+1$\sigma$ Expected 95% CL for the 1SFH $e$ Dirac model.

NMC ran the MRF every 24 h (00 UTC) with 12 h forecasts up to 240 h at a standard resolution of 200 km. The data cutoff for the model runs was 6 h and the output was ON85. There are 18 sigma levels. The output includes pressure, geopotential altitude, u and v wind components, virtual temperature, and relative humidity, among others.

`*.npz` and `*.asdf` files containing visibilities are in the TMS format (opposite that of CASA).

logo_cube.noise.npz visibilities have been rescaled such that data - model / sigma follows the expected Gaussian envelope.

HD 143006 continuum visibilities have flagged outliers removed and weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.

AS 209 continuum visibilities have been averaged across frequency and have their weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.

BackgroundMultiple studies have indicated that the minimal model of hip structure can enhance hip fracture risk assessment. This study aimed to investigate the independent association between minimal model variables and hip fracture risk in Han Chinese individuals.MethodsThis cross-sectional study included 937 Han Chinese patients (248 with hip fractures). Minimal model variables were calculated from the hip structural analysis, including bone mineral density (BMD), femoral neck width (FNW), and Delta and Sigma values.ResultsThis study included 937 patients (293 men; mean age = 68.3 years). In logistic regression analyses, BMD increase (per 0.1 g/cm2) correlated with a 45% reduction in the hip fracture risk (odds ratio [OR] = 0.55; 95% confidence interval [CI]: 0.45–0.68) after adjusting for all covariates. However, FNW (per 0.1 cm) and Sigma (per 0.01 cm) and Delta values (per 0.01 cm) were associated with increased risks (OR = 1.28; 95% CI: 1.18–1.37; OR = 1.06; 95% CI: 1.03–1.09; OR = 1.06; 95% CI: 1.03–1.09, respectively). When the Delta was >0.17 cm, the risk of hip fracture rose considerably by 13% (OR = 1.13; 95% CI: 1.08–1.18) for every 0.01 cm that the Delta value increased. The area under the curve (AUC) for hip fracture prediction from BMD alone was significantl lower than those of minimal model (0.781 vs 0.838, p

Based on professional technical analysis and AI models, deliver precise price‑prediction data for SIGMA on 2025-12-01. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for SIGMA on 2025-11-25. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

+2$\sigma$ Expected 95% CL for the 1SFH $e$ Dirac model.

Problem Statement

Welcome to Sigma Cab Private Limited - a cab aggregator service. Their customers can download their app on smartphones and book a cab from any where in the cities they operate in. They, in turn search for cabs from various service providers and provide the best option to their client across available options. They have been in operation for little less than a year now. During this period, they have captured surge_pricing_type from the service providers.

You have been hired by Sigma Cabs as a Data Scientist and have been asked to build a predictive model, which could help them in predicting the surge_pricing_type pro-actively. This would in turn help them in matching the right cabs with the right customers quickly and efficiently.

Data

Variable Definition Trip_ID - ID for TRIP (Can not be used for purposes of modelling) Trip_Distance - The distance for the trip requested by the customer Type_of_Cab - Category of the cab requested by the customer Customer_Since_Months - Customer using cab services since n months; 0 month means current month Life_Style_Index - Proprietary index created by Sigma Cabs showing lifestyle of the customer based on their behaviour Confidence_Life_Style_Index - Category showing confidence on the index mentioned above Destination_Type - Sigma Cabs divides any destination in one of the 14 categories Customer_Rating - Average of life time ratings of the customer till date Cancellation_Last_1Month - Number of trips cancelled by the customer in last 1 month Var1, Var2 and Var3 - Continuous variables masked by the company. Can be used for modelling purposes Gender - Gender of the customer Surge_Pricing_Type - Predictor variable can be of 3 types

The design and synthesis of a series of 2,7-diazaspiro[4.4]nonane derivatives as potent sigma receptor (SR) ligands, associated with analgesic activity, are the focus of this work. In this study, affinities at S1R and S2R were measured, and molecular modeling studies were performed to investigate the binding pose characteristics. The most promising compounds were subjected to in vitro toxicity testing and subsequently screened for in vivo analgesic properties. Compound 9d (AD258) exhibited negligible in vitro cellular toxicity and a high binding affinity to both SRs (KiS1R = 3.5 nM, KiS2R = 2.6 nM), but not for other pain-related targets, and exerted high potency in a model of capsaicin-induced allodynia, reaching the maximum antiallodynic effect at very low doses (0.6–1.25 mg/kg). Functional activity experiments showed that S1R antagonism is needed for the effects of 9d and that it did not induce motor impairment. In addition, 9d exhibited a favorable pharmacokinetic profile.

Here we present a 1200 yr long benthic foraminiferal Mg/Ca based temperature and oxygen isotope record from a ~900 m deep sediment core off northwest Africa to show that atmosphere-ocean interactions in the eastern subpolar gyre are transferred at central water depth into the eastern boundary of the subtropical gyre. Further we link the variability of the NAO (over the past 165 yrs) and solar irradiance (Late Holocene) and their control on subpolar mode water formation to the multidecadal variability observed at mid-depth in the eastern subtropical gyre. Our results show that eastern North Atlantic central waters cooled by up to ~0.8± 0.7 °C and densities decreased by Sigma theta=0.3±0.2 during positive NAO years and during minima in solar irradiance during the Late Holocene. The presented records demonstrate the sensitivity of central water formation to enhanced atmospheric forcing and ice/freshwater fluxes into the eastern subpolar gyre and the importance of central water circulation for cross-gyre climate signal propagation during the Late Holocene.

The files contain outputs from numerical simulations using a combination of the numerical models Geospace Environment Model of Ion-Neutral Interactions (GEMINI) and Satellite-beacon Ionospheric- scintillation Global Model of the upper Atmosphere (SIGMA). The outputs provide simulated time series of Global Positioning System (GPS) scintillations through density structures generated by the Kelvin Helmholtz instability (KHI), as explained in detail in the publication. The numerical codes used to generate the outputs are described in the following publications: • Zettergren, M., Semeter, J., & Dahlgren, H. (2015). “Dynamics of density cavities generated by frictional heating: Formation, distortion, and instability”. Geophysical Research Letters, 42(23). [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066806 ] • Deshpande, K. B., Bust, G. S., Clauer, C. R., Rino, C. L., & Carrano, C. S. (2014). “Satellite-beacon Ionospheric-scintillation Global Model of the upper Atmosphere (SIGMA) I: High latitude sensitivity study of the model parameters”. Journal of Geophysical Research: Space Physics, 119, 4026-4043. doi:10.1002/2013JA019699811. [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JA019699 ] • Deshpande, K. B., & Zettergren, M. D. (2019). “Satellite-Beacon Ionospheric Scintillation Global Model of the Upper Atmosphere (SIGMA) III: Scintillation Simulation Using A Physics-Based Plasma Model”. Geophysical Research Letters, 46(9), 4564-4572. doi:10.1029/2019GL082576. [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL082576 ] GEMINI is free, open-source software and can be downloaded from GitHub at https://github.com/gemini3d/. Build instructions, example simulations, and documentation are also included on this website. Uploaded by A.S.

Data archived here are the external iron input data and model output data discussed in a paper entitled "Slowly sinking particles underlie dissolved iron transport across the Pacific Ocean" submitted to Global Biogeochemical Cycles. The model used in this study was developed by coupling Regional Ocean Modeling System (Shchepetkin and McWilliams, 2005) and Biogeochemical Elemental Cycling model (Moore et al., 2013). The model covers the whole North Pacific Ocean. The model horizontal resolution was set to 1/4° mesh. The external iron input data are iron fluxes due to atmospheric deposition and dissolution from seabed sediments. The model output data are dissolved iron concentrations simulated by the model and were only presented for the data in the intermediate layer (26.6-27.4 sigma-theta divided by 0.02 sigma-theta). The simulated data were regridded 1° mesh to reduce the size of the data. The model was calculated for 100 years and the simulated dissolved iron concentration are in quasi-steady state. For more details about the individual archived data, please refer to README.pdf included in the data. […]

As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the key regulatory elements, which recruit the transcriptional machinery through binding to a variety of regulatory proteins (known as sigma factors). The identification of the promoter regions is very challenging because these regions do not adhere to specific sequence patterns or motifs and are difficult to determine experimentally. Machine learning represents a promising and cost-effective approach for computational identification of prokaryotic promoter regions. However, the quality of the predictors depends on several factors including: i) training data; ii) data representation; iii) classification algorithms; iv) evaluation procedures. In this work, we create several variants of E. coli promoter data sets and utilize them to experimentally examine the effect of these factors on the predictive performance of E. coli σ70 promoter models. Our results suggest that under some combinations of the first three criteria, a prediction model might perform very well on cross-validation experiments while its performance on independent test data is drastically very poor. This emphasizes the importance of evaluating promoter region predictors using independent test data, which corrects for the over-optimistic performance that might be estimated using the cross-validation procedure. Our analysis of the tested models shows that good prediction models often perform well despite how the non-promoter data was obtained. On the other hand, poor prediction models seems to be more sensitive to the choice of non-promoter sequences. Interestingly, the best performing sequence-based classifiers outperform the best performing structure-based classifiers on both cross-validation and independent test performance evaluation experiments. Finally, we propose a meta-predictor method combining two top performing sequence-based and structure-based classifiers and compare its performance with some of the state-of-the-art E. coli σ70 promoter prediction methods.

NRL HYCOM 1/25 deg model output, Gulf of Mexico, 10.04 Expt 31.0, 2009-2014, At Surface The HYCOM consortium is a multi-institutional effort sponsored by the National Ocean Partnership Program (NOPP), as part of the U. S. Global Ocean Data Assimilation Experiment (GODAE), to develop and evaluate a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model (called HYbrid Coordinate Ocean Model or HYCOM).

Each row corresponds to a specified training set while each column corresponds to a specified test set.AUC scores for Naive Bayes classifier with DNID features (NB_DNID) trained using seven versions of CV data and in each time tested on the seven versions of the independent test data.

The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.

See Methods Section for more information about these test sets. For each data set, the rank of each classifier is shown in parentheses.AUC scores for selected classifiers (trained using CV_Mixed data) and tested on different versions of independent test set (e.g., TS_Random and TS_Coding).

Each row corresponds to a specified training set while each column corresponds to a specified test set.AUC scores for Naive Bayes classifier with 4-mer features (NB_4-mer) trained using seven versions of CV data and in each time tested on the seven versions of the independent test data.

ECMWF has in the past produced three major re-analyses: FGGE, ERA-15 and ERA-40. Now progress is being made in producing 'ERA-Interim'. ERA-Interim has progressed beyond the end of ERA-40. After completion of the first four years it was decided to revise the configuration of the system and to rerun the initial segment. Other shorter reruns for later periods have been completed as needed for technical reasons. The re-analysis is continued in near-real time in order to support climate monitoring. This experiment contains assimilated data on 60 model (sigma) levels with a time-step of six hours.

Please see this link for detailed terms of use: https://apps.ecmwf.int/datasets/licences/general/

Given its key homeostatic role affecting mitochondria, ionotropic, and metabotropic receptors as well as voltage-gated ion channels, Sigma-1 receptor (Sig1R) represents an interesting target for epilepsy management. Antiseizure effects of the positive allosteric modulator E1R have already been reported in acute seizure models. While modulation of serotonergic neurotransmission is considered the main mechanism of action of fenfluramine, its interaction with Sig1R may be of additional relevance. To further explore the potential of Sig1R as a target, we assessed the efficacy and tolerability of E1R and fenfluramine in two chronic mouse models, including an amygdala kindling paradigm and the intrahippocampal kainate model. The relative contribution of the interaction with Sig1R was analyzed using combination experiments with the Sig1R antagonist NE-100. While E1R exerted pronounced dose-dependent antiseizure effects at well-tolerated doses in fully kindled mice, only limited effects were observed in response to fenfluramine without a clear dose-dependency. In the intrahippocampal kainate model, E1R failed to influence electrographic seizure activity. In contrast, fenfluramine significantly reduced the frequency of electrographic seizure events and their cumulative duration. Pretreatment with NE-100 reduced the effects of E1R and fenfluramine in the kindling model. Surprisingly, pre-exposure to NE-100 in the intrahippocampal kainate model rather enhanced and prolonged fenfluramine’s antiseizure effects.In conclusion, the kindling data further support Sig1R as an interesting target for novel antiseizure medications. However, it is necessary to further explore the preclinical profile of E1R in chronic epilepsy models with spontaneous seizures. Despite the rather limited effects in the kindling paradigm, the findings from the intrahippocampal kainate model suggest that it is of interest to further assess a possible broad-spectrum potential of fenfluramine.Raw datasetskdl_data.xlsxThis raw dataset contains all the raw used for the figures related to the amygdala Kindling experiments. Each row correspond to a different animal and different parameter and each column to a different compound.The vehicle is recorded to the left of their respective drug experiment (the arrow indicate the association). Abbreviations: adt, afterdisharge threshold; add1, afterdischarge duration 1; add2, total afterdischarge duration; sd, seizure duration; d_adt, delta adt; gst, generalized seizure threshold; d_gst, delta gst; PTT, pretreatment time.s1r-kainate_eeg-sz-frequency_raw-data.csvContains the count of each type of seizure in the respective time window per animal.s1r-kainate_eeg-sz-cumulative-duration_raw-data.csvContains the sum of the duration of each type of seizure in the respective time window per animal.s1r-kainate_eeg-sz-mean-duration_raw-data.csvContains the mean duration of the seizures in the respective time window per animal.