On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions. Multi-modal models are expected to benefit from cross-modal interactions on the basis of ensuring uni-modal feature learning. However, recent supervised multi-modal late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality. We prove that this phenomenon does hurt the model's generalization ability. To this end, we propose to choose a targeted late-fusion learning method for the given supervised multi-modal task from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on various multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs

Optimization of a Real-Time Wavelet-Based Algorithm for Improving Speech Intelligibility

The optimization of a wavelet-based algorithm to improve speech intelligibility along with the full data set and results are reported. The discrete-time speech signal is split into frequency sub-bands via a multi-level discrete wavelet transform. Various gains are applied to the sub-band signals before they are recombined to form a modified version of the speech. The sub-band gains are adjusted while keeping the overall signal energy unchanged, and the speech intelligibility under various background interference and simulated hearing loss conditions is enhanced and evaluated objectively and quantitatively using Google Speech-to-Text transcription. A universal set of sub-band gains can work over a range of noise-to-signal ratios up to 4.8 dB. For noise-free speech, overall intelligibility is improved, and the Google transcription accuracy is increased by 16.9 percentage points on average and 86.7 maximum by reallocating the spectral energy toward the mid-frequency sub-bands. For speech already corrupted by noise, improving intelligibility is challenging but still realizable with an increased transcription accuracy of 9.5 percentage points on average and 71.4 maximum. The proposed algorithm is implementable for real-time speech processing and comparatively simpler than previous algorithms. Potential applications include speech enhancement, hearing aids, machine listening, and a better understanding of speech intelligibility.Comment: 16 pages, 7 figures, 4 table

Experimental Investigation on the Pyrolysis and Conversion Characteristics of Organic-Rich Shale by Supercritical Water

Organic-rich shale oil reservoirs with low-medium maturity have attracted increasing attention because of their enormous oil and gas potential. In this work, a series of experiments on pyrolysis of the particle and core samples were carried out in a self-made supercritical water pyrolysis apparatus to evaluate the feasibility and benefits of supercritical water in promoting the transformation efficiency and oil yield of the low-medium maturity organic-rich shale. Core samples had a mass loss of 8.4% under supercritical water pyrolysis, and many microcracks were generated, which increased the pyrolysis efficiency substantially. The oil yield of shale pyrolysis could reach 72.40% under supercritical water conditions at 23 MPa and 400 °C, which was 53.02% higher than that under anhydrous conditions. In supercritical water conditions, oxygen-containing compounds are less abundant than in anhydrous conditions, suggesting that supercritical water can inhibit their formation. Also, supercritical water conditions produced higher yields for light fraction, medium fraction, and heavy fraction shale oil than those under anhydrous conditions. These results indicate that supercritical water pyrolysis is feasible and has excellent advantages for low-medium maturity organic-rich shale

The Cardiotoxicity Induced by Arsenic Trioxide is Alleviated by Salvianolic Acid A via Maintaining Calcium Homeostasis and Inhibiting Endoplasmic Reticulum Stress

Arsenic trioxide (ATO) has been verified as a breakthrough with respect to the management of acute promyelocytic leukemia (APL) in recent decades but associated with some serious adverse phenomena, particularly cardiac functional abnormalities. Salvianolic acid A (Sal A) is a major effective component in treating ATO-induced cardiotoxicity. Therefore, the objective of our study was to assess whether Sal A had protective effects by the regulation of calcium homeostasis and endoplasmic reticulum (ER) stress. For the in vivo study, BALB/c mice were treated with ATO and/or Sal A via daily tail vein injections for two weeks. For the in vitro study, we detected the effects of ATO and/or Sal A in real time using adult rat ventricular myocytes (ARVMs) and an IonOptix MyoCam system. Our results showed that Sal A pretreatment alleviated cardiac dysfunction and Ca2+ overload induced by ATO in vivo and vitro. Moreover, Sal A increased sarcoplasmic reticulum (SR) Ca2+-ATPase (SERCA) activity and expression, alleviated [Ca2+]ER depletion, and decreased ER stress-related protein expression. Sal A protects the heart from ATO-induced injury and its administration correlates with the modulation of SERCA, the recovery of Ca2+ homeostasis, and the down-regulation of ER stress-mediated apoptosis

Type I J-Domain NbMIP1 Proteins Are Required for Both <i>Tobacco Mosaic Virus</i> Infection and Plant Innate Immunity

<div><p>Tm-2² is a coiled coil-nucleotide binding-leucine rich repeat resistance protein that confers durable extreme resistance against <i>Tomato mosaic virus</i> (ToMV) and <i>Tobacco mosaic virus</i> (TMV) by recognizing the viral movement protein (MP). Here we report that the <i>Nicotiana benthamiana</i> J-domain MIP1 proteins (NbMIP1s) associate with tobamovirus MP, Tm-2² and SGT1. Silencing of <i>NbMIP1s</i> reduced TMV movement and compromised <i>Tm-2²</i>-mediated resistance against TMV and ToMV. Furthermore, silencing of <i>NbMIP1s</i> reduced the steady-state protein levels of ToMV MP and Tm-2². Moreover, NbMIP1s are required for plant resistance induced by other <i>R</i> genes and the nonhost pathogen <i>Pseudomonas syringae pv. tomato</i> (<i>Pst</i>) DC3000. In addition, we found that SGT1 associates with Tm-2² and is required for <i>Tm-2²</i>-mediated resistance against TMV. These results suggest that NbMIP1s function as co-chaperones during virus infection and plant immunity.</p></div