S4Net: Single Stage Salient-Instance Segmentation

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320x320). We evaluate our approach on a publicly available benchmark and show that it outperforms other alternative solutions. We also provide a thorough analysis of the design choices to help readers better understand the functions of each part of our network. The source code can be found at \url{https://github.com/RuochenFan/S4Net}

Observation of a dissipative time crystal in a strongly interacting Rydberg gas

The notion of spontaneous symmetry breaking has been well established to characterize classical and quantum phase transitions of matters, such as in condensation, crystallization, and quantum magnetism, etc. Generalizations of this paradigm to the time dimension can further lead to an exotic dynamical phase, the time crystal, which spontaneously breaks the time translation symmetry of the system [1]. While the existence of a continuous time crystal at equilibrium has been challenged by the no-go theorems [2, 3], the difficulty can be circumvented by the dissipation in an open system. Here, we report the experimental observation of such a dissipative time crystalline order in a room-temperature atomic gas, where ground-state atoms are continuously driven to Rydberg states via electromagnetically induced transparency (EIT). The emergent time crystal is revealed by persistent oscillations of the probe-field transmission, with ultralong lifetime and no observable damping during the measurement. We show that the observed limit cycles arise from the coexistence and competition between distinct Rydberg components, in agreement with a mean-field analysis derived from the microscopic model. The random phase distribution of the oscillation for repeated realizations, together with the robustness against temporal noises further supports our realization of a dissipative time crystal.Comment: 6 pages, 4 figure

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Relationship between Time in Green Spaces and Stress Management Ability

The purpose of this study was to determine the potential benefits of campus green spaces for students' well-being. Previous studies show mental health benefits when doing activities in natural environments, which reduces stress. We are interested in the correlation between the time spent in green space and the ability to manage stress. We conducted a correlation study at the University of British Columbia (UBC) and asked 147 UBC students to fill out a self-report survey, where we obtained 100 valid responses. We hypothesized that students who spend more time in green spaces tend to have a better ability to alleviate stress. Our data were analyzed by using the Pearson r test in JASP. Unfortunately, our results do not provide support for our hypothesis. However, we recommend further research on the possible relationship between time spent in green space and the ability to manage stress. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Arts, Faculty ofPsychology, Department ofUnreviewedUndergraduat

Quantitative Proteomic Analysis of Outer Membrane Vesicles from Fusobacterium nucleatum Cultivated in the Mimic Cancer Environment

ABSTRACT Fusobacterium nucleatum is a Gram-negative bacterium that has been identified as an important pathogenic gut bacterium associated with colorectal cancer. Compared with the normal intestine, the pH value of the tumor microenvironment is weakly acidic. The metabolic changes of F. nucleatum in the tumor microenvironment, especially the protein composition of its outer membrane vesicles, remain unclear. Here, we systematically analyzed the effect of environmental pH on the proteome of outer membrane vesicles (OMVs) from F. nucleatum by tandem mass tag (TMT) labeling–high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. A total of 991 proteins were identified in acidic OMVs (aOMVs) and neutral OMVs (nOMVs), including known virulence proteins and putative virulence proteins. Finally, 306 upregulated proteins and 360 downregulated proteins were detected in aOMVs, and approximately 70% of the expression of OMV proteins was altered under acidic conditions. A total of 29 autotransporters were identified in F. nucleatum OMVs, and 13 autotransporters were upregulated in aOMVs. Interestingly, three upregulated autotransporters (D5REI9, D5RD69, and D5RBW2) show homology to the known virulence factor Fap2, suggesting that they may be involved in various pathogenic pathways such as the pathway for binding with colorectal cancer cells. Moreover, we found that more than 70% of MORN2 domain-containing proteins may have toxic effects on host cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated that a number of proteins were significantly enriched in multiple pathways involving fatty acid synthesis and butyrate synthesis. Seven metabolic enzymes involved in fatty acid metabolism pathways were identified in the proteomic data, of which 5 were upregulated and 2 were downregulated in aOMVs, while 14 metabolic enzymes involved in the butyric acid metabolic pathway were downregulated in aOMVs. In conclusion, we found a key difference in virulence proteins and pathways in the outer membrane vesicles of F. nucleatum between the tumor microenvironment pH and normal intestinal pH, which provides new clues for the prevention and treatment of colorectal cancer. IMPORTANCE F. nucleatum is an opportunistic pathogenic bacterium that can be enriched in colorectal cancer tissues, affecting multiple stages of colorectal cancer development. OMVs have been demonstrated to play key roles in pathogenesis by delivering toxins and other virulence factors to host cells. By employing quantitative proteomic analysis, we found that the pH conditions could affect the protein expression of the outer membrane vesicles of F. nucleatum. Under acidic conditions, approximately 70% of the expression of proteins in OMVs was altered. Several virulence factors, such as type 5a secreted autotransporter (T5aSSs) and membrane occupation and recognition nexus (MORN) domain-containing proteins, were upregulated under acidic conditions. A large number of proteins showed significant enrichments in multiple pathways involving fatty acid synthesis and butyrate synthesis. Proteomics analysis of the outer membrane vesicles secreted by pathogenic bacteria in the acidic tumor microenvironment is of great significance for elucidating the pathogenicity mechanism and its application in vaccine and drug delivery vehicles

Shift in controlling factors of carbon stocks across biomes on the Qinghai-Tibetan Plateau

The Qinghai-Tibetan Plateau (TP) accumulated a large amount of organic carbon, while its size and response to environmental factors for the whole area remain uncertain. Here, we synthesized a dataset to date with the largest data volume and broadest geographic coverage over the TP, composing of 7196 observations from multiple field campaigns since the 1980s, and provided a comprehensive assessment of the size and spatial distribution of carbon pools for both plant and soils on the TP using machine learning algorithms. The estimated soil organic carbon (SOC) storage to 1 m depth was ${\text{32}}{\text{.01}}_{{\text{19}}{\text{.69}}}^{{\text{47}}{\text{.9}}}$ Pg ( ${\text{11}}{\text{.72}}_{{\text{7}}{\text{.2}}}^{{\text{17}}{\text{.53}}}$ kg m ^−2 on average), accounting for approximately ${\text{37}}{\text{.2}}_{{\text{22}}{\text{.9}}}^{{\text{55}}{\text{.6}}}$ % of China’s SOC stock on its <30% land area. There was ${\text{15}}{\text{.52}}_{{\text{9}}{\text{.91}}}^{{\text{23}}{\text{.52}}}{ }$ Pg C stored in grassland soils (1 m), which played as the largest C pool on the TP, followed by shrubland ( ${\text{7}}{\text{.52}}_{{\text{4}}{\text{.8}}}^{11.6}$ Pg) and forest ( ${\text{3}}{\text{.72}}_{{\text{2}}{\text{.5}}}^{{\text{5}}{\text{.36}}}$ Pg). The estimated plant C pool was ${\text{2}}{\text{.4}}_{{\text{0}}{\text{.95}}}^{{\text{5}}{\text{.16}}}$ Pg ( ${\text{1}}{\text{.03}}_{{\text{0}}{\text{.2}}}^{{\text{2}}{\text{.7}}}$ Pg in aboveground biomass (AGB) and ${\text{1}}{\text{.37}}_{{\text{0}}{\text{.75}}}^{{\text{2}}{\text{.45}}}$ Pg in belowground biomass). Soil and biomass C density presented a similar spatial pattern, which generally decreased from the east and southeast parts to the central and western parts. We found both vegetation and soil C (1 m depth) were primarily regulated by climatic variables and C input across the entire TP. However, main driving factors of the C stocks varied among vegetation types and depth intervals. Though AGB played as an important role in SOC variation for both topsoil (0–30 cm) and subsoil (30–100 cm), the strength of the correlation weakened with depth and was gradually attenuated from grassland to shrubland, and forest. The outcomes of this study provided an updated geospatial estimate of SOC stocks for the entire TP and their relationships with environmental factors, which are essential to carbon model benchmarking and better understanding the feedbacks of C stocks to global change