Miracle-dz/UniFAR-FTUs huggingface dataset:
A deep learning pipeline for segmentation of Proteus mirabilis colony patterns

The motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring boundary segmentation achieved test Dice scores of 93.28% and 83.24%, respectively. The predicted masks at times improved on the ground truths from our automated annotation algorithms. We demonstrate how application of our pipeline to a typical swarming assay enables ease of colony analysis and precise measurements of more complex pattern features than those which have been historically quantified.
Community deception in weighted networks

Techniques to hide a community from community detection algorithms are emerging as a new way to protect the privacy of users. Existing techniques either adapt optimization criteria derived from community detection (e.g., minimizing instead of maximizing modularity) or define new ones (e.g., community safeness) to identify a set of updates (e.g., edge addition/deletions) that deceive community detection algorithms from recovering the original structure of a target community C. However, all existing approaches do not take into account the fact that network's edges can be weighted to take into account node similarity or relation strength. The goal of this paper is to present SECRETORUM, a novel community deception approach for community deception in weighted networks.
A chromosome-scale genome assembly of the false clownfish, Amphiprion ocellaris

Background The false clownfish Amphiprion ocellaris is a popular fish species and an emerging model organism for studying the ecology, evolution, adaptation, and developmental biology of reef fishes. Despite this, high-quality genomic resources for this species are scarce, hindering advanced genomic analyses. Leveraging the power of PacBio long-read sequencing and Hi-C chromosome conformation capture techniques, we constructed a high-quality chromosome-scale genome assembly for the clownfish A. ocellaris. Results The initial genome assembly comprised of 1,551 contigs of 861.42 Mb, with an N50 of 863.85 kb. Hi-C scaffolding of the genome resulted in 24 chromosomes containing 856.61 Mb. The genome was annotated with 26,797 protein-coding genes and had 96.62 % completeness of conserved actinopterygian genes, making this genome the most complete and high quality among published anemonefish genomes. Transcriptomic analysis identified tissue-specific gene expression patterns, with the brain and optic lobe having the largest number of expressed genes. Further, comparative genomic analysis revealed 91 genome elements conserved only in A. ocellaris and its sister species Amphiprion percula, and not in other anemonefish species. These elements are close to genes that are involved in various nervous system functions and exhibited distinct expression patterns in brain tissue, potentially highlighting the genetic toolkits involved in lineage-specific divergence and behaviors of the clownfish branch. Conclusions Overall, our study provides the highest quality A. ocellaris genome assembly and annotation to date, whilst also providing a valuable resource for understanding the ecology and evolution of reef fishes.
Design of a 30 nm Germanium FinFET by Parameter Optimization

Germanium (Ge) is envisioned as a suitable channel candidate for field-effect transistors (FET). Properties of Ge such as high carrier mobility, compatibility with Si and adaptability with high-k materials makes it comparable to silicon. This paper presents a detailed design of a 30 nm Ge based FinFET by parameter optimization using Silvaco software. Poisson and Schrodinger equation is used to come up with an analytical quantum model. The quantum model is developed based on theory of a double gate (DG) FET but the final design is a trigate (TG) device since they are more scalable. The quantum attributes of DG MOSFET are acquired by adopting the coupled Poisson–Schrodinger equation with the aid of the variational approach. The ratio of channel length (LC) to fin height (Hfin) to fin thickness (tfin) is 4:2:1. The channel length is taken as the gate length (LG) although they are slightly differ mathematically due to side diffusion of the implanted ions. Simulation results show that physical parameters such as dimensions influence electrical characteristics of the device such as threshold voltage (VTH). Much focus is on optimization of the on/off current ratio (ION/OFF) and VTH performances. ION/OFF≈ 106 is achieved at carrier concentration in the range 1 × 1018≤ nd≤ 1.22 × 1018 and in this scenario, VTH = 0.4V . Systematical investigation is presented using IV characteristics to demonstrate the sensitivity or how critical design parameters of Ge FinFET are to the device’s figure of merits. Device performs well at low voltages but breaks down at higher drain voltages (VDS≥ 4V). Gate source voltages (VGS) range between 0.05V≤ VGS ≤ 1V and conductance is dependent on it. Effects of DIBL, which is around 0.031, and velocity saturation are studied to determine how they can be suppressed during the design process.
Algorithm for Screening Phasor Measurement Unit Data for Power System Events and Categories and Common Characteristics for Events Seen in Phasor Measurement Unit Relative Phase-Angle Differences and Frequency Signals

A network of multiple phasor measurement units (PMU) was created, set up, and maintained at the University of Texas at Austin to obtain actual power system measurements for power system analysis. Power system analysis in this report covers a variety of time ranges, such as short- term analysis for power system disturbances and their effects on power system behavior and long- term power system behavior using modal analysis. The first objective of this report is to screen the PMU data for events. The second objective of the report is to identify and describe common characteristics extracted from power system events as measured by PMUs. The numerical characteristics for each category and how these characteristics are used to create selection rules for the algorithm are also described. Trends in PMU data related to different levels and fluctuations in wind power output are also examined.
A Constrained Multi-Objective Reinforcement Learning Framework

: Many real-world problems, especially in robotics, require that reinforcement learning (RL) agents learn policies that not only maximize an environment reward, but also satisfy constraints. We propose a high-level framework for solving such problems, that treats the environment reward and costs as separate objectives, and learns what preference over objectives the policy should optimize for in order to meet the constraints. We call this Learning Preferences and Policies in Parallel (LP3). By making different choices for how to learn the preference and how to optimize for the policy given the preference, we can obtain existing approaches (e.g., Lagrangian relaxation) and derive novel approaches that lead to better performance. One of these is an algorithm that learns a set of constraint-satisfying policies, useful for when we do not know the exact constraint a priori.
Embodied Semantic Scene Graph Generation

: Semantic scene graph provides an effective way for intelligent agents to better understand the environment and it has been extensively used in many robotic applications. Existing work mainly focuses on generating the scene graph from the sensory information collected from a pre-deﬁned path, while the environment should be exhaustively explored with a carefully designed path in order to obtain a comprehensive semantic scene graph efﬁciently. In this paper, we pro-pose a new task of Embodied Semantic Scene Graph Generation, which exploits the embodiment of the intelligent agent to autonomously generate an appropriate path to explore the environment for scene graph generation. To this end, a learning framework with the paradigms of imitation learning and reinforcement learning is proposed to help the agent generate proper actions to explore the environment and the scene graph is incrementally constructed. The proposed method is evaluated on the AI2Thor environment using both the quantitative and qualitative performance indexes. Additionally, we implement the proposed method on a streaming video captioning task and promising experimental results are achieved.
Corporate Competing Culture and Environmental Investment

Using Chinese listed companies as research setting, this paper constructs a measure of corporate competing culture through textual analysis on firms’ management discussion and analysis (MD&A) disclosures, and examines the impact of corporate competing culture on environmental investment. The results show that competing culture has a significant and positive impact on firms’ environmental investment, and the results remain robust to a battery of robustness tests. Moreover, the mediating analysis indicates that competing culture promotes corporate environmental investment through enhancing firms’ internal control quality. Furthermore, the heterogeneity results show that the positive impact of corporate competing culture on environmental investment is more pronounced in firms with larger size, stronger corporate governance, in high-polluting industry, and located in less developed regions. Our findings shed light on the importance of corporate competing culture and provide practical implications for corporate sustainable development.
Comprehensive Analyses of Glucose Metabolism in Glioma Reveal the Glioma-Promoting Effect of GALM

Glioma is the most common tumor with the worst prognosis in the central nervous system. Current studies showed that glucose metabolism could affect the malignant progression of tumors. However, the study on the dysregulation of glucose metabolism in glioma is still limited. Herein, we firstly screened 48 differentially expressed glucose metabolism-related genes (DE-GMGs) by comparing glioblastomas to low-grade gliomas. Then a glucose metabolism-related gene (GMG)-based model (PC, lactate dehydrogenase A (LDHA), glucuronidase beta (GUSB), galactosidase beta 1 (GLB1), galactose mutarotase (GALM), or fructose-bisphosphatase 1 (FBP1)) was constructed by a protein–protein interaction (PPI) network and Lasso regression. Thereinto, the high-risk group encountered a worse prognosis than the low-risk group, and the M2 macrophage was positively relevant to the risk score. Various classical tumor-related functions were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Since protein GALM was rarely studied in glioma, we detected high expression of GALM by western blot and immunohistochemistry in glioma tissues. And experiments in vitro showed that GALM could promote the epithelial-to-mesenchymal transition (EMT) process of glioma cells and could be regulated by TNFAIP3 in glioma cells. Overall, our study revealed the critical role of glucose metabolism in the prognosis of patients with glioma. Furthermore, we demonstrated that GALM was significantly related to the malignancy of glioma and could promote glioma cells’ EMT process.
Climate-Driven High Primary Production and Contrasting Export Production in the Eastern North Pacific Subtropical Gyre

The eastern North Pacific subtropical gyre (NPSG) contributes significantly to global primary production (PP) and export production (EP). In this study, we have investigated the impact of the North Pacific gyre oscillation (NPGO) mode on the temporal changes in the relationship between PP and EP in the eastern NPSG, using long-term time series of oceanographic observations at Station ALOHA. The positive NPGO phases (N2+: 1998–2004, N4+: 2007–2013), exhibiting a deeper mixed layer depth (MLD), coincided with high PP. Moreover, the N2+ phase showed high EP, associated with an increase in the nano-sized phytoplankton group, and inorganic and organic nitrogen-to-phosphorus ratios. However, multiple physical and biogeochemical factors, such as thermocline depression, increase in pico-sized phytoplankton groups, smallest-sized mesozooplankton, and heterotrophic bacteria, have induced low EP during the N4+ phase, despite deep MLD and increased PP conditions. Enhanced stratification under prolonged warming indicates that the surface eastern NPSG may experience a permanent shift toward small cells.
Taxonomic, Phylogenetic and Functional Diversity of Bird Assemblages in Urban Green Spaces: Null Model Analyses, Temporal Variation and Ecological Drivers

Urban expansion is a pervasive driver of biodiversity loss. To understand the effects of urbanization on diversity, we investigated the response of bird taxonomic, phylogenetic, and functional diversity to urban green spaces’ characteristics in thirty-one green spaces of Mexico City. Selected sites encompassed variation of environmental factors along a transformation gradient, from natural protected areas to landscaped parks. Bird observations were conducted during winter (non-breeding season), spring (dry breeding season), and summer (wet breeding season). We used multi-model inference to assess the relationship of green space area, shape, isolation, tree richness, habitat diversity, and vegetation cover with species richness, Faith’s phylogenetic diversity, and functional richness. We calculated standardized effect sizes from null models to assess phylogenetic and functional structure. We registered 91 species belonging to 28 families across all sites and seasons. The number of detected species was largest in winter and decreased toward the dry breeding season, and then toward the wet breeding season. We found a moderate to strong positive relationship of species richness with phylogenetic diversity and functional richness. Overall, phylogenetic and functional structure of bird communities in green spaces was neither clustered nor over-dispersed. However, few cases of functional clustering, phylogenetic clustering, or both were observed, and they corresponded to natural protected areas. Results showed a predominant role of green space area in determining community diversity and phylogenetic structure while it did not influence functional structure. Contrary to our predictions, habitat diversity had a negative effect on species richness and phylogenetic diversity and this effect was detected only during the wet breeding season, whereas isolation showed a positive relationship with phylogenetic and functional structure, during the wet breeding season and winter, respectively. This study emphasizes the use of complementary measures of diversity together with the comparison between observed and expected values to get a better insight into the mechanisms by which green spaces’ characteristics affect bird diversity across the seasons.
Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

Objective This study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC). Materials and Methods Firstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis. Results Eight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS. Conclusion The results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.
Audio Emotion Recognition by Deep Neural Networks and Machine Learning Algorithms

One of the most stimulating tasks is speech emotion recognition. In our work, to make use of sound files as input for machine learning algorithm (SVM) and deep learning algorithm (CNN-1D,2D), for identification of emotions using sample audio files from the Berlin dataset (EMO-DB), we introduced an architecture that excerpts the MFCC and Mel-spectrogram from those audio files. To achieve an improvement in classification performance, we applied an incremental methodology to our initial model. Both machine learning and deep learning models can work directly with raw data. Based on our tentative results, the new development set is initiated for our existing framework of EMO-DB by the best-performing model. The proposed framework obtained 92.5% performance with 535 samples of EMO-DB in 7 classes. It is one of the desirable ways to recognize emotions. We used a neural network in deep learning for better results, which helps to extract the right feature from audio files for emotion classification.
Negative Life Events, Social Ties, and Depressive Symptoms for Older Adults in China

Although it is widely acknowledged that older adults who have gone through negative life events are more likely to develop depression, there is limited evidence on whether and which type of social ties moderate this perceived relationship. Based on 2016 and 2018 waves of Chinese Longitudinal Aging Social Survey (4,466 individuals, 8,932 observations), we apply linear fixed effects models and confirm that negative life events are associated with depressive symptoms for older adults (Coef. = 0.35; 95% CIs 0.11–0.61), and social ties are negatively associated with depression (Coef. = −0.08; 95% CIs −0.10 to −0.07). Our study further suggests that the association between negative life events and depressive symptoms is significantly moderated by friendship ties (Coef. = −0.18, 95% CIs −0.30 to −0.07), rather than family ties (Coef. = −0.03, 95% CIs −0.09 to 0.15). Moreover, the buffering effects of friendship ties are more prominent for the less resilient and less privileged groups, namely male, rural, and less educated older adults. Our findings point to the importance of expanding and strengthening social networks for Chinese older adults in promoting their psychological health.
Inhibition of Orai Channel Function Regulates Mas-Related G Protein-Coupled Receptor-Mediated Responses in Mast Cells

Mast cells (MCs) are tissue resident immune cells that play important roles in the pathogenesis of allergic disorders. These responses are mediated via the cross-linking of cell surface high affinity IgE receptor (FcϵRI) by antigen resulting in calcium (Ca2+) mobilization, followed by degranulation and release of proinflammatory mediators. In addition to FcϵRI, cutaneous MCs express Mas-related G protein-coupled receptor X2 (MRGPRX2; mouse ortholog MrgprB2). Activation of MRGPRX2/B2 by the neuropeptide substance P (SP) is implicated in neurogenic inflammation, chronic urticaria, mastocytosis and atopic dermatitis. Although Ca2+ entry is required for MRGPRX2/B2-mediated MC responses, the possibility that calcium release-activated calcium (CRAC/Orai) channels participate in these responses has not been tested. Lentiviral shRNA-mediated silencing of Orai1, Orai2 or Orai3 in a human MC line (LAD2 cells) resulted in partial inhibition of SP-induced Ca2+ mobilization, degranulation and cytokine/chemokine generation (TNF-α, IL-8, and CCL-3). Synta66, which blocks homo and hetero-dimerization of Orai channels, caused a more robust inhibition of SP-induced responses than knockdown of individual Orai channels. Synta66 also blocked SP-induced extracellular signal-regulated kinase 1/2 (ERK1/2) and Akt phosphorylation and abrogated cytokine/chemokine production. It also inhibited SP-induced Ca2+ mobilization and degranulation in primary human skin MCs and mouse peritoneal MCs. Furthermore, Synta66 attenuated both SP-induced cutaneous vascular permeability and leukocyte recruitment in mouse peritoneum. These findings demonstrate that Orai channels contribute to MRGPRX2/B2-mediated MC activation and suggest that their inhibition could provide a novel approach for the modulation of SP-induced MC/MRGPRX2-mediated disorders.
Identification of Tumor Antigens and Design of mRNA Vaccine for Colorectal Cancer Based on the Immune Subtype

mRNA vaccines have become a promising alternative to conventional cancer immunotherapy approaches. However, its application on colorectal cancer (CRC) remains poorly understood. We herein identified potential antigens for designing an effective mRNA vaccine, further to build an immune landscape for the accurate selection of patients for mRNA vaccine therapy. Raw transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were retrieved. Consensus clustering algorithm was applied to divide the CRC samples into four immune subtypes. Immunogenomics analysis was further integrated to characterize the immune microenvironment of each immune subtype. Six tumor antigens were found to be associated with poor prognosis and infiltration of antigen-presenting cells (APCs) in CRC patients. Furthermore, each of the immune subtypes showed differential cellular and molecular features. The IS2 and IS4 exhibited significantly improved survival and higher immune cell infiltration compared with IS1 and IS3. Immune checkpoint molecules and human leukocyte antigen also showed significant differential expression in four immune subtypes. Moreover, we performed graph structure learning-based dimensionality reduction to visualize the immune landscape of CRC. Our results revealed a complex immune landscape that may provide directions for mRNA vaccine treatment of CRC and define appropriate vaccination patients.