The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One ...significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.
In this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.
In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Transition metal sulfides with a multi‐elemental nature represent a class of promising catalysts for oxygen evolution reaction (OER) owing to their good catalytic activity. However, their synthesis ...remains a challenge due to the thermodynamic immiscibility of the constituent multimetallic elements in a sulfide structure. Herein, for the first time the synthesis of high‐entropy metal sulfide (HEMS, i.e., (CrMnFeCoNi)Sx) solid solution nanoparticles is reported. Computational and X‐ray photoelectron spectroscopy analysis suggest that the (CrMnFeCoNi)Sx exhibits a synergistic effect among metal atoms that leads to desired electronic states to enhance OER activity. The (CrMnFeCoNi)Sx nanoparticles show one of the best activities (low overpotential 295 mV at 100 mA cm−2 in 1 m KOH solution) and good durability (only slight polarization after 10 h by chronopotentiometry) compared with their unary, binary, ternary, and quaternary sulfide counterparts. This work opens up a new synthesis paradigm for high‐entropy compound nanoparticles for highly efficient electrocatalysis applications.
High‐entropy metal sulfide (HEMS) nanoparticles are achieved through a pulse thermal decomposition method by overcoming the immiscibility of multiple metallic constituents. Benefiting from synergistic effects and high‐entropy stabilization, nanoscale HEMS greatly promotes oxygen evolution reaction performance. Thus, a new synthesis paradigm for high‐entropy nanomaterials is established for renewable energy conversion and storage applications.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, ...unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent years, technological advances in areas such as micro-technology, sensors and navigation have influenced the various applications of UAVs. In this study, an all-in-one camera-based target detection and positioning system is developed and integrated into a fully autonomous fixed-wing UAV. The system presented in this paper is capable of on-board, real-time target identification, post-target identification and location and aerial image collection for further mapping applications. Its performance is examined using several simulated search and rescue missions, and the test results demonstrate its reliability and efficiency.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This paper reports an acceleration sensing method based on two weakly coupled resonators (WCRs) using the phenomenon of mode localization. When acceleration acts on the proof masses, differential ...electrostatic stiffness perturbations will be applied to the WCRs, leading to mode localization, and thus, mode shape changes. Therefore, acceleration can be sensed by measuring the amplitude ratio shift. The proposed mode localization with the differential perturbation method leads to a sensitivity enhancement of a factor of 2 than the common single perturbation method. The theoretical model of the sensitivity, bandwidth, and linearity of the accelerometer is established and verified. The measured relative shift in amplitude ratio (~312162 ppm/g) is 302 times higher than the shift in resonance frequency (~1035 ppm/g) within the measurement range of ±1 g. The measured resolution based on the amplitude ratio is 0.619 mg and the nonlinearity is ~3.5% in the open-loop measurement operation.
Development of platinum group metal (PGM)-free catalysts for oxygen reduction reaction (ORR) is essential for affordable proton exchange membrane fuel cells. Herein, a new type of atomically ...dispersed Co doped carbon catalyst with a core–shell structure has been developed via a surfactant-assisted metal–organic framework approach. The cohesive interactions between the selected surfactant and the Co-doped zeolitic imidazolate framework (ZIF-8) nanocrystals lead to a unique confinement effect. During the thermal activation, this confinement effect suppressed the agglomeration of Co atomic sites and mitigated the collapse of internal microporous structures of ZIF-8. Among the studied surfactants, Pluronic F127 block copolymer led to the greatest performance gains with a doubling of the active site density relative to that of the surfactant-free catalyst. According to density functional theory calculations, unlike other Co catalysts, this new atomically dispersed Co–N–C@F127 catalyst is believed to contain substantial CoN2+2 sites, which are active and thermodynamically favorable for the four-electron ORR pathway. The Co–N–C@F127 catalyst exhibits an unprecedented ORR activity with a half-wave potential (E1/2) of 0.84 V (vs. RHE) as well as enhanced stability in the corrosive acidic media. It also demonstrated high initial performance with a power density of 0.87 W cm−2 along with encouraging durability in H2–O2 fuel cells. The atomically dispersed Co site catalyst approaches that of the Fe–N–C catalyst and represents the highest reported PGM-free and Fe-free catalyst performance.
The limited availability of annotated data often hinders real-world applications of machine learning. To efficiently learn from small quantities of multimodal data, we leverage the linguistic ...knowledge from a large pre-trained language model (PLM) and quickly adapt it to new domains of image captioning. To effectively utilize a pretrained model, it is critical to balance the visual input and prior linguistic knowledge from pretraining. We propose VisualGPT, which employs a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the PLM with a small amount of in-domain image-text data. The proposed self-resurrecting activation unit produces sparse activations that prevent accidental overwriting of linguistic knowledge. When trained on 0.1%, 0.5% and 1% of the respective training sets, VisualGPT surpasses the best baseline by up to 10.0% CIDEr on MS COCO 43 and 17.9% CIDEr on Conceptual Captions 63. Furthermore, VisualGPT achieves the state-of-the-art result on IU X-ray 15, a medical report generation dataset. Our code is available at https://github.com/Vision-CAIR/VisualGPT.
The advancement of efficient and stable single-atom catalysts (SACs) has become a pivotal pursuit in the field of proton exchange membrane fuel cells (PEMFCs) and metal-air batteries (MABs), aiming ...to enhance the utilization of clean and sustainable energy sources. The development of such SACs has been greatly significant in facilitating the oxygen reduction reaction (ORR) process, thereby contributing to the progress of these energy conversion technologies. However, while transition metal-based SACs have been extensively studied, there has been comparatively less exploration of SACs based on p-block main-group metals. In this study, we conducted an investigation into the potential of p-block main-group Sn-based SACs as a cost-effective and efficient alternative to platinum-based catalysts for the ORR. Our approach involved employing density functional theory (DFT) calculations to systematically examine the catalyst properties of Sn-based N-doped graphene SACs, the ORR mechanism, and their electrocatalytic performance. Notably, we employed an H atom-decorated N-based graphene matrix as a support to anchor single Sn atoms, creating a contrast catalyst to elucidate the differences in activity and properties compared to pristine Sn-based N-doped graphene SACs. Through our theoretical analysis, we gained a comprehensive understanding of the active structure of Sn-based N-doped graphene electrocatalysts, which provided a rational explanation for the observed high four-electron reactivity in the ORR process. Additionally, we analyzed the relationship between the estimated overpotential and the electronic structure properties, revealing that the single Sn atom was in a +2 oxidation state based on electronic analysis. Overall, this work represented a significant step towards the development of efficient and cost-effective SACs for ORR which could alleviate environmental crises, advance clean and sustainable energy sources, and contribute to a more sustainable future.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to ...linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.
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