Diabetes and the risk of prostate cancer

Zarówno cukrzyca, jak i rak prostaty są chorobami, które obecnie uważa się za światowe epidemie. Cukrzyca jest jedną z najszybciej rozpowszechniających się chorób, zaś rak prostaty jest drugim najczęściej rozpoznawanym na świecie nowotworem wśród mężczyzn i charakteryzuje się największym po raku płuca wskaźnikiem śmiertelności w Stanach Zjednoczonych Ameryki (USA). Większość badań epidemiologicznych wskazuje, iż występowanie cukrzycy wiąże się ze zmniejszonym ryzykiem rozwoju raka prostaty. Jednak przyczyna tego zjawiska pozostaje niewyjaśniona. Na rozwój obu powyższych jednostek chorobowych mają wpływ zarówno czynniki środowiskowe, hormonalne, immunologiczne, jak i genetyczne. W niniejszym artykule przedstawiono  wybrane czynniki, które mogą tłumaczyć odwrotną korelację pomiędzy ryzykiem występowania raka prostaty a cukrzycą oraz  hipotezy dotyczące tej zależności.Diabetes mellitus (DM) is one of the fastest deadlygrowing diseases around the world and prostate cancer (CaP) is one of the most common types ofcancer among men and it has the highest mortalityafter lung cancer in the United States of America(USA). It is interesting that epidemiologic evidencesuggests that the occurrence of DM is related toa decreased CaP risk. The cause of this associationremains largely unknown. DM and CaP are influencedby many factors, e.g.: environmental, hormonal,immunological and genetic. The aim of this studywas to underline the role of certain factors thatmay influence the inverse relationship betweenprostate cancer and diabetes and to asses thecurrently proposed hypotheses for understandingthe mecha-nism of this process

Hybrid Carbon-Based Scaffolds for Applications in Soft Tissue Reconstruction

Current biomedical scaffolds utilized in surgery to repair soft tissues commonly fail to meet the optimal combination of biomechanical and tissue regenerative properties. Carbon is a scaffold alternative that potentially optimizes the balance between mechanical strength, durability, and function as a cell and biologics delivery vehicle that is necessary to restore tissue function while promoting tissue repair. The goals of this study were to investigate the feasibility of fabricating hybrid fibrous carbon scaffolds modified with biopolymer, polycaprolactone and to analyze their mechanical properties and ability to support cell growth and proliferation. Environmental scanning electron microscopy, micro-computed tomography, and cell adhesion and cell proliferation studies were utilized to test scaffold suitability as a cell delivery vehicle. Mechanical properties were tested to examine load failure and elastic modulus. Results were compared to an acellular dermal matrix scaffold control (GraftJacket® [GJ] Matrix), selected for its common use in surgery for the repair of soft tissues. Results indicated that carbon scaffolds exhibited similar mechanical maximums and capacity to support fibroblast adhesion and proliferation in comparison with GJ. Fibroblast adhesion and proliferation was collinear with carbon fiber orientation in regions of sparsely distributed fibers and occurred in clusters in regions of higher fiber density and low porosity. Overall, fibroblast adhesion and proliferation was greatest in lower porosity carbon scaffolds with highly aligned fibers. Stepwise multivariate regression showed that the variability in maximum load of carbon scaffolds and controls were dependent on unique and separate sets of parameters. These finding suggested that there were significant differences in the functional implications of scaffold design and material properties between carbon and dermis derived scaffolds that affect scaffold utility as a tissue replacement construct

On intrinsic Stokes shift in wide GaN/AlGaN polar quantum wells

International audienc

"Sea Archer" Distributed Aviation Platform

Unlike past conflicts which were characterized by major naval battles in the open ocean, present day threats are mostly associated with rogue nations and terrorist cells. These threats are of a different nature to past threats and may strike at unsuspected times and locations. The United States Navy may operate from a Sea Base which projects power ashore through the use of surface and air assets. These assets must transit from the Sea Base in the blue water through the littoral region in order to reach the objective area. Total ship system designs of a group of high-speed littoral combat ships (LCS) are required which are capable of operating in these regions and defending the Sea Base and the surface and air assets from an asymmetric threat. With the modular design and the ability to carry multiple helicopters and underwater vehicles (UUV), the SEA SWAT LCS concept can be quickly employed as a force multiplier capable of operating as an Air Warfare or Undersea/Mine Warfare mission platform. With the addition of the core and Surface Warfare sensors and weapons to one of these modular mission packages, the SEA SWAT LCS concept for sea base defense will ensure air, surface and subsurface superiority during conflict. An advanced electrical power system in conjunction with an integrated propulsion system and zonal power distribution provides sustained combat capability against multiple asymmetric threats. Its enclosed super-structure allows for high survivability in a CBR environment.Approved for public release; distribution is unlimited

Probability in the grammar of German and Dutch: Interfixation in tri-constituent compounds

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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc