BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

Geriatric nutritional risk index and mortality from all-cause, cancer, and non-cancer in US cancer survivors: NHANES 2001–2018

BackgroundMalnutrition is strongly correlated with worsened treatment outcomes, reduced standard of living, and heightened mortality rates among individuals with cancer. Our research explores how the Geriatric Nutritional Risk Index (GNRI), a measure of nutritional status, relates to all-cause mortality, cancer-specific, and non-cancer mortality among middle-aged and older adult cancer patients.MethodsWe enrolled 3,253 participants aged 40 and above who were diagnosed with cancer. The data was obtained from the National Health and Nutrition Examination Survey (NHANES) dataset covering the period from 2001 to 2018, with a median follow-up duration of 83 months. According to the GNRI levels, patients in the study were classified into two distinct groups: the group with a low GNRI (<98) and the group with a high GNRI (≥ 98). We conducted a Kaplan-Meier survival analysis to assess how survival rates vary with different nutritional conditions. Multivariable Cox regression analyses were performed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality, as well as cancer-specific and non-cancer-related mortality. Restricted cubic spline (RCS) analyses and subgroup evaluations were performed to augment the robustness and validity of our findings.ResultsA total of 1,171 deaths were documented, with 383 attributed to cancer, and 788 from other causes. After adjusting for potential confounders, the analysis demonstrated that, within a specified range, an elevation in the GNRI is inversely associated with mortality from all causes, cancer-specific, and non-cancer causes. Moreover, Kaplan-Meier survival curves for all-cause, cancer-specific, and non-cancer mortality distinctly showed a more pronounced decrease in survival rates among individuals in the low GNRI group (<98). Notably, the restricted cubic spline regression model (RCS) revealed statistically significant non-linear associations between GNRI scores and mortality rates. The P-values were ≤0.001 for both all-cause and non-cancer mortality, and 0.024 for cancer-specific mortality.ConclusionOur study conclusively demonstrated a robust correlation between GNRI scores and mortality rates among cancer patients, encompassing all-cause mortality as well as specific mortality related to both cancerous and non-cancerous causes. The GNRI may be a valuable prognostic tool for predicting cancer mortality outcomes, offering insights that may inform nutritional management and influence the clinical treatment strategies for cancer survivors

SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding

Simple Summary: Cotton is a crucial economic crop, but it is often threatened by various pests and diseases during its growth, significantly impacting its yield and quality. Earlier image classification methods often suffer from low accuracy and struggle to perform effectively in complex real-world environments. This paper proposes a novel image classification network named SpemNet, specifically designed for cotton pest and disease recognition. By introducing the Efficient Multi-Scale Attention (EMA) module and the Stacking Patch Embedding (SPE) module, the network enhances the ability to learn local features and integrate multi-scale information, thereby significantly improving the accuracy and efficiency of cotton pest and disease recognition. Extensive experiments conducted on the publicly available CottonInsect and IP102 datasets, as well as a self-collected cotton leaf disease dataset, demonstrate that SpemNet exhibits significant advantages in key metrics such as precision, recall, and F1 score, confirming its effectiveness and superiority in the task of cotton pest and disease recognition. Abstract: We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance

The antioxidant activity of polysaccharides from Armillaria gallica

The purpose of this study was to investigate the antioxidant activity of Armillaria gallica polysaccharides. It explored whether Armillaria gallica polysaccharides (AgP) could prevent HepG2 cells from H2O2-induced oxidative damage. The results demonstrated that HepG2 cells were significantly protected by AgP, and efficiently suppressed the production of reactive oxygen species (ROS) in HepG2 cells. Additionally, AgP significantly decreased the abnormal leakage of alanine aminotransferase (ALT) and lactate dehydrogenase (LDH) caused by H2O2, protecting cell membrane integrity. It was discovered that AgP was also found to regulate the activities of antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-PX), while reducing malondialdehyde (MDA), thus protecting cells from oxidative damage. According to the flow cytometry analysis and measurement of caspase-3, caspase-8, and caspase-9 activities, AgP could modulate apoptosis-related proteins and attenuate ROS-mediated cell apoptosis

Safety evaluation of recombinant Newcastle disease virus expressing IBV multi-epitope chimeric live vaccine

Newcastle Disease (ND) and Infectious Bronchitis (IB) are two significant diseases that pose threats to the poultry industry, caused by Newcastle disease virus (NDV) and Infectious bronchitis virus (IBV), respectively. Currently, the control and prevention of these diseases primarily rely on vaccination. However, commercial ND and IB vaccines face challenges such as poor cross-protection of inactivated IBV strains and interference from live vaccines when used together, leading to immunization failures. Previously, we reported the successful rescue of a recombinant NDV expressing multiple epitopes of IBV, named rNDV-IBV-T/B, which showed promising immunoprotective efficacy against both NDV and IBV. This study focuses on the biosafety of the genetically modified recombinant vaccine candidate rNDV-IBV-T/B. Immunization was performed on day-old chicks, ducklings, goslings, and ICR mice. Observations were recorded on clinical symptoms, body weight changes, and post-mortem examination of organs, as well as histopathological preparations of tissue samples. The results indicated that the rNDV-IBV-T/B vaccine candidate had no adverse effects on the growth of targeted animals (chickens) and non-target species (ducks, geese) as well as in mammals (mice). Additionally, histopathological slides confirmed that the vaccine is safe for all tested species. Further studies evaluated the potential of rNDV-IBV-T/B to spread horizontally and vertically post-immunization, and its environmental safety. The findings revealed that the vaccine candidate lacks the capability for both horizontal and vertical transmission and does not survive in the environment. In conclusion, the rNDV-IBV-T/B strain is safe and holds potential as a new chimeric live vaccine for ND and IB

Evaluating the role of serum uric acid in the risk stratification and therapeutic response of patients with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD)

Background: Pulmonary arterial hypertension (PAH) is a malignant pulmonary vascular disease that negatively impacts quality of life, exercise capacity, and mortality. This study sought to investigate the relationship between serum uric acid (UA) level and the disease severity and treatment response of patients with PAH and congenital heart disease (PAH-CHD).Methods: This study included 225 CHD patients and 40 healthy subjects. Serum UA was measured in all patients, and UA levels and haemodynamic parameters were re-evaluated in 20 patients who had received PAH-specific drug treatment for at least 7 ± 1 month.Results: Serum UA levels were significantly higher in PAH-CHD patients than in CHD patients with a normal pulmonary artery pressure and normal subjects (347.7 ± 105.7 μmol/L vs. 278.3 ± 84.6 μmol/L; 347.7 ± 105.7 μmol/L vs. 255.7 ± 44.5 μmol/L, p < 0.05). UA levels in the intermediate and high risk groups were significantly higher than those in the low-risk group (365.6 ± 107.8 μmol/L vs. 311.2 ± 82.8 μmol/L; 451.6 ± 117.6 μmol/L vs. 311.2 ± 82.8 μmol/L, p < 0.05). Serum UA levels positively correlated with mean pulmonary arterial pressure, WHO functional class, pulmonary vascular resistance, and NT-proBNP (r = 0.343, 0.357, 0.406, 0.398; p < 0.001), and negatively with mixed venous oxygen saturation (SvO2) and arterial oxygen saturation (SaO2) (r = −0.293, −0.329; p < 0.001). UA significantly decreased from 352.7 ± 97.5 to 294.4 ± 56.8 μmol/L (p = 0.001) after PAH-specific drug treatment for at least 6 months, along with significant decreases in mean pulmonary arterial pressure and pulmonary vascular resistance and increases in cardiac index and mixed SvO2.Conclusion: Serum UA can be used as a practical and economic biomarker for risk stratification and the evaluation of PAH-specific drug treatment effects for patients with PAH-CHD

Classification, replication, and transcription of Nidovirales

Nidovirales is one order of RNA virus, with the largest single-stranded positive sense RNA genome enwrapped with membrane envelope. It comprises four families (Arterividae, Mesoniviridae, Roniviridae, and Coronaviridae) and has been circulating in humans and animals for almost one century, posing great threat to livestock and poultry，as well as to public health. Nidovirales shares similar life cycle: attachment to cell surface, entry, primary translation of replicases, viral RNA replication in cytoplasm, translation of viral proteins, virion assembly, budding, and release. The viral RNA synthesis is the critical step during infection, including genomic RNA (gRNA) replication and subgenomic mRNAs (sg mRNAs) transcription. gRNA replication requires the synthesis of a negative sense full-length RNA intermediate, while the sg mRNAs transcription involves the synthesis of a nested set of negative sense subgenomic intermediates by a discontinuous strategy. This RNA synthesis process is mediated by the viral replication/transcription complex (RTC), which consists of several enzymatic replicases derived from the polyprotein 1a and polyprotein 1ab and several cellular proteins. These replicases and host factors represent the optimal potential therapeutic targets. Hereby, we summarize the Nidovirales classification, associated diseases, “replication organelle,” replication and transcription mechanisms, as well as related regulatory factors

Systematic biases in determining dust attenuation curves through galaxy SED fitting

While the slope of the dust attenuation curve ($\delta$) is found to correlate with effective dust attenuation ($A_V$) as obtained through spectral energy distribution (SED) fitting, it remains unknown how the fitting degeneracies shape this relation. We examine the degeneracy effects by fitting SEDs of a sample of local star-forming galaxies (SFGs) selected from the Galaxy And Mass Assembly survey, in conjunction with mock galaxy SEDs of known attenuation parameters. A well-designed declining starburst star formation history is adopted to generate model SED templates with intrinsic UV slope ($\beta_0$) spanning over a reasonably wide range. The best-fitting $\beta_0$ for our sample SFGs shows a wide coverage, dramatically differing from the limited range of $\beta_0<-2.2$ for a starburst of constant star formation. Our results show that strong degeneracies between $\beta_0$, $\delta$, and $A_V$ in the SED fitting induce systematic biases leading to a false $A_V$--$\delta$ correlation. Our simulation tests reveal that this relationship can be well reproduced even when a flat $A_V$--$\delta$ relation is taken to build the input model galaxy SEDs. The variations in best-fitting $\delta$ are dominated by the fitting errors. We show that assuming a starburst with constant star formation in SED fitting will result in a steeper attenuation curve, smaller degeneracy errors, and a stronger $A_V$--$\delta$ relation. Our findings confirm that the $A_V$--$\delta$ relation obtained through SED fitting is likely driven by the systematic biases induced by the fitting degeneracies between $\beta_0$, $\delta$, and $A_V$.Comment: 21 pages, 13 figures, accepted for publication in the MNRAS, Comments welcome

Identification of Heat-Tolerant Genes in Non-Reference Sequences in Rice by Integrating Pan-Genome, Transcriptomics, and QTLs.

The availability of large-scale genomic data resources makes it very convenient to mine and analyze genes that are related to important agricultural traits in rice. Pan-genomes have been constructed to provide insight into the genome diversity and functionality of different plants, which can be used in genome-assisted crop improvement. Thus, a pan-genome comprising all genetic elements is crucial for comprehensive variation study among the heat-resistant and -susceptible rice varieties. In this study, a rice pan-genome was firstly constructed by using 45 heat-tolerant and 15 heat-sensitive rice varieties. A total of 38,998 pan-genome genes were identified, including 37,859 genes in the reference and 1141 in the non-reference contigs. Genomic variation analysis demonstrated that a total of 76,435 SNPs were detected and identified as the heat-tolerance-related SNPs, which were specifically present in the highly heat-resistant rice cultivars and located in the genic regions or within 2 kbp upstream and downstream of the genes. Meanwhile, 3214 upregulated and 2212 downregulated genes with heat stress tolerance-related SNPs were detected in one or multiple RNA-seq datasets of rice under heat stress, among which 24 were located in the non-reference contigs of the rice pan-genome. We then mapped the DEGs with heat stress tolerance-related SNPs to the heat stress-resistant QTL regions. A total of 1677 DEGs, including 990 upregulated and 687 downregulated genes, were mapped to the 46 heat stress-resistant QTL regions, in which 2 upregulated genes with heat stress tolerance-related SNPs were identified in the non-reference sequences. This pan-genome resource is an important step towards the effective and efficient genetic improvement of heat stress resistance in rice to help meet the rapidly growing needs for improved rice productivity under different environmental stresses. These findings provide further insight into the functional validation of a number of non-reference genes and, especially, the two genes identified in the heat stress-resistant QTLs in rice