Miracle-dz/UniFAR-FTUs huggingface dataset:
Global ammonia emissions from synthetic nitrogen fertilizer applications in agricultural systems: Empirical and process‐based estimates and uncertainty

Excessive ammonia (NH3) emitted from nitrogen (N) fertilizer applications in global croplands plays an important role in atmospheric aerosol production, resulting in visibility reduction and regional haze. However, large uncertainty exists in the estimates of NH3 emissions from global and regional croplands, which utilize different data and methods. In this study, we have coupled a process‐based Dynamic Land Ecosystem Model (DLEM) with the bidirectional NH3 exchange module in the Community Multiscale Air‐Quality (CMAQ) model (DLEM‐Bi‐NH3) to quantify NH3 emissions at the global and regional scale, and crop‐specific NH3 emissions globally at a spatial resolution of 0.5° × 0.5° during 1961–2010. Results indicate that global NH3 emissions from N fertilizer use have increased from 1.9 ± 0.03 to 16.7 ± 0.5 Tg N/year between 1961 and 2010. The annual increase of NH3 emissions shows large spatial variations across the global land surface. Southern Asia, including China and India, has accounted for more than 50% of total global NH3 emissions since the 1980s, followed by North America and Europe. Rice cultivation has been the largest contributor to total global NH3 emissions since the 1990s, followed by corn and wheat. In addition, results show that empirical methods without considering environmental factors (constant emission factor in the IPCC Tier 1 guideline) could underestimate NH3 emissions in context of climate change, with the highest difference (i.e., 6.9 Tg N/year) occurring in 2010. This study provides a robust estimate on global and regional NH3 emissions over the past 50 years, which offers a reference for assessing air quality consequences of future nitrogen enrichment as well as nitrogen use efficiency improvement.
Learning Urban Navigation via Value Iteration Network

Choosing an appropriate route is a critical problem in urban navigation. Being familiar with roads topology and other vehicles’ routes, experienced drivers could usually find a near optimal route. However, the nowadays navigation applications hardly catch the domain knowledge and drivers’ interaction in this scenario. In fact, they only recommend several routes and leave the most difficult decision to driver. Hence the route is often congested by many vehicles whose drivers make a similar choose. To intelligently make right decision on navigation and improve traffic efficiency for each vehicle, we propose a neural network structure which learns to plan coarse-grained route in complex urban areas.Focused on learning to navigate, this paper first formalizes urban map and vehicle route model. The city map is segmented into grids and each vehicle’s route is mapped to grids. Based on grid-world model, we solicit both global traffic status and driving actions from large-scale taxicab GPS data. The learn-to-plan problem is therefore to find a policy function from a global status representation to an experienced driver’s action under that status. Traditional neural network is difficult to learn to this plan-involved function, so we leverage and modify value iteration network (VIN), which explicitly takes long-term plan into consideration. Finally we evaluate the performance of proposed network on real map and trajectory data in Beijing, China. The results show that VIN can achieve human driver performance in most cases, with high success rate and less commuting time.
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing

We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.
Multi-Channel Auto-Encoder for Speech Emotion Recognition

Inferring emotion status from users' queries plays an important role to enhance the capacity in voice dialogues applications. Even though several related works obtained satisfactory results, the performance can still be further improved. In this paper, we proposed a novel framework named multi-channel auto-encoder (MTC-AE) on emotion recognition from acoustic information. MTC-AE contains multiple local DNNs based on different low-level descriptors with different statistics functions that are partly concatenated together, by which the structure is enabled to consider both local and global features simultaneously. Experiment based on a benchmark dataset IEMOCAP shows that our method significantly outperforms the existing state-of-the-art results, achieving $64.8\%$ leave-one-speaker-out unweighted accuracy, which is $2.4\%$ higher than the best result on this dataset.
Cigarette smoking and personality: interrogating causality using Mendelian randomisation

Abstract Background Despite the well-documented association between smoking and personality traits such as neuroticism and extraversion, little is known about the potential causal nature of these findings. If it were possible to unpick the association between personality and smoking, it may be possible to develop tailored smoking interventions that could lead to both improved uptake and efficacy. Methods Recent genome-wide association studies (GWAS) have identified variants robustly associated with both smoking phenotypes and personality traits. Here we use publicly available GWAS summary statistics in addition to individual-level data from UK Biobank to investigate the link between smoking and personality. We first estimate genetic overlap between traits using LD score regression and then use bidirectional Mendelian randomisation methods to unpick the nature of this relationship. Results We found clear evidence of a modest genetic correlation between smoking behaviours and both neuroticism and extraversion. We found some evidence that personality traits are causally linked to certain smoking phenotypes: among current smokers each additional neuroticism risk allele was associated with smoking an additional 0.07 cigarettes per day (95% CI 0.02–0.12, p = 0.009), and each additional extraversion effect allele was associated with an elevated odds of smoking initiation (OR 1.015, 95% CI 1.01–1.02, p = 9.6 × 10−7). Conclusion We found some evidence for specific causal pathways from personality to smoking phenotypes, and weaker evidence of an association from smoking initiation to personality. These findings could be used to inform future smoking interventions or to tailor existing schemes.
Safety, efficacy, and dose response of the maturation inhibitor GSK3532795 (formerly known as BMS-955176) plus tenofovir/emtricitabine once daily in treatment-naive HIV-1-infected adults: Week 24 primary analysis from a randomized Phase IIb trial

GSK3532795 (formerly known as BMS-955176) is a second-generation maturation inhibitor targeting a specific Gag cleavage site between capsid p24 and spacer peptide 1 of HIV-1. Study 205891 (previously AI468038) investigated the efficacy, safety, and dose response of GSK3532795 in treatment-naive, HIV-1-infected participants. Study 205891 (NCT02415595) was a Phase IIb, randomized, active-controlled, double-blind, international trial. Participants were randomized 1:1:1:1 to one of three GSK3532795 arms at doses 60 mg, 120 mg or 180 mg once daily (QD), or to efavirenz (EFV) at 600 mg QD, each in combination with tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) (300/200 mg QD). Primary endpoint was proportion of participants with plasma HIV-1 RNA <40 copies/mL at Week 24. Between May 2015 and May 2016, 206 participants received treatment. At Week 24, 76–83% participants receiving GSK3532795 and 77% receiving EFV achieved HIV-1 RNA <40 copies/mL. Fifteen participants receiving GSK3532795 and one receiving EFV met resistance testing criteria; 10/15 receiving GSK3532795 had emergent substitutions at reverse transcriptase positions M184, and one at position K65, while the participant receiving EFV did not have any nucleoside reverse transcriptase inhibitor (NRTI)/non-NRTI mutations. EFV, relative to GSK3532795, had more serious adverse events (9% versus 5%) and adverse events leading to discontinuation (17% versus 5%). However, 3–4-fold higher rates of gastrointestinal adverse events were observed with GSK3532795 relative to EFV. GSK3532795 combined with TDF/FTC is efficacious with 24 weeks of therapy. However, GSK3532795 showed a higher rate of gastrointestinal intolerability and treatment-emergent resistance to the NRTI backbone relative to EFV. Trial registration: ClinicalTrials.gov NCT02415595.
Insufficient Sleep Duration Is Associated With Dietary Habits, Screen Time, and Obesity in Children.

STUDY OBJECTIVES To examine sleep duration and the association between insufficient sleep duration and life- style factors in a representative sample of Greek children and adolescents. METHODS Population data derived from a school-based health survey carried out in spring 2015 on 177,091 children (51% male) aged 8 to 17 years (participation rate was approximately 40% of the total population). Dietary habits, sleeping hours (weekdays and weekend), physical activity status, and sedentary activities were assessed through electronic questionnaires completed at school with assistance of teachers. Anthropometric and physical fitness measurements were obtained by physical education teachers. RESULTS A greater proportion of males than females (42.3% versus 37.3%, P < .001) and of children compared with adolescents (42.1% versus 32.8%, P < .001) reported insufficient sleep duration. Insufficient sleep duration was associated with unhealthy dietary habits such as skipping breakfast (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.25-1.35), fast-food consumption (OR 1.35, 95% CI 1.29-1.41), and consuming sweets regularly (OR 1.32, 95% CI 1.25-1.39). Insufficient sleep duration was found to be associated with insufficient dietary habits (OR 0.59, 95% CI 0.54-0.64), increased screen time (OR 1.26, 95% CI 1.21-1.31), and being overweight/obese (OR 1.21, 95% CI 1.17-1.25), after adjusting for several covariates. CONCLUSIONS Insufficient sleep duration was associated with an unhealthy lifestyle profile among children and adolescents in this study. Results support the development of interventions to help students improve sleep duration.
Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics

Recent work has shown that, when countering external forces, the nervous system adjusts not only predictive (i.e., feedforward) control of reaching but also reflex (i.e., feedback) responses to mechanical perturbations. Here we show that altering the physical properties of the arm (i.e., intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning transfers to feedback responses even though the latter were never directly trained. Forty-five human participants (30 females) performed a single-joint elbow reaching task and countered mechanical perturbations that created pure elbow motion. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements when the shoulder joint was either free to rotate or locked by the robotic manipulandum. With the shoulder unlocked, we found robust activation of shoulder flexor muscles for pure elbow flexion trials, as required to counter the interaction torques that arise at the shoulder because of forearm rotation. After locking the shoulder joint, which cancels these interaction torques, we found a substantial reduction in shoulder muscle activity over many trials. In our second experiment, we tested whether such learning transfers to feedback control. Mechanical perturbations applied to the arm with the shoulder unlocked revealed that feedback responses also account for intersegmental dynamics. After locking the shoulder joint, we found a substantial reduction in shoulder feedback responses, as appropriate for the altered intersegmental dynamics. Our work suggests that feedforward and feedback control share an internal model of the arm's dynamics. SIGNIFICANCE STATEMENT Here we show that altering the physical properties of the arm causes people to learn new motor commands and that this learning transfers to their reflex responses to unexpected mechanical perturbations, even though the reflex responses were never directly trained. Our results suggest that feedforward motor commands and reflex responses share an internal model of the arm's dynamics.
Age-related changes in processing speed modulate context use during idiomatic ambiguity resolution

ABSTRACT The goal of the current study was to investigate whether age-related changes in processing speed, as indexed by verbal fluency, modulate context use in the resolution of ambiguous idioms (e.g., tie the knot). Younger and older adults completed a cross-modal priming experiment where they decided whether visual word targets were related in meaning to idiomatically or literally biased auditory sentence primes. Older adults with high verbal fluency, similar to younger adults, showed context-dependent facilitation in both biasing sentence contexts. In contrast, older adults with low verbal fluency did not show facilitation of literal meanings in literally biased sentence contexts, likely because they had difficultly inhibiting the dominant figurative meaning. These findings argue that age-related changes in context use during ambiguity resolution are restricted to older adults with reduced processing speed. The results also suggest that verbal fluency may reflect the speed in recruiting frontally-mediated selection mechanisms during ambiguity resolution.
Extraction of Natural Language Requirements from Breach Reports Using Event Inference

We address the problem of extracting useful information contained in security and privacy breach reports. A breach report tells a short story describing how a breach happened and the follow-up remedial actions taken by the responsible parties. By predicting sentences that may follow a breach description using natural language processing, our goal is to suggest security and privacy requirements for practitioners and end users that can be used to prevent and recover from such breaches. We prepare a curated dataset of structured short breach stories using unstructured breach reports published by the U.S. Department of Health and Human Services. We propose a prediction model for inferring held-out sentences based on Paragraph Vector, a document embedding method, and Long Short-Term Memory networks. The predicted sentences can suggest natural language requirements. We evaluate our model on the curated dataset as well as the ROCStories corpus, a collection of five-sentence commonsense stories, and find that the presented model performs significantly better than the baseline of using average word vectors.
Quantitative Proteomics of the 2016 WHO Neisseria gonorrhoeae Reference Strains Surveys Vaccine Candidates and Antimicrobial Resistance Determinants*

We present the first large-scale quantitative MS-based profiling of cell envelopes and cytoplasmic proteins derived from a diverse panel of WHO gonococcal reference strains possessing all existing AMR profiles and intended for quality assurance in laboratory investigations. We describe nine novel gonorrhea vaccine candidates and six potential global proteomic AMR markers. A reference proteomics databank for gonococcal vaccine and AMR research endeavors is provided, which enables microbiological, clinical, or epidemiological projects and enhances the utility of the WHO reference strains. Graphical Abstract Highlights Quantitative proteomes of the 2016 WHO Neisseria gonorrhoeae reference strains. Novel gonorrhea vaccine candidates and potential global proteomic AMR markers. First large-scale proteomic profiling of gonorrhea vaccine candidates and AMR. A reference proteomics databank for gonococcal vaccine and AMR research endeavors. The sexually transmitted disease gonorrhea (causative agent: Neisseria gonorrhoeae) remains an urgent public health threat globally because of its reproductive health repercussions, high incidence, widespread antimicrobial resistance (AMR), and absence of a vaccine. To mine gonorrhea antigens and enhance our understanding of gonococcal AMR at the proteome level, we performed the first large-scale proteomic profiling of a diverse panel (n = 15) of gonococcal strains, including the 2016 World Health Organization (WHO) reference strains. These strains show all existing AMR profiles - established through phenotypic characterization and reference genome publication - and are intended for quality assurance in laboratory investigations. Herein, these isolates were subjected to subcellular fractionation and labeling with tandem mass tags coupled to mass spectrometry and multi-combinatorial bioinformatics. Our analyses detected 904 and 723 common proteins in cell envelope and cytoplasmic subproteomes, respectively. We identified nine novel gonorrhea vaccine candidates. Expression and conservation of new and previously selected antigens were investigated. In addition, established gonococcal AMR determinants were evaluated for the first time using quantitative proteomics. Six new proteins, WHO_F_00238, WHO_F_00635c, WHO_F_00745, WHO_F_01139, WHO_F_01144c, and WHO_F_01126, were differentially expressed in all strains, suggesting that they represent global proteomic AMR markers, indicate a predisposition toward developing or compensating gonococcal AMR, and/or act as new antimicrobial targets. Finally, phenotypic clustering based on the isolates' defined antibiograms and common differentially expressed proteins yielded seven matching clusters between established and proteome-derived AMR signatures. Together, our investigations provide a reference proteomics data bank for gonococcal vaccine and AMR research endeavors, which enables microbiological, clinical, or epidemiological projects and enhances the utility of the WHO reference strains.
Optimal Kinematic-based Trajectory Planning and Tracking Control of Autonomous Ground Vehicle Using the Variational Approach

In this paper, a novel kinematic-based optimal trajectory planning formulation for an autonomous vehicle is presented. In this new formulation, the quadratic errors of position, velocity, and acceleration are minimized subject to the rear wheel car-like vehicle nonlinear kinematic model. Minimizing the error of velocity and acceleration in addition to the error of position, allows us to obtain both optimal vehicle trajectory and control law. The Variational approach is used to minimize the cost function. Then, optimal trajectory and control inputs are numerically calculated by solving a set of two-point boundary value (TPBV) nonlinear differential equations. Finally, the proposed method is evaluated in two scenarios of lane changing and multi-curvature road which verify the success of the proposed method in generating an optimal trajectory and control inputs.
Ocular Clocks: Adapting Mechanisms for Eye Functions and Health

Vision is a highly rhythmic function adapted to the extensive changes in light intensity occurring over the 24-hour day. This adaptation relies on rhythms in cellular and molecular processes, which are orchestrated by a network of circadian clocks located within the retina and in the eye, synchronized to the day/night cycle and which, together, fine-tune detection and processing of light information over the 24-hour period and ensure retinal homeostasis. Systematic or high throughput studies revealed a series of genes rhythmically expressed in the retina, pointing at specific functions or pathways under circadian control. Conversely, knockout studies demonstrated that the circadian clock regulates retinal processing of light information. In addition, recent data revealed that it also plays a role in development as well as in aging of the retina. Regarding synchronization by the light/dark cycle, the retina displays the unique property of bringing together light sensitivity, clock machinery, and a wide range of rhythmic outputs. Melatonin and dopamine play a particular role in this system, being both outputs and inputs for clocks. The retinal cellular complexity suggests that mechanisms of regulation by light are diverse and intricate. In the context of the whole eye, the retina looks like a major determinant of phase resetting for other tissues such as the retinal pigmented epithelium or cornea. Understanding the pathways linking the cell-specific molecular machineries to their cognate outputs will be one of the major challenges for the future.
Millimeter Wave Radar Target Tracking Based on Adaptive Kalman Filter

With the continuous development of the intelligent transportation industry, target tracking has become an important research direction. Under normal circumstances, due to the complex road environment and changing backgrounds, millimeter wave radar has more interference when detecting targets. In addition to the variety of targets in the road and the different scattering intensity of multiple parts, the interference of the flicker noise on the radar must be considered. The combination of these noises can affect the accuracy of radar measurement and even make the radar to lose the target for a short time. The paper constructs a target tracking model based on adaptive Sage-Husa Kalman filter algorithm to track radar signals. The algorithm can not only estimate the real-time state of the system, but also estimate and modify the parameters of the system and the statistical parameters of the noise, so that the system model is closer to the current real state of the system, thus improving the accuracy of the target tracking. Even if radar loses its target in a short time, the target tracking model can estimate the approximate value of the true value of the target. The experimental results show that this method can track the radar target accurately and estimate the position information of the lost target.
Low-carbohydrate high-protein diet diminishes the insulin response to glucose load via suppression of SGLT-1 in mice

ABSTRACT The purpose of this study was to examine the effects of a low-carbohydrate high-protein (LCHP) diet on the expression of glucose transporters and their relationships to glucose metabolism. Male C57BL/6 mice were fed a normal control or LCHP diet for 2 weeks. An oral glucose tolerance test and insulin tolerance test (ITT) were performed, and the expression of glucose transporters was determined in the gastrocnemius muscle, jejunum and pancreas. The increase in plasma insulin concentrations after glucose administration was reduced in the LCHP group. However, LCHP diet had no effects on peripheral insulin sensitivity or glucose transporters expression in the gastrocnemius and pancreas. Soluble glucose transporter (SGLT)-1 protein content in jejunum was lower in the LCHP group. Taken together, these results suggest that the blunted insulin response after glucose administration in LCHP diet-fed mice might be due to decreased SGLT-1 expression, but not to an increase in peripheral insulin sensitivity. Abbreviations: LCHP: low-carbohydrate high-protein; ITT: insulin tolerance test; GLUT: glucose transporter; SGLT: soluble glucose transporter; OGTT: oral glucose tolerance test; AUC: area under the curve. Graphical Abstract Reduced SGLT-1 expression caused by low-carbohydrate high-protein diet diminished glucose absorption rate and resulted in lower insulin secretion.