Assessing Quantitative Reasoning in a Ninth Grade Science Class Using Interdisciplinary Data Story Assignments

In a data-driven world, it is necessary that students graduate from high school quantitatively literate, with the ability to interpret quantities within a context to make informed decisions for their lives. A critical component of science learning is developing the ability to make sense of data, critically evaluate it, and effectively communicate scientific ideas. The purpose of this study is two-fold: 1) to investigate how 9th grade students in an Earth Science class use quantitative reasoning (QR) skills when constructing evidence-based scientific explanations during Data Story assignments and 2) to provide teachers with supports to incorporate Data Stories into their curriculum. A Data Story is an interdisciplinary, scaffolded written argumentation assignment that requires students to analyze authentic, real-world scientific data and draw their own conclusions. In doing so, students integrate several discrete skills to synthesize an argument that is supported by evidence. Quantitative and qualitative results were used to investigate affordances and challenges students face when constructing a Data Story, what QR skills they use in the process, and what aspects of QR are challenging for them. Two evidence-based learning progressions provided the foundation for the development of two rubrics to score the student Data Stories quantitatively. Four student interviews analyzed using Grounded Theory provided qualitative insight into the role of QR in evidence-based explanations. Results suggest students enjoyed the Data Story assignments, which exposed them to a range of graph-types and data literacy skills. However, students seemed to struggle to develop appropriate evidence to support a claim in the Claim-Evidence- Reasoning (CER) framework and may need additional supports in this area. Further analysis with the QR Rubric and student interviews revealed some aspects of QR that may be hindering science learning and the development of evidence-based reasoning including: 1) not reasoning about variables in the context of a dataset 2) looking only for a correlation or difference and 3) not using quantitative language. These are aspects teachers should consider when implementing Data Story assignments in their own classrooms as a way to enhance students’ abilities in developing appropriate evidence to support a claim

8x8 Reconfigurable quantum photonic processor based on silicon nitride waveguides

The development of large-scale optical quantum information processing circuits ground on the stability and reconfigurability enabled by integrated photonics. We demonstrate a reconfigurable 8x8 integrated linear optical network based on silicon nitride waveguides for quantum information processing. Our processor implements a novel optical architecture enabling any arbitrary linear transformation and constitutes the largest programmable circuit reported so far on this platform. We validate a variety of photonic quantum information processing primitives, in the form of Hong-Ou-Mandel interference, bosonic coalescence/anticoalescence and high-dimensional single-photon quantum gates. We achieve fidelities that clearly demonstrate the promising future for large-scale photonic quantum information processing using low-loss silicon nitride.Comment: Added supplementary materials, extended introduction, new figures, results unchange

Perioperative gait analysis after total hip arthroplasty: Does outpatient surgery compromise patient outcomes?

Background: There has been a continuing trend towards decreasing the length of hospital stay for patients undergoing total hip arthroplasty (THA). Outpatient procedures, discharging patients on the same day as surgery, have now become possible yet little research has been done to examine the impact this might have on patients during early recovery. We aimed to investigate the impact of discharge time on gait and patient-reported outcomes in the early post-operative period. Methods: We measured gait velocity, stride length, single-limb support and single-limb support symmetry preoperatively, at discharge from the hospital, and two-, six- and 12-weeks postoperatively. Participants also completed the Timed Up and Go and a series of questionnaires (WOMAC, SF-12, Harris Hip Score and pain VAS) at each visit. Results: Thirty-six participants undergoing a direct anterior THA with a single surgeon were enrolled in this study. Sixteen participants were discharged on the same day as surgery (outpatient group), while 20 stayed at least one night in hospital (inpatient group). We found pain at time of discharge to be significantly lower in the outpatient group compared to the inpatient group (p=0.04). We found no other differences between the groups for any gait, patient-reported or surgical outcomes (p\u3c0.05). Conclusion: Following a direct anterior total hip arthroplasty, patients who stayed overnight had significantly more pain at discharge than those discharged as outpatients. There were no differences in gait or patient-reported outcomes between groups. Interdisciplinary Reflection: This study incorporates aspects of kinesiology, surgery, rehabilitation and health policy

EPI-001, A Compound Active against Castration-Resistant Prostate Cancer, Targets Transactivation Unit 5 of the Androgen Receptor

ACKNOWLEDGEMENTS We thank J. M. Valverde (IRB) as well as the NMR facilities of the University of Barcelona (CCiT UB) and the Instituto de Química Física Rocasolano (IQFR, CSIC) for their assistance in, respectively, protein production and NMR. This work was supported by IRB, ICREA (X.S.), Obra Social “la Caixa” (Fellowship to E.D.M. and CancerTec grants to X.S.) MICINN (CTQ2009-08850 to X.S.), MINECO (BIO2012-31043 to X.S.; CTQ2014-56361-P to A.R), Marató de TV3 (102030 to X.S. and 102031 to E.E.P) the COFUND programme of the European Commission (C.T.W.P., A. R. and X.S.), the European Research Council (CONCERT, contract number 648201, to X.S.), the Ramón y Cajal program of MICINN (RYC-2011-07873 to C.W.B.) the Serra Hunter Programme (E.E.P.) and AGAUR (SGR-2014-56RR14 to E.E.P). IRB Barcelona is the recipient of a Severo Ochoa Award of Excellence from MINECO (Government of Spain)Peer reviewedPostprin

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning : Average climate versus extremes

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.Peer reviewe

Assimilative real-time models of HF absorption at high latitudes

Improved real-time HF communications frequency management is required for aircraft on trans-polar routes. Polar cap absorption (PCA) models have therefore been adapted to assimilate real-time measurements of zenithal cosmic radio noise absorption (~ 30 MHz) from a large network of online riometers in Canada and Finland. Two types of PCA model have been developed and improvements to model accuracy following optimisation are quantified. Real-time optimisation is performed by age-weighting riometer measurements in a non-linear regression. This reduces root-mean-square errors (RMSE) from 2-3 dB to less than 1 dB and mean errors to within ±0.2 dB over a wide latitude range. This paper extends previous work by further optimising the models’ dependences on solar-zenith angle to account for differences in the ionospheric response at sunrise and sunset (the Twilight Anomaly). Two models of the rigidity cutoff latitudes are compared and one is optimised in real time by regression to riometer measurements. Whilst measurements from the NASA POES satellites may provide a direct measurement of the rigidity cut-off, it is observed that proton flux measurements from POES often need correcting for relativistic electron contamination for several hours at the start of a PCA event. An optimised real-time absorption model will be integrated into HF ray-tracing propagation predictions relating to measurements of HF signal strengths on a network of HF transmitters and receivers in the high northern latitudes