Зміни міждиферонної та внутрішньодиферонної гетероморфії тканин шкіри за умов впливу наночастинок срібла розміром 20, 30, 70 нм

The study is focused on developing of morphological criteria of biological tissue reactions to metal nanoparticles by detecting changes of tissues heteromorphism interacting with NPs. The study of heteromorphism tissue provides an integrated assessment of functional state of the tissue, allowing objectively evaluate the response of biological tissues in metal nanoparticles. Size-dependent effects of silver nanoparticles were identified, namely depending on the nanoparticles size recovery rate of basement membrane structure differs; the increase of mitotic index of the epidermal basal cells; changes of dermal fibroblasts’s heteromorphism, such as increasing of number of functionally active fibroblasts; and the number of collagen fibers of the dermis. Reactive changes of intradifferon heteromorphism of epidermal basal cells and the dermal fibroblasts was described using quantitative histological methods.Метою дослідження є розробка морфологічних критеріїв оцінки реакцій біологічних тканин на металеві наночастинки методом змін внутрішньо- й міждиферонної гетероморфії тканин, які взаємодіють із наночастинками. Вивчення тканинної гетероморфії забезпечує комплексну оцінку функціо нального стану тканини, дозволяючи об’єктивно оцінити реакцію біологічних тканин при взаємодії з наночастинками металів. За допомогою кількісних гістологічних методик описані реактивні зміни внутрішньодиферонної гетероморфії клітин базального шару епідермісу і фібробластів дерми. Виявлені розмірозалежні ефекти впливу наночастинок срібла

2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images

Abstract We present a technique for estimating the spatial layout of humans in still images—the position of the head, torso and arms. The theme we explore is that once a person is localized using an upper body detector, the search for their body parts can be considerably simplified using weak constraints on position and appearance arising from that detection. Our approach is capable of estimating upper body pose in highly challenging uncontrolled images, without prior knowledge of background, clothing, lighting, or the location and scale of the person in the image. People are only required to be upright and seen from the front or the back (not side). We evaluate the stages of our approach experimentally using ground truth layout annotation on a variety of challenging material, such as images from the PASCAL VOC 2008 challenge and video frames from TV shows and feature films. We also propose and evaluate techniques for searching a video dataset for people in a specific pose. To this end, we develop three new pose descriptors and compare their clas

Monocular {3D} Pose Estimation and Tracking by Detection

Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current methods are able to recover 3D pose for a single person in controlled environments, they are severely challenged by real-world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an initial estimate of the 2D articulation and viewpoint of the person from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust image observations to reliably recover 3D pose. We demonstrate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions