Description of the newly observed Ωc∗\Omega^{*}_c states as molecular states

In this work, we study the strong decays of the newly observed $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$ assuming that $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$ as $S$-wave $D\Xi$ and $D^{*}\Xi$ molecular state, respectively. Since the $\Omega_c^{*}$ was observed in the $\Xi_c^{+}K^{-}$ invariant mass distributions, the partial decay width of $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$ into $\Xi_c^{+}K^{-}$ through hadronic loops are evaluated with the help of the effective Lagrangians. Moreover, the decay channel of $\Xi_c^{'}\bar{K}$ is also included. The decay process is described by the $t$-channel $\Lambda$, $\Sigma$ baryons and $D_s$, $D_s^{*}$ mesons exchanges, respectively. By comparison with the LHCb observation, the current results support the $\Omega^{*}_c(3327)$ with$J^P=3/2^{-}$ as pure $D^{*}\Xi$ molecule while the $\Omega^{*}_c(3327)$ with $J^P=1/2^{-}$ can not be well reproduced in the molecular state picture. In addition, the spin-parity $J^P=1/2^{-}$ $D\Xi$ molecular assumptions for the $\Omega^{*}_c(3185)$ can't be conclusively determined. It may be a meson-baryon molecule with a big $D\Xi$ component. Although the decay width of the $\Omega_c^{*}\to{}\bar{K}\Xi_c^{'}$ is of the order several MeV, it can be well employed to test the molecule interpretations of $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$

Higher atmospheric CO2 levels favour C3 plants over C4 plants in utilizing ammonium as a nitrogen source

Photosynthesis of wheat and maize declined when grown with NH4+ as a nitrogen (N) source at ambient CO2 concentration compared to those grown with a mixture of NO3– and NH4+, or NO3– as the sole N source. Interestingly, these N nutritional physiological responses changed when the atmospheric CO2 concentration increases. We studied the photosynthetic responses of wheat and maize growing with various N forms at three levels of growth CO2 levels. Hydroponic experiments were carried out using a C3 plant (wheat, Triticum aestivum L. cv. Chuanmai 58) and a C4 plant (maize, Zea mays L. cv. Zhongdan 808) given three types of N nutrition: sole NO3– (NN), sole NH4+ (AN) and a mixture of both NO3– and NH4+ (Mix-N). The test plants were grown using custom-built chambers where a continuous and desired atmospheric CO2 (Ca) concentration could be maintained: 280 μmol mol–1 (representing the pre-Industrial Revolution CO2 concentration of the 18th century), 400 μmol mol–1 (present level) and 550 μmol mol–1 (representing the anticipated futuristic concentration in 2050). Under AN, the decrease in net photosynthetic rate (Pn) was attributed to a reduction in the maximum RuBP-regeneration rate, which then caused reductions in the maximum Rubisco-carboxylation rates for both species. Decreases in electron transport rate, reduction of electron flux to the photosynthetic carbon [Je(PCR)] and electron flux for photorespiratory carbon oxidation [Je(PCO)] were also observed under AN for both species. However, the intercellular (Ci) and chloroplast (Cc) CO2 concentration increased with increasing atmospheric CO2 in C3 wheat but not in C4 maize, leading to a higher Je(PCR)/ Je(PCO) ratio. Interestingly, the reduction of Pn under AN was relieved in wheat through higher CO2 levels, but that was not the case in maize. In conclusion, elevating atmospheric CO2 concentration increased Ci and Cc in wheat, but not in maize, with enhanced electron fluxes towards photosynthesis, rather than photorespiration, thereby relieving the inhibition of photosynthesis under AN. Our results contributed to a better understanding of NH4+ involvement in N nutrition of crops growing under different levels of CO2

EA-BEV: Edge-aware Bird' s-Eye-View Projector for 3D Object Detection

In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method, which converts features of 2D camera view and 3D lidar view to Bird's-Eye-View (BEV) for feature fusion. However, inaccurate depth estimation (e.g. the 'depth jump' problem) is an obstacle to develop LSS-based methods. To alleviate the 'depth jump' problem, we proposed Edge-Aware Bird's-Eye-View (EA-BEV) projector. By coupling proposed edge-aware depth fusion module and depth estimate module, the proposed EA-BEV projector solves the problem and enforces refined supervision on depth. Besides, we propose sparse depth supervision and gradient edge depth supervision, for constraining learning on global depth and local marginal depth information. Our EA-BEV projector is a plug-and-play module for any LSS-based 3D object detection models, and effectively improves the baseline performance. We demonstrate the effectiveness on the nuScenes benchmark. On the nuScenes 3D object detection validation dataset, our proposed EA-BEV projector can boost several state-of-the-art LLS-based baselines on nuScenes 3D object detection benchmark and nuScenes BEV map segmentation benchmark with negligible increment of inference time

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models. In this work, the transformer's inefficiency has been taken care of from another perspective. We propose Fourier Transformer, a simple yet effective approach by progressively removing redundancies in hidden sequence using the ready-made Fast Fourier Transform (FFT) operator to perform Discrete Cosine Transformation (DCT). Fourier Transformer is able to significantly reduce computational costs while retain the ability to inherit from various large pretrained models. Experiments show that our model achieves state-of-the-art performances among all transformer-based models on the long-range modeling benchmark LRA with significant improvement in both speed and space. For generative seq-to-seq tasks including CNN/DailyMail and ELI5, by inheriting the BART weights our model outperforms the standard BART and other efficient models. \footnote{Our code is publicly available at \url{https://github.com/LUMIA-Group/FourierTransformer}

MoNET: an R package for multi-omic network analysis

The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation.The MoNET package is freely available on https://github.com/JW-Yan/MONET.Supplementary data are available at Bioinformatics online