Miracle-dz/UniFAR-FTUs huggingface dataset:
Facile synthesis of trimetallic AuPtPd alloy nanowires and their catalysis for ethanol electrooxidation

Using ultrathin, high aspect ratio Te nanowires as a template, we demonstrated a facile method to synthesize high-quality AuPtPd trimetallic alloy nanowires in aqueous solution at room temperature within one hour. More importantly, the obtained AuPtPd trimetallic alloy nanowires exhibited significantly enhanced activity towards ethanol electrooxidation in alkaline medium, demonstrating the potential of applying these Pd-based alloy nanowires as effective electrocatalysts for direct alcohol fuel cells. Considering the important role of one-dimensional trimetallic alloy nanostructures in catalytic systems, the present AuPtPd alloy nanowires may also find applications in other fields, such as fuel cells (formic acid, methanol oxidation and oxygen reduction reaction) and electrochemical biosensors.
Forage biomass yield and arbuscular mycorrhizal symbiosis in a legume and C3 and C4 grasses under increasing soil phosphorus availability

Abstract. Phosphorus (P) is the main limiting factor for forage production in grasslands. It is important to determine levels of available P in soil that optimise production with minimum impact on arbuscular mycorrhizal (AM) symbiosis. We investigated the effects of increasing P availability on biomass production, root morphology, AM symbiosis and P acquisition of a forage legume (Lotus tenuis), a C3 grass (Schedonorus arundinaceus) and a C4 grass (Panicum coloratum) growing on a P-deficient soil in pots with P applied at rates of 0–160 mg kg–1 dry soil. The three forage species responded strongly to addition of P, with 90% of maximum shoot growth reached at available P levels of 24.3 mg kg–1 for L. tenuis, 14.4 mg kg–1 for P. coloratum and 11.2 mg kg–1 for S. arundinaceus. Lotus tenuis and P. coloratum produced higher yields of shoot biomass than S. arundinaceus. Root dry weight was higher in the legume than in the grasses, with the root-mass fraction being lowest in P. coloratum. AM colonisation was higher in L. tenuis roots than in grass roots, and decreased with increased soil P availability, especially in grasses. Low to moderate additions of P did not affect, and could even improve, AM colonisation in L. tenuis roots. For L. tenuis, it is possible to increase forage yield while maintaining high values of AM colonisation at 10–20 mg kg–1 of available P, but for grasses, especially S. arundinaceus, it is difficult to achieve both objectives. The presence of L. tenuis in grasslands or pastures may contribute to maintaining the native AM inoculum under a wide range of soil P availability in regions such as the Salado River basin of Argentina.
Evaluation of Biogas Production from Bio-Digestion of Organic Wastes

The present study evaluates the influence of kitchen wastes on animal manures via anaerobic digestion for biogas production. The digestion was done using a digester with a capacity of 5L. The digester was loaded with the slurry of wastes prepared by mixing the wastes with water in ratio 1:1, and operated at mesophilic temperature of 37 ± 2°C for 30 days. The co-digestion of kitchen wastes with poultry droppings produced highest biogas yield (814.0 ml/kg VS fed) and the least (365.84 ml/kg VS fed) was from the co-digestion of kitchen wastes with the mixture of poultry droppings and cow dung. Composition analysis of the biogas showed the highest methane content (63.1%) from kitchen wastes and the lowest (56.2%) from co-digestion of kitchen wastes with poultry droppings. The pH range for optimum biogas production varied between 5.25 and 7.5. The study concluded that biogas yield from co-digestion of substrates, among other factors depends on the composition of participating substrates.
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization

Langevin dynamics (LD) has been proven to be a powerful technique for optimizing a non-convex objective as an efﬁcient algorithm to ﬁnd local minima while eventually visiting a global minimum on longer time-scales. LD is based on the ﬁrst-order Langevin diffusion which is reversible in time. We study two variants that are based on non-reversible Langevin diffusions: the underdamped Langevin dynamics (ULD) and the Langevin dynamics with a non-symmetric drift (NLD). Adopting the techniques of Tzen et al. (2018) for LD to non-reversible diffusions, we show that for a given local minimum that is within an arbitrary distance from the initialization, with high probability, either the ULD trajectory ends up somewhere outside a small neighborhood of this local minimum within a recurrence time which depends on the smallest eigenvalue of the Hessian at the local minimum or they enter this neighborhood by the recurrence time and stay there for a potentially exponentially long escape time. The ULD algorithm improves upon the recurrence time obtained for LD in Tzen et al. (2018) with respect to the dependency on the smallest eigenvalue of the Hessian at the local minimum. Similar results and improvements are obtained for the NLD algorithm. We also show that non-reversible variants can exit the basin of attraction of a local minimum faster in discrete time when the objective has two local minima separated by a saddle point and quantify the amount of improvement. Our analysis suggests that non-reversible Langevin algorithms are
Prophet Attention: Predicting Attention with Future Attention

Recently, attention based models have been used extensively in many sequence-to-sequence learning systems. Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words. However, for each time step in the decoding process, the attention based models usually use the hidden state of the current input to attend to the image regions. Under this setting, these attention models have a"deviated focus"problem that they calculate the attention weights based on previous words instead of the one to be generated, impairing the performance of both grounding and captioning. In this paper, we propose the Prophet Attention, similar to the form of self-supervision. In the training stage, this module utilizes the future information to calculate the"ideal"attention weights towards image regions. These calculated"ideal"weights are further used to regularize the"deviated"attention. In this manner, image regions are grounded with the correct words. The proposed Prophet Attention can be easily incorporated into existing image captioning models to improve their performance of both grounding and captioning. The experiments on the Flickr30k Entities and the MSCOCO datasets show that the proposed Prophet Attention consistently outperforms baselines in both automatic metrics and human evaluations. It is worth noticing that we set new state-of-the-arts on the two benchmark datasets and achieve the 1st place on the leaderboard of the online MSCOCO benchmark in terms of the default ranking score, i.e., CIDEr-c40.
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond

Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed impressive progress propelled by deep learning methods. However, one critical challenge faced by existing methods is to strike a sweet spot of deep model complexity and resulting SISR quality. This paper addresses this pain point by proposing a linearly-assembled pixel-adaptive regression network (LAPAR), which casts the direct LR to HR mapping learning into a linear coefficient regression task over a dictionary of multiple predefined filter bases. Such a parametric representation renders our model highly lightweight and easy to optimize while achieving state-of-the-art results on SISR benchmarks. Moreover, based on the same idea, LAPAR is extended to tackle other restoration tasks, e.g., image denoising and JPEG image deblocking, and again, yields strong performance. The code is available at https://github.com/dvlab-research/Simple-SR.
Progesterone Intramuscularly or Vaginally Administration May Not Change Live Birth Rate or Neonatal Outcomes in Artificial Frozen-Thawed Embryo Transfer Cycles

Backgrounds Previous studies suggested that singletons from frozen-thawed embryo transfer (FET) were associated with higher risk of large, post-date babies and adverse obstetrical outcomes compared to fresh transfer and natural pregnancy. No data available revealed whether the adverse perinatal outcomes were associated with aberrantly high progesterone level from different endometrium preparations in HRT-FET cycle. This study aimed to compare the impact of progesterone intramuscularly and vaginally regimens on neonatal outcomes in HRT-FET cycles. Methods A total of 856 HRT-FET cycles from a fertility center from 2015 to 2018 were retrospectively analyzed. All patients had their first FET with two cleavage-staged embryos transferred. Endometrial preparation was performed with sequential administration of estrogen followed by progesterone intramuscularly 60 mg per day or vaginal gel Crinone 90 mg per day. Pregnancy outcomes including live birth rate, singleton birthweight, large for gestational age (LGA) rate, small for gestational age (SGA) rate, and preterm delivery rate were analyzed. Student’s t test, Mann-Whitney U-test, Chi square analysis, and multivariable logistic regression were used where appropriate. Differences were considered significant if p < 0.05. Results No significant difference of live birth rate was found between different progesterone regimens (Adjusted OR 1.128, 95% CI 0.842, 1.511, p = 0.420). Neonatal outcomes like singleton birthweight (p = 0.744), preterm delivery rate (Adjusted OR 1.920, 95% CI 0.603, 6.11, p = 0.269), SGA (Adjusted OR 0.227, 95% CI 0.027, 1.934, p = 0.175), and LGA rate (Adjusted OR 0.862, 95% CI 0.425, 1.749, p=0.681) were not different between two progesterone regimens. Serum P level >41.82 pmol/L at 14 day post-FET was associated with higher live birth rate than serum P level ≤41.82 pmol/L in HRT-FET cycles when progesterone was intramuscularly delivered (Adjusted OR 1.690, 95% CI 1.002, 2.849, p = 0.049). But singleton birthweight, preterm delivery rate, SGA and LGA rate were not different between these two groups. Conclusions Relatively higher serum progesterone level induced by intramuscular regimen did not change live birth rate or neonatal outcomes compared to vaginal regimen. Monitoring serum progesterone level and optimizing progesterone dose of intramuscular progesterone as needed in HRT-FET cycles has a role in improving live birth rate without impact on neonatal outcomes.
Higher Cho/NAA Ratio in Postoperative Peritumoral Edema Zone Is Associated With Earlier Recurrence of Glioblastoma

Objective: To explore the prognostic significance of metabolic parameters in postoperative peritumoral edema zone (PEZ) of patients with glioblastoma (GBM) based on proton magnetic resonance spectroscopy (MRS). Methods: The postoperative MRS data of 67 patients with GBM from Beijing Tiantan Hospital were retrospectively reviewed. Metabolite ratios including Cho/NAA, Cho/Cr, and NAA/Cr in both postoperative PEZ and contralateral normal brain region were recorded. Log-rank analysis and Cox regression model were used to identify parameters correlated with progression-free survival (PFS) and overall survival (OS). Results: Compared with the contralateral normal brain region, postoperative PEZ showed a lower ratio of NAA/Cr (1.20 ± 0.42 vs. 1.81 ± 0.48, P < 0.001), and higher ratios of Cho/Cr and Cho/NAA (1.36 ± 0.44 vs. 1.02 ± 0.27, P < 0.001 and 1.32 ± 0.59 vs. 0.57 ± 0.14, P < 0.001). Both the ratios of Cho/NAA and NAA/Cr were identified as prognostic factors in univariate analysis (P < 0.05), while only Cho/NAA ≥ 1.31 was further confirmed as an independent risk factor for early recurrence in the Cox regression model (P < 0.01). According to the factors of MGMT promoter unmethylation, without radiotherapy and Cho/NAA ≥ 1.31, a prognostic scoring scale for GBM was established, which could divide patients into low-risk, moderate-risk, and high-risk groups. There was a significant difference of survival rate between the three groups (P < 0.001). Conclusions: Higher Cho/NAA ratio in the postoperative PEZ of GBM predicts earlier recurrence and is associated with poor prognosis. The prognostic scoring scale based on clinical, molecular and metabolic parameters of patients with GBM can help doctors to make more precise prediction of survival time and to adjust therapeutic regimens.
Determination of Trend of a Radial Distribution of Moisture Content within a Log Cross Section by Oven-Drying of Circumferential Slices(II) -For some of domestic softwoods

This study was carried out to investigate a radial distribution of moisture contents (MCs) within a log cross section (LC) during oven-drying of 3 mm-thick circumferential slices cut from several domestic softwoods LCs. For Korean red pine, drying rate of sapwood slices during oven-drying represented much higher values than that of heartwood slices, and so sapwood with higher green MC early reached below fiber saturation point (FSP) rather than heartwood did. However, this distribution of moistures did not last for long duration. For Japanese larch, green MC of sapwood was approximately three times higher than that of heartwood. This similar distribution in MC was lasted until about 20% average MC. The MC was around uniform throughout the sapwood of Ginkgo when green and during oven-drying, although it was somewhat fluctuated. For Japanese cedar, the heartwood with so low moistures around FSP would begin to shrink from the beginning stage of drying, but the sapwood above hygroscopic MC prevents the heartwood from shrinking, and consequently, the heartwood or the transition wood goes into tension stress. The results for Japanese cypress showed that the green MCs of the sapwoods were much lower than those for heartwoods, and then this trend was continued until about 20% in average MC. For Chinese thuja, the green MCs of sapwoods were about 2 times as high as those of heartwood, but this along the radial gradient in MC rapidly became gentle during oven-drying.
SAUR17 and SAUR50 Differentially Regulate PP2C-D1 during Apical Hook Development and Cotyledon Opening in Arabidopsis[OPEN]

SAUR17 is highly expressed in apical hooks and cotyledons in the dark, where it promotes etiolation by protecting the phosphatase activity of PP2C-D1 against cell expansion–inducing SAURs such as SAUR50. Following germination in the dark, Arabidopsis (Arabidopsis thaliana) seedlings undergo etiolation and develop apical hooks, closed cotyledons, and rapidly elongating hypocotyls. Upon light perception, the seedlings de-etiolate, which includes the opening of apical hooks and cotyledons. Here, we identify Arabidopsis Small Auxin Up RNA17 (SAUR17) as a downstream effector of etiolation, which serves to bring about apical hook formation and closed cotyledons. SAUR17 is highly expressed in apical hooks and cotyledons and is repressed by light. The apical organs also express a group of light-inducing SAURs, as represented by SAUR50, which promote hook and cotyledon opening. The development of etiolated or de-etiolated apical structures requires asymmetric differential cell growth. We present evidence that the opposing actions of SAUR17 and SAUR50 on apical development largely result from their antagonistic regulation of Protein Phosphatase 2C D-clade 1 (PP2C-D1), a phosphatase that suppresses cell expansion and promotes apical hook development in the dark. SAUR50 inhibits PP2C-D1, whereas SAUR17 has a higher affinity for PP2C-D1 without inhibiting its activity. PP2C-D1 predominantly associates with SAUR17 in etiolated seedlings, which shields it from inhibitory SAURs such as SAUR50. Light signals turn off SAUR17 and upregulate a subgroup of SAURs including SAUR50 at the inner side of the hook and cotyledon cells, leading to cell expansion and unfolding of the hook and cotyledons.
YAP-mediated mechanotransduction tunes the macrophage inflammatory response

Macrophages sense biophysical cues in the microenvironment via YAP to tune their inflammatory response. Macrophages are innate immune cells that adhere to the extracellular matrix within tissues. However, how matrix properties regulate their function remains poorly understood. Here, we report that the adhesive microenvironment tunes the macrophage inflammatory response through the transcriptional coactivator YAP. We find that adhesion to soft hydrogels reduces inflammation when compared to adhesion on stiff materials and is associated with reduced YAP expression and nuclear localization. Substrate stiffness and cytoskeletal polymerization, but not adhesive confinement nor contractility, regulate YAP localization. Furthermore, depletion of YAP inhibits macrophage inflammation, whereas overexpression of active YAP increases inflammation. Last, we show in vivo that soft materials reduce expression of inflammatory markers and YAP in surrounding macrophages when compared to stiff materials. Together, our studies identify YAP as a key molecule for controlling inflammation and sensing stiffness in macrophages and may have broad implications in the regulation of macrophages in health and disease.
Thermal Treatment of n-Alkanethiolate Monolayers on Gold, As Observed by Infrared Spectroscopy

In the present work, we have used reflection−absorption infrared spectroscopy (RAIRS) to investigate the temperature behavior of n-alkanethiols adsorbed on gold substrates. A careful examination of the spectra (frequencies, integrated intensities, and line widths) reveals at least two different types of thermally induced disorder as the temperature is increased. These observations are discussed in light of other recent studies using complementary techniques. The overall picture that emerges is that an initial stage is characterized by a gradual untilting of the alkyl chain and the appearance of gauche conformational defects near the surface, and both of these effects are largely reversible. The subsequent nonreversible disordering of the monolayer is characterized by a large number of defects located below the surface. Infrared spectroscopy is particularly well suited for detecting these changes.
Production of Methyl Ester (Biodiesel) From Four Plant Species of Brassicaceae: Optimization of the Transesterification Process

Fatty acid methyl ester (FAME; Biodiesel) of four plant species of Brassicaceae family having more than 30% fixed oil in their seeds were examined. Base catalyzed transesterification using 1:6 molar ratio (oil:methanol) at 70°C was the optimal method to convert crude oil into FAMEs. FAME of oil of these four species, Brassica alba (white mustard), Brassica campestris (mustard), Brassica rapa (canola), and Eruca sativa (rocket seed oil), were found suitable for use as biodiesel and they meet the major specification of biodiesel standards of the United States. The selected plants have great potential for biodiesel use in ordinary diesel engine due to their engine efficiency, consumption, and performance, which is in accordance with mineral diesel.
Accommodating interests? Elite journalism, green interest groups and the U.K. reporting of climate change

We know that journalists accommodate green interest groups’ voices in the reporting of climate change. Argument continues, nevertheless, over the extent of groups’ contributions to coverage. The study on which this is article is based traces groups’ presence during a rapid politicization of the issue (2000–2010). Its analysis of U.K. newspaper coverage shows that groups appear alongside a growing number of political messages “to tackle” climate change and act there as a critical foil to these performances and to explain climate consequences.
Self-Supervised Relationship Probing

Structured representations of images that model visual relationships are beneﬁ-cial for many vision and vision-language applications. However, current human-annotated visual relationship datasets suffer from the long-tailed predicate distribution problem which limits the potential of visual relationship models. In this work, we introduce a self-supervised method that implicitly learns the visual relationships without relying on any ground-truth visual relationship annotations. Our method relies on 1) intra-and inter-modality encodings to respectively model relationships within each modality separately and jointly, and 2) relationship probing, which seeks to discover the graph structure within each modality. By leveraging masked language modeling, contrastive learning, and dependency tree distances for self-supervision, our method learns better object features as well as implicit visual relationships. We verify the effectiveness of our proposed method on various vision-language tasks that beneﬁt from improved visual relationship understanding.
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards

Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow. Recent work demonstrated that using a memory buffer of previous successful trajectories can result in more effective policies. However, existing methods may overly exploit past successful experiences, which can encourage the agent to adopt sub-optimal and myopic behaviors. In this work, instead of focusing on good experiences with limited diversity, we propose to learn a trajectory-conditioned policy to follow and expand diverse past trajectories from a memory buffer. Our method allows the agent to reach diverse regions in the state space and improve upon the past trajectories to reach new states. We empirically show that our approach significantly outperforms count-based exploration methods (parametric approach) and self-imitation learning (parametric approach with non-parametric memory) on various complex tasks with local optima. In particular, without using expert demonstrations or resetting to arbitrary states, we achieve the state-of-the-art scores under five billion number of frames, on challenging Atari games such as Montezuma’s Revenge and Pitfall.
Co-exposure Maximization in Online Social Networks

Social media has created new ways for citizens to stay informed on societal matters and participate in political discourse. However, with its algorithmically-curated and virally-propagating content, social media has contributed further to the polarization of opinions by reinforcing users’ existing viewpoints. An emerging line of research seeks to understand how content-recommendation algorithms can be re-designed to mitigate societal polarization ampliﬁed by social-media interactions. In this paper, we study the problem of allocating seed users to opposing campaigns: by drawing on the equal-time rule of political campaigning on traditional media, our goal is to allocate seed users to campaigners with the aim to maximize the expected number of users who are co-exposed to both campaigns. We show that the problem of maximizing co-exposure is NP -hard and its objective function is neither submodular nor supermodular. However, by exploiting a connection to a submodular function that acts as a lower bound to the objective, we are able to devise a greedy algorithm with provable approximation guarantee. We further provide a scalable instantiation of our approximation algorithm by introducing a novel extension to the notion of random reverse-reachable sets for efﬁciently estimating the expected co-exposure. We experimentally demonstrate the quality of our proposal on real-world social networks.