jMOSAiCS: joint analysis of multiple ChIP-seq datasets

The ChIP-seq technique enables genome-wide mapping of in vivo protein-DNA interactions and chromatin states. Current analytical approaches for ChIP-seq analysis are largely geared towards single-sample investigations, and have limited applicability in comparative settings that aim to identify combinatorial patterns of enrichment across multiple datasets. We describe a novel probabilistic method, jMOSAiCS, for jointly analyzing multiple ChIP-seq datasets. We demonstrate its usefulness with a wide range of data-driven computational experiments and with a case study of histone modifications on GATA1-occupied segments during erythroid differentiation. jMOSAiCS is open source software and can be downloaded from Bioconductor [1]

Misregulation of Alternative Splicing in a Mouse Model of Rett Syndrome

Mutations in the human MECP2 gene cause Rett syndrome (RTT), a severe neurodevelopmental disorder that predominantly affects girls. Despite decades of work, the molecular function of MeCP2 is not fully understood. Here we report a systematic identification of MeCP2-interacting proteins in the mouse brain. In addition to transcription regulators, we found that MeCP2 physically interacts with several modulators of RNA splicing, including LEDGF and DHX9. These interactions are disrupted by RTT causing mutations, suggesting that they may play a role in RTT pathogenesis. Consistent with the idea, deep RNA sequencing revealed misregulation of hundreds of splicing events in the cortex of Mecp2 knockout mice. To reveal the functional consequence of altered RNA splicing due to the loss of MeCP2, we focused on the regulation of the splicing of the flip/flop exon of Gria2 and other AMPAR genes. We found a significant splicing shift in the flip/flop exon toward the flop inclusion, leading to a faster decay in the AMPAR gated current and altered synaptic transmission. In summary, our study identified direct physical interaction between MeCP2 and splicing factors, a novel MeCP2 target gene, and established functional connection between a specific RNA splicing change and synaptic phenotypes in RTT mice. These results not only help our understanding of the molecular function of MeCP2, but also reveal potential drug targets for future therapies

Concomitant bladder tumor is a risk factor for bladder recurrence but not upper tract

Objective: To evaluate the clinical outcomes of UTUC patients with or without concurrent bladder tumor. Design, Setting, and Participants: The Clinical Research Office of the Endourology Society-Urothelial Carcinomas of the Upper Tract (CROES-UTUC) Registry included 1134 UTUC patients with or without concurrent bladder tumor treated between 2014 and 2019. Results: In 218 (19.2%) cases, concurrent bladder tumor was present, while in 916 (80.8%) patients, no bladder cancer was found. In the multivariable Cox regression analysis, concomitant bladder tumor (hazard ratio (HR) 1.562, 95% confidence interval (CI) 0.954-2.560, p = 0.076) indicated a trend associated with recurrence-free survival for UTUC. Further data dissection confirmed that concomitant bladder tumor is a risk factor of bladder recurrence (HR 1.874, 95% CI 1.104-3.183, p = 0.020) but not UTUC recurrence (HR 0.876, 95% CI 0.292-2.625, p = 0.812). Kidney-sparing surgery (KSS) (HR 3.940, 95% CI 1.352-11.486, p = 0.012), pathological T staging >= pT2 (HR 2.840, 95% 1.039-7.763, p = 0.042) were significantly associated with UTUC recurrence. KSS does not affect bladder recurrence (HR 0.619, 95% CI 0.242-1.580, p = 0.315). A limitation is the retrospective nature of the present study analysis. Conclusions: The presence of concomitant bladder tumor does not increase risk of UTUC recurrence, but it results in an increased risk of bladder recurrence. KSS does not affect bladder recurrence and can still be considered in patients with concomitant bladder tumor

Topological optimization of an offshore-wind-farm power collection system based on a hybrid optimization methodology

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.Web of Science112art. no. 27

In-situ growth of nitrogen-doped carbon nanotubes on MXene nanosheets for efficient sodium/potassium-ion storage

For game changing for future of large-scale energy storage technologies, sodium-ion and potassium-ion batteries provide a substitute to lithium-ion batteries. As an excellent candidate anode, MXene still suffers from the blockage of active sites caused by restacking of sheets. Herein, an in-situ decoration of MXene nanosheets with nitrogen-doped carbon nanotubes (CNTs) is introduced, to yield MXene@CNTs. The modification of nitrogen-doped CNTs not only prevents the restacking of MXene and increases ion accessibility but also improves the electrode’s overall conductivity, thereby enhancing electron conduction and ion diffusion kinetics significantly. Therefore, MXene@CNTs exhibits superior sodium/potassium-ion storage performance than pure MXene nanosheets. At 0.05 A g-1, it can deliver reversible capacities of 286 mAh g-1 for SIBs and 250 mAh g-1 for PIBs. This research illustrates the significance of the electrode architecture for electrochemical performances, and the in-situ growth strategy could provide some insight on searching for high-performance MXene-based anodes for SIBs and PIBs

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems

The asymmetrical structure of Golgi apparatus membranes revealed by in situ atomic force microscope.

The Golgi apparatus has attracted intense attentions due to its fascinating morphology and vital role as the pivot of cellular secretory pathway since its discovery. However, its complex structure at the molecular level remains elusive due to limited approaches. In this study, the structure of Golgi apparatus, including the Golgi stack, cisternal structure, relevant tubules and vesicles, were directly visualized by high-resolution atomic force microscope. We imaged both sides of Golgi apparatus membranes and revealed that the outer leaflet of Golgi membranes is relatively smooth while the inner membrane leaflet is rough and covered by dense proteins. With the treatment of methyl-β-cyclodextrin and Triton X-100, we confirmed the existence of lipid rafts in Golgi apparatus membrane, which are mostly in the size of 20 nm -200 nm and appear irregular in shape. Our results may be of significance to reveal the structure-function relationship of the Golgi complex and pave the way for visualizing the endomembrane system in mammalian cells at the molecular level