluconazole, itraconazole, voriconazole and amphotericin B. Conclusions This report contributes to knowledge of the epidemiology of C. auris infections in individuals with underlying disease and describes an isolate with a behavior different from what is usually reported.This article reviews critical aspects that have had an impact on the implementation of epidemiological surveillance of extreme maternal morbidity, as a tracer event of quality maternal care at population and institutional level; taking into account that maternal mortality has been usually monitored, and its analysis allows interventions to avoid maternal death. Until 2015, very few countries had been able to meet the goals established in the Millennium Development Goals (MDGs), especially MDG 5 - improving maternal health. As of today, it is observed that maternal mortality rate is quite heterogeneous, with rates from 1 case per 100,000 live births in developed countries, to more than 100 cases per 100,000 live births in developing countries. Therefore, complementary strategies such as surveillance of the extreme maternal morbidity could offer a more effective alternative to identify and implement interventions that allow us to prevent mortality and strengthen the quality of obstetric care. In addition, the importance of extreme maternal morbidity as a quality tracer event is that, unlike what is observed with maternal mortality, this is an event that occurs more frequently, is anticipatory of death, and the surviving pregnant woman is the primary source of information.