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Battery-powered mobile devices are typically featured with fast varying workloads and constrained power supply, demanding more efficient run-time power management. In this paper, we propose a DVFS design and simulation framework to explore the design space for DVFS. The framework leverages the existing tool chains and uses several machine learning models to support the architectural simulation and run-time profiling. When using the proposed framework to evaluate different management methods, the proposed deep-reinforcement-learning approach can achieve 5.8-7.3% performance improvement compared with the other alternatives.

We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-person distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mobility level, and recognizes the group structure by applying the multi-dimensional scaling technique on the matrix of distance between pairwise persons within the same group. We first validate the performance of between-person distance esti- mation via extensive experiments in a three-floor campus building and a shopping center, showing that WiDE system outperforms other machine learning based approaches for between-person distance estimation, with the average absolute error (AAE) of 0.69m and 1.14m for the campus building and shopping center, respectively, and the corridor identification accuracy for the campus building is over 99%. In addition, the experiments in the shopping center show that our approach can accurately detect groups, classify group mobility into fine-grained level and recognize the group structure.

Message matching within MPI is an important performance consideration for applications that utilize two-sided semantics. In this work, we present an instrumentation of the CrayMPI library that allows the collection of detailed message-matching statistics as well as an implementation of hashed matching in software. We use this functionality to profile key DOE applications with complex communication patterns to determine under what circumstances an application might benefit from hardware offload capabilities within the NIC to accelerate message matching. We find that there are several applications and libraries that exhibit sufficiently long match list lengths to motivate a Binned Message Matching approach.

Determining driver situational awareness is one of the most difficult tasks in driver performance profiling. Perception, being the first level of driver situational awareness, is perhaps the only objectively quantifiable factor for driver inattention detection. In the context of this paper, driver perception is defined as their ability to understand their operating environment and peripheral contextual information. One of the most reliable means of determining driver perception is through eye tracking and gaze concentration. In this study, a set of carefully chosen in-vehicle gaze targets are used to determine driver gaze concentration on/off the road. The time drivers spend looking at these predefined gaze targets has been used to characterise driver visual behaviour at roundabouts during normal driving. In total, 24 drivers completed a 22 km route, including two 5-exit two-lane roundabouts in urban and suburban traffic. This study shows that on the approach to roundabouts, driver gaze direction and gaze concentration was often diverted away from the direction of travel of the vehicle and for some drivers it took a significant amount of time to return their gaze towards the direction of travel of the vehicle. The study is based on the premise that a driver not looking at key regions in a scene cannot perceive the contents of those regions with sufficient detail for safe driving. It is not possible to determine a driver's level of understanding of a scene, unless the parameters of the scene as well as the driver's response is determined in real time.

Complexity problems are a common type of performance issues, caused by algorithmic inefficiency. Algorithmic profiling aims to automatically attribute execution complexity to an executed code construct. It can identify code constructs in superlinear complexity to facilitate performance optimizations and debugging. However, existing algorithmic profiling techniques suffer from several severe limitations, missing the opportunity to be deployed in production environment and failing to effectively pinpoint root causes for performance failures caused by complexity problems. In this paper, we design a tool, ComAir, which can effectively conduct algorithmic profiling in production environment. We propose several novel instrumentation methods to significantly lower runtime overhead and enable the production-run usage. We also design an effective ranking mechanism to help developers identify root causes of performance failures due to complexity problems. Our experimental results show that ComAir can effectively identify root causes and generate accurate profiling results in production environment, while incurring a negligible runtime overhead

The recent GPU-based clusters that handle deep learning (DL) tasks have the features of GPU device heterogeneity, a variety of deep neural network (DNN) models, and high computational complexity. Thus, the traditional power capping methods for CPU-based clusters or small-scale GPU devices do not apply to the GPU-based clusters handling DL tasks. This paper develops a cooperative distributed GPU power capping (CD-GPC) system for GPU-based clusters, aiming to minimize the training completion time of invoked DL tasks without exceeding the limited power budget. Specifically, we first design the frequency scaling (FS) approach using the online model estimation based on the recursive least square (RLS) method. This approach achieves the accurate tuning for DL task training time and power usage of GPU devices without needing offline profiling. Then, we formulate the proposed FS problem as a Lagrangian dual decomposition-based economic model predictive control (EMPC) problem for large-scale heterogeneous GPU clusters. We conduct both the NVIDIA GPU-based lab-scale real experiments and real job trace-based simulation experiments for performance evaluation. Experimental results validate that the proposed system improves the power capping accuracy to have a mean absolute error

In modern NUMA systems, increasing number of cores lead to a heavy congestion on shared caches and memory controllers. Therefore, in this paper, we propose a method (RCLB) which is capable of reducing the memory congestion with reconcile locality of communication. It prevents the load imbalancing on memory controllers which identifies the data traffic applying through clusters using MPI communication patterns. The proposed method works at the running time of MPI application with a small modification in the profiling technique. We have tested the kernels of NAS parallel benchmarks and found it as state-of-art. The experimental results show that the proposed method has achieved a bandwidth improvement ranges 12.65–23% in memory-based tests (CLB).

Millions of smart contracts have been deployed onto Ethereum for providing various services, which can be invoked through their functions. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters.Moreover, we develop SigRec, a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.9% accuracy within 0.07 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.

Spatial analysis of microbiomes at single cell resolution with high multiplexity and accuracy has remained challenging. Here we present spatial profiling of a microbiome using sequential error-robust fluorescence in situ hybridization (SEER-FISH), a highly multiplexed and accurate imaging method that allows mapping of microbial communities at micron-scale. We show that multiplexity of RNA profiling in microbiomes can be increased significantly by sequential rounds of probe hybridization and dissociation. Combined with error-correction strategies, we demonstrate that SEER-FISH enables accurate taxonomic identification in complex microbial communities. Using microbial communities composed of diverse bacterial taxa isolated from plant rhizospheres, we apply SEER-FISH to quantify the abundance of each taxon and map microbial biogeography on roots. At micron-scale, we identify clustering of microbial cells from multiple species on the rhizoplane. Under treatment of plant metabolites, we find spatial re-organization of microbial colonization along the root and alterations in spatial association among microbial taxa. Taken together, SEER-FISH provides a useful method for profiling the spatial ecology of complex microbial communities in situ.

Profiling side-channel attacks in which an adversary creates a “profile” of a sensitive device and uses such profile to model a target device with similar implementation has received the lion’s share of attention in the recent years. In particular, template attacks are known to be the most powerful profiling side-channel attacks from an information theoretic point of view. When launching such attack, the adversary first builds a model based on the leakage of the profiling (training) device in his disposal which is then exploited in the second phase of the attack (i.e., matching) to extract the key from the target device. Discrepancies between the device used for modeling and the target device affect the attack success. The effect of process variation and temperature misalignment between the profiling and target devices in the template attack’s success has been studied extensively in literature, while the impact of device aging on the template attack’s success is yet to be investigated thoroughly. This paper moves one step forward and studies the impact of device aging, mainly Bias temperature Instability (BTI) and Hot Carrier Injection (HCI), in the devices that have been protected against power analysis attacks via dual rail logics. In particular, we focus on the Wave Dynamic Differential Logic (WDDL) circuits, and via extensive transistor-level simulations we will show how device aging misalignments between the profiling and target devices can hinder template attacks for both unprotected and WDDL protected counterparts. We mounted several attacks on the PRESENT cipher, with and without WDDL protection, at different temperatures and aging times. Our results shows that the attack is more difficult if there is an aging-duration mismatch between the training and target devices, and the attack-efficiency decrease is especially significant for mismatches of few weeks.

The rapid development and deployment of IoT devices in modern networks and Industry 4.0 have attracted substantial interest from cybersecurity researchers. In this study, we propose a software-defined framework that improves network intrusion detection systems by using manufacturer usage description (MUD) to enhance the behavioral monitoring in IoT networks. We aim to explore whether industrial IoT devices typically serve a common role in cyber-physical systems, and their communications exhibit predictable patterns that can be defined in MUD profile(s) formally and succinctly. We design a framework that utilizes the concept of digital twins and software-defined networking to improve the security of Industrial IoT environments. The MUD data are profiled, and the actions are evaluated on the network digital twin before they are used in the physical network. The behavioral profiling system is updated in real time, thereby improving the overall system security and compliance to policies in the IoT deployment. Evaluation results show that our solution outperforms existing approaches substantially in terms of attack detection accuracy, predicting security incidents, response time, and resource usage.

Reducing tail latency becomes increasingly important to improve user-perceived service experience. User-facing latency-sensitive cloud applications typically contain multiple interactive tiers with complex interaction patterns. However, such interactions between VMs in different tiers are often neglected in previous VM consolidation methods, resulting in poor application performance. In this paper, we propose a novel profiling-based consolidation methodology to satisfy tail latency requirements while reducing the number of used physical machines. To achieve such a goal, we first perform large-scale profiling experiments under various consolidation settings in a KVM virtualized private cluster to establish the empirical performance values. We consider two key factors that affect the tail latency of multi-tier workloads: interference with co-located VMs and interaction between tiers. We model the consolidation of multi-tier workloads as an optimization problem with different objectives and constraints, and derive the consolidation schedule. We implement and evaluate the proposed models, as well as comparing with other methods (i.e., without profiling or without considering interaction influence). Extensive experimental results show that the proposed method is able to reduce up to 5X tail latency, compared with the method without profiling and up to 1.3X tail latency, compared with the method without considering the interaction influence between different tiers.

Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, frozen-/thawed-layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow depth and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel distributed temperature profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of ±0.1 ∘C and enables improving it to ±0.015 ∘C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 s time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow depth and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

The vast majority of high-performance embedded systems implement multi-level CPU cache hierarchies. But the exact behavior of these CPU caches has historically been opaque to system designers. Absent expensive hardware debuggers, an understanding of cache makeup remains tenuous at best. This enduring opacity further obscures the complex interplay among applications and OS-level components, particularly as they compete for the allocation of cache resources. Notwithstanding the relegation of cache comprehension to proxies such as static cache analysis, performance counter-based profiling, and cache hierarchy simulations, the underpinnings of cache structure and evolution continue to elude software-centric solutions. In this paper, we explore a novel method of studying cache contents and their evolution via snapshotting. Our method complements extant approaches for cache profiling to better formulate, validate, and refine hypotheses on the behavior of modern caches. We leverage cache introspection interfaces provided by vendors to perform live cache inspections without the need for external hardware. We present CacheFlow, a proof-of-concept Linux kernel module which snapshots cache contents on an NVIDIA Tegra TX1 SoC (system on chip).

Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces, compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

Motivation High-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity are typically achieved through unsupervised clustering, which crucially relies on a similarity metric. Results We here propose the use of Optimal Transport (OT) as a cell–cell similarity metric for single-cell omics data. OT defines distances to compare high-dimensional data represented as probability distributions. To speed up computations and cope with the high dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over 13 independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters. OT is found to improve cell–cell similarity inference and cell clustering in all simulated and real scRNA-seq data, as well as in scATAC-seq and single-cell DNA methylation data. Availability and implementation All our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics. Supplementary information Supplementary data are available at Bioinformatics online.

Nowadays, GPU accelerators are commonly used to speed up general-purpose computing tasks on a variety of hardware. However, due to the diversity of GPU architectures and processed data, optimization of codes for a particular type of hardware and specific data characteristics can be extremely challenging. The autotuning of performance-relevant source-code parameters allows for automatic optimization of applications and keeps their performance portable. Although the autotuning process typically results in code speed-up, searching the tuning space can bring unacceptable overhead if (i) the tuning space is vast and full of poorly-performing implementations, or (ii) the autotuning process has to be repeated frequently because of changes in processed data or migration to different hardware. In this paper, we introduce a novel method for searching generic tuning spaces. The tuning spaces can contain tuning parameters changing any user-defined property of the source code. The method takes advantage of collecting hardware performance counters (also known as profiling counters) during empirical tuning. Those counters are used to navigate the searching process towards faster implementations. The method requires the tuning space to be sampled on any GPU. It builds a problem-specific model, which can be used during autotuning on various, even previously unseen inputs or GPUs. Using a set of five benchmarks, we experimentally demonstrate that our method can speed up autotuning when an application needs to be ported to different hardware or when it needs to process data with different characteristics. We also compared our method to state of the art and show that our method is superior in terms of the number of searching steps and typically outperforms other searches in terms of convergence time.

In this paper, a solution to effective energy consumption monitoring of fast-response energy systems in industrial environments was proposed, designed, and developed. Moreover, in this research, production systems are characterized as nonlinear dynamic systems, with the hypothesis that the identification and introduction of nonlinear members (variables) can have a significant impact on improving system performance by providing clear insight and realistic representation of system behavior due to a series of nonlinear activities that stimulate the system state changes, which can be spotted through the manner and intensity of energy use in the observed system. The research is oriented towards achieving favorable conditions to deploy dynamic energy management systems by means of the Internet of Things and Big Data, as highly prominent concepts of Industry 4.0 technologies into scientifically-driven industrial practice. The motivation behind this is driven by the transition that this highly digital modern age brought upon us, in which energy management systems could be treated as a continual, dynamic process instead of remaining characterized as static with periodical system audits. In addition, a segmented system architecture of the proposed solution was described in detail, while initial experimental results justified the given hypothesis. The generated results indicated that the process of energy consumption quantification, not only ensures reliable, accurate, and real-time information but opens the door towards system behavior profiling, predictive maintenance, event forensics, data-driven prognostics, etc. Lastly, the points of future investigations were indicated as well.

Since early 2012, the cross-track infrared sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. In this study, the CrIS sensor data record (SDR) data are improved for climate applications with its fine-tuning of calibration coefficients in an NOAA reprocessing project. A specific software system was developed to reprocess the CrIS SDR. This software system was updated with a new calibration algorithm, nonlinearity, and geolocation to improve the SDR data quality and long-term consistency. The calibration coefficients are refined with the latest updates, which were used to calibrate the latest operational SDR products and replace those in the engineering packet (EP) in the raw data record (RDR) data stream. The resampling wavelength was updated based on the metrology laser wavelength and resulted in zero sampling error in the spectral calibration. All the historical SDRs (from February 2012 to March 2017) were generated with the same calibration coefficients and same version of the processing software system, resulting in improved accuracy and stability in terms of spectral and radiometric calibration during the CrIS lifetime mission. The quality of the reprocessed CrIS SDR data at nominal spectral resolution (NSR) is assessed in terms of its radiometric and spectral calibration. Comparisons against the operational SDR data are carried out to demonstrate the improved long-term stability of the reprocessed CrIS SDR data. Overall radiometric biases are found to be small and highly stable over the instrument mission, the FOV-to-FOV differences are less than ~10 mK, and much better than that from the operational SDR data. It is shown that the CrIS metrology laser wavelength varies within 4 ppm as measured by the neon calibration system. The reprocessed SDR data have spectral errors less than 0.5 ppm, which is much better than the operational SDR data with about 4 ppm. This baseline version of the reprocessed SNPP CrIS SDR data is suitable for long-term climate monitoring and model assessments and can provide an infrared reference observation to assess other narrow- or broadband infrared instruments' calibration accuracy.

The wheel re-profiling is an important part of railway wheelset maintenance. Researchers and railway operators have been very concerned about how to minimize the loss of time during wheel re-profiling without decreasing safety. Avoiding wheelset disassembly means considerable time savings, while reducing wheel damage during operation. Underfloor wheel lathes are the most appropriate tool to achieve this double objective, and therefore the most used nowadays. Multi-cut tool lathes have the disadvantage of being extremely expensive. On the other hand, with single tool lathes, re-profiling is not smooth or safe enough when current convex profile support rollers are used. It is well known by the companies that during reprofiling the wheel suffers impacts/damaged. In this article, a methodology to optimize the profile of the support rollers used in underfloor single tool lathes for railway wheel re-profiling is proposed. This novel profile design will minimize damage and increase the safety of such lathes, since it proposes a greater smoothness in the process. Simulations of re-profiling process have been carried out by the finite element method showing that the designed roller profile reduces drastically the impact/damage during the operation. The impact generated between the re-profiling wheel and the rollers is avoided. Profile-optimized support rollers have been used in a real underfloor wheel lathe, showing good results. This work was financially supported by the Spanish Government through the MCYT project 395 “RETOS2015: sistema de monitorización integral de conjuntos mecánicos críticos para la mejora del mantenimiento en el transporte-maqstatus.”

The growing number and diversity of applications make malware detection and app recommendation for users more challenging. In this work, we design a framework APPDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorization and app version detection, etc. We propose to conduct a function-call-graph-based app profiling scheme based on a comprehensive and deep understanding of an app's behaviors. We design a graph-encoding method to convert a large function call graph to a 64-dimensional fixed length vector to achieve robust app profiling. Our extensive evaluations on 86,332 apps demonstrate that our approach performs app profiling with high accuracy and low computation cost: it takes about 46.5 seconds for one app to extract its function call graph; 0.68 seconds to encode a function call graph; it classifies all 4,024 (benign/malware) apps in around 5.06 seconds with accuracy about 93.07%; it classifies all 570 malicious apps' family (21 families in total) in around 0.83 seconds with accuracy 82.3%; it classifies 9,730 apps' functionality into 2 categories with accuracy 88.1%.

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this article we propose Horus: an interference-aware and prediction-based resource manager for DL systems. Horus proactively predicts GPU utilization of heterogeneous DL jobs extrapolated from the DL model’s computation graph features, removing the need for online profiling and isolated reserved GPUs. Through micro-benchmarks and job co-location combinations across heterogeneous GPU hardware, we identify GPU utilization as a general proxy metric to determine good placement decisions, in contrast to current approaches which reserve isolated GPUs to perform online profiling and directly measure GPU utilization for each unique submitted job. Our approach promotes high resource utilization and makespan reduction; via real-world experimentation and large-scale trace driven simulation, we demonstrate that Horus outperforms other DL resource managers by up to 61.5 percent for GPU resource utilization, 23.7–30.7 percent for makespan reduction and 68.3 percent in job wait time reduction.

BACKGROUND: Anthocyanins are one of the key factors contributing to the quality and biological activities in various berries. Particular attention was devoted to Vaccinium L. berries, because of being commonly consumed in daily life and providing a large potential for the development of new pharmaceutical applications. OBJECTIVE: The present work aimed at establishing a novel UPLC-PDA method for profiling anthocyanins in berries and evaluating their distribution in cultivated lingonberries. METHODS: The method was developed and validated using bilberries (Vaccinium myrtillus L.), cranberries (Vaccinium macrocarpon Ait.), and lingonberries (Vaccinium vitis-idaea L.) matrices. Anthocyanins content variation was analyzed among 7 different cultivars and 1 infraspecific taxon of lingonberries. RESULTS: The proposed method ensured the separation of 20 compounds, including major and minor anthocyanins and their aglycones, in bilberries, 15 in cranberries, and 9 in lingonberries, during 12 min analysis. Bilberry matrix was distinguished by the most complex profile and the presence of delphinidin and petunidin, which were not identified either in cranberries or lingonberries. Cranberry anthocyanins consisted mainly of the 3-galactosides of cyanidin and peonidin, whereas lingonberry anthocyanins included cyanidin and its glycosides with just traces of other anthocyanins. Obtained anthocyanins fingerprinting results have guidance function in practice and demonstrate valuable chemophenetic information for studied Vaccinium berries. All determined method validation values were considered to be acceptable. Variation analysis of anthocyanins levels among cultivated lingonberries suggested genetic diversity and because of the highest anthocyanins content—the superiority of Russian cultivars (‘Kostromička’ and ‘Rubin’). To the best of our knowledge, this is the first comprehensive report on the anthocyanins of certain lingonberry cultivars. CONCLUSIONS: This study resulted in the rapid, simple, and validated method, which was shown to be applicable and convenient for routine analysis and authentication of Vaccinium berry samples.

Background: Vildagliptin is a drug for the treatment of diabetes. DPP-IV inhibitor represents a new class of oral antihyperglycemic agents to treat patients with type 2 diabetes. Several RP-HPLC methods have been reported to determine Vildagliptin alone. However, it has been noted that there are no available stability-indicating methods in pharmacopeias (USP/BP/EP/JP) nor in the available literature to quantify known and unknown impurity patterns for vildagliptin in vildagliptin tablets. Objective: The aim of this study is to develop a new single, sensitive, robust and specific gradient RP-HPLC method to quantify known and unknown impurities and degradants of Vildagliptin in Vildagliptin tablets. Methods: Chromatographic separation has been accomplished on the Hypersil ODS column (250 x 4.6) mm, 5 μm with a mobile phase consisting of a mixture of Perchloric acid Buffer, methanol, acetonitrile and Triethylamine delivered at a flow rate of 1.0 mL minute-1 and the detection wavelength 210 nm. The developed method was validated as per ICH guidelines. Results: Vildagliptin was found degraded significantly under oxidative and alkaline stress conditions. The degradation products were well resolved from Vildagliptin and its impurities. An analytical method was found linear, accurate and precise from LOQ (Limit of Quantification) level to 150% of impurity specification limit (0.5%). Conclusion: The method found sensitive, rapid and accurate quantification of known, unknown impurities and degradants. The peak purity results confirmed that the Vildagliptin peak was homogeneous and pure in all stress samples, thus proving the stability-indicating nature of the method.

We introduce a simple figure-of-merit (foM) to schedule task executions in frame-based CPS workloads. The objective is to more effectively harvest slack released by early task completions. The foM is derived based on task execution time probability density functions (obtained by profiling). A nominal voltage/frequency is assigned to each task based on its activity factor; the aim is to keep power consumption reasonably steady. Simulation results show that this approach lengthens battery lifetime and reduces thermally-induced processor aging.

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

Highlights Extracellular vesicle (EV) multiplexing involves chemical, physical spatial, biological, or nanoparticle-based strategies.Multiplexing in EV biomarker development includes high-throughput screening as well as point-of-care testing platforms which to date have been applied mainly to EV surface proteins or internal cargo miRNAs.Multiplexed measurements at single-EV resolution are likely to revolutionize the applicability of EV analytes as biomarkers in complex and heterogeneous diseases., Extracellular vesicles (EVs) are cell-derived membranous particles that play a crucial role in molecular trafficking, intercellular transport and the egress of unwanted proteins. They have been implicated in many diseases including cancer and neurodegeneration. EVs are detected in all bodily fluids, and their protein and nucleic acid content offers a means of assessing the status of the cells from which they originated. As such, they provide opportunities in biomarker discovery for diagnosis, prognosis or the stratification of diseases as well as an objective monitoring of therapies. The simultaneous assaying of multiple EV-derived markers will be required for an impactful practical application, and multiplexing platforms have evolved with the potential to achieve this. Herein, we provide a comprehensive overview of the currently available multiplexing platforms for EV analysis, with a primary focus on miniaturized and integrated devices that offer potential step changes in analytical power, throughput and consistency.

Background : Allergic rhinitis (AR) is one of the most common medical conditions in the westernized world. Recent data suggest AR to be far more immunologically complex than the archetypical allergic Th2-driven eosinophilic inflammation and new methodological approaches are needed to decode this complexity. Methods : This study explores a novel histology-based analysis of circulating leukocytes for detailed profiling of immune cells using routine clinical blood samples. In brief, leukocytes were purified with minimal ex-vivo artefacts, embedded into agarose-paraffin pellets and sectioned for cutting-edge immunohistochemistry-based immune cell profiling. Blood leukocyte mapping was performed in 16 patients with seasonal AR outside and during the birch pollen season. Results : Our methodological feasibility test confirmed that the > 5000 cross sectioned leukocytes typically present in a pellet section had well preserved morphology and cell marker epitopes, allowing for robust quantitative analysis of immune-stained slides. Blood leukocyte samples collected during the allergen season had statistically higher levels of markers for eosinophils, neutrophils, monocytes and CD8 lymphocytes compared to the off-season baseline. No change was observed for CD20 B-lymphocytes, total CD3 T cells and basophils. Subclassification of CD4+ T-helper cells demonstrated a parallel and significant expansion of Th2 and Th17 cells during the pollen season, while Th1 cells remained unchanged. Whereas absolute basophils numbers were unaltered, a significant increase of the basophil markers GATA2 and CPA3 was observed during the pollen season. Conclusions: Apart from representing a positive method feasibility validation, our study provides further evidence of complex and parallel Th2 and Th17 immune signatures in seasonal AR. Our data also forward GATA2 and basophil CPA3 as potential biomarkers for ongoing allergic inflammation. It is thus proposed that the present histology-based approach, with its broad applicability, represents a powerful tool for decoding systemic immune alterations and guide novel biomarker strategies for improved personalized medication.

RP-HPLC method was developed for the estimation of Lisinopril and Hydrochlorothiazide in tablet dosage form with the help of Quality by Design (QbD) approaches. In this method concentration of each drug was obtained by using the absorptivity values calculated for drug wavelength 226.0 nm and solving the equation. The RP-HPLC method was performed C18-(100mm x 4.6 mm,)2.5 μm particle size in gradient mode, and the sample was analysed using methanol 45.0 ml and 55.0 ml (pH 3.3 0.05% OPA with TEA) as a mobile phase at a flow rate of 0.8 ml/min and detection at nm. By the retention time for Lisinopril and Hydrochlorothiazide found 3.39 and 4.59 min respectively. Validation related the method is specific, rapid, accurate, precise, reliable, and reproducible. Calibration plots by both HPLC were linear over the 5-25 and 12.5-62.5 μg/ml for Lisinopril and Hydrochlorothiazide respectively, and recoveries from tablet dosage form were between 99.02 and 100.00 %. The method can be used for routine of the quality control in pharmaceuticals. The degradation profiling of Lisinopril and Hydrochlorothiazide were also carried out.

Salvia sclarea L. or clary sage is cultivated worldwide in temperate and sub-tropical climates, as an ornamental and essential oil (EO) bearing plant. EO is obtained from fresh spikes in full flowering stage and is recognized as an important commercial product for food, beverage and cosmetic industries. This study investigated the EO composition of S. sclarea grown in Serbia (Southeast Europe) obtained by two different methods, steam (SD) and hydrodistillation (HD). GC-MS analysis identified oxygenated monoterpenes as the main class of compounds for all EOs (between 81.8% and 88.2% depending on the distillation process). The most abundant oxygenated monoterpenes were linalyl acetate and linalool. In addition, in vitro antimicrobial (modified resazurin microtitre-plate assay) and antioxidant activities (DPPH· assay) and total polyphenol content of obtained EOs were also evaluated. According to the assay used for the evaluation of the antibacterial activity, Gram-negative bacteria were more sensitive to S. sclarea EO in comparison to Gram-positive bacteria. EOs exhibited low antioxidant capacity, below 3% neutralized DPPH· radicals, reaching up to approximately 400 µg AAE mL-1. This study also investigated a possibility for predicting retention indices (RIs) of compounds isolated from EOs. In total, 78 experimentally obtained RIs were applied to construct the prediction model. The quantitative structure-chromatographic retention relationship (QSRR) model was used to anticipate the experimentally obtained RIs. Five molecular descriptors were selected by factor analysis and genetic algorithm to predict RIs. The obtained accuracy of the QSRR model reached r2 = .912, which showed that these models might be applied for predicting retention indices. This is the accepted peer-reviewed version of the published article [https://doi.org/10.1002/ffj.3684]. Due to publisher`s embargo it will be available after 2 November 2022.

Cells are inherently heterogeneous to achieve a diverse spectrum of biological functions. To understand the underlying protein machinery that achieves these fascinating functions, it is important to develop advanced analytical methods that can profile proteins in their native environment, as protein expression, aggregation, degradation, and regulation define both normal physiology as well as pathogenicity. Genome and transcriptome sequencing have seen major advances at the single-cell levels, but comprehensive proteomic profiling is still challenging. The conventional proteomic methods, such as enzyme-linked immunosorbent assay (ELISA), western blot, and protein chips, can characterize biomarkers of interest. Still, these ensemble techniques are unsuitable for single-cell studies. Increasing evidence has shown the significance of in situ, sensitive, quantitative, and multiplexed profiling of biomarkers in single cells for diagnosis and treatment guidance. Here, we review the recent development of advanced imaging and spectroscopy techniques, including mass cytometry, immunofluorescence, and surface-enhanced Raman spectrometry (SERS) for single-cell proteomic imaging. We also provide our view on the challenges and the outlook.

Advances in single-cell level profiling of the proteome require quantitative and versatile platforms, especially for rare cell analyses such as circulating tumor cell (CTC) profiling. Here we demonstrate an integrated microfluidic chip that uses magnetic nanoparticles to capture single tumor cells with high efficiency, permits on-chip incubation, and facilitates in situ cell-surface protein expression analysis. Combined with phage-based barcoding and next-generation sequencing technology, we were able to monitor changes in the expression of multiple surface markers stimulated in response to CTC adherence. Interestingly, we found fluctuations in the expression of Frizzled2 (FZD2) that reflected the microenvironment of the single cells. This platform has a high potential for in-depth screening of multiple surface antigens simultaneously in rare cells with single-cell resolution, which will provide further insights regarding biological heterogeneity and human disease.

Sequencing-based RNA structure probing can generate transcriptome-wide profiles of RNA secondary structures. Sufficient structural coverage is needed to obtain unbiased insights about RNA structures and functions, yet probing methods often yield uneven coverage, with missing structural scores across many transcripts. To overcome this barrier, we developed StructureImpute, a deep learning framework inspired by depth completion from computer vision that integrates an RNA sequence with available RNA structural information of neighbouring nucleotides to infer missing structure scores. We demonstrate the strong imputation performance of StructureImpute, with accuracy much superior to predictions based on RNA sequence alone. We also show that StructureImpute reliably reconstructs RNA structural patterns at biologically impactful RNA regulation regions, including protein-binding and RNA-modification sites. Strikingly, StructureImpute can use transfer learning to apply a model trained on one dataset to accurately infer missing structural scores in other datasets, even if they were generated with different technologies (for example, icSHAPE and DMS-seq). RNA structure profiling methods suffer from missing values in RNA structurome data. Inspired by a computer vision approach, Gong and colleagues develop a deep learning method that imputes missing RNA structure scores and increases the structural coverage of the transcriptome.

Objective: The aim of the research work to develop a simple, sensitive, rugged, robust and specific novel gradient stability indicating reverse phase HPLC method for quantitative determination of known and unknown impurities profiling of Carvedilol pharmaceutical dosage forms (Tablets). Methods: Chromatographic separation has been achieved on an Inertsil ODS 3V column (150 mm x 4.6 mm, 5μm) with mobile phase consisting Mobile phase-A (Water, Acetonitrile and Trifluroacetic acid in the ratio of 80:20:0.1 v/v/v respectively and pH adjusted to 2.0 with dilute potassium hydroxide solution) and Mobile phase-B (Water and acetonitrile in the ratio of 100:900 v/v respectively) delivered at flow rate of 1.0 ml min-1 and the detection wavelength 240 nm. The column compartment temperature maintained at 40 °C. Results: Resolution between Carvedilol and its impurities has been achieved greater than 1.5. The developed method was validated as per ICH guidelines. Analytical method found Precise, Linear, accurate, specific, rugged and robust. Conclusion: Developed and validated novel analytical method can be used to for impurity profile analysis of Carvedilol Pharmaceutical dosage form (Tablets).

Spectroscopic profiling data (e.g., Raman spectroscopy and mass spectroscopy), combined with machine learning, have provided a data-driven approach for discriminative tasks. In these tasks, researchers often start with simple classification models. If one model doesn’t work, they will try more sophisticated models. If all models fail, the researchers will deem the data set as “inseparable.“ This “trial-and-error” practice reveals a fundamental question: does the dataset possess the necessary statistical power for the current discriminative task? This “classifiability analysis” is an implicit and often neglected step in the data-driven pipeline. This paper aims to design a unified methodological framework for classifiability analysis. In this framework, a meta-learner model combines diversified atom metrics (e.g., Bayes error rate / irreducible error, classification accuracy, information gain / mutual information) into one unified metric (d). We have successfully used the proposed framework to analyze a spectroscopic profiling dataset to discriminate vintage liquors of different ages. A significant difference (d = 1.447. d > 0.8 indicates a significant difference) between 5-year and 16-year liquors.

With the volume growth of delivery business, terminal distribution plays a more and more important role in logistics as it faces consumers directly. User Profiling as an important tool to realize user-centric interaction design can provide more accurate information for terminal distribution. By user profiling, the design team can better understand and satisfy users and their demands for the product and service. This paper studies the problem of terminal delivery route planning considering user logistic profiles. It mainly generates user profiles from two aspects: consumers’ preference for self-pickup services and consumers’ complaint tendencies. Based on the results of user profiles, an Adaptive Large Adjacent Search algorithm is established to design the delivery route of terminal distribution and determine the appropriate delivery strategy to reduce delivery costs and improve customer satisfaction.

Made available in DSpace on 2022-04-28T19:46:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Image registration is a commonly task in medical image analysis. Therefore, a significant number of algorithms have been developed to perform rigid and non-rigid image registration. Particularly, the free-form deformation algorithm is frequently used to carry out non-rigid registration task; however, it is a computationally very intensive algorithm. In this work, we describe an approach based on profiling data to identify potential parts of this algorithm for which parallel implementations can be developed. The proposed approach assesses the efficient of the algorithm by applying performance analysis techniques commonly available in traditional computer operating systems. Hence, this article provides guidelines to support researchers working on medical image processing and analysis to achieve real-time non-rigid image registration applications using common computing systems. According to our experimental findings, significant speedups can be accomplished by parallelizing sequential snippets, i.e., code regions that are executed more than once. For the selected costly functions previously identified in the studied free-form deformation algorithm, the developed parallelization decreased the runtime by up to seven times relatively to the related single thread based implementation. The implementations were developed based on the Open Multi-Processing application programming interface. In conclusion, this study confirms that based on the call graph visualization and detected performance bottlenecks, one can easily find and evaluate snippets which are potential optimization targets in addition to throughput in memory accesses. CNPq National Scientific and Technological Development Council Research Group PIXEL - UNEMAT Programa Doutoral em Engenharia Informática Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Faculdade de Engenharia Universidade do Porto Departamento de Ciências da Computação Faculdade de Ciências Universidade Estadual Paulista-UNESP Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Departamento de Engenharia Mecânica Faculdade de Engenharia Universidade do Porto Departamento de Ciências da Computação Faculdade de Ciências Universidade Estadual Paulista-UNESP

Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

In this article we present our results from the SC19 Student Cluster Competition Reproducibility Challenge. The challenge entails reproducing the article entitled “Computing Planetary Interior Normal Modes with A Highly Parallel Polynomial Filtering Eigensolver” presented at SC’18, which proposes a parallel polynomial filtered Lanczos algorithm to directly calculate the planetary normal modes of heterogeneous planets. The proposed algorithm showed excellent performance with relatively low memory consumption and high parallel efficiency. In this work, we reproduce the scaling tests in that article on a cluster using Intel Cascade Lake architecture and use the proposed algorithm to illustrate specific normal modes of Mars. We compare the results obtained on our cluster with those in the original article. We also design a new metric to better analyze the results. In addition, we use the profiling tool Intel VTune Amplifier to explain our discoveries. Our results demonstrate that the given models show great scalability, which is similar to the original article. The required normal modes of Mars are also successfully calculated and visualized.

Metabolite profiling is an indispensable part of drug discovery and development, enabling a comprehensive understanding of the drug's metabolic behavior. Liquid chromatography-mass spectrometry facilitates metabolite profiling by reducing sample complexity and providing high sensitivity. This review discusses the in vivo metabolite profiling involving LC-MS/MS and the utilization of QTOF, QQQ mass analyzers with a particular emphasis on a mass filter. Further, a summary of sample extraction procedures in biological matrices such as plasma, urine, feces, serum and hair as in vivo samples are outlined. toward the end, we present 15 case studies in biological matrices and their LC-MS/MS conditions to understand the metabolic disposition.

A series of flight experiments have demonstrated a novel approach to measuring path-resolved optical turbulence quantities, such as Cn2, along an air-to-ground slant path. This paper describes the ...

For new developments of manufacturing processes in which cooling lubricants are used, the design of cutting tools plays an important role in achieving effective coolant flow behaviour. In this work, new bottle boring systems for profiling and finishing the ground and the wall of a bore, as well as a novel bottle boring system for the machining of non-circular bore profiles are simulative investigated, because both systems are too complex and costly to carry out experimental studies in the design stage. Since no established measuring equipment can be used due to the inaccessibility, a detailed flow analysis is carried out in this study using computational fluid dynamics (CFD) simulation. Since the two systems consist of numerous components with very complex geometries, a multi-body system has to be processed in order to create the respective fluid models. Subsequently, the fluid models could be generated and a meshing strategy adapted to the geometries and physical properties selected. The shear stress transport (k-ω-SST) turbulence model, which combines the advantages of wall-bounded and free-shear layer flow, was used to resolve the flow areas. The results show that both bottle boring system prototypes have an inadequate coolant supply and the importance of CFD usage in the development process in order to save development costs and to exploit the improvement potential for tool and process is emphasized. Based on the multi-body modelling the CFD simulation model, which considered the chip formation and the different engagement positions of the tool systems, are to be taken into account in further research work and corresponding optimizations are to be carried out.

As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed. In this paper, we present an integrative database of quantitative PTMs in plants named qPTMplants (http://qptmplants.omicsbio.info), which hosts 1 242 365 experimentally identified PTM events for 429 821 nonredundant sites on 123 551 proteins under 583 conditions for 23 PTM types in 43 plant species from 293 published studies, with 620 509 quantification events for 136 700 PTM sites on 55 361 proteins under 354 conditions. Moreover, the experimental details, such as conditions, samples, instruments and methods, were manually curated, while a variety of annotations, including the sequence and structural characteristics, were integrated into qPTMplants. Then, various search and browse functions were implemented to access the qPTMplants data in a user-friendly manner. Overall, we anticipate that the qPTMplants database will be a valuable resource for further research on PTMs in plants.

Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P(t) = W′/t + CP (W′—work capacity above CP; t—time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.

Genomic experiments analyzing human papillomaviruses (HPVs) require a carefully selected list of sequences as a reference database to map millions of reads. The available sources, such as the Papillomavirus Episteme (PaVE), are organized based on variations in the L1 gene rather than the whole HPV sequence. Moreover, the PaVE process uses complex multiple sequence alignments containing hundreds or thousands of sequences. These issues complicate the generation of a reference database for genomics, leading to the generation of per-analysis-defined databases. Here, we propose a de novo strategy considering all HPV sequences reported in the NCBI database to define a subset of highly representative HPV sequences. The strategy is based on oligonucleotide frequency profiling of the whole sequence followed by hierarchical clustering. Using data from HPV capture experiments, we demonstrate that this strategy selects suitable sequences as a reference database to map most mappable reads unambiguously. We provide some recommendations to improve HPV mapping. The generated .fasta files can be accessed at https://github.com/vtrevino/HPV-Ref-Genomes .