Malignant melanoma of soft parts with osteoclast-rich giant cells: A rare tumor of the jejunum

Malignant melanoma of soft parts (MMSP), first described by Franz M. Enzinger, is a rare tumor of unknown cell origin. We describe a case of a 45-year-old male who presented with a 1-year history of abdominal pain, weakness, and anaemia. Computerized tomography enteroclysis showed a mass in the jejunum that was suggestive of a gastrointestinal stromal tumor. An ulceroinfiltrative lesion measuring 6.5 x 4 x 2 cm was identified. Microscopy revealed typical features of MMSP with numerous osteoclasts-like giant cells. The diverse histomorphology and immunohistochemical characteristics of this case involving a rare tumor at a rare site is presented.

Oxygen regulates molecular mechanisms of cancer progression and metastasis

Oxygen is the basic molecule which supports life and it truly is "god's gift to life.” Despite its immense importance, research on "oxygen biology” has never received the light of the day and has been limited to physiological and biochemical studies. It seems that in modern day biology, oxygen research is summarized in one word "hypoxia.” Scientists have focused on hypoxia-induced transcriptomics and molecular-cellular alterations exclusively in disease models. Interestingly, the potential of oxygen to control the basic principles of biology like homeostatic maintenance, transcription, replication, and protein folding among many others, at the molecular level, has been completely ignored. Here, we present a perspective on the crucial role played by oxygen in regulation of basic biological phenomena. Our conclusion highlights the importance of establishing novel research areas like oxygen biology, as there is great potential in this field for basic science discoveries and clinical benefits to the society

Science in the wilderness: the predicament of scientific research in India’s wildlife reserves

Ecology and allied scientific disciplines aim to understand patterns and processes pertaining to wild species, their ecosystems and their relationships with humans. India’s wildlife reserves are important ‘living laboratories’ for these disciplines. Today, there is a disturbing trend across India where scientists are increasingly denied access to wildlife reserves for scientific research or are seriously impeded, without scope for redress. Although official wildlife management rhetoric emphasizes the need for scientific research, in reality, it is viewed as undesirable and permitted, if at all, as a concession, subject to the discretion of individual forest officials. With no enabling legislative or policy framework to promote and apply science in our wildlife reserves, we are concerned that the future of many scientific disciplines in India is being jeopardized. Here, we provide an analysis of this issue and outline steps needed to promote scientific research in our natural areas

The missing links:A global study on uncovering financial network structures from partial data

Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Asymmetric New Product Development Alliances: Win-Win or Win-Lose Partnerships?

Interorganizational alliances are widely recognized as critical to product innovation, particularly in high-technology markets. Many new product development (NPD) alliances tend to be asymmetric, that is, they are formed between a larger firm and a smaller firm. As is the case with alliances in general, asymmetric alliances also typically result in changes in the shareholder values of the partner firms. Are the changes in shareholder values of the partner firms significant? Are asymmetric NPD alliances win-win or win-lose partnerships? Are the gains or losses symmetric for the larger and smaller partner firms? What factors drive the changes in shareholder values of the partner firms? These important questions remain largely unexplored as evidenced by the dearth of empirical research on the effect of asymmetric NPD alliances on shareholder value and on the apportionment of this value between the partner firms. We develop and empirically test a model of short-term changes in shareholder values of larger and smaller firms involved in NPD alliances, using the event study methodology on data covering 167 asymmetric alliances in the information technology and communication industries. In this model, we examine alliance, firm, and partner characteristics as potential determinants of the changes in shareholder values of the partner firms due to an NPD alliance announcement. Our model accounts for selection correction, potential cross-correlation across the residuals from the models of firm value changes for the larger and smaller firms, and unobserved heterogeneity. The results suggest that both the partners experience significant short-term financial gains, but there are considerable asymmetries between the larger and smaller firms with regard to the effects of alliance, partner, and firm characteristics on the gains of the partner firms. The results relating to alliance characteristics suggest that while a broad scope alliance enhances the financial gains for the larger firm, a scale R& D alliance (relative to a link alliance) contributes positively to the financial gains for the smaller firm. With regard to partner characteristics, while partner alliance experience positively influences the financial gains for the larger firm, it has no significant effect on the financial returns for the smaller firm. Further, partner innovativeness is positively associated with the financial gains for the larger firm, but partner reputation is unrelated to the financial gains of the smaller firm. Regarding firm characteristics, the magnitude of the financial gains accruing from a firm's own alliance experience is considerably higher for the smaller firm than it is for the larger firm. We outline the implications of the research findings for future research and management practice.innovation, new product development, strategic alliance, shareholder value, strategic management