Why Would the Rise of Social Media Increase the Influence of Traditional Media on Collective Judgments?

In our original article (Etter, Ravasi & Colleoni, 2018), we argued that the rise of social media is changing how evaluations are made public and impact the formation of organizational reputation. In their counterpoint, [authors] argue in favour of a separation between the construct of media reputation and social media reputation. They further argue that the rise of social media is actually strengthening the impact of traditional media on the evaluations of key stakeholders. Finally, they urge scholars to take a cautious approach to the assumption that social media are introducing more dynamism in the formation of (media) reputation. We agree that, in some circumstances, a conceptual distinction between (traditional) media reputation and social media reputation might be useful to advance future research and theorization of reputational dynamics. In fact, in our original article we highlighted the importance to acknowledge the potential existence of different and separate “reputational arenas” (Aula & Mantere, 2013; see also Bromberg & Fine, 2002). We are less persuaded, however, by the other objections that [authors] raise

Robotic instrument segmentation with image-to-image translation

The semantic segmentation of robotic surgery video and the delineation of robotic instruments are important for enabling automation. Despite major recent progresses, the majority of the latest deep learning models for instrument detection and segmentation rely on large datasets with ground truth labels. While demonstrating the capability, reliance on large labelled data is a problem for practical applications because systems would need to be re-trained on domain variations such as procedure type or instrument sets. In this paper, we propose to alleviate this problem by training deep learning models on datasets that are synthesised using image-to-image translation techniques and we investigate different methods to perform this process optimally. Experimentally, we demonstrate that the same deep network architecture for robotic instrument segmentation can be trained on both real data and on our proposed synthetic data without affecting the quality of the output models' performance. We show this for several recent approaches and provide experimental support on publicly available datasets, which highlight the potential value of this approach

Social Media and the Formation of Organizational Reputation

The rise of social media is changing how evaluative judgments about organizations are produced and disseminated in the public domain. In this article, we discuss how these changes question traditional assumptions that research on media reputation rests upon, and we offer an alternative framework that begins to account for how the more active role of audiences, the changing ways in which they express their evaluations, and the increasing heterogeneity and dynamism that characterizes media reputation influence the formation of organizational reputations

Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery

Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images for robust segmentation of surgical tools. Due to the lack of laparoscopic frames annotated with both segmentation ground truth and kinematic information a new custom dataset was generated using the da Vinci Research Kit (dVRK) and is made available

Influence of Textile Structure and Silica Based Finishing on Thermal Insulation Properties of Cotton Fabrics

The aim of this work is to investigate the influence of weave structures and silica coatings obtained via sol-gel process on the thermal insulation properties of cotton samples. For this reason three main weave structures (plain, satin, and piqué) of cotton fabric were selected with different yarn count, threads per cm, and mass per square meter values. Thereafter, only for the plain weave, the samples were padded using silica sol formed by hydrolysis and subsequent condensation of 3-glycidoxypropyltrimethoxysilane under acidic conditions. The silanized plain weave samples were characterized by TGA and FT-IR techniques. The thermal properties were measured with a home-made apparatus in order to calculate thermal conductivity, resistance, and absorption of all the treated fabric samples. The relationship between the thermal insulation properties of the plain weave fabrics and the concentration of sol solutions has been investigated. Fabrics weave and density were found to strongly influence the thermal properties: piqué always shows the lowest values and satin shows the highest values while plain weave lies in between. The thermal properties of treated high-density cotton plain weave fabric were proved to be strongly influenced by finishing agent concentration

Deep Learning Based Robotic Tool Detection and Articulation Estimation with Spatio-Temporal Layers

Surgical-tool joint detection from laparoscopic images is an important but challenging task in computer-assisted minimally invasive surgery. Illumination levels, variations in background and the different number of tools in the field of view, all pose difficulties to algorithm and model training. Yet, such challenges could be potentially tackled by exploiting the temporal information in laparoscopic videos to avoid per frame handling of the problem. In this letter, we propose a novel encoder-decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos. When tested on benchmark and custom-built datasets, a median Dice similarity coefficient of 85.1% with an interquartile range of 4.6% highlights performance better than the state of the art based on single-frame processing. Alongside novelty of the network architecture, the idea for inclusion of temporal information appears to be particularly useful when processing images with unseen backgrounds during the training phase, which indicates that spatio-temporal features for joint detection help to generalize the solution

Visual kinematic force estimation in robot-assisted surgery – application to knot tying

Robot-assisted surgery has potential advantages but lacks force feedback, which can lead to errors such as broken stitches or tissue damage. More experienced surgeons can judge the tool-tissue forces visually and an automated way of capturing this skill is desirable. Methods to measure force tend to involve complex measurement devices or visual tracking of tissue deformation. We investigate whether surgical forces can be estimated simply from the discrepancy between kinematic and visual measurement of the tool position. We show that combined visual and kinematic force estimation can be achieved without external measurements or modelling of tissue deformation. After initial alignment when no force is applied to the tool, visual and kinematic estimates of tool position diverge under force. We plot visual/kinematic displacement with force using vision and marker-based tracking. We demonstrate the ability to discern the forces involved in knot tying and visualize the displacement force using the publicly available JIGSAWS dataset as well as clinical examples of knot tying with the da Vinci surgical system. The ability to visualize or feel forces using this method may offer an advantage to those learning robotic surgery as well as adding to the information available to more experienced surgeons

Social Media and the Formation of Organizational Reputation

The rise of social media is changing how evaluative judgments about organizations are produced and disseminated in the public domain. In this article, we discuss how these changes question traditional assumptions that research on media reputation rests upon, and we offer an alternative framework that begins to account for how the more active role of organizational audiences, the changing ways in which they express their evaluations, and the increasing heterogeneity and dynamism that characterizes media reputation influence the formation of organizational reputations

Influence of speed of sample processing on placental energetics and signalling pathways: implications for tissue collection.

INTRODUCTION: The placenta is metabolically highly active due to extensive endocrine and active transport functions. Hence, placental tissues soon become ischaemic after separation from the maternal blood supply. Ischaemia rapidly depletes intracellular ATP, and leads to activation of stress-response pathways aimed at reducing metabolic demands and conserving energy resources for vital functions. Therefore, this study aimed to elucidate the effects of ischaemia ex vivo as may occur during tissue collection on phosphorylation of placental proteins and kinases involved in growth and cell survival, and on mitochondrial complexes. METHODS: Eight term placentas obtained from normotensive non-laboured elective caesarean sections were kept at room-temperature and sampled at 10, 20, 30 and 45 min after delivery. Samples were analyzed by Western blotting. RESULTS: Between 10 and 45 min the survival signalling pathway intermediates, P-AKT, P-GSK3α and β, P-4E-BP1 and P-p70S6K were reduced by 30-65%. Stress signalling intermediates, P-eIF2α increased almost 3 fold after 45 min. However, other endoplasmic reticulum stress markers and the Heat Shock Proteins, HSP27, HSP70 and HSP90, did not change. Phosphorylation of AMPK, an energy sensor, was elevated 2 fold after 45 min. Contemporaneously, there was an ∼25% reduction in mitochondrial complex IV subunit I. DISCUSSION AND CONCLUSIONS: These results suggest that for placental signalling studies, samples should be taken and processed within 10 min of caesarean delivery to minimize the impact of ischaemia on protein phosphorylation

Prognosis of selected triple negative apocrine breast cancer patients who did not receive adjuvant chemotherapy

Background: Triple negative breast cancer encompasses several biological entities with different outcomes and is a priority to identify which patients require more treatment to reduce the risk of recurrence and which patients need less treatment. Patients and methods: Among the 210 women with first primary invasive apocrine non metastatic breast cancer operated on between January 1998 and December 2016 at the European Institute Oncology, Milan, we identified 24 patients with a pT1-pT2, node-negative, triple negative subtype and Ki-67 64 20% who did not receive adjuvant chemotherapy (CT). We compared the outcome of this cohort with a similar group of 24 patients with ductal tumors who received adjuvant chemotherapy, matched by pathological stage and biological features and also with a similar group of 12 patients with apocrine tumors who received adjuvant chemotherapy. Results: The median age was 64 and 61 years in the apocrine (w/o CT) and ductal group, respectively. The median value of Ki-67 expression was 12% in the apocrine group (w/o CT) and 16% in the ductal group (p < 0.001). After a median follow-up of 7.5 years, no patients in the apocrine group (w/o CT) experienced a breast cancer related event compared with 4 events in the ductal carcinoma group (Gray test p-value = 0.11). Conclusions: The outcome of selected apocrine triple negative breast cancer patients who did not received adjuvant chemotherapy is excellent and supports a treatment de-escalation. Multicenter projects focusing on the possibility of avoiding adjuvant chemotherapy in selected subtypes of triple negative breast cancers with favorable outcome are warranted