Tag: statistics

Curr Pharm Teach Learn. 2022 Oct;14(10):1269-1273. doi: 10.1016/j.cptl.2022.09.004. Epub 2022 Sep 15.

ABSTRACT

INTRODUCTION: In many ways, traditional education environments contribute to a sedentary lifestyle, and this can lead to several negative health outcomes. This study determined the extent of anti-sedentary physical activity outcomes resulting from conducting pharmacy academia rotation topic discussions while walking.

METHODS: Topic discussions during six, one-month pharmacy resident academia rotations were converted from a traditional sitting environment to walking discussions conducted at a conversational pace. The preceptor’s time, distance, steps, ascent, and caloric expenditure for the discussions were tracked via the hiking feature on Garmin Fenix 3 Global Positioning System watch (Garmin Ltd.). Results were synced to the Garmin Connect mobile application, from which they were manually transcribed to spreadsheet software. Descriptive statistics for the variables were calculated.

RESULTS: A total of 46 topic discussions were conducted while walking. The average walking topic discussion lasted 44.62 min, covered 2.13 miles with 3896 steps, and expended 159 calories.

CONCLUSIONS: Results of this study demonstrate the various physical activity benefits of conducting topic discussions while walking as opposed to more traditional stationary discussions. Walking topic discussions are a simple way to counteract the sedentary aspects of academia by incorporating physical activity into the educational process without additional time or resources. Opportunity exists for other aspects of academia to adopt similar strategies to benefit faculty, staff, student, and resident wellness.

PMID:36283795 | DOI:10.1016/j.cptl.2022.09.004

Radiology. 2022 Oct 25:212403. doi: 10.1148/radiol.212403. Online ahead of print.

ABSTRACT

Background Pre-liver transplant (LT) sarcopenia is associated with poor survival. Methods exist for measuring body composition with use of CT scans; however, it is unclear which components best predict post-LT outcomes. Purpose To quantify the association between abdominal CT-based body composition measurements and post-LT mortality in a large North American cohort. Materials and Methods This was a retrospective cohort of adult first-time deceased-donor LT recipients from 2009 to 2018 who underwent pre-LT abdominal CT scans, including at the L3 vertebral level, at Johns Hopkins Hospital. Measurements included sarcopenia (skeletal muscle index [SMI] <50 in men and <39 in women), sarcopenic obesity, myosteatosis (skeletal muscle CT attenuation <41 mean HU for body mass index [BMI] <25 and <33 mean HU for BMI ≥25), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and VAT/SAT ratio. Covariates in the adjusted models were selected with use of least absolute shrinkage and selection operator regression with lambda chosen by means of 10-fold cross-validation. Cox proportional hazards models were used to quantify associations with post-LT mortality. Model discrimination was quantified using the Harrell C-statistic. Results A total of 454 recipients (median age, 57 years [IQR, 50-62 years]; 294 men) were evaluated. In the adjusted model, pre-LT sarcopenia was associated with a higher hazard ratio (HR) of post-LT mortality (HR, 1.6 [95% CI: 1.1, 2.4]; C-statistic, 0.64; P = .02). SMI was significantly negatively associated with survival after adjustment for covariates. There was no evidence that myosteatosis was associated with mortality (HR, 1.3 [95% CI: 0.86, 2.1]; C-statistic, 0.64; P = .21). There was no evidence that BMI (HR, 1.2 [95% CI: 0.95, 1.4]), VAT (HR, 1.0 [95% CI: 0.98, 1.1]), SAT (HR, 1.0 [95% CI: 0.97, 1.0]), and VAT/SAT ratio (HR, 1.1 [95% CI: 0.90, 1.4]) were associated with mortality (P = .15-.77). Conclusions Sarcopenia, as assessed on routine pre-liver transplant (LT) abdominal CT scans, was the only factor significantly associated with post-LT mortality. © RSNA, 2022 See also the editorial by Ruehm in this issue.

PMID:36283115 | DOI:10.1148/radiol.212403

Neural Comput. 2022 Oct 14:1-38. doi: 10.1162/neco_a_01547. Online ahead of print.

ABSTRACT

This review examines gradient-based techniques to solve bilevel optimization problems. Bilevel optimization extends the loss minimization framework underlying statistical learning to systems that are implicitly defined through a quantity they minimize. This characterization can be applied to neural networks, optimizers, algorithmic solvers, and even physical systems and allows for greater modeling flexibility compared to the usual explicit definition of such systems. We focus on solving learning problems of this kind through gradient descent, leveraging the toolbox of implicit differentiation and, for the first time applied to this setting, the equilibrium propagation theorem. We present the mathematical foundations behind such methods, introduce the gradient estimation algorithms in detail, and compare the competitive advantages of the different approaches.

PMID:36283053 | DOI:10.1162/neco_a_01547

Neural Comput. 2022 Oct 14:1-35. doi: 10.1162/neco_a_01549. Online ahead of print.

ABSTRACT

Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this letter, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.

PMID:36283052 | DOI:10.1162/neco_a_01549

Neural Comput. 2022 Oct 14:1-14. doi: 10.1162/neco_a_01543. Online ahead of print.

ABSTRACT

Our neurons seem capable of handling any type of data, regardless of its scale or statistical properties. In this letter, we suggest that optimal coding may occur at the single-neuron level without requiring memory, adaptation, or evolutionary-driven fit to the stimuli. We refer to a neural circuit as optimal if it maximizes the mutual information between its inputs and outputs. We show that often encountered differentiator neurons, or neurons that respond mainly to changes in the input, are capable of using all their information capacity when handling samples of any statistical distribution. We demonstrate this optimality using both analytical methods and simulations. In addition to demonstrating the simplicity and elegance of neural processing, this result might provide a way to improve the handling of data by artificial neural networks.

PMID:36283043 | DOI:10.1162/neco_a_01543

Neural Comput. 2022 Oct 14:1-27. doi: 10.1162/neco_a_01544. Online ahead of print.

ABSTRACT

Complex systems can be defined by “sloppy” dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and “stiff” dimensions, whose change results in considerable behavioral modification. In the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of asynchronous irregular spiking dynamics with low firing rates despite a diversity of inputs, states, and short- and long-term plasticity. Using simulations on neural networks with first-order spiking statistics matched to firing in murine visual cortex while varying connectivity parameters, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Algorithmically generated connectivity parameter values drawn from a large portion of the parameter space reveal that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across input classes with self-sustaining synaptic architectures following brief input occupying a smaller subspace as compared to the other input classes. Experimentally estimated connectivity probabilities from mouse visual cortex are consistent with the connectivity correlations found and fall in the same region of the parameter space as architectures identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Additionally, coarsegraining cell types does not prevent the generation of accurate, informative, and interpretable models underlying simple spiking activity. This unbiased investigation provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in the neocortex.

PMID:36283042 | DOI:10.1162/neco_a_01544

Rev Med Inst Mex Seguro Soc. 2022 Oct 25;60(6):657-665.

ABSTRACT

OBJECTIVE: Identify risk factors for severe outcome in Mexican patients with COVID-19 in the population of Quintana Roo.

MATERIAL AND METHODS: Study of 5,916 who met the criteria for suspected cases of COVID-19, 2,531 confirmed by qrTPCR-Sars-CoV-2 tests, of which 1,486 were positive, among which they were classified as hospitalized (severe COVID-19) and outpatients. Multivariate logistic regression analysis was performed to explore the factors associated with the severity of COVID-19 and death as clinical outcomes. The basic reproduction number (R0) was calculated Statistical analysis) Endorsement of the ethics committee 2301.

RESULTS: SARS-CoV-2 positive patients presented a high prevalence of hypertension 29.1%, diabetes 23.5%, obesity 24%, and 48.5% have at least one chronic disease. There is a high risk of severity for COVID-19 in patients with diabetes OR=3.14, hypertension OR=1.88, obesity OR=1.68, kidney disease OR=3.2, older than 65 years OR=13.6 and men OR=1.7. These factors also increase the risk of death up to 7.7 times. The maximum R0 during the epidemic was 2.4.

CONCLUSION: Liver and kidney disease, diabetes, hypertension, and obesity are significantly associated with severe COVID-19 and death.

PMID:36283034

Res Number Theory. 2022;8(4):70. doi: 10.1007/s40993-022-00373-2. Epub 2022 Sep 16.

ABSTRACT

We consider spaces of modular forms attached to definite orthogonal groups of low even rank and nontrivial level, equipped with Hecke operators defined by Kneser neighbours. After reviewing algorithms to compute with these spaces, we investigate endoscopy using theta series and a theorem of Rallis. Along the way, we exhibit many examples and pose several conjectures. As a first application, we express counts of Kneser neighbours in terms of coefficients of classical or Siegel modular forms, complementing work of Chenevier-Lannes. As a second application, we prove new instances of Eisenstein congruences of Ramanujan and Kurokawa-Mizumoto type.

PMID:36283022 | PMC:PMC9583295 | DOI:10.1007/s40993-022-00373-2

Pol Merkur Lekarski. 2022 Oct 21;50(299):282-286.

ABSTRACT

Myocardial infarction (MI) of the right ventricle (RV) coexists in 20- 60% of patients with inferior MI. There are electrocardiographic indicators which are connected with RV MI, which may also predict unfavorable clinical outcome of in-hospital follow-up.

AIM: The aim of the study was determination a value of seven electrocardiographic predictors of RV MI in prognosis of in-hospital complications in patients with inferior MI.

MATERIALS AND METHODS: The analysis of hospital files of patients admitted with diagnosis of inferior MI with persistent ST elevation (STEMI) was retrospectively performed. A set of seven RV MI predictors (RVMIP) was assessed from the electrocardiographic tracings (ECG). Finally in group of 167 patients relation between each RVMIP and in-hospital complications was statistically evaluated.

RESULTS: The most often RVMIP was an elevation of ST higher in III lead then in II and aVF (RVMIP-2; recorded in 61,7% patients). In total any RVMIP was found in ECG of 142 patients (85%). Patients who had more RVMIP were more prone for combined adverse cardiac event (CACE, which included artificial respirotherapy, lungs edema and cardiogenic shock) (p=0,012); ventricular arrhythmias (p<0,001) and second/third grade atrioventricular blocks (p=0,028). Advanced atrioventricular blocks were more often observed in patient with ST elevation in V1 and ST depression in aVL (OR=4,91, p=0,005; OR=4,67, p=0,006; respectively). On the other hand second/third grade atrioventricular blocks were also related to higher incidence of CACE, ventricular arrhythmias and atrial fibrillation (AF), respectively: OR=8,1, p<0,001; OR=7,19, p=0,001; OR=5,75, p=0,001).

CONCLUSIONS: The more RVMIPs the higher risk of in-hospital complication in patients with inferior MI. The second/third grade atrioventricular blocks were recorded more often in patients with ST elevation in V1 and ST depression in aVL. Advanced atrioventricular conduction blocks were related to worse outcome of in-hospital followup. A detailed ECG analysis in admission still adds important contribution in determination of in-hospital risk of complications.

PMID:36283009

Multimodal Learn Clin Decis Support (2021). 2021 Oct;13050:1-11. doi: 10.1007/978-3-030-89847-2_1. Epub 2021 Oct 20.

ABSTRACT

Advances in neuroimaging have yielded extensive variety in the scale and type of data available. Effective integration of such data promises deeper understanding of anatomy and disease-with consequences for both diagnosis and treatment. Often catered to particular datatypes or scales, current computational tools and mathematical frameworks remain inadequate for simultaneously registering these multiple modes of “images” and statistically analyzing the ensuing menagerie of data. Here, we present (1) a registration algorithm using a “scattering transform” to align high and low resolution images and (2) a varifold-based modeling framework to compute 3D spatial statistics of multiscale data. We use our methods to quantify microscopic tau pathology across macroscopic 3D regions of the medial temporal lobe to address a major challenge in the diagnosis of Alzheimer’s Disease-the reliance on invasive methods to detect microscopic pathology.

PMID:36283001 | PMC:PMC9582035 | DOI:10.1007/978-3-030-89847-2_1