Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Potential G-Quadruplex Sequences in Archaeal Genomes

Evolutionary conservation or over-representation of the potential G-quadruplex sequences (PQS) in genomes are usually considered as a sign of the functional relevance of these sequences. However, uneven base distribution (GC-content) along the genome may along the genome may result in seeming abundance of PQSs over average in the genome. Apart from this, a number of other conserved functional signals that are encoded in the GC-rich genomic regions may inadvertently result in emergence of G-quadruplex compatible sequences. Here, we analyze the genomes of archaea focusing our search to repetitive PQS (rPQS) motifs within each organism. The probability of occurrence of several identical PQSs within a relatively short archaeal genome is low and, thus, the structure and genomic location of such rPQSs may become a direct indication of their functionality. We have found that the majority of the genomes of Methanomicrobiaceae family of archaea contained multiple copies of the interspersed highly similar PQSs. Short oligonucleotides corresponding to the rPQS formed the G-quadruplex (G4) structure in presence of potassium ions as demonstrated by circular dichroism (CD) and enzymatic probing. However, further analysis of the genomic context for the rPQS revealed a 10–12 nt cytosine-rich track adjacent to 3'-end of each rPQS. Synthetic DNA fragments that included the C-rich track tended to fold into alternative structures such as hairpin structure and antiparallel triplex that were in equilibrium with G4 structure depending on the presence of potassium ions in solution. Structural properties of the found repetitive sequences, their location in the genomes of archaea, and possible functions are discussed.
Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat

Grain protein concentration (GPC) is an important trait in durum cultivar development as a major determinant of the nutritional value of grain and end-use product quality. However, it is challenging to simultaneously select both GPC and grain yield (GY) due to the negative correlation between them. To characterize quantitative trait loci (QTL) for GPC and understand the genetic relationship between GPC and GY in Canadian durum wheat, we performed both traditional and conditional QTL mapping using a doubled haploid (DH) population of 162 lines derived from Pelissier × Strongfield. The population was grown in the field over 5 years and GPC was measured. QTL contributing to GPC were detected on chromosome 1B, 2B, 3A, 5B, 7A, and 7B using traditional mapping. One major QTL on 3A (QGpc.spa-3A.3) was consistently detected over 3 years accounting for 9.4–18.1% of the phenotypic variance, with the favorable allele derived from Pelissier. Another major QTL on 7A (QGpc.spa-7A) detected in 3 years explained 6.9–14.8% of the phenotypic variance, with the beneficial allele derived from Strongfield. Comparison of the QTL described here with the results previously reported led to the identification of one novel major QTL on 3A (QGpc.spa-3A.3) and five novel minor QTL on 1B, 2B and 3A. Four QTL were common between traditional and conditional mapping, with QGpc.spa-3A.3 and QGpc.spa-7A detected in multiple environments. The QTL identified by conditional mapping were independent or partially independent of GY, making them of great importance for development of high GPC and high yielding durum.
Circulating Histones in Sepsis: Potential Outcome Predictors and Therapeutic Targets

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection and is associated with high morbidity and mortality. Circulating histones (CHs), a group of damage-associated molecular pattern molecules mainly derived from neutrophil extracellular traps, play a crucial role in sepsis by mediating inflammation response, organ injury and death through Toll-like receptors or inflammasome pathways. Herein, we first elucidate the molecular mechanisms of histone-induced inflammation amplification, endothelium injury and cascade coagulation activation, and discuss the close correlation between elevated level of CHs and disease severity as well as mortality in patients with sepsis. Furthermore, current state-of-the-art on anti-histone therapy with antibodies, histone-binding proteins (namely recombinant thrombomodulin and activated protein C), and heparin is summarized to propose promising approaches for sepsis treatment.
Desire, Familiarity, and Engagement in Polyamory: Results From a National Sample of Single Adults in the United States

Coupledom and notions of intimacy and family formation with one committed partner are hallmarks of family and relationship science. Recent national surveys in the United States and Canada have found that consensually non-monogamous relationships are common, though prevalence of specific types of consensual non-monogamy are unknown. The present research draws on a United States Census based quota sample of single adults (N = 3,438) to estimate the prevalence of desire for, familiarity with, and engagement in polyamory—a distinct type of consensually non-monogamous relationship where people typically engage in romantic love and sexual intimacy with multiple partners. Results show that 1 out of 6 people (16.8%) desire to engage in polyamory, and 1 out of 9 people (10.7%) have engaged in polyamory at some point during their life. Approximately 1 out of 15 people (6.5%) reported that they knew someone who has been or is currently engaged in polyamory. Among participants who were not personally interested in polyamory, 1 out of 7 (14.2%) indicated that they respect people who engage in polyamory. Few sociodemographic correlates emerged; no differences in prevalence were found based on political affiliation, income, religion, geographic region, or race/ethnicity. Sexual minorities, men, and younger adults reported greater desire to engage in polyamory (compared to heterosexuals, women, and older adults, respectively). Men and people with lower education backgrounds were more likely to have previously engaged in polyamory (compared to women and people with higher education backgrounds, respectively). Given that emotional and sexual intimacy is an important part of most people’s lives, understanding the varied ways in which people navigate their intimate lives is critical for the fields of relationship, sexuality, and family science.
Learned Approximate Query Processing: Make it Light, Accurate and Fast

The advent of learning algorithms has revealed many opportunities for improving Data Systems’ functionality and performance. Ap-proximate Query Processing (AQP) is one such area where machine learning (ML) models have been used to improve query execution efficiency and accuracy, outperforming the traditional sampling-based approaches. Based on our group’s experience in the ML-for-DBs area, [3–7, 29, 37–39], we contribute a novel AQP engine, coined DBEst++ , which extends our previous effort (DBEst, [29]) and sets the state of the art in terms of accuracy and query execution efficiency. The DBEst++ salient design objective is to derive lightweight ML models for the task, allowing a plethora of ML models to coexist, covering a very large fraction of the expected analytical query workload without requiring very large memory footprints. The DBEst++ salient architectural feature rests on a novel blending of word embedding models with neural networks tasked with regression-based predictions for density estimation and aggregation-attribute values. We present design features and motivations/rationale behind DBEst++ and discuss how all the ML models are brought together. We also present how DBEst++ can deal with challenging scenarios, including how to deal with high-cardinality categorical attributes and how to ensure high accuracy under data updates. We provide a detailed experimental evaluation using the TPC-DS and Flights datasets against state of the art learned and sampling-based AQP engines, showcasing DBEst++ ’s gains in terms of accuracy, response-times, and memory space overheads.
Zen: a High-Throughput Log-Free OLTP Engine for Non-Volatile Main Memory

Emerging Non-Volatile Memory (NVM) technologies like 3DX-point promise significant performance potential for OLTP databases. However, transactional databases need to be redesigned because the key assumptions that non-volatile storage is orders of magnitude slower than DRAM and only supports blocked-oriented access have changed. NVMs are byte-addressable and almost as fast as DRAM. The capacity of NVM is much (4-16x) larger than DRAM. Such NVM characteristics make it possible to build OLTP database entirely in NVM main memory. This paper studies the structure of OLTP engines with hybrid NVM and DRAM memory. We observe three challenges to design an OLTP engine for NVM: tuple metadata modifications, NVM write redundancy, and NVM space management. We propose Zen, a high-throughput log-free OLTP engine for NVM. Zen addresses the three design challenges with three novel techniques: metadata enhanced tuple cache, log-free persistent transactions, and light-weight NVM space management. Experimental results on a real machine equipped with Intel Optane DC Persistent Memory show that Zen achieves up to 10.1x improvement compared with existing solutions to run an OLTP database as large as the size of NVM while achieving fast failure recovery.
Ease.ML: A Lifecycle Management System for Machine Learning

We present Ease.ML, a lifecycle management system for machine learning (ML). Unlike many existing works, which focus on improving individual steps during the lifecycle of ML application development, Ease.ML focuses on managing and automating the entire lifecycle itself. We present user scenarios that have motivated the development of Ease.ML, the eight-step Ease.ML process that covers the lifecycle of ML application development; the foundation of Ease.ML in terms of a probabilistic database model and its connection to information theory; and our lessons learned, which hopefully can inspire future research.
A Power Dissipation Monitoring Circuit for Intrusion Detection and Botnet Prevention on IoT Devices

Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and the absence of international standards to achieve this goal. Thus, these devices are becoming vulnerable, with many of them not even showing any signs of malfunction or suspicious behavior. The aim of the present work is to introduce a circuit that is connected in series with the power supply of a smart device, specifically an IP camera, which allows analysis of its behavior. The detection circuit operates in real time (real-time detection), sampling the supply current of the device, processing the sampled values and finally indicating any detection of abnormal activities, based on a comparison to normal operation conditions. By utilizing techniques borrowed by simple power analysis side channel attack, it was possible to detect deviations from the expected operation of the IP camera, as they occurred due to intentional attacks, quarantining the monitored device from the rest of the network. The circuit is analyzed and a low-cost implementation (under 5US$) is illustrated. It achieved 100% success in the test results, showing excellent performance in intrusion detection.
Communications of the Association for Information Systems Communications of the Association for Information Systems

: The coronavirus disease of 2019 (COVID-19) pandemic has changed the way we work, learn, and interact with others in society. Academic institutions have responded to the pandemic by shifting face-to-face teaching to online instruction. However, whether online instruction succeeds also depends on students’ social and physical learning environment, particularly in developing countries. In this paper, we discuss how learning space challenges exacerbate the digital divide. We argue that weak digital infrastructure, combined with family and social dynamics, create learning space inequality that negatively influence learning outcomes. We provide recommendations on how academic institutions can reimagine content delivery, evaluation, and student support to mitigate learning space inequalities.
Hamming Tree: The Case for Memory-Aware Bit Flipping Reduction for NVM Indexing

Non-Volatile Memory (NVM) is improving the performance and cost-efficiency of data management systems. However, they introduce salient challenges that were not considered in traditional memory architectures. In particular, write endurance in NVM is significantly lower than other memory technologies which threat-ens the practicality and longevity of NVM devices. In this paper, we introduce Hamming Tree , an indexing structure that can be augmented with existing database indexing technologies to increase the write endurance of NVM. Hamming Tree proposes a new way of increasing write endurance by directing write operations to memory locations that would minimize the number of incurred bit flips. This method is capable of significantly increasing the endurance of NVM compared to existing write endurance methods that remain agnostic to the underlying memory such as local write optimizations and write amplification techniques. Our evaluations show that Hamming Tree can reduce bit flipping (hence increasing write endurance) by up to %93 on both traditional and optimized NVM indexing technologies with minimal changes to their indexing structure.
Adaptive Code Generation for Data-Intensive Analytics

Modern database management systems employ sophisticated query optimization techniques that enable the generation of efficient plans for queries over very large data sets. A variety of other applications also process large data sets, but cannot leverage database-style query optimization for their code. We therefore identify an opportunity to enhance an open-source programming language compiler with database-style query optimization. Our system dynamically generates execution plans at query time, and runs those plans on chunks of data at a time. Based on feedback from earlier chunks, alternative plans might be used for later chunks. The compiler extension could be used for a variety of data-intensive applications, allowing all of them to benefit from this class of performance optimizations.
ParaX: Boosting Deep Learning for Big Data Analytics on Many-Core CPUs

Despite the fact that GPUs and accelerators are more efficient in deep learning (DL), commercial clouds like Facebook and Amazon now heavily use CPUs in DL computation because there are large numbers of CPUs which would otherwise sit idle during off-peak periods. Following the trend, CPU vendors have not only released high-performance many-core CPUs but also developed efficient math kernel libraries. However, current DL platforms cannot scale well to a large number of CPU cores, making many-core CPUs inefficient in DL computation. We analyze the memory access patterns of various layers and identify the root cause of the low scalability, i.e., the per-layer barriers that are implicitly imposed by current platforms which assign one single instance (i.e., one batch of input data) to a CPU. The barriers cause severe memory bandwidth contention and CPU starvation in the access-intensive layers (like activation and BN). This paper presents a novel approach called ParaX, which boosts the performance of DL on many-core CPUs by effectively alleviating bandwidth contention and CPU starvation. Our key idea is to assign one instance to each CPU core instead of to the entire CPU, so as to remove the per-layer barriers on the executions of the many cores. ParaX designs an ultralight scheduling policy which sufficiently overlaps the access-intensive layers with the compute-intensive ones to avoid contention, and proposes a NUMA-aware gradient server mechanism for training which leverages shared memory to substantially reduce the overhead of per-iteration parameter synchronization. We have implemented ParaX on MXNet. Extensive evaluation on a two-NUMA Intel 8280 CPU shows that ParaX significantly improves the training/inference throughput for all tested models (for image recognition and natural language processing) by 1.73X ~ 2.93X.
On the String Matching with k Differences in DNA Databases

In this paper, we discuss an efficient and effective index mechanism for the string matching with k differences, by which we will find all the substrings of a target string y of length n that align with a pattern string x of length m with not more than k insertions, deletions, and mismatches. A typical application is the searching of a DNA database, where the size of a genome sequence in the database is much larger than that of a pattern. For example, n is often on the order of millions or billions while m is just a hundred or a thousand. The main idea of our method is to transform y to a BWT-array as an index, denoted as BWT ( y ), and search x against it. The time complexity of our method is bounded by O( k · | T |), where T is a tree structure dynamically generated during a search of BWT ( y ). The average value of | T | is bounded by O(|Σ| 2 k ), where Σ is an alphabet from which we take symbols to make up target and pattern strings. This time complexity is better than previous strategies when k ≤ O(log |Σ| n ). The general working process consists of two steps. In the first step, x is decomposed into a series of l small subpatterns, and BWT ( y ) is utilized to speedup the process to figure out all the occurrences of such subpatterns with ⌊ k/l ⌋ differences. In the second step, all the found occurrences in the first step will be rechecked to see whether they really match x , but with k differences. Extensive experiments have been conducted, which show that our method for this problem is promising.
On Analyzing Graphs with Motif-Paths

Path-based solutions have been shown to be useful for various graph analysis tasks, such as link prediction and graph clustering. However, they are no longer adequate for handling complex and gigantic graphs. Recently, motif-based analysis has attracted a lot of attention. A motif, or a small graph with a few nodes, is often considered as a fundamental unit of a graph. Motif-based analysis captures high-order structure between nodes, and performs better than traditional "edge-based" solutions. In this paper, we study motif-path , which is conceptually a concatenation of one or more motif instances. We examine how motif-paths can be used in three path-based mining tasks, namely link prediction, local graph clustering and node ranking. We further address the situation when two graph nodes are not connected through a motif-path, and develop a novel defragmentation method to enhance it. Experimental results on real graph datasets demonstrate the use of motif-paths and defragmentation techniques improves graph analysis effectiveness.
Organosoluble and light‐colored fluorinated polyimides from 4,4′‐bis(4‐amino‐2‐trifluoromethylphenoxy)biphenyl and aromatic dianhydrides

A novel fluorinated diamine monomer, 9,9-bis[4-(4-amino-2-trifluoromethylphenoxy)phenyl]fluorene (II) was prepared via the nucleophilic substitution reaction of 2-chloro-5-nitrobenzotrifluoride with 9,9-bis(4-hydroxyphenyl)fluorene in the presence of potassium carbonate, followed by catalytic reduction with hydrazine and Pd/C. Polyimides V were synthesized from diamine II and various aromatic dianhydrides IIIa-f via thermal imidization. These polymers had inherent viscosities ranging from 0.84 to 1.03 dL/g and were soluble in a variety of organic solvents such as NMP, DMAc, DMF, and DMSO, and some could even be dissolved in less polar solvents such as m-cresol, pyridine, and dioxane. Polyimide films Va-f had tensile strengths of 85–105 MPa, elongations to break of 7–9%, and initial moduli of 2.13–2.42 GPa. The glass transition temperature of these polymers were in the range of 277–331 °C, their 10% weight loss temperatures were in the range of 539–594 °C in nitrogen and above 544 °C in air, and their char yields at 800 °C in nitrogen ranged between 55–65 wt%. Compared with nonfluorinated polyimides VI, V showed better solubility and lower color intensity. Low dielectric constants (2.68–2.85 at 1 MHz) and low moisture absorptions (0.12–0.24 wt%) were also observed. In particular, Vc-f afforded lightly-colored films, which had cutoff wavelengths lower than 385 nm and b* values ranging from 6 to 22.
Contention and Space Management in B-Trees

Classical B-Trees were not designed for the modern hardware. They often do not scale on today’s many-core CPUs because they do not handle high-contention workloads well. Moreover, their fixed-size nodes result in underfull nodes and a space utilization below 70%. This low space utilization is economically undesirable, especially with fast but relatively expensive NVMe SSDs, and causes unnecessary read and write amplification. In this paper, we address these two shortcomings by introducing two techniques: The first, Contention Split detects unnecessary contention on nodes and splits them to allow higher concurrency. The second technique, XMerge, saves space by lazily merging neighboring nodes. Both techniques are lightweight, easy to integrate into existing database systems, and result in substantial space and performance improvements.
Bridging the Chasm between Science and Reality

When a research-prototype of a database management system becomes a product in the market, then more insight is required from the actual workloads to steer its industrial hardening. Unfortunately, few customers are willing to share their database schema, data samples, query load and execution traces for business and legal reasons. The technical challenge then becomes, how to enable customers to share their workloads in such a way that it will not leak sensitive information, while still provide sufﬁcient information to allow DBMS researchers and developers to assess and improve their technology. In this paper, we report on ongoing research to address this challenge in the context of MonetDB 1 , as it is increasingly adopted in the enterprise market. This paper sheds light on the importance of a good proﬁling tool during system construction and the techniques deployed to gain permission from customers’ legal departments to share proﬁling traces captured on live production systems.
Combination of synthetic peptides derived from bone morphogenetic proteins and biomaterials for medical applications

A critical-size bone defect cannot repair itself. These defects are presently filled by bone grafts or with biomaterials that mimic bone properties. The activity of bone cells is modulated by cytokines like the bone morphogenetic proteins (BMPs). This review described the peptides derived from BMPs or extracellular matrix proteins which can be immobilised on biomaterials to increase their action on bone cells and promote healing. However, the development of such materials requires peptides that can act in synergy. This requires the use of model surfaces to better understand how cells perceive biomaterials. La reparation des pertes osseuses ne se fait pas au dela d'une taille critique. Les greffes ou les biomateriaux mimant les proprietes de ce tissu sont actuellement utilises pour combler ces pertes. Les cellules osseuses sont regulees par de nombreuses cytokines, telles les proteines morphogenetiques osseuses (BMPs). Cette revue de la litterature decrit l'immobilisation sur les biomateriaux de peptides derives des BMPs ou des proteines de la matrice extracellulaire qui permet leur action selective pour favoriser une meilleure guerison. Cependant, le developpement de ces materiaux implique non seulement la selection de peptides agissant en synergie, mais aussi une meilleure connaissance des interactions cellules-biomateriaux en utilisant des surfaces modeles.