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Background Sheep milk is a nutritional and health-promoting food source for humans. The DEATH superfamily is a conserved protein family, and some of its members are closely related to lactation. Systematic studies of the members of the DEATH superfamily are important for further understanding its functions in the mammary gland during lactation; however, there studies are currently lacking. Results Herein, 74 members of the DEATH superfamily were identified in sheep, and phylogenetic analyses indicated that four subfamilies were strongly correlated in evolution. The Ka/Ks calculations demonstrated that negative selection was the primary pressure acting on DEATH members; however, the immune-related gene IFI203 was undergoing strong positive selection in sheep. Furthermore, in the late pregnancy and lactation period, these DEATH genes exhibited similar expression patterns under different nutritional conditions in the mammary gland, and four subfamilies were positively correlated in expression patterns. Additionally, half or more DEATH genes were upregulated in the lactation period, which implied their crucial roles in the lactation of sheep. Conclusions The current research contributes to a better understanding of the evolutionary characteristics of the DEATH superfamily and their roles in sheep lactation, and it also provides potential target genes for the molecular breeding of dairy sheep. Graphical Abstract

Objective. To clarify the application value of 5-hydroxymethylcytosine (5hmC) in evaluating the progression of chronic hepatitis B (CHB) to hepatocellular carcinoma (HCC) based on difference analysis. Methods. A total of 180 patients were enrolled. Among them, 84 patients with chronic hepatitis B virus (HBV) infection while no progression to hepatocellular carcinoma (HCC) were included in the control group (CG), and 96 patients with HCC developed from HBV infection were included in the research group (RG). Two-thirds of the samples were used in the training set and 1/3 samples in the validation set to detect the level of 5hmC in both groups based on the modified nano-hmC-Seal technique. The expression levels of 5hmC-related genes TET2 and TET3 were quantified by qPCR, and the correlation between TET3 and 5hmC was analyzed by Pearson’s correlation coefficients. Receiver operating characteristic (ROC) curves were drawn to evaluate the application value of the TET3-based 5hmC prediction model in the early diagnosis of HCC. Results. (i) The expression of 5hmC in RG was lower than that in CG, no matter in the training set or the validation set. (ii) 5hmC was significantly enriched in the region between the transcription initiation site and the transcription end site but was depleted in the flanking region. (iii) 5hmC-related genes TET2 and TET3 were significantly downregulated in HCC patients, whether in the training set or the validation set. (iv) In both the training and validation sets, TET3 showed a positive association with 5hmC. (v) ROC analysis results showed that the 5hmC prediction model could be used to predict the progression of CHB to HCC (training set: AUC = 0.81 , 0.729-0.893; validation set: AUC = 0.84 , 0.739-0.936). Conclusions. TET3 expression based on 5hmC sequencing is a landmark molecule for evaluating the progression of HCC in CHB patients, which is worthy of further study and promotion.

Heat stress is one of the most important factors limiting the milk yields of dairy animals. This decline can be attributed to the heat-stress-induced apoptosis of mammary epithelial cells (MECs). The cytosolic arginine sensor for mTORC1 subunit 1 (CASTOR1) is a crucial upstream regulator of the mechanistic target of rapamycin complex 1 (mTORC1) signaling, which has close connections with apoptosis. However, the specific roles of CASTOR1 in regulating the apoptosis and lactation of MECs are still obscure. In the present study, we found that heat stress promotes apoptosis and CASTOR1's expression in HC11 cells. Downregulation of CASTOR1 inhibits heat-stress-induced apoptosis through a ROS-independent pathway. In addition, silencing of CASTOR1 promotes cell proliferation, cell cycle progression, and milk component synthesis, and overexpressing of CASTOR1 reverses these observations. Furthermore, we found that silencing of CASTOR1 contributes to the nuclear transport of SREBP1 and promotes lipid synthesis. This study demonstrates the pivotal roles of CASTOR1 in heat-stress-induced apoptosis and milk component synthesis in MECs.

LIM-domain proteins have been shown to be associated with heart development and diseases. Systematic studies of LIM family members at the genome-wide level, which are crucial to further understand their functions in cardiac hypertrophy, are currently lacking. Here, 70 LIM genes were identified and characterised in mice. The expression patterns of LIM genes differ greatly during cardiac development and in the case of hypertrophy. Both Crip2 and Xirp2 are differentially expressed in cardiac hypertrophy and during heart failure. In addition, the hypertrophic state of cardiomyocytes is controlled by the relative expression levels of Crip2 and Xirp2. This study provides a foundation for further understanding of the special roles of LIM proteins in mammalian cardiac development and hypertrophy.

Background Sheep milk is an attractive nutritional and functional food source. Some Dishevelled, Egl-10 and pleckstrin (DEP) domain-containing proteins can regulate lactation performance in mammals. However, systematic study of the role of sheep DEP family members in regulating lactation is currently lacking. This study aimed to reveal the roles of sheep DEP family members in lactation by exploring their genetic characteristics and functional features at the whole genome-wide level. Results Twenty DEP family members were identified in the sheep genome, and they can be divided into four major groups. Ka/Ks calculations suggest that the purifying selection is the main pressure acting on DEP genes. In the late pregnancy and lactation periods, the expression levels of eight DEP genes exhibited significant differences in the mammary gland. In addition, nutritional conditions have a great influence on the expression of DEP family members, and the DEP gene family underwent more expansion than the average gene family in the early stages of biological evolution. The Mirrortree assays indicated that the DEP family members coevolved in biological evolution. Conclusions Our research provides a better understanding of the characters of the DEP domain-containing protein family and their potential roles in regulating lactation in sheep. Moreover, these results of our study may contribute to the genetic improvement of milk performance in dairy sheep breed. Graphical Abstract

We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.

Oxidative carbonylation has attracted broad interest from both academia and industry in recent years. Oxycarbonylation of ethanol to diethyl carbonate (DEC) has been of great challenge due to the difficulty in the simultaneous activation of ethanol and CO. This work reports a heterogeneous Cu-Pd pair catalysis for simultaneous activation of ethanol and CO, affording a selectivity of 85.4% to DEC and a space–time yield (STY) of up to 207 mg∙g−1∙h−1. Well distributed Cu-Pd pair affords more Cu (I) sites, promoting CO activation and the followed insertion to adsorbed ethanol. Further increasing ratio of Cu (I) species via the pre-treatment of catalyst with CO and ethanol promotes simultaneously the activation of ethanol and CO, affording a STY of 325 mg∙g−1∙h−1 of DEC.

BackgroundIn our clinical work, we found that cancer patients were susceptible to coronary atherosclerotic heart disease (CAD). However, less is known about the relationship between CAD and cancer. The present study aimed to identify the risk factors for CAD and cancer, as well as the relationship between CAD and cancer.MethodsIn this retrospective study, 1600 patients between January 2012 and June 2019 were enrolled and divided into groups according to whether they had CAD or cancer. Single-factor and multivariate analysis methods were applied to examine the risk factors for CAD and cancer.Results(1) Cancer prevalence was significantly higher in patients with CAD than in patients without CAD (47.2 vs. 20.9%). The prevalence of CAD in cancer and non-cancer patients was 78.9 and 52.4%, respectively. (2) Multivariable logistic regression showed that patients with cancer had a higher risk of developing CAD than non-cancer patients (OR: 2.024, 95% CI: 1.475 to 2.778, p < 0.001). Respiratory (OR: 1.981, 95% CI: 1.236–3.175, p = 0.005), digestive (OR: 1.899, 95% CI: 1.177–3.064, p = 0.009) and urogenital (OR: 3.595, 95% CI: 1.696–7.620, p = 0.001) cancers were significantly associated with a higher risk of CAD compared with no cancer. (3) Patients with CAD also had a higher risk of developing cancer than non-CAD patients (OR = 2.157, 95% CI: 1.603 to 2.902, p < 0.001). Patients in the Alanine aminotransferase (ALT) level ≥ 40 U/L group had a lower risk of cancer than patients in the ALT level < 20 U/L group (OR: 0.490, 95% CI: 0.333–0.722, p < 0.001). (4) An integrated variable (Y = 0.205 × 10–1 age − 0.595 × 10–2 HGB − 0.116 × 10–1 ALT + 0.135 FIB) was identified for monitoring the occurrence of cancer among CAD patients, with an AUC of 0.720 and clinical sensitivity/specificity of 0.617/0.711.Conclusion(1) We discovered that CAD was an independent risk factor for cancer and vice versa. (2) Digestive, respiratory and urogenital cancers were independent risk factors for CAD. (3) We created a formula for the prediction of cancer among CAD patients. (4) ALT, usually considered a risk factor, was proven to be a protective factor for cancer in this study.

H2 production from aqueous phase reforming (APR) is of great importance, but an enhancement in the H2 production efficiency is required. Our previous work reported the catalytic production of H2 over metal oxide (MMO)-supported Ni derived from Ni-containing layered double hydroxides (Ni-MgAl-LDHs). Herein, we report the catalytic production of H2 from APR of cellulose over MMO-supported defect-rich Ni derived from Ni-MgAl-LDHs with abundant Mg(II) vacancies (VMg). The well-retained VMg during calcination under an H2 atmosphere induced the formation of Ni vacancies not only at the Ni–MMO interface, but also on the surface of the Ni particles. An H2 yield of up to 70.4% was achieved via the APR of cellulose over the defect-rich Ni–MMO, which is almost 2.5 times that from the APR of cellulose reported to date (∼30%). A combined investigation via quasi in situ XPS, EPR, PAS, and in situ FT-IR verified that the Mg vacancies on the support surface facilitate the cleavage of the C–H bond, while the Ni vacancies on the Ni particles boost the breakage of the C–C bond.

Background and aims Liver regeneration after extreme hepatocyte loss occurs through transdifferentiation of biliary epithelial cells (BECs), which includes dedifferentiation of BECs into bipotential progenitor cells (BPPCs) and subsequent redifferentiation into nascent hepatocytes and BECs. Although multiple molecules and signaling pathways have been implicated to play roles in the BEC-mediated liver regeneration, mechanisms underlying the dedifferentiation-redifferentiation transition and the early phase of BPPC redifferentiation that is pivotal for both hepatocyte and BEC directions remain largely unknown. Approach and results The zebrafish extreme liver damage model, genetic mutation, pharmacological inhibition, transgenic lines, whole-mount and fluorescent in situ hybridizations and antibody staining, single-cell RNA sequencing, quantitative real-time PCR, and heat shock-inducible overexpression were used to investigate roles and mechanisms of farnesoid X receptor (FXR; encoded by nuclear receptor subfamily 1, group H, member 4 [nr1h4]) in regulating BPPC redifferentiation. The nr1h4 expression was significantly up-regulated in response to extreme liver injury. Genetic mutation or pharmacological inhibition of FXR was ineffective to BEC-to-BPPC dedifferentiation but blocked the redifferentiation of BPPCs to both hepatocytes and BECs, leading to accumulation of undifferentiated or less-differentiated BPPCs. Mechanistically, induced overexpression of extracellular signal-related kinase (ERK) 1 (encoded by mitogen-activated protein kinase 3) rescued the defective BPPC-to-hepatocyte redifferentiation in the nr1h4 mutant, and ERK1 itself was necessary for the BPPC-to-hepatocyte redifferentiation. The Notch activities in the regenerating liver of nr1h4 mutant attenuated, and induced Notch activation rescued the defective BPPC-to-BEC redifferentiation in the nr1h4 mutant. Conclusions FXR regulates BPPC-to-hepatocyte and BPPC-to-BEC redifferentiations through ERK1 and Notch, respectively. Given recent applications of FXR agonists in the clinical trials for liver diseases, this study proposes potential underpinning mechanisms by characterizing roles of FXR in the stimulation of dedifferentiation-redifferentiation transition and BPPC redifferentiation.

The Wnt signaling pathway is an evolutionarily conserved signaling pathway that plays essential roles in embryonic development, organogenesis, and many other biological activities. Both Wnt proteins and DIX proteins are important components of Wnt signaling. Systematic studies of Wnt and DIX families at the genome-wide level may provide a comprehensive landscape to elucidate their functions and demonstrate their relationships, but they are currently lacking. In this report, we describe the correlations between mouse Wnt and DIX genes in family expansion, molecular evolution, and expression levels in cardiac hypertrophy at the genome-wide scale. We observed that both the Wnt and DIX families underwent more expansion than the overall average in the evolutionarily early stage. In addition, mirrortree analyses suggested that Wnt and DIX were co-evolved protein families. Collectively, these results would help to elucidate the evolutionary characters of Wnt and DIX families and demonstrate their correlations in mediating cardiac hypertrophy.

Background: Sepsis-induced myocardial dysfunction (SIMD), which is one of the features of multiple organ dysfunction in sepsis with extremely high mortality, is characterized by impaired myocardial compliance. To date, there are few effective treatment options to cure sepsis. Tannic acid (TA) is reportedly protective during sepsis. However, the underlying mechanisms by which TA protects against septic heart injury remains elusive. Methods: We investigated the potential effects and underlying mechanisms of TA in alleviating lipopolysaccharide (LPS)-induced H9C2 cardiomyocyte cell apoptosis. H9C2 cells were treated with LPS (15 μg/mL), TA (10 μM) and TA+LPS. Control cells were treated with media only. Apoptosis was measured using flow cytometry, RT-PCR, and Western blotting analysis. Additionally, laser confocal immunofluorescence analysis detected the production of reactive oxygen species (ROS). Western blot and RT-PCR were employed to detect ER stress-associated functional proteins. Results: The results demonstrated that TA reduced the degree of LPS-induced H9C2 cells injury, including the inhibition of ROS production and endoplasmic reticulum (ER) stress-associated apoptosis. ER stress-associated functional proteins, including ATF6, PERK, IRE1, XBP1s, and CHOP were suppressed in response to TA treatment. Furthermore, the expression levels of ER stress-associated apoptotic proteins, including JNK, Bax, Cyt, Caspase3, Caspase12, and Caspase9 were reduced following treatment with TA. Additionally, the protective effects of TA on LPS-induced H9C2 cells were strengthened following treatment with the ROS inhibitor, N-Acetylcysteine (NAC), which demonstrated that ROS-mediated ER stress-associated apoptosis and TA decreased ROS-mediated ER stress-associated apoptosis. Conclusion: Our findings demonstrated that the protective effects of TA against LPS-induced H9C2 cells apoptosis may be associated with the amelioration of ROS-mediated ER stress. These findings may assist the development of potential novel therapeutic methods to inhibit the progression of myocardial cell injury. (TA alleviates LPS-induced H9C2 cell apoptosis.)

Deep learning plays an important role in the field of artificial intelligence, but in most situation, deep learning requires a larger number of training samples to complete high-precision learning. Few-shot learning, which only uses a small dataset to learn different tasks, has been extensively researched. It can be summarized into three methods: metric learning, optimization-based and model-based. In this paper, we develop a novel method based on Matching Networks: focusing on the relation between samples. We have proposed a simple method based on the idea that we can aggregate intra-class samples and separate inter-class samples. Our experiments on the Omniglot dataset and miniImageNet dataset show that our method outperforms the baseline networks.

Sepsis‑induced myocardial dysfunction is one of the features of multiple organ dysfunction in sepsis, which is associated with extremely high mortality and is characterized by impaired myocardial compliance. To date, there are few effective treatment options available to cure sepsis. Tannic acid (TA) is reportedly protective during sepsis; however, the underlying mechanisms by which TA protects against septic heart injury remain elusive. The present study investigated the potential effects and underlying mechanisms of TA in alleviating lipopolysaccharide (LPS)‑induced H9C2 cardiomyocyte cell apoptosis. H9C2 cells were treated with LPS (15 µg/ml), TA (10 µM) and TA + LPS; control cells were treated with medium only. Apoptosis was measured using flow cytometry, reverse transcription‑quantitative PCR (RT‑qPCR) and western blot analysis. Additionally, the levels of cellular reactive oxygen species (ROS), malondialdehyde and nicotinamide adenine dinucleotide phosphate were evaluated. Western blotting and RT‑qPCR were also employed to detect the expression levels of endoplasmic reticulum (ER) stress‑associated functional proteins. The present findings demonstrated that TA reduced the degree of LPS‑induced H9C2 cell injury, including inhibition of ROS production and ER stress (ERS)‑associated apoptosis. ERS‑associated functional proteins, including activating transcription factor 6, protein kinase‑like ER kinase, inositol‑requiring enzyme 1, spliced X box‑binding protein 1 and C/EBP‑homologous protein were suppressed in response to TA treatment. Furthermore, the expression levels of ERS‑associated apoptotic proteins, including c‑Jun N‑terminal kinase, Bax, cytochrome c, caspase‑3, caspase‑12 and caspase‑9 were reduced following treatment with TA. Additionally, the protective effects of TA on LPS‑induced H9C2 cells were partially inhibited following treatment with the ROS inhibitor N‑acetylcysteine, which demonstrated that ROS mediated ERS‑associated apoptosis and TA was able to decrease ROS‑mediated ERS‑associated apoptosis. Collectively, the present findings demonstrated that the protective effects of TA against LPS‑induced H9C2 cell apoptosis may be associated with the amelioration of ROS‑mediated ERS. These findings may assist the development of potential novel therapeutic methods to inhibit the progression of myocardial cell injury.

Background Arginine vasopressin (AVP) is elevated in patients with heart failure, and the increase in the AVP concentration in plasma is positively correlated with disease severity and mortality. Metoprolol (Met) is a beta blocker that is widely used in the clinic to treat pathological cardiac hypertrophy and to improve heart function. However, the specific mechanism by which Met alleviates AVP-induced pathological cardiac hypertrophy is still unknown. Our current study aimed to evaluate the inhibitory effects of Met on AVP-induced cardiomyocyte hypertrophy and the underlying mechanisms. Methods AVP alone or AVP plus Met was added to the wild type or AKT1-overexpressing rat cardiac H9C2 cell line. The cell surface areas and ANP/BNP/β-MHC expressions were used to evaluate the levels of hypertrophy. Western bolting was used to analyze AKT1/P-AKT1, AKT2/P-AKT2, total AKT, SERCA2, and Phospholamban (PLN) expression. Fluo3-AM was used to measure the intracellular Ca2+ stores. Results In the current study, we found that AKT1 but not AKT2 mediated the pathogenesis of AVP-induced cardiomyocyte hypertrophy. Sustained stimulation (48 h) with AVP led to hypertrophy in the H9C2 rat cardiomyocytes, resulting in the downregulation of AKT1 (0.48 fold compared to control) and SERCA2 (0.62 fold), the upregulation of PLN (1.32 fold), and the increase in the cytoplasmic calcium concentration (1.52 fold). In addition, AKT1 overexpression increased the expression of SERCA2 (1.34 fold) and decreased the expression of PLN (0.48 fold) in the H9C2 cells. Moreover, we found that Met could attenuate the AVP-induced changes in AKT1, SERCA2 and PLN expression and decreased the cytoplasmic calcium concentration in the H9C2 cells. Conclusions Our results demonstrated that the AKT1–SERCA2 cascade served as an important regulatory pathway in AVP-induced pathological cardiac hypertrophy.

Ribosome biogenesis is a significant process in cells. Dysfunction in this process will result in the defects of protein synthesis and consequently cause the development of specific diseases called ribosomopathies. Mutations in ribosome biogenesis protein Rps19, Rpl5, or Rpl11 can lead to hematopoietic defects in human, thus triggering the disease Diamond Blackfan anemia. However, the regulatory mechanisms of ribosome biogenesis in hematopoiesis remain incompletely understood. In this study, we describe a zebrafish mutant cq42, which carries a nonsense mutation in the gene that encodes ribosome biogenesis 2 homolog (Urb2). Urb2 is strongly expressed in the caudal hematopoietic tissue (CHT) during hematopoietic stem cells (HSCs) expanding. Molecular characterization of urb2cq42 larvae suggest that urb2 deficiency notably decrease the population of HSCs in CHT and early T cells in thymus. Further analysis shows that compromised cell proliferation and superfluous apoptosis are observed in the CHT of urb2cq42 mutant. P53 pathway is upregulated in the urb2cq42 larvae and loss-of-function of P53 can fully rescue the hematopoietic defects in urb2cq42 mutant. These data demonstrate that urb2 is essential for HSCs development through the regulation of P53 pathway.

The role of the AKT2/NBA1/SPK1 signaling cascade in macrophage migration regulation and post-ischemic cardiac remodeling was investigated. We determined that the AKT2/NBA1/SPK1 signaling cascade regulated macrophage migration. A novel role for NBA1 in macrophage migration was discovered. Elevated AKT2 phosphorylation, NBA1, SPK1 (along with phosphorylated SPK1) levels, macrophage recruitment, apoptosis, and fibrosis were found within the infarct area. Atorvastatin had a beneficial effect on cardiac remodeling following myocardial infarction by inhibiting AKT2/NBA1/SPK1-mediated macrophage recruitment, apoptosis, and collagen deposition while increasing angiogenesis in the infarct area. Atorvastatin-related protection of cardiac remodeling following myocardial infarction was abolished in SPK1-KO mice. The AKT2/NAB1/SPK1 pathway is a novel regulating factor of macrophage migration and cardiac remodeling after myocardial infarction.

Visualizations in organizational research have primarily been used in the context of traditional survey data, where individual data points (e.g., responses) can typically be plotted, and qualitative (e.g., language data) and quantitative (e.g., frequency data) information are not typically combined. Moreover, visualizations are typically used in a hypothetico-deductive fashion to showcase significant hypothesized results. With the advent of big data, which has been characterized as being particularly high in volume, variety, and velocity of collection, visualizations need to more explicitly and formally consider the issues of (a) identification (isolating or highlighting relevant data pertaining to the phenomena of interest), (b) integration (combining different modes of data to reveal insights about a phenomenon of interest), (c) immediacy (examining real-time data in a time-sensitive manner), and (d) interactivity (inductively uncovering and identifying new patterns). We discuss basic ideas for addressing these issues and provide illustrative examples of visualizations that incorporate and highlight ways of addressing these issues. Examples in our article include visualizing multiple performance criteria for police officers, publication network of organizational researchers, and social media language of Fortune 500 companies.

Performance analysis often considers numerous factors contributing to performance, and the relative importance of these factors is evolving based on dynamic conditions and requirements. Investigating large numbers of factors and understanding individual factors' predictability within the ultimate performance are challenging tasks. A visual analytics approach that integrates interactive analysis, novel visual representations, and predictive machine learning models can provide new capabilities to examine performance effectively and thoroughly. Currently, only limited research has been done on the possible applications of visual analytics for performance evaluation. In this dissertation, two specific types of performance analysis are presented: (1) organizational employee performance evaluation and (2) performance improvement of machine learning models with interactive feature selection. Both application scenarios leverage the human-in-the-loop approach to assist the identification of influential factors. For organizational employee performance evaluation, a novel visual analytics system, MetricsVis, is developed to support exploratory organizational performance analysis. MetricsVis incorporates hybrid evaluation metrics that integrate quantitative measurements of observed employee achievements and subjective feedback on the relative importance of these achievements to demonstrate employee performance at and between multiple levels regarding the organizational hierarchy. MetricsVis II extends the original system by including actual supervisor ratings and user-guided rankings to capture preferences from users through derived weights. Comparing user preferences with objective employee workload data enables users to relate user evaluation to historical observations and even discover potential bias. For interactive feature selection and model evaluation, a visual analytics system, FeatureExplorer, allows users to refine and diagnose a model iteratively by selecting features based on their domain knowledge, interchangeable features, feature importance, and the resulting model performance. FeatureExplorer enables users to identify stable, trustable, and credible predictive features that contribute significantly to a prediction model.

Additional file 1. Fig. S1: P-AKT1 (Thr308) was downregulated in the AVP-induced hypertrophic H9C2 cells. Fig. S2: AKT1 overexpression upregulated the protein expression of SERCA2 and downregulated the protein expression of PLN.

Toll-like receptors (TLRs) are the earliest reported pathogen recognition receptors (PRRs), and these receptors play pivotal roles in the innate immune system. Systematic studies of TLR family at the genome-wide level are important to understand its functions but are currently lacking in the insect lineage. Here, 6 TLR genes were identified and characterized in housefly (Musca domestica). The TLR genes of housefly were classified into five families according to the phylogenetic analysis of insect TLRs. The domain organization analyses indicated that the TLRs were composed by three major components: a leucine-rich repeat (LRR) domain, a transmembrane region (TM) and a Toll/interleukin-1 receptor (TIR) domain. Primary and tertiary structure analysis showed that the ectodomains of arthropod TLRs were longer than that of other phyla or classes. The mRNA expression levels of all 6 TLRs downregulated in the resistant housefly strain. Moreover, the expression levels of 6 TLRs varied between tissue and gender. Additionally, the 3D structures of the TIR domain were highly conserved during evolution. Collectively, these results help elucidate the crucial roles of TLRs in the immune response of housefly and provide a foundation for further understanding of innate immunity of the housefly.

Rho GTPases play essential roles in various life activities. Rho GTPase-activating protein (RhoGAP) and Rho guanine nucleotide exchange factor (RhoGEF) are the main regulators of Rho GTPases. RhoGAP, RhoGEF and Rho make up a molecular switch and exert crucial roles in signaling pathways. The genome-wide studies can provide us a comprehensive information of special protein family, but the genome-wide information of RhoGAP and RhoGEF families are still lacking in the mammal lineage. Here, we report the correlations between mouse RhoGAPs and RhoGEFs in gene quantities, evolution, molecular function, and their expression levels in heart embryonic development and cardiovascular medicine treatment at genome-wide scale. Besides, we find that the 3D structures of RhoGAP domains between different species are highly conserved, but that of RhoGEF domains are variable between species. Our present study contributes to a better understanding of the complex regulation mechanisms of RhoGAP and RhoGEF families.

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains., Comment: To appear in 2019 IEEE Transactions on Visualization and Computer Graphics

Social media platforms such as Twitter are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve and evade traditional detection techniques. In this work, we propose VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling techniques, which offer new insights that enable the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We conducted a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool., Comment: IEEE VIS (VAST) 2019

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance., Comment: To appear in IEEE VIS 2019 Short Papers

Macrophage recruitment to the inflammation site is essential for LPS-induced myocarditis, although the underlying mechanism remains elusive. This study was designed to examine the role of the P-AKT2/SPK1 (P-SPK1) and P-MEK/P-ERK signaling cascades in the regulation of macrophage migration and LPS-induced myocarditis. Our data revealed that (1) the P-AKT2/SPK1 (P-SPK1) and P-MEK/P-ERK signaling cascades acted separately in the regulation of macrophage migration; (2) P-AKT2/SPK1 (P-SPK1) played a relatively important role compared to P-MEK/P-ERK cell signaling in LPS-induced macrophage migration; (3) atorvastatin (ATV) inhibited macrophage migration by inhibiting P-AKT2/SPK1 (P-SPK1) cell signaling, but ATV could increase P-MEK and P-ERK protein expression; (4) ATV has a beneficial effect on LPS-induced myocarditis via inhibition of P-AKT2/SPK1-mediated macrophage recruitment, apoptosis, TNFα, IL-1β, and IL-6; (5) ATV-offered protection against LPS-induced myocarditis was eliminated from SPK1-KO mice; (6) SPK1 may play a harmful role in LPS-induced myocarditis. Taken together, our data revealed that SPK1 represents a novel regulating factor for macrophage migration and cardiac protection under LPS-induced myocarditis.

Accurate phenotyping with unmanned aerial vehicles is a remote sensing application that has received recent attention as plant breeders seek to automate the expensive and time consuming traditional manual acquisition of measurements of plant traits. This paper focuses on the prediction of sorghum biomass utilizing high temporal and spatial resolution remote sensing data. Two methods are investigated for biomass prediction. The first uses nonlinear regression models to predict biomass directly from remote sensing data, based on features from Light Detection And Ranging (LiDAR) point clouds and hyperspectral data. The second strategy focuses on the biophysical sorghum crop model, APSIM, first, using remote sensing data to parametrize the crop model, and then simulating the biomass. Results from both approaches are provided and evaluated for an agricultural test field at the Agronomy Center for Research and Education (ACRE) at Purdue University.

High throughput plant phenotyping has gained significant interest in the plant science community due to its potential impact in advancing the use of advanced plant genetics for problems ranging from global food security to biomass-based energy crops. While traditional collection of field-based phenotypes is manual, automated remote sensing-based methods can reduce the manual requirements, expand the number of sampled points, and accelerate associations with genotypes. In this preliminary work, we use multiple types of features derived from multi-temporal UAV-based hyperspectral and RGB image data for prediction of sorghum biomass. Considering the nonlinear properties of the spectral input features, multiple layer perception (MLP) neural networks and support vector regression (SVR) are explored for predicting dry biomass. The analysis is conducted on datasets acquired during June-August 2016 over an agricultural test field at the Agronomy Center for Research and Education (ACRE) at Purdue University.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

Pseudomonas aeruginosa causes serious acute and chronic infections in humans. Major differences exist in disease pathogenesis, clinical treatment and outcomes between acute and chronic infections. P. aeruginosa acute infection characteristically involves the type III secretion systems (T3SS) while chronic infection is often associated with the formation of biofilms, a major cause of difficulties to eradicate chronic infections. The choice between acute and chronic infection or the switch between them by P. aeruginosa is controlled by regulatory pathways that control major virulence factors and genes associated with biofilm formation. In this study, we characterized a hybrid sensor kinase PA1611 that controls the expression of genes associated with acute and chronic infections in P. aeruginosa PAO1. Expression of PA1611 completely repressed T3SS and swarming motility while it promoted biofilm formation. The protein PA1611 regulates two small RNAs (sRNAs), rsmY and rsmZ which in turn control RsmA. Independent of phosphate relay, PA1611 interacts directly with RetS in vivo. The positive effect of RetS on factors associated with acute infection could presumably be restrained by PA1611 when chronic infection conditions are present. This RetS-PA1611 interaction, together with the known RetS-GacS interaction, may control disease progression and the lifestyle choice of P. aeruginosa.

We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-forth dialogue with the computational model by alternating between manually predicting future changes through interaction and letting the model automatically determine the most likely outcomes, to eventually come to a common prediction using the model. This computer-supported prediction approach allows for harnessing the user's knowledge of factors influencing future behavior, as well as sophisticated computational models drawing on past performance. To validate the TimeFork technique, we conducted a user study in a stock market prediction game. We present evidence of improved performance for participants using TimeFork compared to fully manual or fully automatic predictions, and characterize qualitative usage patterns observed during the user study.

Our visual analytics system that we developed as part of the 2015 VAST Challenge provided us with insights to characterize the communication patterns of visitors.

The 2015 VAST challenge features movement tracking (Mini- Challenge 1 (MC1)) and communication information (Mini- Challenge 2 (MC2)) datasets of all visitors in an amusement park over a three-day weekend. The data includes around 25 million individual movement records, along with 4 million communication records. Analyzing and exploring such large-scale datasets require intelligent data mining methods that characterize the overall trends and anomalies, as well as interactive visual interfaces to support investigation at different spatiotemporal granularities. The objective of MC1 was to characterize the behavior of different groups of visitors, compare different activity patterns over the three days, and discover anomalies or unusual behavior patterns that relate to the crime that occurred during the weekend. We utilized both movement data provided in MC1 and communication data provided in MC2 to answer the questions asked in MC1.

We developed a visual analytics system to analyze the provided heterogeneous 2015 VAST Challenge data. This system utilized several analytic models and visualization techniques. Currently, the underlying data models and clustering techniques have limitations in processing the large volume of data in real time. Therefore, for future work, we will improve the scalability of our system to support real time interactivity and analysis.

The 2014 VAST Grand Challenge required us to find victims, suspects, and criminal motivations based on three separate dataseis. We developed three VA tools (AnnotatedTimeTree, Dodeca-Ring Map and SMART) to facilitate the understanding of heterogeneous multivariate dataseis. These tools were integrated to gain insights into the source data and find connections among complex information (Fig. 1). AnnotedTimeTree aims to identify the cause clues and timeline of kidnapping based on analysis of text and network data. Dodeca-Rings Map allows analysts to interact with geospatial, temporal, and card transaction data to find suspicious personal behaviors and social networks. SMART is a visual analytics tool that enables text stream analysis by dynamically visualizing microblog data on the map over time.

We present TimeFork, an analytics technique for predicting the behavior of multivariate time-series data originating from modern disciplines such as economics (stock market) and meteorology (climate), with human-in-the-loop. We identify two types of machine-generated predictions for such datasets: temporal prediction that predicts the future of an attribute; and spatial prediction that predicts an attribute based on the other attributes in the dataset. Visual exploration of this prediction space, constituting of these predictions of different confidences, by chunking and chaining predictions over time promises accurate user-guided predictions. In order to utilize TimeFork technique, we created a visual analytics application for user-guided prediction over different time periods, thus allowing for visual exploration of time-series data.

This article describes a real-time visual analytics process based on microblog and emergency call data to solve VAST 2014 Mini Challenge 3. We extended SMART system (Social Media Analytics and Reporting Toolkit), developed by the U.S. Department of Homeland Security's VACCINE Center. Our system consists of multiple linked views to allow the analyst monitor topic evolution, identify influential microblog users, observe geo-location patterns and examine correlations among different data sources. Extensions to our previous work include a time series view, a reply/retweet networks view, and integration of emergency call data.

Pseudomonas aeruginosa causes serious acute and chronic infections in humans. Major differences exist in disease pathogenesis, clinical treatment and outcomes between acute and chronic infections. P. aeruginosa acute infection characteristically involves the type III secretion systems (T3SS) while chronic infection is often associated with the formation of biofilms, a major cause of difficulties to eradicate chronic infections. The choice between acute and chronic infection or the switch between them by P. aeruginosa is controlled by regulatory pathways that control major virulence factors and genes associated with biofilm formation. In this study, we characterized a hybrid sensor kinase PA1611 that controls the expression of genes associated with acute and chronic infections in P. aeruginosa PAO1. Expression of PA1611 completely repressed T3SS and swarming motility while it promoted biofilm formation. The protein PA1611 regulates two small RNAs (sRNAs), rsmY and rsmZ which in turn control RsmA. Independent of phosphate relay, PA1611 interacts directly with RetS in vivo. The positive effect of RetS on factors associated with acute infection could presumably be restrained by PA1611 when chronic infection conditions are present. This RetS-PA1611 interaction, together with the known RetS-GacS interaction, may control disease progression and the lifestyle choice of P. aeruginosa.