Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Organic Anion Transporting Polypeptide Uptake in HEK293 Overexpressing Cells in the Presence and Absence of Human Plasma

Generating accurate in vitro data is crucial for in vitro to in vivo extrapolation and pharmacokinetic predictions. The use of human embryonic kidney (HEK) 293 cells overexpressing organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 in protein-free buffer and 100% human plasma incubations was explored for the uptake of four OATP substrates: pravastatin, rosuvastatin, repaglinide, and pitavastatin. Differences were observed for each parameter [unbound Michaelis constant (Km,u), Vmax, intrinsic clearance (CLint), and unbound passive diffusion Pdif,u] obtained from the buffer and plasma incubations in both cells, and the fold differences were greatest for the highly protein bound compounds. The fold change in Km,u values ranged from 1.91 to 619, and the fold change in Vmax values ranged from 1.22 to 97.4. As a result, in both cells, the CLint values generated in the plasma incubations were higher by 0.762- to 31.7-fold than the values generated in the protein-free buffer. The passive diffusion was also higher in the plasma incubations for all four compounds, with a fold difference range of 1.73–23.4. These shifts in the presence and absence of human plasma suggest that plasma proteins may play a role in both the active uptake and passive diffusion processes. The results also support the idea of a transporter-induced protein-binding shift, where high protein binding may not limit the uptake of compounds that have high affinity for transporters. The addition of plasma to incubations leading to higher CLint values for transporter substrates helps mitigate the underprediction commonly noted with in vitro to in vivo extrapolation. SIGNIFICANCE STATEMENT The current investigation brings a new perspective on how to mitigate the underprediction commonly noted with in vitro to in vivo extrapolation for OATP substrates by using HEK293 cells overexpressing OATP1B1 and OATP1B3. It also supports the idea of a transporter-induced protein-binding shift, where high protein binding may not limit the uptake of compounds that have high affinity for transporters.
Orion: A Distributed File System for Non-Volatile Main Memory and RDMA-Capable Networks

High-performance, byte-addressable non-volatile main memories (NVMMs) force system designers to rethink tradeoffs throughout the system stack, often leading to dramatic changes in system architecture. Conventional distributed file systems are a prime example. When faster NVMM replaces block-based storage, the dramatic improvement in storage performance makes networking and software overhead a critical bottleneck. In this paper, we present Orion, a distributed file system for NVMM-based storage. By taking a clean slate design and leveraging the characteristics of NVMM and high-speed, RDMA-based networking, Orion provides high-performance metadata and data access while maintaining the byte addressability of NVMM. Our evaluation shows Orion achieves performance comparable to local NVMM file systems and outperforms existing distributed file systems by a large margin.
GearDB: A GC-free Key-Value Store on HM-SMR Drives with Gear Compaction

Host-managed shingled magnetic recording drives (HMSMR) give a capacity advantage to harness the explosive growth of data. Applications where data is sequentially written and randomly read, such as key-value stores based on Log-Structured Merge Trees (LSM-trees), make the HMSMR an ideal solution due to its capacity, predictable performance, and economical cost. However, building an LSMtree based KV store on HM-SMR drives presents severe challenges in maintaining the performance and space efficiency due to the redundant cleaning processes for applications and storage devices (i.e., compaction and garbage collections). To eliminate the overhead of on-disk garbage collections (GC) and improve compaction efficiency, this paper presents GearDB, a GC-free KV store tailored for HMSMR drives. GearDB proposes three new techniques: a new on-disk data layout, compaction windows, and a novel gear compaction algorithm. We implement and evaluate GearDB with LevelDB on a real HM-SMR drive. Our extensive experiments have shown that GearDB achieves both good performance and space efficiency, i.e., on average 1.71× faster than LevelDB in random write with a space efficiency of 89.9%.
Broad-Spectrum Disease Resistance Conferred by the Overexpression of Rice RLCK BSR1 Results from an Enhanced Immune Response to Multiple MAMPs

Plants activate their immune system through intracellular signaling pathways after perceiving microbe-associated molecular patterns (MAMPs). Receptor-like cytoplasmic kinases mediate the intracellular signaling downstream of pattern-recognition receptors. BROAD-SPECTRUM RESISTANCE 1 (BSR1), a rice (Oryza sativa) receptor-like cytoplasmic kinase subfamily-VII protein, contributes to chitin-triggered immune responses. It is valuable for agriculture because its overexpression confers strong disease resistance to fungal and bacterial pathogens. However, it remains unclear how overexpressed BSR1 reinforces plant immunity. Here we analyzed immune responses using rice suspension-cultured cells and sliced leaf blades overexpressing BSR1. BSR1 overexpression enhances MAMP-triggered production of hydrogen peroxide (H2O2) and transcriptional activation of the defense-related gene in cultured cells and leaf strips. Furthermore, the co-cultivation of leaves with conidia of the blast fungus revealed that BSR1 overexpression allowed host plants to produce detectable oxidative bursts against compatible pathogens. BSR1 was also involved in the immune responses triggered by peptidoglycan and lipopolysaccharide. Thus, we concluded that the hyperactivation of MAMP-triggered immune responses confers BSR1-mediated robust resistance to broad-spectrum pathogens.
Tailoring Stakeholder Interests to Task-Oriented Functional Requirements

Without a specific functional context, non-functional requirements can only be approached as cross-cutting concerns and treated uniformly across all features of an application. This neglects, however, the heterogeneity of non-functional requirements that arises from stakeholder interests and the distinct functional scopes of software systems, which mutually influence how these non-functional requirements have to be satisfied. Earlier studies showed that the different types and objectives of non-functional requirements result in either vague or unbalanced specification of non-functional requirements. We propose a task analytic approach for eliciting and modeling user tasks to approach the stakeholders' pursued interests towards the software product. Stakeholder interests are structurally related to user tasks and each interest can be specified individually as a constraint of a specific user task. These constraints support DevOps teams with important guidance on how the interest of the stakeholder can be satisfied in the software lifecycle sufficiently. We propose a structured approach, intertwining task-oriented functional requirements with non-functional stakeholder interests to specify constraints on the level of user tasks. We also present results of a case study with domain experts, which reveals that our task modeling and interest-tailoring method increases the comprehensibility of non-functional requirements as well as their impact on the functional requirements, i.e., the users' tasks.
Tiresias: A GPU Cluster Manager for Distributed Deep Learning

Deep learning (DL) training jobs bring some unique challenges to existing cluster managers, such as unpredictable training times, an all-or-nothing execution model, and inﬂex-ibility in GPU sharing. Our analysis of a large GPU cluster in production shows that existing big data schedulers cause long queueing delays and low overall performance. We present Tiresias, a GPU cluster manager tailored for distributed DL training jobs, which efﬁciently schedules and places DL jobs to reduce their job completion times (JCT). Given that a DL job’s execution time is often unpredictable, we propose two scheduling algorithms – Discretized Two-Dimensional Gittins index relies on partial information and Discretized Two-Dimensional LAS is information-agnostic – that aim to minimize the average JCT. Additionally, we describe when the consolidated placement constraint can be re-laxed, and present a placement algorithm to leverage these observations without any user input. Experiments on the Michigan ConFlux cluster with 60 P100 GPUs and large-scale trace-driven simulations show that Tiresias improves the average JCT by up to 5 . 5 × over an Apache YARN-based resource manager used in production. More importantly, Tire-sias’s performance is comparable to that of solutions assuming perfect knowledge.
Alembic: Automated Model Inference for Stateful Network Functions

Network operators today deploy a wide range of complex, stateful network functions (NFs). Typically, they only have access to the NFs’ binary executables, configuration interfaces, and manuals from vendors. To ensure correct behavior of NFs, operators use network testing and verification tools, which often rely on models of the deployed NFs. The effectiveness of these tools depends on the fidelity of such models. Today, models are handwritten, which can be error prone, tedious, and does not account for implementation-specific artifacts. To address this gap, our goal is to automatically infer behavioral models of stateful NFs for a given configuration. The problem is challenging because NF configurations can contain diverse rule types and the space of dynamic and stateful NF behaviors is large. In this work, we present Alembic, which synthesizes NF models viewed as an ensemble of finite-state machines (FSMs). Alembic consists of an offline stage that learns symbolic FSM representations for each NF rule type and an online stage that generates a concrete behavioral model for a given configuration using these symbolic FSMs. We demonstrate that Alembic is accurate, scalable, and sheds light on subtle differences across NF implementations.
The Pglyrp1-Regulated Microbiome Enhances Experimental Allergic Asthma

Key Points Pglyrp1 enhances allergic asthma in mice through its effect on the intestinal microbiome. This effect is strong in asthma-prone mice and weak in asthma-resistant outbred mice. This effect requires germ-free mice before colonization with the Pglyrp1 microbiome. Changes in intestinal or respiratory microbiomes in infants correlate with increased incidence of asthma, but the causative role of microbiome in the susceptibility to asthma and the host genes that regulate these changes in microbiome are mostly unknown. In this study, we show that decreased responsiveness to allergic asthma in Pglyrp1−/− mice (lacking bactericidal peptidoglycan recognition protein 1) could be transferred to germ-free wild-type mice by colonization of mothers and newborns with microbiota from Pglyrp1−/− mice. These colonized mice had decreased airway resistance and fewer inflammatory cells, less severe histopathology, and lower levels of IgE and proallergic cytokines and chemokines in the lungs. This microbiome-dependent decreased responsiveness to asthma was most pronounced in colonized germ-free BALB/c mice (genetically predisposed to asthma), only partially evident in outbred germ-free Swiss Webster mice, and marginal in conventional BALB/c mice following depletion of microbiome with antibiotics. Mice with a low asthmatic response colonized with microbiota from Pglyrp1−/− mice had increased abundance of Bacteroidetes and decreased abundance of Firmicutes, Tenericutes, Deferribacteres, and Spirochaetes in the feces and increased abundance of Pasteurella in the oropharynx. These changes in bacterial abundance in the feces and oropharynx correlated with lower asthmatic responses in the lungs. Thus, our results show that Pglyrp1 enhances allergic asthmatic responses primarily through its effect on the host intestinal microbiome and identify several bacteria that may increase or decrease sensitivity to asthma. This effect of microbiome is strong in asthma-prone BALB/c mice and weak in asthma-resistant outbred mice and requires germ-free conditions before colonization with microbiota from Pglyrp1−/− mice.
Hyperscan: A Fast Multi-pattern Regex Matcher for Modern CPUs

Regular expression matching serves as a key functionality of modern network security applications. Unfortunately, it often becomes the performance bottleneck as it involves compute-intensive scan of every byte of packet payload. With trends towards increasing network bandwidth and a large ruleset of complex patterns, the performance requirement gets ever more demanding. In this paper, we present Hyperscan, a high performance regular expression matcher for commodity server machines. Hyperscan employs two core techniques for efficient pattern matching. First, it exploits graph decomposition that translates regular expression matching into a series of string and finite automata matching. Unlike existing solutions, string matching becomes a part of regular expression matching, eliminating duplicate operations. Decomposed regular expression components also increase the chance of fast DFA matching as they tend to be smaller than the original pattern. Second, Hyperscan accelerates both string and finite automata matching using SIMD operations, which brings substantial throughput improvement. Our evaluation shows that Hyperscan improves the performance of Snort by a factor of 8.7 for a real traffic trace.
Shenango: Achieving High CPU Efficiency for Latency-sensitive Datacenter Workloads

Datacenter applications demand microsecond-scale tail latencies and high request rates from operating systems, and most applications handle loads that have high variance over multiple timescales. Achieving these goals in a CPU-efficient way is an open problem. Because of the high overheads of today’s kernels, the best available solution to achieve microsecond-scale latencies is kernel-bypass networking, which dedicates CPU cores to applications for spin-polling the network card. But this approach wastes CPU: even at modest average loads, one must dedicate enough cores for the peak expected load. Shenango achieves comparable latencies but at far greater CPU efficiency. It reallocates cores across applications at very fine granularity—every 5 μs—enabling cycles unused by latency-sensitive applications to be used productively by batch processing applications. It achieves such fast reallocation rates with (1) an efficient algorithm that detects when applications would benefit from more cores, and (2) a privileged component called the IOKernel that runs on a dedicated core, steering packets from the NIC and orchestrating core reallocations. When handling latency-sensitive applications, such as memcached, we found that Shenango achieves tail latency and throughput comparable to ZygOS, a state-of-the-art, kernel-bypass network stack, but can linearly trade latency-sensitive application throughput for batch processing application throughput, vastly increasing CPU efficiency.
Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure

Serverless computing is poised to fulfill the long-held promise of transparent elasticity and millisecond-level pricing. To achieve this goal, service providers impose a finegrained computational model where every function has a maximum duration, a fixed amount of memory and no persistent local storage. We observe that the fine-grained elasticity of serverless is key to achieve high utilization for general computations such as analytics workloads, but that resource limits make it challenging to implement such applications as they need to move large amounts of data between functions that don’t overlap in time. In this paper, we present Locus, a serverless analytics system that judiciously combines (1) cheap but slow storage with (2) fast but expensive storage, to achieve good performance while remaining cost-efficient. Locus applies a performance model to guide users in selecting the type and the amount of storage to achieve the desired cost-performance trade-off. We evaluate Locus on a number of analytics applications including TPC-DS, CloudSort, Big Data Benchmark and show that Locus can navigate the costperformance trade-off, leading to 4⇥-500⇥ performance improvements over slow storage-only baseline and reducing resource usage by up to 59% while achieving comparable performance with running Apache Spark on a cluster of virtual machines, and within 2⇥ slower compared to Redshift.
Early Risk Detection of Pre-eclampsia for Pregnant Women Using Artificial Neural Network

Pre-eclampsia still dominates maternal mortality cases in Indonesia. One effort that can be done is to establish early detection of the risk of pre-eclampsia in pregnant women. Automated devices with high accuracy are needed to detect the risk of pre-eclampsia so that the maternal mortality ratio can be reduced. This study aims to design an early detection system for the risk of pre-eclampsia based on artificial neural networks. The system is designed with 11 input parameters in the form of risk factors and output in the form of positive or negative risk of pre-eclampsia. The classification tool used in this study is backpropagation neural network with cross validation scenario at the training stage. The advantage of this system is the weighting of risk factor parameters by obstetric and gynecology specialists so that the results of testing the device show high accuracy. In addition, the device for early detection of pre-eclampsia was also conducted by user acceptance tests for a number of pregnant women.
A CAD System for the Early Detection of Lung Nodules Using Computed Tomography Scan Images

In this paper, a computer-aided detection system is developed to detect lung nodules at an early stage using Computed Tomography (CT) scan images where lung nodules are one of the most important indicators to predict lung cancer. The developed system consists of four stages. First, the raw Computed Tomography lung images were preprocessed to enhance the image contrast and eliminate noise. Second, an automatic segmentation procedure for human's lung and pulmonary nodule canddates (nodules, blood vessels) using a two-level thresholding technique and morphological operations. Third, a feature fusion technique that fuses four feature extraction techniques: the statistical features of first and second order, value histogram features, histogram of oriented gradients features, and texture features of gray level co-occurrence matrix based on wavelet coefficients was utilised to extract the main features. The fourth stage is the classifier. Three classifiers were used and their performance was compared in order to obtain the highest classification accuracy. These are; multi-layer feed-forward neural network, radial basis function neural network and support vector machine. The performance of the proposed system was assessed using three quantitative parameters. These are: the classification accuracy rate, the sensitivity and the specificity. Forty standard computed tomography images containing 320 regions of interest obtained from an early lung cancer action project association were used to test and evaluate the developed system. The images consists of 40 computed tomography scan images. The results have shown that the fused features vector resulting from genetic algorithm as a feature selection technique and the support vector machine classifier give the highest classification accuracy rate, sensitivity and specificity values of 99.6%, 100% and 99.2%, respectively.
Analysis of an Intelligent Graphical Tutoring System using the Internet of Things (IoT) to Develop the Competency of Embedded Systems

The objectives of this research are to analyse and assess an intelligent graphical tutoring system using the internet of things (IoT) to develop the efficiency of embedded systems. The research findings suggest that an intelligent system is composed of six modules: 1) student, 2) graphical tutoring, 3) expert, 4) knowledge, 5) evaluation and 6) user interface. The result of a composition assessment performed by experts indicates that the overall results are at a high level. The system suitability scored the highest level which can be applied to real life situations.
Computational Optimal Transport

Optimal transport (OT) theory can be informally described using the words of the French mathematician Gaspard Monge (1746-1818): A worker with a shovel in hand has to move a large pile of sand lying on a construction site. The goal of the worker is to erect with all that sand a target pile with a prescribed shape (for example, that of a giant sand castle). Naturally, the worker wishes to minimize her total effort, quantified for instance as the total distance or time spent carrying shovelfuls of sand. Mathematicians interested in OT cast that problem as that of comparing two probability distributions, two different piles of sand of the same volume. They consider all of the many possible ways to morph, transport or reshape the first pile into the second, and associate a "global" cost to every such transport, using the "local" consideration of how much it costs to move a grain of sand from one place to another. Recent years have witnessed the spread of OT in several fields, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.
A comparative study of single-particle cryo-EM with liquid-nitrogen and liquid-helium cooling

Radiation damage is the most fundamental limitation for achieving high resolution in cryo-EM, and is expected to be reduced at liquid-helium temperature. Surprisingly, cryo-EM reconstructions of apoferritin samples cooled with liquid helium showed no improvement in resolution over liquid-nitrogen-cooled samples, but showed substantially more beam-induced particle motion.
A Routing Algorithm Based on Zigbee Technology

To realize the exploration of A routing algorithm for ZigBee technology, a kind of intelligent parking system based on ZigBee wireless sensor network is designed, and the parking space is managed by online and offline interaction. First of all, the status of parking at home and abroad is studied, and the demand for parking is analyzed. Secondly, the intelligent scheme of parking system is studied, including parking guidance technology and parking space intelligent recommendation technology. The former is based on the A routing algorithm to search the shortest path, and uses Unity for route planning simulation, and the latter is based on collaboration filtering algorithm to discuss the recommendation method of similar parking spaces. The result shows that the prototype of an intelligent parking system based on ZigBee is eventually realized, and the functions of online viewing, online parking reservation, online path planning and parking guidance are realized. As a result, the purpose of managing offline parking spaces through online is achieved, the utilization rate of resources in the existing parking lot is improved and the use rate of parking space is promoted. At last, it effectively alleviates the urban parking chaos and has certain application value.