Notes

Outcomes of thorough lymphadenectomy regarding individuals with innovative phase ovarian carcinoma along with rare histologic sub-types.
We show that the crystal exhibits uniaxial anisotropy in the permittivity below 105 K when the structure becomes tetragonal, and exhibits biaxial anisotropy in the permittivity below 51 K when the structure becomes orthorhombic.Diffuse reverberation clutter often significantly degrades the visibility of abdominal structures. Reverberation clutter acts as a temporally stationary haze that originates from the multiple scattering within the subcutaneous layers and has a narrow spatial correlation length. We recently presented an adaptive beamforming technique, Lag-one Spatial Coherence Adaptive Normalization (LoSCAN), that can recover the contrast suppressed by incoherent noise. LoSCAN successfully suppressed reverberation clutter in numerous clinical examples. However, reverberation clutter is a three-dimensional phenomenon and can often exhibit a finite partial correlation between receive channels. Due to a strict noise-incoherence assumption, LoSCAN does not eliminate correlated reverberation clutter. This work presents a 2D matrix array-based LoSCAN method and evaluates matrix-LoSCAN based strategies to suppress partially correlated reverberation clutter. We validated the proposed matrix LoSCAN method using Field II simulations of a 64 × 64 symmetric 2D array. We show that a sub-aperture beamforming (SAB) method tuned to the direction of noise correlation is an effective method to enhance LoSCAN's performance. We evaluated the efficacy of the proposed methods using fundamental and harmonic channel data acquired from the liver of two healthy volunteers using a 64 × 16 custom 2D array. Compared to azimuthal LoSCAN, the proposed approach increased the contrast by up to 5.5 dB and generalized contrast to noise ratio (gCNR) by up to 0.07. We also present analytic models to understand the impact of partially correlated reverberation clutter on LoSCAN images and explain the proposed methods' mechanism of image quality improvement.We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras. While multi-camera setups are widespread for applications such as surveillance and traffic monitoring, existing trajectory forecasting methods typically focus on single-camera trajectory forecasting (SCTF), limiting their use for such applications. Brusatol supplier Furthermore, using a single camera limits the field-of-view available, making long-term trajectory forecasting impossible. We address these shortcomings of SCTF by developing an MCTF framework that simultaneously uses all estimated relative object locations from several viewpoints and predicts the object's future location in all possible viewpoints. Our framework follows a Which-When-Where approach that predicts in which camera(s) the objects appear and when and where within the camera views they appear. To this end, we propose the concept of trajectory tensors a new technique to encode trajectories across multiple camera views and the associated uncertainties. We develop several encoder-decoder MCTF models for trajectory tensors and present extensive experiments on our own database (comprising 600 hours of video data from 15 camera views) created particularly for the MCTF task. Results show that our trajectory tensor models outperform coordinate trajectory-based MCTF models and existing SCTF methods adapted for MCTF.CNN-based salient object detection (SOD) methods achieve impressive performance. However, the way semantic information is encoded in them and whether they are category-agnostic is less explored. One major obstacle in studying these questions is the fact that SOD models are built on top of the ImageNet pre-trained backbones which may cause information leakage and feature redundancy. To remedy this, here we first propose an extremely light-weight holistic model tied to the SOD task that can be freed from classification backbones and trained from scratch, and then employ it to study the semantics of SOD models. With the holistic network and representation redundancy reduction by a novel dynamic weight decay scheme, our model has only 100K parameters, 0.2% of parameters of large models, and performs on par with SOTA on popular SOD benchmarks. Using CSNet, we find that a) SOD and classification methods use different mechanisms, b) SOD models are category insensitive, c) ImageNet pre-training is not necessary for SOD training, and d) SOD models require far fewer parameters than the classification models. The source code is publicly available at https//mmcheng.net/sod100k/.Conventional salient object detection models cannot differentiate the importance of different salient objects. Two works have proposed to detect saliency ranking by assigning different degrees of saliency to different objects. However, one of these models cannot differentiate object instances and the other focuses more on sequential attention shift order inference. In this paper, we investigate a practical problem setting which requires to simultaneously segment salient instances and infer their relative saliency rank order. We present a novel unified model as the first end-to-end solution, where an improved Mask R-CNN is first used to segment salient instances and a saliency ranking branch is then added to infer the relative saliency. For relative saliency ranking, we build a novel graph reasoning module by combining four graphs to incorporate the instance interaction relation, local contrast, global contrast, and a high-level semantic prior, respectively. A novel loss function is proposed to effectively train the saliency ranking branch. Besides, a new dataset and an evaluation metric are proposed for this task, aiming at pushing forward this field of research. Finally, experimental results demonstrate that our proposed model is more effective than previous methods. We show an example of its practical usage on adaptive image retargeting.
Cellular sensitivity to heat is highly variable depending on the cell line. The aim of this paper is to assess the cellular sensitivity of the A375 melanoma cell line to continuous (CW) millimeter-waves (MMW) induced heating at 58.4 GHz, between 37 C and 47 C C to get a deeper insight into optimization of thermal treatment of superficial skin cancer.

Phosphorylation of heat shock protein 27 (HSP27) was mapped within an area of about 30 mm2 to visualize the variation of heat-induced cellular stress as a function of the distance from the waveguide aperture (MMW radiation source). A multiphysics computational approach was then adopted to yield both electromagnetic and thermal field distributions as well as corresponding specific absorption rate (SAR) and temperature elevation. Induced temperature rise was experimentally measured using a micro-thermocouple (TC).

Coupling of the incident electromagnetic (EM) field with TC leads was first characterized, and optimal TC placing was identified. HSP27 phosphorylation was induced at temperatures 41 C, and its level increases as a function of the thermal dose delivered, remaining mostly focused within 3 mm2.

Phosphorylation of HSP27 represents a valuable marker of cellular stress of A375 melanoma cells under MMW exposure, providing both quantitative and spatial information about the distribution of the thermal stress.

These results may contribute to the design of thermal treatments of superficial melanoma through MMW-induced heating in the hyperthermic temperature range.
These results may contribute to the design of thermal treatments of superficial melanoma through MMW-induced heating in the hyperthermic temperature range.
Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts -- a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects' wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep.

Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths.

The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events.

During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration.

The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects' normal sleep environment.
The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects' normal sleep environment.The effects of untreated OSA on cardiopulmonary function remain unclear. Cardiorespiratory fitness (CRF), commonly reflected by VO2 max measured during cardiopulmonary exercise testing (CPET), has gained popularity in evaluating numerous cardiopulmonary conditions and may provide a novel means of identifying OSA patients with the most clinically significant disease. This emerging testing modality provides simultaneous assessment of respiratory and cardiovascular function with results helping uncover evidence of evolving pathology in either organ system. In this review, we highlight the current state of the literature in regards to OSA and CRF with a specific focus on changes in cardiovascular function that have been previously noted. While OSA does not appear to limit respiratory function during exercise, studies seem to suggest an abnormal cardiovascular exercise response in this population including decreased cardiac output, a blunted heart rate response (i.e., chronotropic incompetence) and exaggerated blood pressure response. Surprisingly, despite these observed changes in the cardiovascular response to exercise, results involving VO2 max in OSA remain inconclusive. This is reflected by VO2 max studies involving middle-aged OSA patients showing both normal and reduced CRF. As prior studies have not extensively characterized oxygen desaturation burden, we propose that reductions in VO2 max may exist in OSA patients with only the most significant disease (as reflected by nocturnal hypoxia). Further characterizing this relationship remains important as some research suggests that positive airway pressure (PAP) therapy or aerobic exercise may improve CRF in patients with OSA. In conclusion, while it likely that severe OSA, via an abnormal cardiovascular response to exercise, is associated with decreased CRF; further study is clearly warranted to include determining if OSA with decreased CRF is associated with increased morbidity or mortality.
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