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The COVID-19 pandemic has caused severe health problems worldwide and unprecedented decimation of the global economy. Moreover, after more than 2 years, many populations are still under pressure of infection. Thus, a broader perspective in developing antiviral strategies is still of great importance. Inspired by the observed multiple benefits of heparin in the treatment of thrombosis, the potential of low molecular weight heparin (LMWH) for the treatment of COVID-19 have been explored. Clinical applications found that LMWH decreased the level of inflammatory cytokines in COVID-19 patients, accordingly reducing lethality. Furthermore, several in vitro studies have demonstrated the important roles of heparan sulfate in SARS-CoV-2 infection and the inhibitory effects of heparin and heparin mimetics in viral infection. These clinical observations and designed studies argue for the potential to develop heparin mimetics as anti-SARS-CoV-2 drug candidates. In this review, we summarize the properties of heparin as an anticoagulant and the pharmaceutical possibilities for the treatment of virus infection, focusing on the perspectives of developing heparin mimetics via chemical synthesis, chemoenzymatic synthesis, and bioengineered production by microbial cell factories. The ultimate goal is to pave the eminent need for exploring novel compounds to treat coronavirus infection-caused diseases.

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue.

Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the assorted intensities of user interests in different clicks. In this paper, we aim to achieve multi-granularity Click confidence Learning via Self-Distillation in recommendation (CLSD). Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method. After that, we define a local confidence function to adapt confidence scores at the user group level, since the confidence distributions can be varied among user groups. With the combination of multi-granularity confidence learning, we can distinguish the quality of clicks and model user interests more accurately without involving extra data and model structures. The significant improvements over different backbones on industrial offline and online experiments in a real-world recommender system prove the effectiveness of our model. Recently, CLSD has been deployed on a large-scale recommender system, affecting over 400 million users.

Personalized recommendation relies on user historical behaviors to provide user-interested items, and thus seriously struggles with the data sparsity issue. A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels. In this work, we propose a novel model-agnostic Diversified self-distillation guided positive augmentation (DivSPA) for accurate and diverse positive item augmentations. Specifically, DivSPA first conducts three types of retrieval strategies to collect high-quality and diverse positive item candidates according to users' overall interests, short-term intentions, and similar users. Next, a self-distillation module is conducted to double-check and rerank these candidates as the final positive augmentations. Extensive offline and online evaluations verify the effectiveness of our proposed DivSPA on both accuracy and diversity. DivSPA is simple and effective, which could be conveniently adapted to other base models and systems. Currently, DivSPA has been deployed on multiple widely-used real-world recommender systems.

Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential component in learning the evolving user preferences through historical records. Except for randomly sampling negative samples from a uniformly distributed subset, many delicate methods have been proposed to mine negative samples with high quality. However, due to the inherent randomness of negative sampling, false negative samples are inevitably collected in model training. Current strategies mainly focus on removing such false negative samples, which leads to overlooking potential user interests, lack of recommendation diversity, less model robustness, and suffering from exposure bias. To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance. We first devise a simple strategy to extract false negative samples and then transfer these samples to positive samples in the following training process. Furthermore, we construct a teacher model to provide soft labels for false negative samples and design a consistency loss to regularize the predictions of these samples from the student model and the teacher model. To the best of our knowledge, this is the first work to utilize false negative samples instead of simply removing them for the sequential recommendation. Experiments on three benchmark public datasets are conducted using three widely applied SOTA models. The experiment results demonstrate that our proposed UFNRec can effectively draw information from false negative samples and further improve the performance of SOTA models. The code is available at https://github.com/UFNRec-code/UFNRec.

Nitrate water pollution, which is mainly caused by agricultural activities, remains an international problem. Too much nitrate in water bodies can cause serious long-term environmental issues and threaten both economic and human health. This presentation introduces the experiences of using numerical modelling to help handle nitrate water pollution in the United Kingdom. Since it is critical to prevent diffuse water pollution from agricultural sources, this talk also covers the ongoing efforts of adopting innovative modelling technologies to help improve water and fertiliser efficiencies and hence farmers’ income, while reducing agricultural activities' adverse impacts on the water environment.

Hydraulic system lubricating oil is subject to serialized Variable Static Loads (VSL) following-up performance. An improved Self-Turbulent Flow (STF) algorithm, based on the real-time acquisition and monitoring of Lubricating Oil Static Pressure (LOSP) in hydraulic system to simulate VSL, is proposed. In this topic, a mathematical model of the Electro-Hydraulic Servo (EHS) control system for LOSP acquisition is presented, and the STF controller is designed for numerical analysis. The Self-Anti-Disturbance (SAD) control strategy for LOSP of EHS system is discussed, which is used for quadratic optimization, pole placement, PID and STF control, and the LOSP simulation model of STF control is constructed by SIMULINK module. Numerical simulation results indicate that the overshoot is significantly reduced. The proposed SAD control algorithm is verified by experiments, and the LOSP acquisition followability and monitoring accuracy are greatly improved. Toward this goal, a variable hydraulic LOSP acquisition and monitoring can be effectively and stability adjusted by pre-designed EHS control system in the field of power hydraulic fluid lubrication.

As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, these embeddings are usually assigned a unified dimension, which suffers from the following issues: (1) high memory usage and computation cost. (2) sub-optimal performance due to inferior dimension assignments. In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems. However, they either require well-designed search space for hyperparameters or need time-consuming optimization procedures. In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. Specifically, it introduces a criterion for identifying the importance of each embedding dimension for each feature field. As a result, SSEDS could automatically obtain mixed-dimensional embeddings by explicitly reducing redundant embedding dimensions based on the corresponding dimension importance ranking and the predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic, meaning that it could be integrated into different base recommendation models. The extensive offline experiments are conducted on two widely used public datasets for CTR prediction tasks, and the results demonstrate that SSEDS can still achieve strong recommendation performance even if it has reduced 90\% parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription platform for practical recommendation services. The 7-day online A/B test results show that SSEDS can significantly improve the performance of the online recommendation model.

Biological applications of nanomaterials as delivery carriers have been embedded in traditional biomedical research for decades. Despite lagging behind, recent significant breakthroughs in the use of nanocarriers as tools for plant biotechnology have created great interest. In this Perspective, we review the outstanding recent works in nanocarrier-mediated plant transformation and its agricultural applications. We analyze the chemical and physical properties of nanocarriers determining their uptake efficiency and transport throughout the plant body.

Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and perform recommendations accordingly. State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations. Though effective and performing well, the models based on contrastive learning require careful selection of data augmentation methods and pretext tasks, efficient negative sampling strategies, and massive hyper-parameters validation. In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. Specifically, we present a simple yet effective \textbf{C}onsistency \textbf{T}raining method for sequential \textbf{Rec}ommendation (CT4Rec) in which only two extra training objectives are utilized without any structural modifications and data augmentation. Experiments on three benchmark datasets and one large newly crawled industrial corpus demonstrate that our proposed method outperforms SOTA models by a large margin and with much less training time than these based on contrastive learning. Online evaluation on real-world content recommendation system also achieves 2.717\% improvement on the click-through rate and 3.679\% increase on the average click number per capita. Further exploration reveals that such a simple method has great potential for CTR prediction. Our code is available at \url{https://github.com/ct4rec/CT4Rec.git}.

Clinical use of antimicrobials faces great challenges from the emergence of multidrug-resistant pathogens. The overexpression of drug efflux pumps is one of the major contributors to multidrug resistance (MDR). Reversing the function of drug efflux pumps is a promising approach to overcome MDR by reversing the function of drug efflux pumps. In the life-threatening fungal pathogen Candida albicans, the major facilitator superfamily (MFS) transporter Mdr1p can excrete many structurally unrelated antifungals, leading to multidrug resistance. Here we report a counterintuitive case of reversing MDR in C. albicans by using a natural product berberine to hijack the overexpressed Mdr1p for its own importation. Moreover, we illustrate that the imported berberine accumulates in mitochondria and compromises the mitochondrial function by impairing mitochondrial membrane potential and mitochondrial Complex I. This results in the selective elimination of Mdr1p overexpressed C. albicans cells. Furthermore, we show that berberine treatment can prolong the mean survival time (MST) of mice with blood-borne dissemination of Mdr1p overexpressed multidrug-resistant candidiasis. This study provides a potential direction of novel anti-MDR drug discovery by screening for multidrug efflux pump converters.

The current global demand for novel anti-TB drugs has drawn urgent attention on the discovery of natural product compounds with anti-TB activity. Lots of efforts have emphasized on environmental samples from unexplored or underexplored natural habits and identified numerous rare actinomycete taxa producing structurally diverse bioactive natural products. Herein, we report a survey of the rare actinobacteria diversity in Xinjiang region together with the discovery of anti-TB active natural products from these strains. We have collected 17 soil samples at different sites with different environmental conditions, from which 39 rare actinobacteria were identified by using a selective isolation strategy with 5 media variations. Among those isolated strains, XJ31 was identified as a new Nocardia sp. based on 16S rRNA gene analysis. Through one strain-many compounds (OSMAC) strategy combined with anti-Bacillus Calmette-Guérin bioassay-guided isolation, two groups of compounds were identified. They were twelve siderophores (nocardimicins, 1-12) and two anthraquinones (brasiliquinones, 13 and 14) and ten of them were identified as new compounds. The structures of the purified compounds were elucidated using HR-ESI-MS, 1D NMR, and 2D NMR techniques. The anti-TB bioassays revealed that the two benz[α]anthraquinones have potent activity against BCG (MICs = 25 μM), which can be used as a promising start point for further anti-TB drug development.

The use of antimicrobial growth promoters in chicken farming has been commonly associated with high levels of antimicrobial resistance (AMR) in humans. Most of this work, however, has been focused on intensive large-scale operations. Intensive small-scale farming that regularly uses antibiotics is increasing worldwide and has different exposure pathways compared with large-scale farming, most notably the spatial connection between chickens and households. In these communities, free-ranging backyard chickens (not fed antibiotics) can roam freely, whereas broiler chickens (fed antibiotics) are reared in the same husbandry environment but confined to coops. We conducted an observational field study to better understand the spatial distribution of AMR in communities that conduct small-scale farming in northwestern Ecuador. We analyzed phenotypic resistance of Escherichia coli sampled from humans and backyard chickens to 12 antibiotics in relation to the distance to the nearest small-scale farming operation within their community. We did not find a statistically significant relationship between the distance of a household to small-scale farming and antibiotic-resistant E. coli isolated from chicken or human samples. To help explain this result, we monitored the movement of backyard chickens and found they were on average 17 m (min-max: 0-59 m) from their household at any given time. These backyard chickens on average ranged further than the average distance from any study household to its closest neighbor. This level of connectivity provides a viable mechanism for the spread of antimicrobial-resistant bacteria and genes throughout the community.

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different programs, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Instead of flush-based or physical isolation of hardware resources, we want to achieve isolation of the content in these hardware tables with some lightweight processing using randomization as follows. (1) Content encoding. We propose to use hardware-based thread-private random numbers to encode the contents of the branch predictor tables (both direction and destination histories) which we call XOR-BP. Specifically, the data is encoded by XOR operation with the key before written in the table and decoded after read from the table. Such a mechanism obfuscates the information adding difficulties to cross-process or cross-privilege level analysis and perception. It achieves a similar effect of logical isolation but adds little in terms of space or time overheads. (2) Index encoding. We propose a randomized index mechanism of the branch predictor (Noisy-XOR-BP). Similar to the XOR-BP, another thread-private random number is used together with the branch instruction address as the input to compute the index of the branch predictor. This randomized indexing mechanism disrupts the correspondence between the branch instruction address and the branch predictor entry, thus increases the noise for malicious perception attacks. Our analyses using an FPGA-based RISC-V processor prototype and additional auxiliary simulations suggest that the proposed mechanisms incur a very small performance cost while providing strong protection., Comment: 13 pages, 10 figures, submitted to MICRO 2020

Small-scale farming may have large impacts on the selection and spread of antimicrobial resistance to humans. We conducted an observational study to evaluate antibiotic-resistant Escherichia coli populations from poultry and humans in rural northwestern Esmeraldas, Ecuador. Our study site is a remote region with historically low resistance levels of third-generation antibiotics such cefotaxime (CTX), a clinically relevant antibiotic, in both poultry and humans. Our study revealed 1) high CTX resistance (66.1%) in farmed broiler chickens, 2) an increase in CTX resistance over time in backyard chicken not fed antibiotics (2.3-17.9%), and 3) identical bla CTX-M sequences from human and chicken bacteria, suggesting a spillover event. These findings provide evidence that small-scale meat production operations have direct impacts on the spread and selection of clinically important antibiotics among underdeveloped settings.

Background: The exponential growth of gigantic biological data from various sources, such as protein-protein interaction (PPI), genome sequences scaffolding, Mass spectrometry (MS) molecular networking and metabolic flux, demands an efficient way for better visualization and interpretation beyond the conventional, two-dimensional visualization tools.Results: We developed a 3D Cytoscape Client/Server (3DScapeCS) plugin, which adopted Cytoscape in interpreting different types of data, and UbiGraph for three-dimensional visualization. The extra dimension is useful in accommodating, visualizing, and distinguishing large-scale networks with multiple crossed connections in five case studies.Conclusions: Evaluation on several experimental data using 3DScapeCS and its special features, including multilevel graph layout, time-course data animation, and parallel visualization has proven its usefulness in visualizing complex data and help to make insightful conclusions.

There is an urgent demand for privacy-preserving techniques capable of supporting compute and data intensive (CDI) computing in the era of big data. However, none of existing TEEs can truly support CDI computing tasks, as CDI requires high throughput accelerators like GPU and TPU but TEEs do not offer security protection of such accelerators. This paper present HETEE (Heterogeneous TEE), the first design of TEE capable of strongly protecting heterogeneous computing with unsecure accelerators. HETEE is uniquely constructed to work with today's servers, and does not require any changes for existing commercial CPUs or accelerators. The key idea of our design runs security controller as a stand-alone computing system to dynamically adjust the boundary of between secure and insecure worlds through the PCIe switches, rendering the control of an accelerator to the host OS when it is not needed for secure computing, and shifting it back when it is. The controller is the only trust unit in the system and it runs the custom OS and accelerator runtimes, together with the encryption, authentication and remote attestation components. The host server and other computing systems communicate with controller through an in memory task queue that accommodates the computing tasks offloaded to HETEE, in the form of encrypted and signed code and data. Also, HETEE offers a generic and efficient programming model to the host CPU. We have implemented the HETEE design on a hardware prototype system, and evaluated it with large-scale Neural Networks inference and training tasks. Our evaluations show that HETEE can easily support such secure computing tasks and only incurs a 12.34% throughput overhead for inference and 9.87% overhead for training on average., Comment: 16 pages, 7 figures

Rapid growth of datacenter (DC) scale, urgency of cost control, increasing workload diversity, and huge software investment protection place unprecedented demands on the operating system (OS) efficiency, scalability, performance isolation, and backward-compatibility. The traditional OSes are not built to work with deep-hierarchy software stacks, large numbers of cores, tail latency guarantee, and increasingly rich variety of applications seen in modern DCs, and thus they struggle to meet the demands of such workloads. This paper presents XOS, an application-defined OS for modern DC servers. Our design moves resource management out of the OS kernel, supports customizable kernel subsystems in user space, and enables elastic partitioning of hardware resources. Specifically, XOS leverages modern hardware support for virtualization to move resource management functionality out of the conventional kernel and into user space, which lets applications achieve near bare-metal performance. We implement XOS on top of Linux to provide backward compatibility. XOS speeds up a set of DC workloads by up to 1.6X over our baseline Linux on a 24-core server, and outperforms the state-of-the-art Dune by up to 3.3X in terms of virtual memory management. In addition, XOS demonstrates good scalability and strong performance isolation., Comment: Accepted for publication in IEEE BigData 2018. 10 pages, 6 figures, 3 tables

SummarySmall-scale food animal production is widely practiced around the globe, yet it is often overlooked in terms of the environmental health risks. Evidence suggests that small-scale food animal producers often employ the use of antimicrobials to improve the survival and growth of their animals, and that this practice leads to the development of antimicrobial resistance (AMR) that can potentially spread to humans. The nature of human-animal interactions in small-scale food animal production systems, generally practiced in and around the home, likely augments spillover events of AMR into the community on a scale that is currently unrecognized and deserves greater attention. https://doi.org/10.1289/EHP2116.

Staphylococcus aureus is a serious human pathogen and antibiotic resistant, community-associated strains, such as the methicillin resistant S. aureus (MRSA) strain USA300, continue to spread. To avoid resistance, anti-virulence therapy has been proposed where toxicity is targeted rather than viability. Previously we have shown that norlichexanthone, a small non-reduced tricyclic polyketide produced by fungi and lichens, reduces expression of hla encoding α-hemolysin as well as the regulatory RNAIII of the agr quorum sensing system in S. aureus 8325–4. The aim of the present study was to further characterise the mode of action of norlichexanthone and its effect on biofilm formation. We find that norlichexanthone reduces expression of both hla and RNAIII also in strain USA300. Structurally, norlichexanthone resembles ω-hydroxyemodin that recently was shown to bind the agr two component response regulator, AgrA, which controls expression of RNAIII and the phenol soluble modulins responsible for human neutrophil killing. We show that norlichexanthone reduces S. aureus toxicity towards human neutrophils and interferes directly with AgrA binding to its DNA target. In contrast to ω-hydroxyemodin however, norlichexanthone reduces staphylococcal biofilm formation. Transcriptomic analysis revealed that genes regulated by the SaeRS two-component system are repressed by norlichexanthone when compared to untreated cells, an effect that was mitigated in strain Newman carrying a partially constitutive SaeRS system. Our data show that norlichexanthone treatment reduces expression of key virulence factors in CA-MRSA strain USA300 via AgrA binding and represses biofilm formation

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Multi-drug resistance of pathogenic microorganisms is becoming a serious threat, particularly to immunocompromised populations. The high mortality of systematic fungal infections necessitates novel antifungal drugs and therapies. Unfortunately, with traditional drug discovery approaches, only echinocandins was approved by FDA as a new class of antifungals in the past two decades. Drug efflux is one of the major contributors to multi-drug resistance, the modulator of drug efflux pumps is considered as one of the keys to conquer multi-drug resistance. In this study, we combined structure-based virtual screening and whole-cell based mechanism study, identified a natural product, beauvericin (BEA) as a drug efflux pump modulator, which can reverse the multi-drug resistant phenotype of Candida albicans by specifically blocking the ATP-binding cassette (ABC) transporters; meantime, BEA alone has fungicidal activity in vitro by elevating intracellular calcium and reactive oxygen species (ROS). It was further demonstrated by histopathological study that BEA synergizes with a sub-therapeutic dose of ketoconazole (KTC) and could cure the murine model of disseminated candidiasis. Toxicity evaluation of BEA, including acute toxicity test, Ames test, and hERG (human ether-à-go-go-related gene) test promised that BEA can be harnessed for treatment of candidiasis, especially the candidiasis caused by ABC overexpressed multi-drug resistant C. albicans.

Fluoroquinolone resistance can be conferred through chromosomal mutations or by the acquisition of plasmids carrying genes such as the quinolone resistance gene (qnr). In this study, 3,309 strains of commensal Escherichia coli were isolated in Ecuador from: (i) humans and chickens in a rural northern coastal area (n = 2368, 71.5%) and (ii) chickens from an industrial poultry operation (n = 827, 25%). In addition, 114 fluoroquinolone-resistant strains from patients with urinary tract infections who were treated at three urban hospitals in Quito, Ecuador were analyzed. All of the isolates were subjected to antibiotic susceptibility screening. Fluoroquinolone-resistant isolates (FRIs) were then screened for the presence of qnrB genes. A significantly higher phenotypic resistance to fluoroquinolones was determined in E. coli strains from chickens in both the rural area (22%) and the industrial operation (10%) than in strains isolated from humans in the rural communities (3%). However, the rates of qnrB genes in E. coli isolates from healthy humans in the rural communities (11 of 35 isolates, 31%) was higher than in chickens from either the industrial operations (3 of 81 isolates, 6%) or the rural communities (7 of 251 isolates, 2.8%). The occurrence of qnrB genes in human FRIs obtained from urban hospitals was low (1 of 114 isolates, 0.9%). These results suggested that the qnrB gene is more widely distributed in rural settings, where antibiotic usage is low, than in urban hospitals and industrial poultry operations. The role of qnrB in clinical resistance to fluoroquinolones is thus far unknown. [Int Microbiol 2015; 18(2):85-90]Keywords: Escherichia coli · gene qnrB · quinolone resistance · urban hospitals · industral poultry operations

Big data analytics applications play a significant role in data centers, and hence it has become increasingly important to understand their behaviors in order to further improve the performance of data center computer systems, in which characterizing representative workloads is a key practical problem. In this paper, after investigating three most impor- tant application domains in terms of page views and daily visitors, we chose 11 repre- sentative data analytics workloads and characterized their micro-architectural behaviors by using hardware performance counters, so as to understand the impacts and implications of data analytics workloads on the systems equipped with modern superscalar out-of-order processors. Our study reveals that big data analytics applications themselves share many inherent characteristics, which place them in a different class from traditional workloads and scale-out services. To further understand the characteristics of big data analytics work- loads we performed a correlation analysis of CPI (cycles per instruction) with other micro- architecture level characteristics and an investigation of the big data software stack impacts on application behaviors. Our correlation analysis showed that even though big data ana- lytics workloads own notable pipeline front end stalls, the main factors affecting the CPI performance are long latency data accesses rather than the front end stalls. Our software stack investigation found that the typical big data software stack significantly contributes to the front end stalls and incurs bigger working set. Finally we gave several recommen- dations for architects, programmers and big data system designers with the knowledge acquired from this paper., Comment: arXiv admin note: substantial text overlap with arXiv:1307.8013

Microbes hold the key to life. They hold the secrets to our past (as the descendants of the earliest forms of life) and the prospects for our future (as we mine their genes for solutions to some of the planet's most pressing problems, from global warming to antibiotic resistance). However, the piecemeal approach that has defined efforts to study microbial genetic diversity for over 20 years and in over 30,000 genome projects risks squandering that promise. These efforts have covered less than 20% of the diversity of the cultured archaeal and bacterial species, which represent just 15% of the overall known prokaryotic diversity. Here we call for the funding of a systematic effort to produce a comprehensive genomic catalog of all cultured Bacteria and Archaea by sequencing, where available, the type strain of each species with a validly published name (currently∼11,000). This effort will provide an unprecedented level of coverage of our planet's genetic diversity, allow for the large-scale discovery of novel genes and functions, and lead to an improved understanding of microbial evolution and function in the environment.

Microbes hold the key to life. They hold the secrets to our past (as the descendants of the earliest forms of life) and the prospects for our future (as we mine their genes for solutions to some of the planet's most pressing problems, from global warming to antibiotic resistance). However, the piecemeal approach that has defined efforts to study microbial genetic diversity for over 20 years and in over 30,000 genome projects risks squandering that promise. These efforts have covered less than 20% of the diversity of the cultured archaeal and bacterial species, which represent just 15% of the overall known prokaryotic diversity. Here we call for the funding of a systematic effort to produce a comprehensive genomic catalog of all cultured Bacteria and Archaea by sequencing, where available, the type strain of each species with a validly published name (currently∼11,000). This effort will provide an unprecedented level of coverage of our planet's genetic diversity, allow for the large-scale discovery of novel genes and functions, and lead to an improved understanding of microbial evolution and function in the environment.

Microbes hold the key to life. They hold the secrets to our past (as the descendants of the earliest forms of life) and the prospects for our future (as we mine their genes for solutions to some of the planet's most pressing problems, from global warming to antibiotic resistance). However, the piecemeal approach that has defined efforts to study microbial genetic diversity for over 20 years and in over 30,000 genome projects risks squandering that promise. These efforts have covered less than 20% of the diversity of the cultured archaeal and bacterial species, which represent just 15% of the overall known prokaryotic diversity. Here we call for the funding of a systematic effort to produce a comprehensive genomic catalog of all cultured Bacteria and Archaea by sequencing, where available, the type strain of each species with a validly published name (currently~11,000). This effort will provide an unprecedented level of coverage of our planet's genetic diversity, allow for the large-scale discovery of novel genes and functions, and lead to an improved understanding of microbial evolution and function in the environment.

Background: The exponential growth of gigantic biological data from various sources, such as protein-protein interaction (PPI), genome sequences scaffolding, Mass spectrometry (MS) molecular networking and metabolic flux, demands an efficient way for better visualization and interpretation beyond the conventional, two-dimensional visualization tools.Results: We developed a 3D Cytoscape Client/Server (3DScapeCS) plugin, which adopted Cytoscape in interpreting different types of data, and UbiGraph for three-dimensional visualization. The extra dimension is useful in accommodating, visualizing, and distinguishing large-scale networks with multiple crossed connections in five case studies.Conclusions: Evaluation on several experimental data using 3DScapeCS and its special features, including multilevel graph layout, time-course data animation, and parallel visualization has proven its usefulness in visualizing complex data and help to make insightful conclusions.

A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biological extracts. We demonstrate here that molecular networking, an approach that organizes MS/MS data based on chemical similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with molecular networks requires MS/MS spectra of the natural product mixture along with MS/MS spectra of known standards, synthetic compounds, or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Molecular networking not only dereplicates known molecules from complex mixtures, it also captures related analogues, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based molecular networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 molecules including analogues.

Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we focus on measuring the performance impacts of diverse applications and scalable volumes of data sets on big data systems. For four typical data analysis applications---an important class of big data applications, we find two major results through experiments: first, the data scale has a significant impact on the performance of big data systems, so we must provide scalable volumes of data sets in big data benchmarks. Second, for the four applications, even all of them use the simple algorithms, the performance trends are different with increasing data scales, and hence we must consider not only variety of data sets but also variety of applications in benchmarking big data systems., Comment: 16 pages, 3 figures

As the amount of data explodes rapidly, more and more corporations are using data centers to make effective decisions and gain a competitive edge. Data analysis applications play a significant role in data centers, and hence it has became increasingly important to understand their behaviors in order to further improve the performance of data center computer systems. In this paper, after investigating three most important application domains in terms of page views and daily visitors, we choose eleven representative data analysis workloads and characterize their micro-architectural characteristics by using hardware performance counters, in order to understand the impacts and implications of data analysis workloads on the systems equipped with modern superscalar out-of-order processors. Our study on the workloads reveals that data analysis applications share many inherent characteristics, which place them in a different class from desktop (SPEC CPU2006), HPC (HPCC), and service workloads, including traditional server workloads (SPECweb2005) and scale-out service workloads (four among six benchmarks in CloudSuite), and accordingly we give several recommendations for architecture and system optimizations. On the basis of our workload characterization work, we released a benchmark suite named DCBench for typical datacenter workloads, including data analysis and service workloads, with an open-source license on our project home page on http://prof.ict.ac.cn/DCBench. We hope that DCBench is helpful for performing architecture and small-to-medium scale system researches for datacenter computing., Comment: 11 pages, 12 figures, IISWC2013

The lattice-misfit InN/GaN (0001) interface supports a triangular network of alpha-core 90 degrees partial misfit dislocations. These misfit dislocations provide excellent strain relief. However, in their unreconstructed form the dislocation contains numerous high-energy N dangling bonds, which must be eliminated by reconstructing the dislocation core. Existing single-period (SP) and double-period (DP) dislocation reconstruction models eliminate these dangling bonds via a like-atom dimerization, such as N-N dimers. However, we show that these N-N dimers are unstable for the III-N materials, so an entirely new reconstruction mechanism is needed. A "triple-period" (TP) structural model is developed which eliminates N dangling bonds via the formation of N vacancies instead of N-N dimers. The model contains no N-N (or III-III) bonds, fully bonds all N atoms to four group-Ill neighboring atoms, and satisfies the "electron counting rule" by transferring charge from In dangling bonds to Ga dangling bonds. (c) 2012 Elsevier B.V. All rights reserved.

For the first time, this paper systematically identifies three categories of throughput oriented workloads in data centers: services, data processing applications, and interactive real-time applications, whose targets are to increase the volume of throughput in terms of processed requests or data, or supported maximum number of simultaneous subscribers, respectively, and we coin a new term high volume computing (in short HVC) to describe those workloads and data center computer systems designed for them. We characterize and compare HVC with other computing paradigms, e.g., high throughput computing, warehouse-scale computing, and cloud computing, in terms of levels, workloads, metrics, coupling degree, data scales, and number of jobs or service instances. We also preliminarily report our ongoing work on the metrics and benchmarks for HVC systems, which is the foundation of designing innovative data center computer systems for HVC workloads., Comment: 10 pages

The High-Performance Computing ecosystem consists of a large variety of execution platforms that demonstrate a wide diversity in hardware characteristics such as CPU architecture, memory organization, interconnection network, accelerators, etc. This environment also presents a number of hard boundaries (walls) for applications which limit software development (parallel programming wall), performance (memory wall, communication wall) and viability (power wall). The only way to survive in such a demanding environment is by adaptation. In this paper we discuss how dynamic information collected during the execution of an application can be utilized to adapt the execution context and may lead to performance gains beyond those provided by static information and compile-time adaptation. We consider specialization based on dynamic information like user input, architectural characteristics such as the memory hierarchy organization, and the execution prole of the application as obtained from the execution platform's performance monitoring units. One of the challenges of future execution platforms is to allow the seamless integration of these various kinds of information with information obtained from static analysis (either during ahead-of-time or just-in-time) compilation. We extend the notion of information-driven adaptation and outline the architecture of an infrastructure designed to enable information ow and adaptation throughout the life-cycle of an application.

Extrapulmonary tuberculosis (EPTB) is an important health problem that may cause serious morbidity and diagnostic challenges. We conducted a case-control study involving 5,684, approximately 99% of bacteriologically confirmed TB patients (including 1,925 EPTB cases) diagnosed in Denmark and Greenland during 1992-2007 to gain insight to the role of host factors in EPTB pathogenesis. Among patients from Somalia and Asia, persons 25-44 and 45-64 years of age were more likely to have EPTB than persons 15-24 years of age. In contrast, among persons from Greenland, the two oldest age groups were significantly less likely to have EPTB than the youngest age group. For all the age groups, the odds for having EPTB was significantly higher among patients from Somalia and Asia and significantly lower among the patients from Greenland than among patients from Denmark. Furthermore, the occurrence of specific types of EPTB significantly varied among different age groups or origins.

Mycobacterium tuberculosis may survive for decades in the human body in a state termed latent tuberculosis infection (LTBI). We investigated the occurrence during LTBI of insertion/deletion events in a selected set of mononucleotide simple sequence repeats, DNA sequence changes in four M. tuberculosis genes, and large sequence variations in 4750 M. tuberculosis open reading frames. We studied 13 paired M. tuberculosis clinical isolates, with each pair representing a reactivation of LTBI more than three decades after primary infection. Absence of sequence variations between paired isolates in nearly all investigated loci suggests a low likelihood of bacterial replication during LTBI.

Intracellular 14-3-3 proteins bind to many proteins, via a specific phosphoserine motif, regulating diverse cellular tasks including cell signalling and disease progression. The 14-3-3 isoform is a molecular chaperone, preventing the stressinduced aggregation of target proteins in a manner comparable with that of the unrelated sHsps (small heat-shock proteins). 1H-NMR spectroscopy revealed the presence of a flexible and unstructured C-terminal extension, 12 amino acids in length, which protrudes from the domain core of 14-3-3 and is similar in structure and length to the C-terminal extension of mammalian sHsps. The extension stabilizes 14-3-3, but has no direct role in chaperone action. Lys 49 is an important functional residue within the ligand-binding groove of 14-3-3 with K49E 14-3-3 exhibiting markedly reduced binding to phosphorylated and non-phosphorylated ligands. The R18 peptide binds to the binding groove of 14-3-3 with high affinity and also reduces the interaction of 14-3-3 ligands. However, neither the K49E mutation nor the presence of the R18 peptide affected the chaperone activity of 14-3-3, implying that the C-terminal extension and binding groove of 14-3-3 do not mediate interaction with target proteins during chaperone action. Other region(s) in 14-3-3 are most likely to be involved, i.e. the protein's chaperone and phosphoserine-binding activities are functionally and structurally separated. C The Authors Journal compilation C 2011 Biochemical Society.

Traditional multilevel SRAM-based cache hierarchies, especially in the context of chip multiprocessors (CMPs), present many challenges in area requirements, core--to--cache balance, power consumption, and design complexity. New advancements in technology enable caches to be built from other technologies, such as Embedded DRAM (EDRAM), Magnetic RAM (MRAM), and Phase-change RAM (PRAM), in both 2D chips or 3D stacked chips. Caches fabricated in these technologies offer dramatically different power-performance characteristics when compared with SRAM-based caches, particularly in the areas of access latency, cell density, and overall power consumption. In this article, we propose to take advantage of the best characteristics that each technology has to offer through the use of Hybrid Cache Architecture (HCA) designs.

Metagenomic studies characterize both the composition and diversity of uncultured viral and microbial communities. BLAST-based comparisons have typically been used for such analyses; however, sampling biases, high percentages of unknown sequences, and the use of arbitrary thresholds to find significant similarities can decrease the accuracy and validity of estimates. Here, we present Genome relative Abundance and Average Size (GAAS), a complete software package that provides improved estimates of community composition and average genome length for metagenomes in both textual and graphical formats. GAAS implements a novel methodology to control for sampling bias via length normalization, to adjust for multiple BLAST similarities by similarity weighting, and to select significant similarities using relative alignment lengths. In benchmark tests, the GAAS method was robust to both high percentages of unknown sequences and to variations in metagenomic sequence read lengths. Re-analysis of the Sargasso Sea virome using GAAS indicated that standard methodologies for metagenomic analysis may dramatically underestimate the abundance and importance of organisms with small genomes in environmental systems. Using GAAS, we conducted a meta-analysis of microbial and viral average genome lengths in over 150 metagenomes from four biomes to determine whether genome lengths vary consistently between and within biomes, and between microbial and viral communities from the same environment. Significant differences between biomes and within aquatic sub-biomes (oceans, hypersaline systems, freshwater, and microbialites) suggested that average genome length is a fundamental property of environments driven by factors at the sub-biome level. The behavior of paired viral and microbial metagenomes from the same environment indicated that microbial and viral average genome sizes are independent of each other, but indicative of community responses to stressors and environmental conditions.

T cell antigen receptor (TCR) signaling in CD4+CD8+ double-positive thymocytes determines cell survival and lineage commitment, but the genetic and molecular basis of this process is poorly defined. To address this issue, we used ethylnitrosourea mutagenesis to identify a previously unknown T lineage-specific gene, Themis, which is critical for the completion of positive selection. Themis contains a tandem repeat of a unique globular domain (called 'CABIT' here) that includes a cysteine motif that defines a family of five uncharacterized vertebrate proteins with orthologs in most animal species. Themis-deficient thymocytes showed no substantial impairment in early TCR signaling but did show altered expression of genes involved in the cell cycle and survival before and during positive selection. Our data suggest a unique function for Themis in sustaining positive selection.

Caching techniques have been an efficient mechanism for mitigating the effects of the processor-memory speed gap. Traditional multi-level SRAM-based cache hierarchies, especially in the context of chip multiprocessors (CMPs), present many challenges in area requirements, core-to-cache balance, power consumption, and design complexity. New advancements in technology enable caches to be built from other technologies, such as Embedded DRAM (EDRAM), Magnetic RAM (MRAM), and Phase-change RAM (PRAM), in both 2D chips or 3D stacked chips. Caches fabricated in these technologies offer dramatically different power and performance characteristics when compared with SRAM-based caches, particularly in the areas of access latency, cell density, and overall power consumption. In this paper, we propose to take advantage of the best characteristics that each technology offers, through the use of Hybrid Cache Architecture (HCA) designs. We discuss and evaluate two types of hybrid cache architectures: inter cache Level HCA (LHCA), in which the levels in a cache hierarchy can be made of disparate memory technologies; and intra cache level or cache Region based HCA (RHCA), where a single level of cache can be partitioned into multiple regions, each of a different memory technology. We have studied a number of different HCA architectures and explored the potential of hardware support for intra-cache data movement and power consumption management within HCA caches. Utilizing a full-system simulator that has been validated against real hardware, we demonstrate that an LHCA design can provide a geometric mean 7% IPC improvement over a baseline 3-level SRAM cache design under the same area constraint across a collection of 25 workloads. A more aggressive RHCA-based design provides 12% IPC improvement over the baseline. Finally, a 2-layer 3D cache stack (3DHCA) of high density memory technology within the same chip footprint gives 18% IPC improvement over the baseline. Furthermore, up to 70%

Caches made of non-volatile memory technologies, such as Magnetic RAM (MRAM) and Phase-change RAM (PRAM), offer dramatically different power-performance characteristics when compared with SRAM-based caches, particularly in the areas of static/dynamic power consumption, read and write access latency and cell density. In this paper, we propose to take advantage of the best characteristics that each technology has to offer through the use of read-write aware Hybrid Cache Architecture (RWHCA) designs, where a single level of cache can be partitioned into read and write regions, each of a different memory technology with disparate read and write characteristics. We explore the potential of hardware support for intra-cache data movement within RWHCA caches. Utilizing a full-system simulator that has been validated against real hardware, we demonstrate that a RWHCA design with a conservative setup can provide a geometric mean 55% power reduction and yet 5% IPC improvement over a baseline SRAM cache design across a collection of 30 workloads. Furthermore, a 2-layer 3D cache stack (3DRWHCA) of high density memory technology with the same chip footprint still gives 10% power reduction and boost performance by 16% IPC improvement over the baseline.

For heteroepitaxy of InN on GaN(0001) by molecular-beam epitaxy, the lattice misfit strain is relieved by misfit dislocations (MDs) formed at the interface between InN and GaN. Imaging by scanning tunneling microscopy (STM) of the surfaces of thin InN epifilms reveals line feature parallel to < 11 (2) over bar0 >. Their contrast becomes less apparent for thicker epifilms. From the interline spacing as well as a comparison with transmission electron microscopy studies, it is suggested that they correspond to the MDs beneath the surface. The STM contrast originates from both the surface distortion caused by the local strain at MDs and the electronic states of the defects. (C) 2008 American Institute of Physics.