Notes

Overtraining Malady (OTS) as well as Relative Power Insufficiency within Sport (RED-S): Discussed Path ways, Symptoms as well as Complexity.
Inbred lines CKL1522, CKL05010, and CKL05017 had significantly lower Rust 3 scores and AUDPC compared to the resistant check CML444 and are potential donors of CR resistance alleles. The genetic correlations between CR disease resistance parameters were positive and strong. A neighbor-joining (NJ) tree and STRUCTURE suggested the presence of three major groups among the lines, with lines highly resistant to CR spread across the three groups. The genetic diversity among the highly resistant lines can be exploited by recycling genetically distant lines to develop new multiple disease resistant inbred lines for hybrid development and deployment.Fundamental mathematical constants such as e and π are ubiquitous in diverse fields of science, from abstract mathematics and geometry to physics, biology and chemistry1,2. Nevertheless, for centuries new mathematical formulas relating fundamental constants have been scarce and usually discovered sporadically3-6. Such discoveries are often considered an act of mathematical ingenuity or profound intuition by great mathematicians such as Gauss and Ramanujan7. Here we propose a systematic approach that leverages algorithms to discover mathematical formulas for fundamental constants and helps to reveal the underlying structure of the constants. We call this approach 'the Ramanujan Machine'. Our algorithms find dozens of well known formulas as well as previously unknown ones, such as continued fraction representations of π, e, Catalan's constant, and values of the Riemann zeta function. Several conjectures found by our algorithms were (in retrospect) simple to prove, whereas others remain as yet unproved. We present two algorithms that proved useful in finding conjectures a variant of the meet-in-the-middle algorithm and a gradient descent optimization algorithm tailored to the recurrent structure of continued fractions. Both algorithms are based on matching numerical values; consequently, they conjecture formulas without providing proofs or requiring prior knowledge of the underlying mathematical structure, making this methodology complementary to automated theorem proving8-13. Our approach is especially attractive when applied to discover formulas for fundamental constants for which no mathematical structure is known, because it reverses the conventional usage of sequential logic in formal proofs. Instead, our work supports a different conceptual framework for research computer algorithms use numerical data to unveil mathematical structures, thus trying to replace the mathematical intuition of great mathematicians and providing leads to further mathematical research.Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates1. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems2. Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models3. Bayesian optimization has also been recently applied in chemistry4-9; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software toota-driven decisions about which experiments to run.In the quest for post-CMOS (complementary metal-oxide-semiconductor) technologies, driven by the need for improved efficiency and performance, topologically protected ferromagnetic 'whirls' such as skyrmions1-8 and their anti-particles have shown great promise as solitonic information carriers in racetrack memory-in-logic or neuromorphic devices1,9-11. However, the presence of dipolar fields in ferromagnets, which restricts the formation of ultrasmall topological textures3,6,8,9,12, and the deleterious skyrmion Hall effect, when skyrmions are driven by spin torques9,10,12, have thus far inhibited their practical implementation. Antiferromagnetic analogues, which are predicted to demonstrate relativistic dynamics, fast deflection-free motion and size scaling, have recently become the subject of intense focus9,13-19, but they have yet to be experimentally demonstrated in natural antiferromagnetic systems. Here we realize a family of topological antiferromagnetic spin textures in α-Fe2O3-an Earth-abundant oxide insulator-capped with a platinum overlayer. see more By exploiting a first-order analogue of the Kibble-Zurek mechanism20,21, we stabilize exotic merons and antimerons (half-skyrmions)8 and their pairs (bimerons)16,22, which can be erased by magnetic fields and regenerated by temperature cycling. These structures have characteristic sizes of the order of 100 nanometres and can be chemically controlled via precise tuning of the exchange and anisotropy, with pathways through which further scaling may be achieved. Driven by current-based spin torques from the heavy-metal overlayer, some of these antiferromagnetic textures could emerge as prime candidates for low-energy antiferromagnetic spintronics at room temperature1,9-11,23.Following early hypotheses about the possible existence of Arctic ice shelves in the past1-3, the observation of specific erosional features as deep as 1,000 metres below the current sea level confirmed the presence of a thick layer of ice on the Lomonosov Ridge in the central Arctic Ocean and elsewhere4-6. Recent modelling studies have addressed how an ice shelf may have built up in glacial periods, covering most of the Arctic Ocean7,8. So far, however, there is no irrefutable marine-sediment characterization of such an extensive ice shelf in the Arctic, raising doubt about the impact of glacial conditions on the Arctic Ocean. Here we provide evidence for at least two episodes during which the Arctic Ocean and the adjacent Nordic seas were not only covered by an extensive ice shelf, but also filled entirely with fresh water, causing a widespread absence of thorium-230 in marine sediments. We propose that these Arctic freshwater intervals occurred 70,000-62,000 years before present and approximately 150,000-131,000 years before present, corresponding to portions of marine isotope stages 4 and 6.
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