Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing the fast consolidation hypothesis of retrieval-mediated learning

The testing-effect, or retrieval-mediated learning, is one of the most robust effects in memory research. It shows that actively and repeatedly retrieving information, compared to merely restudying it, improves long-term retention. Surprisingly, little is known about the neurocognitive mechanisms underlying this phenomenon. Attempting to fill this gap, a recent framework proposed that retrieval acts as a fast memory consolidation mechanism, stabilizing memories through online reactivation, similar to memory replay during offline (e.g. sleep) consolidation. In this fMRI study, we empirically tested the predictions derived from this framework. We predicted that reactivation during retrieval allows memories to become embedded in neocortex, creating an additional route to access the memory trace and rendering it less hippocampus-dependent. Participants encoded scene-object pairs and either retrieved or restudied the objects over two sessions, two days apart. We analysed univariate and multivariate changes in brain activity specific to retrieval but not restudy, and tested whether the predicted changes occur rapidly within a session, or evolve slowly, across the two days. Results showed that medial prefrontal cortex activation increased across retrieval trials within one session, consistent with a fast consolidation account. Hippocampal activity decreased across sessions, suggesting a slower mechanism. Moreover, Representational Similarity Analyses (RSA) showed that consecutive retrieval attempts strengthen both higher-level semantic and episode-specific information in parietal areas, both across but not within sessions. Our findings suggest that retrieval supports the online creation of a neocortical trace, which becomes increasingly relevant at long delays when hippocampus-dependent episodic details would otherwise have faded. Significance statement Repeated remembering strengthens memories much more so than repeated learning. The aim of this study was to shed light onto the poorly understood neurocognitive underpinnings of retrieval-mediated learning. We tested a novel framework proposing that a memory’s stabilization via retrieval relies on mechanisms akin to those involved in offline systems consolidation. Observing the retrieval-induced neural pattern changes across different timescales, we find that retrieval stabilizes semantic and episodic aspects of the original memories, and produces increases in prefrontal activity and decreases in hippocampal activity that are consistent with the consolidation view, but not necessarily with a fast acting mechanism. Our findings inform cognitive theories of the testing effect, suggesting that retrieval produces its benefits by interacting with hippocampal-neocortical consolidation mechanisms.
Ambient Temperature CO Oxidation Using Palladium–Platinum Bimetallic Catalysts Supported on Tin Oxide/Alumina

A series of Pt-based catalysts were synthesised and investigated for ambient temperature CO oxidation with the aim to increase catalytic activity and improve moisture resistance through support modification. Initially, bimetallic PtPd catalysts supported on alumina were found to exhibit superior catalytic activity compared with their monometallic counterparts for the reaction. Following an investigation into the effect of Pt/Pd ratio, a composition of 0.1% Pt/0.4% Pd was selected for further studies. Following this, SnO2/Al2O3 supports were synthesised from a variety of tin oxide sources. Catalytic activity was improved using sodium stannate and tin oxalate precursors compared with a traditional tin oxide slurry. Catalytic activity versus tin concentration was found to vary significantly across the three precursors, which was subsequently investigated by X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDX).
Influence of Physical and Chemical Characteristics of Sediment on Macroinvertebrate Communities in Agricultural Headwater Streams

Agricultural land use leads to changes in physical and chemical characteristics of sediment that influence macroinvertebrate community diversity and abundance in streams. To the best of our knowledge the joint influence of sediment’s physical and chemical characteristics on stream macroinvertebrates has not been assessed. We measured sediment’s physical and chemical characteristics and sampled macroinvertebrates in eight agricultural headwater streams in Indiana, Michigan, and Ohio, United States, in 2017 and 2018 to determine the physical and chemical conditions of the sediment, to evaluate the relationships between physical and chemical characteristics of the sediment, and the relationship of macroinvertebrate communities with the sediment’s physical and chemical characteristics. Sediments within most sites were dominated by sand or silt. pH was suitable for macroinvertebrates and nitrate, herbicide, and trace metal concentrations were below concentration levels anticipated to affect macroinvertebrate survival. Linear mixed effect model analysis results indicated that a physical gradient of percent small gravel and percent silt was positively correlated (p < 0.05) with a chemical gradient of potassium concentrations, magnesium concentrations, and percent total nitrogen in the sediments. Our linear mixed effect model analysis results also indicated that Invertebrate Community Index scores were negatively correlated (p < 0.05) with a chemical gradient of simazine and calcium concentrations and were negatively correlated (p < 0.05) with physical gradient of grain size diversity and percent sand. Our results suggest that watershed management plans need to address physical and chemical degradation of sediment to improve macroinvertebrate biotic integrity within agricultural headwater streams in the Midwestern United States.
Expression Analysis of miRNA 164c During Rice Regeneration in Different indica Rice Genotypes

A highly efficient and reproducible tissue culture regeneration system is an important prerequisite for achieving successful transformation. However, most indica rice varieties remain recalcitrant to genetic transformation and require refinement of the tissue culture protocol for the generation of embryogenic calli or regeneration of fertile plants from transformed cells. In this study, standardized regeneration system for indica rice cultivars was carried out. Further the expression of miR164c was investigated in the different rice genotypes. The expression of miR164c was proportional to the percentage of regeneration of the rice genotypes. These results provide interesting insights that will significantly improve our understanding of direct regeneration of rice from callus tissue.
Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.
Microdosing and standard‐dosing take‐home buprenorphine from the emergency department: A feasibility study

Emergency department (ED)–initiated buprenorphine may prevent overdose. Microdosing is a novel approach that does not require withdrawal, which can be a barrier to standard inductions. We aimed to evaluate the feasibility of an ED‐initiated buprenorphine/naloxone program providing standard‐dosing and microdosing take‐home packages and of randomizing patients to either intervention.
Soil Organic Carbon Depletion from Forests to Grasslands Conversion in Mexico: A Review

Land use change from forests to grazing lands is one of the important sources of greenhouse gas emissions in many parts of the tropics. The objective of this study was to analyze the extent of soil organic carbon (SOC) loss from the conversion of native forests to pasturelands in Mexico. We analyzed 66 sets of published research data with simultaneous measurements of soil organic carbon stocks between native forests and pasturelands in Mexico. We used a generalized linear mixed effect model to evaluate the effect of land use change (forest versus pasture), soil depth, and original native forest types. The model showed that there was a significant reduction in SOC stocks due to the conversion of native forests to pasturelands. The median loss of SOC ranged from 31.6% to 52.0% depending upon the soil depth. The highest loss was observed in tropical mangrove forests followed by highland tropical forests and humid tropical forests. Higher loss was detected in upper soil horizon (0–30 cm) compared to deeper horizons. The emissions of CO2 from SOC loss ranged from 46.7 to 165.5 Mg CO2 eq. ha−1 depending upon the type of original native forests. In this paper, we also discuss the effect that agroforestry practices such as silvopastoral arrangements and other management practices like rotational grazing, soil erosion control, and soil nutrient management can have in enhancing SOC stocks in tropical grasslands. The results on the degree of carbon loss can have strong implications in adopting appropriate management decisions that recover or retain carbon stocks in biomass and soils of tropical livestock production systems.
Inhibitory activity of salivary glycoproteins on phytohemagglutins (PHA): Possible molecules to enhance nutritional quality of red kidney beans

Food allergy caused by red kidney bean (Phaseolus vulgaris L.) is of serious health concern and is mainly due to its phytohemagglutinins (PHA) content. PHA can enter the circulation after oral uptake and cause IgE mediated allergy. However, studies describing enhancement of nutritional quality of red kidney beans by targeting PHA are not reported. This study was carried out to identify, PHA-inhibitory molecules present in saliva secretions. Results describe that PHA can be effectively inhibited by salivary glycoproteins. Fractionation of salivary proteins by ammonium sulphate precipitation revealed that, PHA-inhibitory proteins can be specifically precipitated at 30-60% of ammonium sulphate saturation. Gel filtration chromatography and lectin-blot analysis of 30-60% ammonium sulphate fraction suggest that only high molecular weight glycoproteins can act as potent inhibitors of PHA. In conclusion, human saliva secretions contain inhibitory glycoproteins which can be used to inhibit PHA effectively. If these glycoproteins are purified to homogeneity, can be used as potent food supplements in order to neutralize allergic potential of PHA, thus increasing the nutritional value of red kidney beans.
Can the Federal Reserve Effectively Target Main Street? Evidence from the 1970s Recession

Modern central bankers confront a challenge of providing economic stimulus even when the policy rate is constrained by a lower bound. This challenge has led to substantial innovation by policymakers and a proliferation of new policy tools. In this paper, I offer evidence on the efficacy of a new tool known as funding for lending, which provides banks with subsidized funding to make additional loans. I focus on a historical episode from the United States in which the Federal Reserve provided banks with steeply subsidized loans to promote the expansion of credit within their local communities. I show that the cheap funding succeeded in generating more lending by countering banks' excessive liquidity preference. The additional credit benefited the real economy. Local areas enjoyed higher rates of small business formation and more rapid employment growth. Finally, I show that the cost of the subsidy provided by the government was more than offset by the additional payroll taxes paid out of higher wages and salaries. These results suggest that funding for lending programs deserve consideration for the modern central banker's toolkit and demonstrate that certain unconventional tools can offer monetary policymakers the means to pursue more targeted objectives.
COMPORTAMIENTO PRODUCTIVO Y DIGESTIBILIDAD DE NUTRIENTES DENOVILLOS EN ENGORDA SUPLEMENTADOS CON UN ADITIVO A BASE DEPROBIÓTICOS Y ENZIMAS DIGESTIVAS

The objective was to evaluate an additive made with probiotics and digestive enzymes on animal performance on feedlot cattle and nutrient digestibility on finishing steers. Thirty steers (Charolais × Beefmaster) averaging an initial body weight of 321.83 ± 3.73 kg were used. Steers were randomly assigned to 1 of 2 treatments: 1) Control (CON; Basal diet) and 2) Treatment (TX; Basal diet + 30 g / animal / day of additive). Dry matter intake (DMI) was recorded daily. Body weight (BW) was measured at the beginning and each 28 d intervals in order to obtain average daily gain (ADG). Feed efficiency (G:F) was calculated per period. The digestibility of DM, CP, and NDF were evaluated in the finishing phase in 10 steers per treatment. Residual feed intake (RFI) was calculated using DMI, BW, and ADG data. Data was analyzed using a completely randomized design with measures repeated over time (except for nutrient digestibility). Residual feed intake was calculated as the difference among the observed and the expected DMI. For DMI, ADG, and G:F neither differences (P > 0.05) were found among treatments nor interaction effect among periods (P > 0.05). Average daily gain was different (P 0.05) among treatments. The use of this additive on feedlot cattle does not increase both animal performance and nutrient digestibility
Performance Assessment of Pareto and Non-Pareto Approaches for the Optimal Allocation of DG and DSTATCOM in the Distribution System

: This paper proposes a Differential Evolution (DE) optimization algorithm and a Pareto-frontier Multi-Objective Differential Evolution (MODE) optimization algorithm for the optimal allocation of Distributed Generation (DG) and Distribution Static Compensator (DSTATCOM) in a radial distribution system. It considers the minimization of active power dissipation, voltage drop and the annual cost as the objectives of this optimization problem. The proposed techniques are tested on an IEEE 33 bus radial distribution system. To compare the performance of the MODE and DE, the weighted sum approach is carried out. This helps to select one solution from the Pareto front of the MODE. Case studies show that the allocation of both DG and DSTATCOM results in a noticeable reduction of system losses, voltage drop and annual cost. Comparative studies also show that the global convergence characteristics of MODE are better than several other optimization algorithms.
Multimodal treatment based on thyroidectomy improves survival in patients with metastatic anaplastic thyroid carcinoma: a SEER analysis from 1998 to 2015.

Background The aim of this retrospective study was to evaluate multimodal treatments consisting of surgery, radiotherapy (RT) and chemotherapy (CT) in metastatic anaplastic thyroid carcinoma (ATC) patients using the Surveillance, Epidemiology, and End Results (SEER) database. Methods Patients with a histopathologic diagnosis of ATC between 1998 and 2015 were included. The endpoint of this study was overall survival (OS). The prognostic significance in terms of OS was analyzed by univariate and multivariate analyses. Results A total of 433 metastatic ATC patients were identified. The median OS was two months, with a 1-year OS rate of 6.9%. In the multivariate analysis, the factors significantly correlated with OS were age [<68 vs. ≥68 years old, P=0.032, hazard ratio (HR) =1.252], tumor size (<7 vs. ≥7 cm, P=0.004, HR =1.418; <7 cm vs. unknown, P=0.005, HR =1.424), surgery at the primary site (none/unknown vs. less than thyroidectomy, P<0.001, HR =0.623; none/unknown vs. thyroidectomy, P=0.001, HR =0.616), use of RT (P<0.001, HR =0.562) and use of CT (P<0.001, HR =0.621). In the subgroup analysis, the combination of thyroidectomy, RT and CT demonstrated the best OS outcome when compared with other therapeutic modalities. Conclusions Surgery, RT and CT were all strong prognostic factors for improved OS in metastatic ATC patients. In addition, treatment approaches consisting of thyroidectomy were beneficial compared with nonsurgical treatment. However, given the limited benefit of only a few months in the clinic, we suggest that thyroidectomy in combination with RT and CT should be delivered in selected patients with caution for better management of metastatic ATC.
A taxonomic conundrum resolved: the transfer of Grimmia horrida to Coscinodon based on sporophyte discovery in France, with support from molecular data

Abstract Coscinodon horridus, formerly Grimmia horrida, is reported as new to France from two localities. Sporophytes are described for the first time. Morphological and molecular evidence are presented supporting the transfer of G. horrida to Coscinodon. The variability of the species and reproductive traits are discussed and it is compared with morphologically similar taxa.
A Review of 3D Human Pose Estimation from 2D Images

Human pose estimation task takes images as input and extracts a set of locations representing the predefined body joints and the sparse connections between the joints, called the body parts. A pose can be estimated from single or multiple frames, in a single (monocular) or multi-view (stereo) setup and for a single person or multiple people in the scene. In this work, we provide an overview of the classic and deep learning-based 3D pose estimation approaches. We also point out relevant evaluation metrics, pose parametrizations, body models, and 3D human pose datasets. Finally, we review state-of-the-art pose estimation results, briefly discuss open problems, and propose possible future research directions.
Identifying and Interpreting Subgroups in Health Care Utilization Data with Count Mixture Regression Models

Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures such as length of stay and spending include zero inflation, over-dispersion, and skewness, all of which complicate statistical modeling. Moreover, latent subgroups of patients may have distinct patterns of utilization and relationships between that utilization and observed covariates. In this work, we apply and compare likelihood-based and parametric Bayesian mixtures of Negative Binomial and zero-inflated Negative Binomial regression models. In a simulation, we find that the Bayesian approach finds the true number of mixture components more accurately than using information criteria to select among likelihood-based finite mixture models. When we apply the models to data on hospital lengths of stay for patients with lung cancer, we find distinct subgroups of patients with different means and variances of hospital days, health and treatment covariates, and relationships between covariates and length of stay.
A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 s, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance.