Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study.

BACKGROUND Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. OBJECTIVE The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). METHODS Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. RESULTS Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. CONCLUSIONS SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated

How Many Topics? Stability Analysis for Topic Models

Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given corpus. Choosing too few topics will produce results that are overly broad, while choosing too many will result in the "over-clustering" of a corpus into many small, highly-similar topics. In this paper, we propose a term-centric stability analysis strategy to address this issue, the idea being that a model with an appropriate number of topics will be more robust to perturbations in the data. Using a topic modeling approach based on matrix factorization, evaluations performed on a range of corpora show that this strategy can successfully guide the model selection process.Comment: Improve readability of plots. Add minor clarification

Deriving Telescope Mueller Matrices Using Daytime Sky Polarization Observations

Telescopes often modify the input polarization of a source so that the measured circular or linear output state of the optical signal can be signficantly different from the input. This mixing, or polarization "cross-talk", is defined by the optical system Mueller matrix. We describe here an efficient method for recovering the input polarization state of the light and the full 4 x 4 Mueller matrix of the telescope with an accuracy of a few percent without external masks or telescope hardware modification. Observations of the bright, highly polarized daytime sky using the Haleakala 3.7m AEOS telescope and a coude spectropolarimeter demonstrate the technique.Comment: Accepted for publication in PAS

Sparse and Distributed Coding of Episodic Memory in Neurons of the Human Hippocampus

Neurocomputational models hold that sparse distributed coding is the most efficient way for hippocampal neurons to encode episodic memories rapidly. We investigated the representation of episodic memory in hippocampal neurons of nine epilepsy patients undergoing intracranial monitoring as they discriminated between recently studied words (targets) and new words (foils) on a recognition test. On average, single units and multiunits exhibited higher spike counts in response to targets relative to foils, and the size of this effect correlated with behavioral performance. Further analyses of the spike-count distributions revealed that (i) a small percentage of recorded neurons responded to any one target and (ii ) a small percentage of targets elicited a strong response in any one neuron. These findings are consistent with the idea that in the human hippocampus episodic memory is supported by a sparse distributed neural code

KELT-11b: A Highly Inflated Sub-Saturn Exoplanet Transiting the V=8 Subgiant HD 93396

We report the discovery of a transiting exoplanet, KELT-11b, orbiting the bright ($V=8.0$) subgiant HD 93396. A global analysis of the system shows that the host star is an evolved subgiant star with $T_{\rm eff} = 5370\pm51$ K, $M_{*} = 1.438_{-0.052}^{+0.061} M_{\odot}$, $R_{*} = 2.72_{-0.17}^{+0.21} R_{\odot}$, log $g_*= 3.727_{-0.046}^{+0.040}$, and [Fe/H]$= 0.180\pm0.075$. The planet is a low-mass gas giant in a $P = 4.736529\pm0.00006$ day orbit, with $M_{P} = 0.195\pm0.018 M_J$, $R_{P}= 1.37_{-0.12}^{+0.15} R_J$, $\rho_{P} = 0.093_{-0.024}^{+0.028}$ g cm$^{-3}$, surface gravity log ${g_{P}} = 2.407_{-0.086}^{+0.080}$, and equilibrium temperature $T_{eq} = 1712_{-46}^{+51}$ K. KELT-11 is the brightest known transiting exoplanet host in the southern hemisphere by more than a magnitude, and is the 6th brightest transit host to date. The planet is one of the most inflated planets known, with an exceptionally large atmospheric scale height (2763 km), and an associated size of the expected atmospheric transmission signal of 5.6%. These attributes make the KELT-11 system a valuable target for follow-up and atmospheric characterization, and it promises to become one of the benchmark systems for the study of inflated exoplanets.Comment: 15 pages, Submitted to AAS Journal

Borrelia recurrentis employs a novel multifunctional surface protein with anti-complement, anti-opsonic and invasive potential to escape innate immunity

Borrelia recurrentis, the etiologic agent of louse-borne relapsing fever in humans, has evolved strategies, including antigenic variation, to evade immune defence, thereby causing severe diseases with high mortality rates. Here we identify for the first time a multifunctional surface lipoprotein of B. recurrentis, termed HcpA, and demonstrate that it binds human complement regulators, Factor H, CFHR-1, and simultaneously, the host protease plasminogen. Cell surface bound factor H was found to retain its activity and to confer resistance to complement attack. Moreover, ectopic expression of HcpA in a B. burgdorferi B313 strain, deficient in Factor H binding proteins, protected the transformed spirochetes from complement-mediated killing. Furthermore, HcpA-bound plasminogen/plasmin endows B. recurrentis with the potential to resist opsonization and to degrade extracellular matrix components. Together, the present study underscores the high virulence potential of B. recurrentis. The elucidation of the molecular basis underlying the versatile strategies of B. recurrentis to escape innate immunity and to persist in human tissues, including the brain, may help to understand the pathological processes underlying louse-borne relapsing fever