Miracle-dz/UniFAR-FTUs huggingface dataset:
Sediment Delivery FROM Reopened Forest Roads AT Stream Crossings IN the Virginia Piedmont Physiographic Region, USA

Efforts to control surface runoff and erosion from forest roads at stream crossings are critical for the protection of aquatic ecosystems in forests. In this research, annual and event-based sediment delivery rates were estimated for reopened legacy roads at forest stream crossings in the Virginia Piedmont physiographic region, USA. Sediment delivery rates were compared among stream crossing approaches with diverse intensities of best management practice (BMP) implementations for surface cover and water control. Model predictions from the Water Erosion Prediction Project (WEPP) were compared to field observations of surface runoff and sediment delivery to evaluate model performance. Annual sediment delivery rates from reopened (bare) legacy road approaches to stream crossings were 7.5 times higher than those of completely graveled approaches. Sediment delivery rates ranged from 34 to 287 Mg ha -1 year -1 for the bare approaches and from 10 to 16 Mg ha -1 year -1 for the graveled approaches. Event-based surface runoff and associated total suspended solids (TSS) concentrations were compared among a succession of gravel surfacing treatments that represented increasing intensities of BMP implementations on reopened approaches. The three treatments were No Gravel (10-19% cover), Low Gravel (34-60% cover), and High Gravel (50-99% cover). Median TSS concentration of surface runoff for the No Gravel treatment (2.84 g L -1 ) was greater than Low Gravel (1.10 g L -1 ) and High Gravel (0.82 g L -1 ) by factors of 2.6 and 3.5,
Long noncoding RNA LINC00515 promotes cell proliferation and inhibits apoptosis by sponging miR-16 and activating PRMT5 expression in human glioma

Introduction: In recent years, an increasing amount of literature has demonstrated the functional role of long non-coding RNA (lncRNA) in human diseases. LINC00515 is a newly defined lncRNA and is reported to act as an oncogene in multiple myeloma. However, the function of LINC00515 in glioma is still uncertain. Materials and methods: We examined the expression levels of LINC00515 in human glioma tissues and cell lines using real-time PCR analysis. In addition, we confirmed the distribution of LINC00515 in glioma cells and suppressed LINC00515 expression with siRNAs. CCK-8, colony formation assay and apoptosis analysis were used to study the function of LINC00515 in glioma progression. Then, we used bioinformatics prediction and subsequent experiments to reveal the underlying molecular mechanism. Results: We found that LINC00515 was up-regulated in glioma tissues and cell lines. LINC00515 was mainly located in the cytoplasm in glioma cells. Knockdown of LINC00515 led to decreased proliferation and increased apoptosis of glioma cells. Mechanistically, our data indicated that there was a LINC00515/miR-16/PRMT5 regulatory axis in glioma. LINC00515 could activate PRMT5 expression and promote glioma progression by acting as a sponge of miR-16. Conclusion: LINC00515 expression is elevated in human glioma and promotes growth and inhibits apoptosis of glioma cells. The regulatory cascade LINC00515/miR-16/PRMT5 plays a critical role in glioma progression.
Prognostic impact of the absence of biallelic deletion at the TRG locus for pediatric patients with T-cell acute lymphoblastic leukemia treated on the MRC UKALL 2003 trial by

Citation: Nadine Farah, Amy A. Kirkwood, Sunniyat Rahman, Theresa Leon, Sarah Jenkinson, Rosemary E. Gale, Katharine Patrick, Jeremy Hancock, Sujith Samarasinghe, David C. Linch, Anthony V. Moorman, Nicholas Goulden, Ajay Vora, and Marc R. Mansour. Prognostic impact of the absence of biallelic deletion at the TRG locus for pediatric patients with T-cell acute lymphoblastic leukemia treated on the MRC UKALL2003 trial. Haematologica. 2018; 103:xxx doi:10.3324/haematol.2017. 185801
Unintended Consequences of Antismoking Pricing Policies: Insights from Smokers’ Household Expenditure on Smoking Behavior and Public Health

Despite e-cigarettes’ increasing popularity, traditional cigarette smoking, particularly with roll-your-own (RYO) tobacco, persists as the predominant form of global tobacco consumption. We examine the effect of an antismoking pricing policy on smokers’ behavior by assessing changes in their households’ consumption of tobacco products. To do so, we use a pricing policy implemented in Israel that introduced significant tax increases on RYO tobacco and factory-made cigarettes as our research setting. We analyze data compiled by a national agency on 8000 households representing the smoking population, evaluating changes in smoking behavior through their expenditures on tobacco products. Our model assesses the policy’s impact on tobacco and cigarettes consumed, as well as the probability of households consuming these products. We find that the policy reduced neither overall tobacco consumption nor cigarettes smoked, contrary to expectations. Instead, it was associated with an increased likelihood of households choosing RYO cigarettes and the number of RYO cigarettes consumed. The study emphasizes that anti-smoking pricing policies may inadvertently promote RYO tobacco use, potentially harming public health. To mitigate this, we recommend designing price policies that eliminate perceived gaps between RYO tobacco and factory-made cigarettes. This approach can discourage substitution and foster the deconsumption of both products.
Low : ! Beer > Wine > Liquor ! Liquor : ! Steak + Dessert ! Wine : ! Fish + Salad ! Beer : ! Appetizer + Burger ! Companion ! Location ! Spend ! Food ! Drink !

Many of the seminal papers in preference handling have used food preferences as motivating examples for their work; for example (Boutilier et al. 2004; Chomicki 2002). As foodies, the authors find this particularly motivating. While we think that there is both research and commercial potential in preference-based software for restaurants, we believe that serious application of the MPREF community’s technology to the problem of personal preference-driven presentation of menus, seating, etc., will require significant further innovation. We broadly survey the current use of preferences in making the dining-out experience more enjoyable, and we look at the states of the art for preference representation and reasoning, and for restaurant software. We illustrate some of our points with a short story.
Biases and Uncertainties when Estimating the Hazard of the Next Nankai Earthquake

After a renewal process is estimated for data on repeated occurrences of large-scale earthquakes, we predict the hazard of the next rupture as a function of time using the estimated parameters. However, when the number of events in the data is small, the hazard function based on the maximum likelihood estimates (MLE) has not only a large uncertainty in the estimation but also sometimes has a serious bias. To see the uncertainty of estimated hazard functions, we use a Bayesian inference. In this manuscript I discuss the uncertainty of the estimated hazard functions of the forthcoming Nankaido earthquake based on historical data consisting of ten large events. Moreover, assuming the same distribution of the ratio of the time-interval length between the successive events to the slip size of the first event of the pair, an extended renewal process for the Shimazaki-Nakata's Time-predictable model can be considered. Thus we estimate not only the alternative hazard function of the next Nankaido event but also its uncertainty based on occurrence time data for the last three large events associated with records of coseismic uplift sizes. The same extended renewal model is applied to the same ten large events noted above associated with the estimated slip sizes that are calculated from Usami's magnitude estimates. The three datasets lead to significantly different predictions, even if the estimation uncertainty is taken into consideration.
Validation and Re ﬁ nement of Uni ﬁ ed Analytic Model for Flexible and Semi ﬂ exible Polymer Melt Entanglement

: We combine molecular dynamics simulations and topological analyses (TA) to validate and re ﬁ ne a recently proposed uni ﬁ ed analytic model [Hoy, R. S. ; Kro ̈ ger, M. Phys. Rev. Lett. 2020 , 124 , 147801] for the reduced entanglement length, tube diameter, and plateau modulus of polymer melts. While the functional forms of the previously published expressions are insensitive to the choice of the TA method and N e -estimator, obtaining better statistics and eliminating all known sources of systematic error in the N e - estimation alters their numerical coe ﬃ cients. Our revised expressions quantitatively match bead − spring simulation data over the entire range of chain sti ﬀ nesses for which systems remain isotropic, semiquantitatively match all available experimental data for ﬂ exible, semi ﬂ exible, and sti ﬀ polymer melts (including new data for conjugated polymers that lie in a previously unpopulated sti ﬀ ness regime), and outperform previously developed uni ﬁ ed scaling theories.
TRANSFORMATIONS OF SPORTS AUDIOVISUAL LANGUAJE DURING COVID-19

Introduction : The research focuses on the transformations experienced by Ibero-American sports channels during the COVID-19 pandemic. The main objective is to describe the changes applied to the audiovisual language of these media during this period of crisis. Methodology : A qualitative approach was employed, using the semi-structured interview technique. The selected sample included journalists and producers affiliated with sports channels. The interview was structured to inquire about specific changes in key elements of the audiovisual language, such as image, sound
High-Grade Puriﬁcation of Third-Generation HIV-Based Lentiviral Vectors by Anion Exchange Chromatography for Experimental Gene and Stem Cell Therapy Applications

Lentiviral vectors (LVs) have been increasingly used in clinical gene therapy applications particularly due to their efﬁcient gene transfer ability, lack of interference from preexisting viral immunity, and long-term gene expression they provide. Purity of LVs is essential in in vivo applications, for a high therapeutic beneﬁt with minimum toxicity. Accordingly, laboratory scale production of LVs frequently involves transient cotrans-fection of 293T cells with packaging and transfer plasmids in the presence of CaPO 4 . After clearance of the cellular debris by low-speed centrifugation and ﬁltration, lentivectors are usually concentrated by high-speed ultracentrifugation in sucrose cushion. Concentrated viral samples are then puriﬁed by anion exchange chromatography (AEX) after benzonase treatment to remove the residual cellular DNA. Here, we describe an improved practical method for LV puriﬁcation using AEX, useful for experimental studies concerning gene and stem cell therapy.
Reproductive Medicine; Endocrinology and Infertility

OBJECTIVE: The primary objective of our study was to compare the endometrial microbiota profiles of women with unexplained infertility and fertile women. STUDY DESIGN: A total of 15 patients with unexplained infertility and 15 patients who had a live birth in the last two years with proven fertility were recruited. Endometrial samples were collected and analyzed through next-generation sequencing of the bacteria-specific 16S ribosome gene. RESULTS: Lactobacillus species represented the majority of the microbiome profile in both groups. The median percentage of the endometrial Lactobacillus between infertile patients and fertile patients was not statistically significant (p=0.9). Groups were divided into two categories: Lactobasillus dominant (Lactobacillus spp.> 90%, LD) and Non-Lactobasillus dominant (NLD). Lactobacillus dominance was detected in six of nine patients (66.7%) in the study group and four of nine patients (44.5%) in the control group, and no significant difference was observed between them (p=0.64). CONCLUSION: According to our study results, no significant difference was observed between the endometrial microbiota profile of infertile and fertile patients. Further studies with larger sample sizes are needed to characterize the endometrial microbiota and its impact on reproduction.
Remote Sensing of Environment

Rivers are essential to the Earth’s water cycle and deeply impact many human societies and ecosystems, yet they are currently monitored poorly at the global scale. In-situ gauging stations are distributed sparsely and heterogeneously and do not cover much of the world, whereas remotely sensed images are spatially and temporally dense and available globally. Remotely sensed multispectral images, such as the ones acquired by Landsat missions, are available to enable the analysis and surveying of rivers using suitable algorithms. However, existing algorithms are limited in ways that restrict the coverage of the produced results and that prevent the automated analysis of river networks at large scales over short periods of time. Ideally, river maps should be as “live” as possible, e.g., computed quickly and continuously as new Earth imaging data becomes available. Towards advancing progress on this problem, we describe an automated river analysis and mapping engine, RivaMap, that enables the computation of large-scale hydrography data sets from remotely sensed data in a short period of time. RivaMap facilitates water resource management by providing tools to delineate rivers and to estimate their width. As a practical application of RivaMap, we present a continental-scale centerline and width data set of North American rivers, that is automatically computed on Landsat data. We validate our mapping engine by comparing the RivaMap-generated data to a similar data set, NARWidth, and also to in-situ measurements. Our experimental results show that RivaMap is able to efficiently and accurately extract rivers from remotely sensed images at large scales. The outcomes of this research, the software, and the computed exemplary data set are publicly available. © 2017 Elsevier Inc. All rights reserved.
Neurobiology of Aging

Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.
Emotional Lateralization-inspired Spatiotemporal Neural Networks for EEG-based Emotion Recognition

—Due to the volume conduction effects in the brain, multi-channel electroencephalogram (EEG) contain redundant information. Thus it is necessary to employ the channel selection method to ascertain vital channel in the EEG emotion recognition task. The existing studies select channels by the correlation analysis or the supervised learning. However, such methods are impacted by the inherent limitations of data sets such as the limited numbers and unbalanced distribution, which tend to overﬁt high-frequency emotion and underﬁt low-frequency ones. As a consequence, these selected channels are high related with certain emotion, but low related with the overall emotion. Hence, it is an essential problem in current research to select effective channel subset using unsupervised learning to fully improve the universal adaptability in multi-label classiﬁcation tasks. In this paper, we propose a channel selection method named emotional lateralization-inspired spatiotemporal neural networks (ELSTNN) for EEG emotion recognition. In ELSTNN, the neuroscience ﬁnding of emotional lateralization is applied to guide channel selection with unsupervised learning, which could remove redundant channels. Then, ELSTNN with three convolution layers and one long short-term memory layer is applied to mine and integrate the deep spatiotemporal feature from the selected EEG signals. To evaluate the effectiveness of ELSTNN, extensive experiments are carried out in two public datasets, DEAP and DREAMER. Our proposed method achieves better performance compared with various state-of-the-art methods, which obtains the mean accuracy 95.67% in valence and 94.97% in arousal on DEAP, and 93.72% in valence and 93.57% in arousal on DREAMER.
Robotics-Assisted Stroke Rehabilitation with Machine Learning-Based Residual Severity Classification

Background: Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. This study uses supervised learning methods to address a clinician’s autonomous classification of stroke residual severity labeled data towards improving in-home robotics-assisted stroke rehabilitation. Methods: Thirty-three stroke patients participate in in-home therapy sessions using the Motus Nova robotics rehabilitation technology to capture upper and lower body motion. The therapy session summary data is based on high-resolution movement and assistance data and clinician-informed discrete stroke residual severity labels. This arises from a final processed dataset of 32 , 902 patient sessions based on the maximum score per patient per session. Four machine learning algorithms are used to classify stroke residual severity: light gradient boosting, extra trees, deep neural networks, and classical logistic regression. Their performance measures are evaluated to identify which method maximizes stroke residual severity classification accuracy. Results: We demonstrated that the light gradient boosting method provides the most reliable autonomous detection of stroke severity. This method achieved an average of 94% accuracy, measured using the F1-score performance measure with 10-fold cross-validation. All variables collected from each patient session impact the model’s classification accuracy and contribute to 95% of the explained variance. Conclusion: We showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class with efforts to enhance in-home self-guided, individualized stroke rehabilitation. As data from rehabilitation practices are often of comparable size and nature to the data collected in our study, this suggests that the light gradient boosting method should be considered a standard, more efficient tool for this analysis.
Neuroscience and Biobehavioral Reviews

The ability to observe, interpret, and learn behaviors and emotions from conspeci ﬁ cs is crucial for survival, as it bypasses direct experience to avoid potential dangers and maximize rewards and bene ﬁ ts. The anterior cingulate cortex (ACC) and its extended neural connections are emerging as important networks for the detection, encoding, and interpretation of social signals during observational learning. Evidence from rodents and primates (including humans) suggests that the social interactions that occur while individuals are exposed to important information in their environment lead to transfer of information across individuals that promotes adaptive behaviors in the form of either social a ﬃ liation, alertness, or avoidance. In this review, we ﬁ rst showcase anatomical and functional connections of the ACC in primates and rodents that contribute to the perception of social signals. We then discuss species-speci ﬁ c cognitive and social functions of the ACC and di ﬀ erentiate be-tween neural activity related to ‘ self ’ and ‘ other ’ , extending into the di ﬀ erence between social signals received and processed by the self, versus observing social interactions among others. We next describe behavioral and neural events that contribute to social learning via observation. Finally, we discuss some of the neural mechanisms underlying observational learning within the ACC and its extended network.
Diminution of feeding response in Helicoverpa armigera by inhibition or silencing of sugar gustatory receptor

Gustatory receptor (GR) is one of the essential chemosensory molecules in Lepidopteran pests. GR is involved in sensing several canonical tastes which in turn regulate the diverse behavioral and physiological responses of these insects. In this article, we have evaluated the alteration in feeding response of Helicoverpa armigera by blocking and silencing of sugar-sensing gustatory receptor 9 (HarmGr9). Sf9 cells based assay showed that glucose analogue, Miglitol, can bind to the expressed HarmGr9. This binding might lead to an inhibition of receptor activation and downstream signaling, indicated by reduced intracellular Ca2+ fluorescence. Further, the in-vivo study illustrated the feeding rate reduced on a diet containing miglitol as compared to the larvae fed on the artificial diet. Reduction in feeding rate was prolonged in insects fed on the miglitol containing diet even after switching to the control glucose diet. Competitive cell-based assay and feeding assay, using equimolar glucose and miglitol, also showed an inhibitory effect on HarmGr-9 activation and insect feeding rate. We have observed similar feeding rate reduction in HarmGr9 knockdown in H. armigera larvae. We believe this unique approach of H. armigera feeding response inhibition by blocking the sugar receptor can be further used to develop a novel strategy for agricultural pest management.