Strategic Scientific Disclosure – Evidence from the Leahy-Smith America Invents Act

We examine the impact of technological competition on voluntary innovation disclosure using changes scientific publications around the enactment of Leahy-Smith America Invents Act of 2011 (AIA). The AIA changes the patent system from first-to-invent to first-inventor-to-file system and induces a patent “race” that increases technological competition. Firms with resource constraints tend to be slow in filing a patent and are disadvantaged in this race. Using a difference-in-differences design, we show that financially constrained firms strategically increase scientific publications in an attempt to block competitors from obtaining a patent and extend the patent race after the enactment of AIA. This effect is more pronounced among firms (1) that are less capital intensive, and whose competitors have a lower cost of entry; (2) that face more patent competition; and (3) whose patents have longer lifecycles. The findings suggest that technological competition is a key determinant of firms’ scientific publications. The positive effect of the AIA on corporate scientific publications is consistent with the policy makers’ goal to promote knowledge spillover in society

CD4+CD25+FOXP3+ regulatory T cells: a potential “armor” to shield “transplanted allografts” in the war against ischemia reperfusion injury

Despite the advances in therapeutic interventions, solid organ transplantation (SOT) remains the “gold standard” treatment for patients with end-stage organ failure. Recently, vascularized composite allotransplantation (VCA) has reemerged as a feasible treatment option for patients with complex composite tissue defects. In both SOT and VCA, ischemia reperfusion injury (IRI) is inevitable and is a predominant factor that can adversely affect transplant outcome by potentiating early graft dysfunction and/or graft rejection. Restoration of oxygenated blood supply to an organ which was previously hypoxic or ischemic for a period of time triggers cellular oxidative stress, production of both, pro-inflammatory cytokines and chemokines, infiltration of innate immune cells and amplifies adaptive alloimmune responses in the affected allograft. Currently, Food and Drug Administration (FDA) approved drugs for the treatment of IRI are unavailable, therefore an efficacious therapeutic modality to prevent, reduce and/or alleviate allograft damages caused by IRI induced inflammation is warranted to achieve the best-possible transplant outcome among recipients. The tolerogenic capacity of CD4+CD25+FOXP3+ regulatory T cells (Tregs), have been extensively studied in the context of transplant rejection, autoimmunity, and cancer. It was not until recently that Tregs have been recognized as a potential cell therapeutic candidate to be exploited for the prevention and/or treatment of IRI, owing to their immunomodulatory potential. Tregs can mitigate cellular oxidative stress, produce anti-inflammatory cytokines, promote wound healing, and tissue repair and prevent the infiltration of pro-inflammatory immune cells in injured tissues. By using strategic approaches to increase the number of Tregs and to promote targeted delivery, the outcome of SOT and VCA can be improved. This review focuses on two sections: (a) the therapeutic potential of Tregs in preventing and mitigating IRI in the context of SOT and VCA and (b) novel strategies on how Tregs could be utilized for the prevention and/or treatment of IRI

Sequential approach to joint flow-seismic inversion for improved characterization of fractured media

Seismic interpretation of subsurface structures is traditionally performed without any account of flow behavior. Here we present a methodology for characterizing fractured geologic reservoirs by integrating flow and seismic data. The key element of the proposed approach is the identification—within the inversion—of the intimate relation between fracture compliance and fracture transmissivity, which determine the acoustic and flow responses of a fractured reservoir, respectively. Owing to the strong (but highly uncertain) dependence of fracture transmissivity on fracture compliance, the modeled flow response in a fractured reservoir is highly sensitive to the geophysical interpretation. By means of synthetic models, we show that by incorporating flow data (well pressures and tracer breakthrough curves) into the inversion workflow, we can simultaneously reduce the error in the seismic interpretation and improve predictions of the reservoir flow dynamics. While the inversion results are robust with respect to noise in the data for this synthetic example, the applicability of the methodology remains to be tested for more complex synthetic models and field cases.Eni-MIT Energy Initiative Founding Member ProgramKorea (South). Ministry of Land, Transportation and Maritime Affairs (15AWMP-B066761-03

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

Large-scale genetic study in East Asians identifies six new loci associated with colorectal cancer risk

Known genetic loci explain only a small proportion of the familial relative risk of colorectal cancer (CRC). We conducted the largest genome-wide association study in East Asians with 14,963 CRC cases and 31,945 controls and identified six new loci associated with CRC risk (P = 3.42 × 10−8 to 9.22 × 10−21) at 10q22.3, 10q25.2, 11q12.2, 12p13.31, 17p13.3 and 19q13.2. Two of these loci map to genes (TCF7L2 and TGFB1) with established roles in colorectal tumorigenesis. Four other loci are located in or near genes involved in transcription regulation (ZMIZ1), genome maintenance (FEN1), fatty acid metabolism (FADS1 and FADS2), cancer cell motility and metastasis (CD9) and cell growth and differentiation (NXN). We also found suggestive evidence for three additional loci associated with CRC risk near genome-wide significance at 8q24.11, 10q21.1 and 10q24.2. Furthermore, we replicated 22 previously reported CRC loci. Our study provides insights into the genetic basis of CRC and suggests new biological pathways

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi