Miracle-dz/UniFAR-FTUs huggingface dataset:
“On-Water” Synthesis of Quinazolinones and Dihydroquinazolinones Starting from o-Bromobenzonitrile

A versatile and practical “on-water” protocol was newly developed to synthesize quinazolinones using o-bromobenzonitrile as a novel starting material. Studies have found that air as well as water plays an important role in synthesis of quinazolinones. Further investigation indicated that dihydroquinazolinones can be prepared with this protocol under the protection of N2. The protocol can be extended to other substrates and various quinazolinones and dihydroquinazolinones were obtained. o-Bromobenzamide, o-aminobenzonitrile, and o-aminobenzamide were also evaluated as starting materials, and the results further proved the versatility of this protocol, especially towards dihydroquinazolinones.
Deep Spectral Correspondence for Matching Disparate Image Pairs

A novel, non-learning-based, saliency-aware, shape-cognizant correspondence determination technique is proposed for matching image pairs that are significantly disparate in nature. Images in the real world often exhibit high degrees of variation in scale, orientation, viewpoint, illumination and affine projection parameters, and are often accompanied by the presence of textureless regions and complete or partial occlusion of scene objects. The above conditions confound most correspondence determination techniques by rendering impractical the use of global contour-based descriptors or local pixel-level features for establishing correspondence. The proposed deep spectral correspondence (DSC) determination scheme harnesses the representational power of local feature descriptors to derive a complex high-level global shape representation for matching disparate images. The proposed scheme reasons about correspondence between disparate images using high-level global shape cues derived from low-level local feature descriptors. Consequently, the proposed scheme enjoys the best of both worlds, i.e., a high degree of invariance to affine parameters such as scale, orientation, viewpoint, illumination afforded by the global shape cues and robustness to occlusion provided by the low-level feature descriptors. While the shape-based component within the proposed scheme infers what to look for, an additional saliency-based component dictates where to look at thereby tackling the noisy correspondences arising from the presence of textureless regions and complex backgrounds. In the proposed scheme, a joint image graph is constructed using distances computed between interest points in the appearance (i.e., image) space. Eigenspectral decomposition of the joint image graph allows for reasoning about shape similarity to be performed jointly, in the appearance space and eigenspace.
PPtaxi: Non-Stop Package Delivery via Multi-Hop Ridesharing

City-wide package delivery has become popular due to the dramatic rise of online shopping. It places a tremendous burden on the traditional logistics industry, which relies on dedicated couriers and is labor-intensive. Leveraging the ridesharing systems is a promising alternative, yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays. In this paper, we propose a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free. Specifically, a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting successive passengers. We tackle it with a two-phase solution, named PPtaxi. In the first phase, we use the Multivariate Gaussian distribution and Bayesian inference to predict the passenger orders. In the second phase, both the computation efficiency and solution effectiveness are considered to plan package delivery routes. We evaluate PPtaxi with a real-world dataset from an online taxi-taking platform and compare it with multiple benchmarks. The results show that the successful delivery rate of packages with our solution can reach 95 percent on average during the daytime, and is at most 46.9 percent higher than those of the benchmarks.
Depth Estimation and Blur Removal from a Single Out-of-focus Image

This paper presents a depth estimation method that leverages rich representations learned from cascaded convolutional and fully connected neural networks operating on a patch-pooled set of feature maps. Our method is very fast and it substantially improves depth accuracy over the state-of-the-art alternatives, and from this, we computationally reconstruct an all-focus image and achieve synthetic re-focusing, all from a single image. Our experiments on benchmark datasets such as Make3D and NYU-v2 demonstrate superior performance in comparison to other available depth estimation methods by reducing the root-mean-squared error by 57% & 46%, and blur removal methods by 0.36 dB & 0.72 dB in PSNR, respectively. This improvement is also demonstrated by the superior performance using real defocus images.
Physical Layer Security in Uplink NOMA Multi-Antenna Systems With Randomly Distributed Eavesdroppers

The physical layer security of uplink non-orthogonal multiple access (NOMA) is analyzed. A stochastic geometry approach is applied to analyze the coverage probability and effective secrecy throughput (EST) of the $kth$ NOMA user, where a fixed or an adaptive transmission rate can be used. We consider a protected zone around the legitimate terminals to establish an eavesdropper-exclusion area. We assume that the channel state information associated with eavesdroppers is not available at the base station. We also consider that the base station is equipped with multiple antennas. The impact of imperfect successive interference cancellation is also taken into account in this paper. Our framework allows to compute, numerically, the wiretap code rates that maximize the EST. In addition, our framework also allows an optimum selection of other system parameters, such as the transmit power or the eavesdropper-exclusion radius.
Status of Retinoids and Carotenoids and Associations with Clinical Outcomes in Maternal-Infant Pairs in Nigeria

Vitamin A is an essential nutrient in pregnancy, and other carotenoids have been independently associated with maternal-infant outcomes. The objective of this study was to quantify the status of vitamin A and carotenoids in Nigerian maternal-infant pairs at delivery, compare these to a cohort from a developed nation, and determine the impact on clinical outcomes. Maternal and cord blood samples were collected in 99 Nigerian mother-infant pairs. Concentrations of lutein + zeaxanthin, β-cryptoxanthin, lycopene, α- and β-carotenes, and retinol were measured using HPLC. Descriptive statistics were calculated and Spearman coefficients were used to assess correlations between maternal and cord measurements; Mann-Whitney tests were used to compare median plasma values between dichotomous variables. Linear regression models were used to adjust for relevant confounders. A p < 0.05 was considered statistically significant. Thirty-five percent of mothers had plasma retinol concentrations ≤0.70 µmol/L; 82% of infants had plasma retinol concentrations ≤0.70 µmol/L at delivery. Maternal and infant concentrations of vitamin A compounds were highly correlated and were associated with newborn growth and Apgar scores. Despite plasma concentrations of pro-vitamin A carotenoids higher than those reported in other populations, pregnant Nigerian women have a high prevalence of vitamin A deficiency. As vitamin A related compounds are modifiable by diet, future research determining the clinical impact of these compounds is warranted.
H19 contributes to poor clinical features in NSCLC patients and leads to enhanced invasion in A549 cells through regulating miRNA-203-mediated epithelial-mesenchymal transition

Recent studies have demonstrated that the overexpression of H19 may contribute towards development of tumorigenesis in various types of cancer. To investigate the role of H19 in the development of non-small cell lung cancer (NSCLC), 76 NSCLC tissues samples and their adjacent normal tissue samples were collected. Expression level of H19, and its association with clinicopathological features and overall survival was analyzed. It was found that compared with normal adjacent tissues, H19 expression was elevated in NSCLC tissues along with a decreased miR-203 expression level. It was also found that patients who were in advanced clinical stages had a higher H19 and a lower miR-203 expression compared to normal tissues. The overall survival time of patients with higher H19 expression was shorter compared with the lower H19 expression group. Upregulation of A549 enhanced cell proliferation and promoted invasion. Overexpression of H19 stimulated the epithelial-mesenchymal transition (EMT) process in lung cancer cells and demonstrated typical morphological characteristics of EMT. The level of mesenchymal marker protein, such as Vimentin and SNAI1 increased; while CDH1 protein level decreased. Also, H19 negatively regulated miR-203. Inhibition of H19 attenuated miR-203 induced EMT process. Upregulation of H19 contributes to poor clinical features in patients with NSCLC, induces occurrence of EMT, promotes proliferation and stimulates cell invasion in NSCLC cell line through regulating miRNA-203 mediated EMT.
Anticancer activity of paclitaxel-loaded keratin nanoparticles in two-dimensional and perfused three-dimensional breast cancer models

Purpose Taxanes are highly effective cytotoxic drugs for progressing breast cancer treatment. However, their poor solubility and high toxicity urge the development of innovative formulations of potential clinical relevance. Materials and methods By using a simple and straightforward aggregation method, we have generated paclitaxel (PTX) loaded in keratin nanoparticles (KER-NPs-PTX). Their activities were tested against human breast cancer MCF-7 and MDA MB 231 cell lines in conventional two-dimensional (2D) cultures and in a dynamic three-dimensional (3D) model with perfused bioreactor (p3D). Moreover, KER-NPs-PTX activity was compared to free PTX and to PTX loaded in albumin nanoparticles (HSA-NPs-PTX). Cell viability, induction of apoptosis, and gene expression analysis were used as readouts. Results In 2D cultures, KER-NPs-PTX was able to inhibit tumor cell viability and to induce apoptosis similarly to PTX and HSA-NPs-PTX. In the p3D model, a lower sensitivity of tumor cells to treatments was observed. Importantly, only KER-NPs-PTX was able to induce a statistically significant increase in apoptotic cell percentages following 24 h treatment for MCF-7 (16.7±4.0 early and 11.3±4.9 late apoptotic cells) and 48 h treatment for MDA MB 231 (21.3±11.2 early and 10.5±1.8 late apoptotic cells) cells. These effects were supported, at least for MCF-7 cells, by significant increases in the expression of proapoptotic BAX gene (5.8±0.5) 24 h after treatment and of cleaved caspase 3 (CC3) protein. Conclusion KER-NPs-PTX, generated by a simple procedure, is characterized by high water solubility and enhanced PTX-loading ability, as compared to HSA-NPs-PTX. Most importantly, it appears to be able to exert effective anticancer activities on breast cancer cells cultured in 2D or in p3D models.
Priority Switches in Visual Working Memory are Supported by Frontal Delta and Posterior Alpha Interactions

Abstract Visual working memory (VWM) distinguishes between representations relevant for imminent versus future perceptual goals. We investigated how the brain sequentially prioritizes visual working memory representations that serve consecutive tasks. Observers remembered two targets for a sequence of two visual search tasks, thus making one target currently relevant, and the other prospectively relevant. We show that during the retention interval prior to the first search, lateralized parieto-occipital EEG alpha (8–14 Hz) suppression is stronger for current compared with prospective search targets. Crucially, between the first and second search task, this difference in posterior alpha lateralization reverses, reflecting the change in priority states of the two target representations. Connectivity analyses indicate that this switch in posterior alpha lateralization is driven by frontal delta/low-theta (2–6 Hz) activity. Moreover, this frontal low-frequency signal also predicts task performance after the switch. We thus obtained evidence for large-scale network interactions underlying the flexible shifting between the priority states of multiple memory representations in VWM.
Sparse Deep Feature Representation for Object Detection from Wearable Cameras

We propose a novel sparse feature representation for the faster RCNN detection framework and apply it for object detection from wearable cameras. Two main ideas, sparse convolution and sparse ROI pooling, are developed to reduce model complexity as well as computational cost. Sparse convolution approximates a full kernel by skipping weights in the kernel while sparse ROI pooling performs dimensionality reduction on the ROI pooling layer by skipping identical features at consecutive locations in the output feature maps. We demonstrate the effectiveness of our approach on two challenging body camera datasets including realistic police-generated clips. Our approach achieves a significant reduction of model size by a factor of over 10× as well as a computational speedup of about 2×, yet without compromising much detection accuracy compared to the VGG16-based baseline detector.
Nederland in 2030 en 2050: Twee Referentiescenario’s, : Toekomstverkenning Welvaart en Leefomgeving

De studie ‘Nederland in 2030-2050: twee referentiescenario’s - Toekomstverkenning Welvaart en Leefomgeving’ (WLO) is de basis voor veel beleidsbeslissingen op het gebied van de fysieke leefomgeving in Nederland
A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.
Effect of pupil dilation on biometric measurements and intraocular lens power calculations in schoolchildren

Purpose To investigate the effect of pupil dilation on ocular biometric parameters and intraocular lens (IOL) power calculation in schoolchildren using the Lenstar LS 900. Methods One hundred forty eyes of 140 healthy schoolchildren were included in the analysis. Axial length (AL), central corneal thickness (CCT), aqueous depth (AD), anterior chamber depth (ACD), lens thickness (LT), flat keratometry (K), steep K, astigmatism, white-to-white (WTW), and iris/pupil barycenter distance were measured, before and after pupil dilation. Anterior segment length (ASL) was defined as the sum of ACD and LT, and lens position (LP) was defined as ACD plus half of the LT. The relative lens position (RLP) was defined as LP divided by AL. IOL power was calculated using the eight formulas (Hill-RBF, Barrett, Haigis, Hoffer Q, Holladay, Olsen, SRK II, and SRK/T) integrated in the Lenstar LS 900. Parameters before and after pupil dilation were compared. Results AL, AD, ACD, LT, ASL, LP, RLP, flat K, iris barycenter distance, pupil barycenter distance, and PD differed significantly after pupil dilation (P < 0.001 in all cases), as compared to before dilation. The Olsen formula demonstrated significant differences in the magnitude of astigmatism (P = 0.010) and IOL power (P = 0.003) after pupil dilation. Using the different formulas, 23.6–40.7% of participants had IOL power changes of more than 0.50 diopters, while 0.7–1.4% had IOL changes of more than 1.0 diopter after pupil dilation. Conclusions Dilated and undilated pupil size affected the Lenstar LS 900 measurement of some ocular biometric parameters, and pupil dilation led to IOL power changes exceeding 0.50 diopters with a high percentage (from 23.6% to 40.7%) in schoolchildren, which should be noticed in clinical practice.
The Effects of Hedge Fund Interventions on Strategic Firm Behavior

We examine the impact of hedge fund interventions on target firms’ strategic behavior, specifically their voluntary disclosure and earnings management strategies. We find a decrease in both the likelihood and the frequency of management earnings forecasts conveying bad news and an increase in the level of real earnings management following interventions by hedge fund activists. Additional evidence suggests that managers substitute between voluntary disclosure and earnings management strategies in resisting hedge fund attacks. We also find that withholding of bad news is more pronounced when hedge fund activists pose greater threats to the target firm’s management and when the intervention lasts for a relatively short time period. Our results are consistent with firms behaving strategically in response to heightened career/reputation concerns and endangered corporate control arising from hedge fund activism. The online appendix is available at https://doi.org/10.1287/mnsc.2017. 2816. This paper was accepted.
A comprehensive portrait of the venom of the giant red bull ant, Myrmecia gulosa, reveals a hyperdiverse hymenopteran toxin gene family

Characterization of the venom of an ant reveals a diverse and multifunctional hymenopteran toxin superfamily. Ants (Hymenoptera: Formicidae) are diverse and ubiquitous, and their ability to sting is familiar to many of us. However, their venoms remain largely unstudied. We provide the first comprehensive characterization of a polypeptidic ant venom, that of the giant red bull ant, Myrmecia gulosa. We reveal a suite of novel peptides with a range of posttranslational modifications, including disulfide bond formation, dimerization, and glycosylation. One venom peptide has sequence features consistent with an epidermal growth factor fold, while the remaining peptides have features suggestive of a capacity to form amphipathic helices. We show that these peptides are derived from what appears to be a single, pharmacologically diverse, gene superfamily (aculeatoxins) that includes most venom peptides previously reported from the aculeate Hymenoptera. Two aculeatoxins purified from the venom were found to be capable of activating mammalian sensory neurons, consistent with the capacity to produce pain but via distinct mechanisms of action. Further investigation of the major venom peptide MIITX1-Mg1a revealed that it can also incapacitate arthropods, indicative of dual utility in both defense and predation. MIITX1-Mg1a accomplishes these functions by generating a leak in membrane ion conductance, which alters membrane potential and triggers neuronal depolarization. Our results provide the first insights into the evolution of the major toxin gene superfamily of the aculeate Hymenoptera and provide a new paradigm in the functional evolution of toxins from animal venoms.
Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach

Using a comprehensive return data set and an array of 27 macroeconomic, stock, and bond predictors, we find that corporate bond returns are highly predictable based on an iterated combination model. The large set of predictors outperforms traditional predictors substantially, and predictability generated by the iterated combination is both statistically and economically significant. Stock market and macroeconomic variables play an important role in forming expected bond returns. Return forecasts are closely linked to the evolution of real economy. Corporate bond premia have strong predictive power for business cycle, and the primary source of this predictive power is from the low-grade bond premium. The Internet appendix is available at https://doi.org/10.1287/mnsc.2017. 2734. This paper was accepted by Lauren Cohen, finance.
Applications of Recurrent Neural Networks in Environmental Factor Forecasting: A Review

Abstract Analysis and forecasting of sequential data, key problems in various domains of engineering and science, have attracted the attention of many researchers from different communities. When predicting the future probability of events using time series, recurrent neural networks (RNNs) are an effective tool that have the learning ability of feedforward neural networks and expand their expression ability using dynamic equations. Moreover, RNNs are able to model several computational structures. Researchers have developed various RNNs with different architectures and topologies. To summarize the work of RNNs in forecasting and provide guidelines for modeling and novel applications in future studies, this review focuses on applications of RNNs for time series forecasting in environmental factor forecasting. We present the structure, processing flow, and advantages of RNNs and analyze the applications of various RNNs in time series forecasting. In addition, we discuss limitations and challenges of applications based on RNNs and future research directions. Finally, we summarize applications of RNNs in forecasting.