Ultrafast Error-Bounded Lossy Compression for Scientific Datasets

Today\u27s scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in the scientific community because it not only can significantly reduce the data volumes but also can strictly control the data distortion based on the user-specified error bound. Existing lossy compressors, however, cannot offer ultrafast compression speed, which is highly demanded by numerous applications or use cases (such as in-memory compression and online instrument data compression). In this paper, we propose a novel ultrafast error-bounded lossy compressor that can obtain fairly high compression performance on both CPUs and GPUs and with reasonably high compression ratios. The key contributions are threefold. (1) We propose a generic error-bounded lossy compression framework - -called SZx - -that achieves ultrafast performance through its novel design comprising only lightweight operations such as bitwise and addition/subtraction operations, while still keeping a high compression ratio. (2) We implement SZx on both CPUs and GPUs and optimize the performance according to their architectures. (3) We perform a comprehensive evaluation with six real-world production-level scientific datasets on both CPUs and GPUs. Experiments show that SZx is 2∼16x faster than the second-fastest existing error-bounded lossy compressor (either SZ or ZFP) on CPUs and GPUs, with respect to both compression and decompression

Case report: Giant cystic ileal gastrointestinal stromal tumor with an atypical intratumoral abscess

BackgroundGastrointestinal stromal tumors (GISTs) are typically solid, sometimes with small cystic areas, but rarely manifest as predominantly cystic neoplasms. In addition, cystic intestinal GISTs with intratumoral abscess formation are rare.Case presentationWe present the case of a 49-year-old male patient with a history of frequent and urgent urination for 2 weeks. Radiologic studies revealed a large cystic mass in the lower abdomen. The patient underwent abdominal laparotomy, which revealed a large cystic mass arising from the distal ileum invading the sigmoid mesocolon and apex vesicae. Partial resection of the ileum along with the tumor and the adjacent bladder was performed. Macroscopic examination revealed that the cystic mass contained a large amount of foul-smelling pus and a tumor-bowel fistula. The final pathology revealed an abdominal stromal tumor. Postoperative recovery was uneventful, and adjuvant imatinib mesylate 400 mg was administered daily. No tumor recurrence or metastasis was observed during the 9-month follow-up period.ConclusionFingings of a cystic tumor in the abdomen should raise concern for cystic GISTs. This case report reviews a rare presentation of an ileal giant cystic GIST with atypical intratumoral abscess formation. Complete surgical resection and adjuvant imatinib is still the mainstay treatment for GISTs

Global Intelligent Governance—A Collaborative Platform

The purpose of this panel on “Global Intelligent Governance—A Collaborative Platform (GIG-CP)” is to discuss the feasibility and need for developing a collaborative platform to facilitate a global network-to-network collaboration of research in intelligent governance (IG). The discussion could provide a guide to establish the platform which will enable collaboration among international research networks. The platform will facilitate establishing common protocols for sharing high quality and high value open data. It would transform data-driven public engagement in collaborative decision making processes. There are three aims of the project: (i) to facilitate the development of research network collaboration; (ii) to enable the design of a global data hub, and (iii) to examine the IG skills required for the future workforce

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck when training surrogates with large datasets. In practice, surrogates are usually trained with high-resolution scientific data, which can easily reach the terabyte scale. Several state-of-the-art data loaders are proposed to improve the loading throughput in general CNN training; however, they are sub-optimal when applied to the surrogate training. In this work, we propose SOLAR, a surrogate data loader, that can ultimately increase loading throughput during the training. It leverages our three key observations during the benchmarking and contains three novel designs. Specifically, SOLAR first generates a pre-determined shuffled index list and accordingly optimizes the global access order and the buffer eviction scheme to maximize the data reuse and the buffer hit rate. It then proposes a tradeoff between lightweight computational imbalance and heavyweight loading workload imbalance to speed up the overall training. It finally optimizes its data access pattern with HDF5 to achieve a better parallel I/O throughput. Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24.4X speedup over PyTorch Data Loader and 3.52X speedup over state-of-the-art data loaders.Comment: 14 pages, 15 figures, 5 tables, submitted to VLDB '2

The Protective Antibodies Induced by a Novel Epitope of Human TNF-α Could Suppress the Development of Collagen-Induced Arthritis

Tumor necrosis factor alpha (TNF-α) is a major inflammatory mediator that exhibits actions leading to tissue destruction and hampering recovery from damage. At present, two antibodies against human TNF-α (hTNF-α) are available, which are widely used for the clinic treatment of certain inflammatory diseases. This work was undertaken to identify a novel functional epitope of hTNF-α. We performed screening peptide library against anti-hTNF-α antibodies, ELISA and competitive ELISA to obtain the epitope of hTNF-α. The key residues of the epitope were identified by means of combinatorial alanine scanning and site-specific mutagenesis. The N terminus (80–91 aa) of hTNF-α proved to be a novel epitope (YG1). The two amino acids of YG1, proline and valine, were identified as the key residues, which were important for hTNF-α biological function. Furthermore, the function of the epitope was addressed on an animal model of collagen-induced arthritis (CIA). CIA could be suppressed in an animal model by prevaccination with the derivative peptides of YG1. The antibodies of YG1 could also inhibit the cytotoxicity of hTNF-α. These results demonstrate that YG1 is a novel epitope associated with the biological function of hTNF-α and the antibodies against YG1 can inhibit the development of CIA in animal model, so it would be a potential target of new therapeutic antibodies

TAT-Ngn2 Enhances Cognitive Function Recovery and Regulates Caspase-Dependent and Mitochondrial Apoptotic Pathways After Experimental Stroke

Neurogenin-2 (Ngn2) is a basic helix-loop-helix (bHLH) transcription factor that contributes to the identification and specification of neuronal fate during neurogenesis. In our previous study, we found that Ngn2 plays an important role in alleviating neuronal apoptosis, which may be viewed as an attractive candidate target for the treatment of cerebral ischemia. However, novel strategies require an understanding of the function and mechanism of Ngn2 in mature hippocampal neurons after global cerebral ischemic injury. Here, we found that the expression of Ngn2 decreased in the hippocampus after global cerebral ischemic injury in mice and in primary hippocampal neurons after oxygen glucose deprivation (OGD) injury. Then, transactivator of transcription (TAT)-Ngn2, which was constructed by fusing a TAT domain to Ngn2, was effectively transported and incorporated into hippocampal neurons after intraperitoneal (i.p.) injection and enhanced cognitive functional recovery in the acute stage after reperfusion. Furthermore, TAT-Ngn2 alleviated hippocampal neuronal damage and apoptosis, and inhibited the cytochrome C (CytC) leak from the mitochondria to the cytoplasm through regulating the expression levels of brain-derived neurotrophic factor (BDNF), phosphorylation tropomyosin-related kinase B (pTrkB), Bcl-2, Bax and cleaved caspase-3 after reperfusion injury in vivo and in vitro. These findings suggest that the downregulation of Ngn2 expression may have an important role in triggering brain injury after ischemic stroke and that the neuroprotection of TAT-Ngn2 against stroke might involve the modulation of BDNF-TrkB signaling that regulates caspase-dependent and mitochondrial apoptotic pathways, which may be an attractive therapeutic strategy for cerebral ischemic injury

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

Article describes how today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. In this paper, the authors develop a fast and high- ratio error-bounded lossy compressor on GPUs for scientific data (called FZ-GPU)

Plant biomass allocation and driving factors of grassland revegetation in a Qinghai-Tibetan Plateau chronosequence

Biomass allocation is a key factor in understanding how ecosystems respond to changing environmental conditions. The role of soil chemistry in the above- and belowground plant biomass allocation in restoring grassland is still incompletely characterized. Consequently, it has led to two competing hypotheses for biomass allocation: optimal partitioning, where the plants allocate biomass preferentially to optimize resource use; and the isometric hypothesis, which postulates that biomass allocation between roots and shoots is fixed. Here we tested these hypotheses over a chronosequence of alpine grasslandsion undergoing restoration in the Qinghai-Tibetan Plateau, these range from severely degraded to those with 18 years of revegetation with an intact grassland (as a reference). A high proportion of biomass was allocated to the roots in the revegetated grasslands, and more biomass to shoots in the degraded and intact grasslands. The grasslands gradually decreased their root to shoot ratio as revegetation continued, with the lowest value in year 18 of revegetation. Our results showed that aboveground biomass (AGB) was increased by available phosphorus (P), soil moisture, and negatively related to bulk density, while belowground biomass (BGB) was positively impacted by total P and negatively by nitrate nitrogen (N). The trade-off between them was positively associated with available P and nitrate-N, and soil nutrient availability is more linked to increased AGB relative to BGB. Our study indicates that biomass allocation is highly variable during the revegetation period from degraded grassland, and is linked with soil properties, thus supporting the optimal partitioning hypothesis.</p

Association between plasma trimethylamine N -oxide and neoatherosclerosis in patients with very late stent thrombosis

Abstract(#br)Background(#br)Trimethylamine N -oxide (TMAO) has been shown to promote the development of atherosclerosis. However, the relationship between plasma TMAO and neoatherosclerosis, an important underlying mechanism of very late stent thrombosis (VLST), is unknown.(#br)Methods(#br)This post hoc study investigated the association between TMAO and neoatherosclerosis in two independent cohorts. These included a control group of 50 healthy volunteers and a study cohort of 50 patients with VLST who presented with ST-segment elevation myocardial infarction and underwent optical coherence tomography examination. Of the 50 patients with VLST, 23 had neoatherosclerosis and 27 did not have neoatherosclerosis. Patients with neoatherosclerosis were further divided into two subgroups, including 14 patients with plaque rupture and 9 without plaque rupture.(#br)Results(#br)The plasma TMAO levels, detected using mass spectrometry, were significantly higher in patients with VLST than in healthy individuals (median [interquartile range]: 2.50 [1.67-3.84] vs. 1.32 [0.86-2.44] μM; P < 0.001). Among the VLST patients, the plasma TMAO levels were significantly higher in patients with neoatherosclerosis than in those without neoatherosclerosis (3.69 [2.46-5.29] vs. 1.96 [1.39-2.80] μM; P<0.001). In addition, in patients with neoatherosclerosis, patients with plaque rupture had significantly higher plasma TMAO concentrations than those without plaque rupture (4.51 [3.41-5.85] vs. 2.46 [2.05-3.55] μM; P=0.005). Multivariate analysis indicated that TMAO was an independent predictor of neoatherosclerosis (odds ratio 3.41; 95% confidence interval: 1.59-7.30; P=0.002). Moreover, the area under the receiver operating characteristic curve for TMAO, differentiated by neoatherosclerosis, was 0.85.(#br)Conclusions(#br)Plasma TMAO was significantly correlated with neoatherosclerosis and plaque rupture in patients with VLST