Universal Sleep Decoder: Aligning awake and sleep neural representation across subjects

Decoding memory content from brain activity during sleep has long been a goal in neuroscience. While spontaneous reactivation of memories during sleep in rodents is known to support memory consolidation and offline learning, capturing memory replay in humans is challenging due to the absence of well-annotated sleep datasets and the substantial differences in neural patterns between wakefulness and sleep. To address these challenges, we designed a novel cognitive neuroscience experiment and collected a comprehensive, well-annotated electroencephalography (EEG) dataset from 52 subjects during both wakefulness and sleep. Leveraging this benchmark dataset, we developed the Universal Sleep Decoder (USD) to align neural representations between wakefulness and sleep across subjects. Our model achieves up to 16.6% top-1 zero-shot accuracy on unseen subjects, comparable to decoding performances using individual sleep data. Furthermore, fine-tuning USD on test subjects enhances decoding accuracy to 25.9% top-1 accuracy, a substantial improvement over the baseline chance of 6.7%. Model comparison and ablation analyses reveal that our design choices, including the use of (i) an additional contrastive objective to integrate awake and sleep neural signals and (ii) the pretrain-finetune paradigm to incorporate different subjects, significantly contribute to these performances. Collectively, our findings and methodologies represent a significant advancement in the field of sleep decoding

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation

kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus. As a result, the constructed datastore is usually large and possibly redundant. In this paper, we investigate the interpretability issue of this approach: what knowledge does the NMT model need? We propose the notion of local correctness (LAC) as a new angle, which describes the potential translation correctness for a single entry and for a given neighborhood. Empirical study shows that our investigation successfully finds the conditions where the NMT model could easily fail and need related knowledge. Experiments on six diverse target domains and two language-pairs show that pruning according to local correctness brings a light and more explainable memory for kNN-MT domain adaptation

Large-eddy simulation of ow and combustion dynamics in a lean partially premixed swirling combustor

A lean partially premixed swirling combustor was studied by resolving the complete flow path from the swirl vanes to the chamber outlet with large-eddy simulation (LES). The flow and combustion dynamics for non-reacting and reacting situations was analysed, where the intrinsic effects of swirl vanes and counter flows on the vortex formation, vorticity distribution for non-reacting cases were examined. A modified flame index was introduced to identify the flame regime during the partially premixed combustion. The combustion instability phenomenon was examined by applying Fourier spectra analysis. Several scalar variables were monitored to investigate the combustion dynamics at different operating conditions. The effects of swirl number, equivalence ratio and nitrogen dilution on combustion dynamics and NOx emissions were found to be significant.This work is supported by the UK EPSRC through Grant EP/K036750/1 and the National Natural Science Foundation of China through Grant No. 51376107. The computation is supported by the Tsinghua National Laboratory for Information Science and TechnologyPeer ReviewedPostprint (author's final draft

Reduction of Hox Gene Expression by Histone H1 Depletion

The evolutionarily conserved homeotic (Hox) genes are organized in clusters and expressed collinearly to specify body patterning during embryonic development. Chromatin reorganization and decompaction are intimately connected with Hox gene activation. Linker histone H1 plays a key role in facilitating folding of higher order chromatin structure. Previous studies have shown that deletion of three somatic H1 subtypes together leads to embryonic lethality and that H1c/H1d/H1e triple knockout (TKO) embryonic stem cells (ESCs) display bulk chromatin decompaction. To investigate the potential role of H1 and higher order chromatin folding in the regulation of Hox gene expression, we systematically analyzed the expression of all 39 Hox genes in triple H1 null mouse embryos and ESCs by quantitative RT-PCR. Surprisingly, we find that H1 depletion causes significant reduction in the expression of a broad range of Hox genes in embryos and ESCs. To examine if any of the three H1 subtypes (H1c, H1d and H1e) is responsible for decreased expression of Hox gene in triple-H1 null ESCs, we derived and characterized H1c−/−, H1d−/−, and H1e−/− single-H1 null ESCs. We show that deletion of individual H1 subtypes results in down-regulation of specific Hox genes in ESCs. Finally we demonstrate that, in triple-H1- and single-H1- null ESCs, the levels of H3K4 trimethylation (H3K4me3) and H3K27 trimethylation (H3K27me3) were affected at specific Hox genes with decreased expression. Our data demonstrate that marked reduction in total H1 levels causes significant reduction in both expression and the level of active histone mark H3K4me3 at many Hox genes and that individual H1 subtypes may also contribute to the regulation of specific Hox gene expression. We suggest possible mechanisms for such an unexpected role of histone H1 in Hox gene regulation

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

IntroductionThe vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.MethodsThis study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.ResultsThe study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region

Single-cell genomics and regulatory networks for 388 human brains.

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Erratum: Entanglement distribution with minimal memory requirements using time-bin photonic qudits [PRX Quantum 3, 040319 (2022)]

Recently we became aware of an important reference that was published during the preparations of our manuscript, which we failed to cite in the original paper. In Ref. [1], the authors propose a similar scheme for the generation of multiple entangled pairs between qubit registers using a high-dimensional photonic qudit and cavity-mediated spin-photon gates. Contrary to Ref. [1], we show that such photonic qudit-mediated entanglement generation schemes have similar distribution rates as standard (parallel) qubit approaches but the memory requirements are significantly relaxed for the qudit schemes.Erratum DOI 10.110/PRXQuantum.3.040319QID/Borregaard GroupApplied SciencesQN/Borregaard groe

The Relationship between Electron Transport and Microstructure in Ge2Sb2Te5 Alloy

Phase-change random-access memory (PCRAM) holds great promise for next-generation information storage applications. As a mature phase change material, Ge2Sb2Te5 alloy (GST) relies on the distinct electrical properties of different states to achieve information storage, but there are relatively few studies on the relationship between electron transport and microstructure. In this work, we found that the first resistance dropping in GST film is related to the increase of carrier concentration, in which the atomic bonding environment changes substantially during the crystallization process. The second resistance dropping is related to the increase of carrier mobility. Besides, during the cubic to the hexagonal phase transition, the nanograins grow significantly from ~50 nm to ~300 nm, which reduces the carrier scattering effect. Our study lays the foundation for precisely controlling the storage states of GST-based PCRAM devices