Miracle-dz/UniFAR-FTUs huggingface dataset:
CITRUS: A TREASURE TROVE OF HEALTH-PROMOTING PHYTOCHEMICALS

Citrus species belong to the family Rutaceae. Their unique taste, texture and their wide range of beneficial properties have led citrus to be among the most highly-consumed fruits in the world. In recent years, per capita citrus consumption has increased due to accumulating evidence of the beneficial roles of certain bioactive compounds also known as phytochemicals present in citrus fruits. Besides the well-known vitamin C, citrus fruits contain a wide array of phytochemicals considered to have many healthpromoting properties. Phytochemicals are secondary metabolites that are naturally synthesized by plants, and their primary role is to protect the plant from various pests and diseases. Due to various genetic, environmental and physiochemical changes, different citrus species synthesize numerous phytochemicals of varying contents and kinds. These phytochemicals are classified into different groups based on their structural scaffolds. This chapter describes the various classes of phytochemicals present in citrus fruits and their beneficial properties for human health, such as decreasing the risk of cardiovascular disease, various types of cancer, and other chronic diseases.
Trophic ecology of red snapper Lutjanus campechanus on natural and artificial reefs: interactions between annual variability, habitat, and ontogeny

In the Gulf of Mexico (GOM), oil and gas platforms have created an expansive network of artificial reefs. Generally, policies mandate removal of these structures post-production; however, many enter ‘Rigs-to-Reefs’ (RTR) programs that convert the rig materials into artificial reefs (‘reefing’). Despite the growth of RTR programs worldwide, the functionality of the resulting habitats remains uncertain, particularly due to the lack of comparative studies with natural systems. To address this data gap as it relates to trophic ecology, we compared annual, ontogenetic, and habitat-specific diet and stable isotope signatures (δ13C and δ15N) of adult red snapper Lutja nus campe chanus (n = 1585) from relic coralgal natural reefs to those of fish from standing and reefed platforms located in similar depth strata of the northwestern GOM. Stomach content analyses revealed significant effects of year, habitat, and total length on prey composition. Subsequent analyses of stable isotope values by size class identified a non-linear relationship with ontogeny. δ13C and δ15N values at reefed platforms and natural reefs decreased in the medium size class (401− 600 mm total length), whereas fish from standing platforms exhibited more consistent feeding patterns across ontogeny. Annual variability was also observed in δ13C and δ15N values, with 2013 and 2014 significantly different from 2015. These findings suggest that the trophic impacts of habitat type on reef fishes are more complex than previously considered in the GOM and that reefed platforms provide foraging opportunities more similar to natural reefs than standing platforms.
Cloning and Analysis of Heteroplasmy in Hypervariable Segment 1 (HVS1) D-loop in Mitochondrial DNA of Human Isolates of Timika and Wamena in Highlands of Papuan Province, Indonesia

Analysis of mutation and heteroplasmy in the normal human population and is associated with mitochondrial disease have been studied. The aims of this study was to determine the cause of heteroplasmy that is not successful determination of the nucleotide sequence of the D-loop HVS1 region with poly-C sequence by direct sequencing. Determination of the nucleotide sequence of the D-loop sequencing performed successfully when after going through the process of cloning. The research was conducted on isolates Timika and Wamena, the individuals of tribes in the central highlands province of Papua, Indonesia. Determination of the nucleotide sequence of the recombinant DNA clones on some isolates HVS1 D-loop of several samples that have a poly-C sequence using the Sanger dideoxy method. In this research has been obtained HVS1 mtDN sequences derived from three clone Mimika and two clone isolates. Nucleotide sequence analysis showed a series of poly-C is caused by mutations T16189C. Length variation of poly-C sequence in isolates of the same sample showed subpopulation of mtDNA in certain isolates. This phenomenon is strongly suspected the cause of unreadable sequence of human mtDNA HVS1 region containing the poly-C by direct sequencing. These results occur because of several distinct subpopulation within a sample, which causes the detector sequencing receiver two different fluorescent signals in the same position. Signal difference is due to the shift due to differences in nucleotide base sequence length poly-C.
Characterization of Antimicrobial, Antioxidant, Cytotoxic Activity and Chemical Composition of Punica granatum Rind Extract

This study was carried out to characterize antimicrobial, antioxidant and cytotoxic activities of Punica granatum rind extract as well as its chemical composition. The main objective of the present study is to reveal the medicinal values of P. granatum rind. Antimicrobial property of P. granatum rind extract was revealed by using two-fold broth microdilution method against Aeromonas hydrophila, Escherichia coli, Edwardsiella tarda, Flavobacterium spp. , Klebsiella pneumonia, Salmonella typhi, Vibrio alginolyticus, V. parahaemolyticus, V. cholerae and Pseudomonas aeruginosa whereas the antioxidant activity of the plant extract was determined with ? , ? -diphenyl-? - picrylhydrazyl (DPPH) radical scavenging method. Cytotoxic activity of the plant extract was evaluated with Colorimetric MTT (tetrazolium) assay against a human breast cell line (MCF-7). Chemical compounds of the plant extract were screened and identified by using gas chromatography-mass spectrometry (GC-MS). The results of the present study showed that the MIC values of P. granatum rind extract ranged from 3.91 to 15.63 mg/l against the tested bacterial isolates. The value of IC50 of the plant extract against DPPH and MCF-7 cell line was 0.144 ± 0.087 ppt and 0.54 ± 0.03 ? g/ml, respectively. A total of 14 chemical compounds were successfully identified from P. granatum rind extract and the major compound was Hexanamide, N-phenyl (68.52%.). The findings of the present study indicate promising medicinal values of the plant extract.
In silico based Identification of Potential Therapeutic Marine Metabolitesagainst the Ocular Pathogen Chlamydia trachomatis

Granular conjunctivitis is one of the leading causes of infectious blindness worldwide. It is caused by Chlamydia trachomatis bacterium which produces a characteristic roughening in the inner surface of the eyelids. Though antibacterial drugs have been identified so far, none give the successful remedy. In recent years, genome-sequencing projects of pathogens and bioinformatic techniques have revolutionized microbial drug target identification. In this work, codon adaptation index (CAI) was used as a measure to predict the frequency of codon usage in the highly expressed genes and was coupled with other protein sequence analysis for mining potential drug targets. The chosen genes were filtered against non-homologous to human proteins. The functional significance, sub-cellular location and other parameters were used to narrow down the target. On the other hand, the drug molecules were screened from marine secondary metabolites. The compounds were collected from literature and were screened for drug-likeness property. Five therapeutic targets were identified from the results, among ATPase DnaA and DNA polymerase III subunit alpha could be the good drug targets as they are involved in essential functionalities like DNA replication and regulation. Also, for quick permeable drugs, oligopeptide-binding proteins namely replicative DNA helicase, DNA polymerase-I and protein translocate subunit Sec A were identified. The molecular docking studies of those marine compounds with the therapeutic target reveals that chloriolin A from Jaspis marine sponge may act effective against C. trachomatis .
Influence of Roughage Level and Feed Intake Level on Digestive Function

for the reduced trial gains. During the latter phase, gains were 4.1 and 9.9 percent slower for animals with liver abscess scores of2 and 3, respectively. These data suggest that the adverse effects ofliver abscesses on performance are evident only after 56 days on feed. Possibly animals which consume large amounts offeed and make rapid gains may overeat and be more susceptible to severe liver abscesses. Alternatively, the presence of minor liver damage might stimulate the immune system and increase animal performance. The most severe abscess score was associated with a reduced dressing percentage (0.7 percentage points) and increased cutability (0.29 percentage points). The lower gains and carcass weights of abscessed cattle may account for the slightly lower dressing percentage, fat thickness and increased cutability.
You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis

—To subvert recent advances in perimeter and host security, the attacker community has developed and employed various attack vectors to make a malware much stealthier than before to penetrate the target system and prolong its presence. Such advanced malware or “stealthy malware” makes use of various techniques to impersonate or abuse benign applications and legitimate system tools to minimize its footprints in the target system. It is thus difﬁcult for traditional detection tools, such as malware scanners, to detect it, as the malware normally does not expose its malicious payload in a ﬁle and hides its malicious behaviors among the benign behaviors of the processes. In this paper, we present P ROV D ETECTOR , a provenance-based approach for detecting stealthy malware. Our insight behind the P ROV D ETECTOR approach is that although a stealthy malware attempts to blend into benign processes, its malicious behaviors inevitably interact with the underlying operating system (OS), which will be exposed to and captured by provenance monitoring. Based on this intuition, P ROV D ETECTOR ﬁrst employs a novel selection algorithm to identify possibly malicious parts in the OS-level provenance data of a process. It then applies a neural embedding and machine learning pipeline to automatically detect any behavior that deviates signiﬁcantly from normal behaviors. We evaluate our approach on a large provenance dataset from an enterprise network and demonstrate that it achieves very high detection performance of stealthy malware (an average F1 score of 0.974). Further, we conduct thorough interpretability studies to understand the internals of the learned machine learning models
Robust Design of Large Area Flexible Electronics via Compressed Sensing

Large area flexible electronics (FE) is emerging for low-cost, light-weight wearable electronics, artificial skins and IoT nodes, benefiting from its low-cost fabrication and mechanical flexibility. How-ever, the low temperature requirement for fabrication on a flexible substrate and the large-area nature of flexible sensor arrays inevitably result in inadequate device yield, reliability and stability. Therefore, it is essential to develop design methodologies for large area sensing applications which can ensure system robustness with-out relying on highly reliable devices. Based on the observation that most signals sensed by body sensor arrays exhibit sparse statistical characteristics, we propose a system design method which lever-ages the sparse nature via compressed sensing (CS). Specifically, we use flexible circuitry to implement a CS encoder and decode the compressed signal in the silicon side. As a system demonstration, we fabricated the temperature sensor array, shift register and amplifier to illustrate the feasibility of the encoder design using carbon-nanotube-based flexible thin-film transistors. To evaluate the improvement of system robustness achieved by the proposed sensing schema, we conducted two case studies: temperature imaging and tactile-sensor based object recognition. With ∼10% sparse errors (due to either device defects or transient errors), we achieved reduction of root-mean-square-error (RMSE) from 0.20 to 0.05 for temperature sensing and boost the classification accuracy from 65% to 84% for tactile-sensing based object recognition.
Fact and Fiction: Challenging the Honest Majority Assumption of Permissionless Blockchains

Honest majority is the key security assumption of Proof-of-Work (PoW) based blockchains. However, the recent 51% attacks render this assumption unrealistic in practice. In this paper, we challenge this assumption against rational miners in the PoW-based blockchains in reality. In particular, we show that the current incentive mechanism may encourage rational miners to launch 51% attacks in two cases. In the first case, we consider a miner of a stronger blockchain launches 51% attacks on a weaker blockchain, where the two blockchains share the same mining algorithm. In the second case, we consider a miner rents mining power from cloud mining services to launch 51% attacks. As 51% attacks lead to double-spending, the miner can profit from these two attacks. If such double-spending is more profitable than mining, miners are more intended to launch 51% attacks rather than mine honestly. We formally model such behaviours as a series of actions through a Markov Decision Process. Our results show that, for most mainstream PoW-based blockchains, 51% attacks are feasible and profitable, so profit-driven miners are incentivised to launch 51% attacks to gain extra profit. In addition, we leverage our model to investigate the recent 51% attack on Ethereum Classic (on 07/01/2019), which is suspected to be an incident of 51% attacks. We provide insights on the attacker strategy and expected revenue, and show that the attacker's strategy is near-optimal.
HFL: Hybrid Fuzzing on the Linux Kernel

Hybrid fuzzing, combining symbolic execution and fuzzing, is a promising approach for vulnerability discovery because each approach can complement the other. However, we observe that applying hybrid fuzzing to kernel testing is challenging because the following unique characteristics of the kernel make a naive adoption of hybrid fuzzing inefficient: 1) having indirect control transfers determined by system call arguments, 2) controlling and matching internal system state via system calls, and 3) inferring nested argument type for invoking system calls. Failure to handling such challenges will render both fuzzing and symbolic execution inefficient, and thereby, will result in an inefficient hybrid fuzzing. Although these challenges are essential to both fuzzing and symbolic execution, to the best of our knowledge, existing kernel testing approaches either naively use each technique separately without handling such challenges or imprecisely handle a part of challenges only by static analysis. To this end, this paper proposes HFL, which not only combines fuzzing with symbolic execution for hybrid fuzzing but also addresses kernel-specific fuzzing challenges via three distinct features: 1) converting indirect control transfers to direct transfers, 2) inferring system call sequence to build a consistent system state, and 3) identifying nested arguments types of system calls. As a result, HFL found 24 previously unknown vulnerabilities in recent Linux kernels. Additionally, HFL achieves 15% and 26% higher code coverage than Moonshine and Syzkaller, respectively, and over kAFL/S2E/TriforceAFL, achieving even four times better coverage, using the same amount of resources (CPU, time, etc.). Regarding vulnerability discovery performance, HFL found 13 known vulnerabilities more than three times faster than Syzkaller.
Are You Going to Answer That? Measuring User Responses to Anti-Robocall Application Indicators

Robocalls are inundating phone users. These automated calls allow for attackers to reach massive audiences with scams ranging from credential hijacking to unnecessary IT support in a largely untraceable fashion. In response, many applications have been developed to alert mobile phone users of incoming robocalls. However, how well these applications communicate risk with their users is not well understood. In this paper, we identify common real-time security indicators used in the most popular anti-robocall applications. Using focus groups and user testing, we first identify which of these indicators most effectively alert users of danger. We then demonstrate that the most powerful indicators can reduce the likelihood that users will answer such calls by as much as 43%. Unfortunately, our evaluation also shows that attackers can eliminate the gains provided by such indicators using a small amount of targetspecific information (e.g., a known phone number). In so doing, we demonstrate that anti-robocall indicators could benefit from significantly increased attention from the research community.
Intelligent Agents Coordination in Ad Hoc Teams

—This document’s main target is the scientiﬁc ﬁeld of Ad Hoc teamwork, in which an intelligent agent secures a position in a previously assembled team of agents. It must be able to coordinate with that team, without formerly acquired knowledge about their team strategies or communication protocols with the purpose of achieving a common goal (in our case, winning a RoboCup 2D Simulation League match). The difﬁculty of developing a solution that performs in complex domains poses a great challenge for us to solve. We assume an approach that consists in identifying the task, teammates and planning towards achieving that goal. By applying machine learning techniques, we deployed our agent in a dynamic multi-agent environment (RoboCup 2D Simulation League), whose complexity is far bigger than the conventional settings used in Ad Hoc teamwork problems. To achieve this goal, we modeled a Markov Decision Process (MDP) where the reward is initially unknown and is later obtained using Inverse Reinforcement Learning (IRL). The other agents and the formation are classiﬁed using supervised learning techniques. Finally, the planning consists in solving the MDP via a Policy Iteration algorithm. With the results obtained with this approach, we prove that it is possible to successfully use Ad Hoc teamwork in complex multi-agent domains.
View-OS: Change your View on Virtualization.

The View-OS concept is deﬁned as allowing each process to have its own view of its execution environment . For example, each process can have its own view for its ﬁle system, networking support, user-id, system-name, etc. Umview and kmview are two proof of concept implementations of the View-OS concept. Technically, umview and kmview are user-level, system-call based, partial, modular, virtual machine monitors. They virtualize a subset of the kernel requests (system calls) depending upon which umview (or kmview) modules have been loaded and upon which kind of virtualization the user conﬁgured. The effectiveness of this approach is best illustrated by providing some examples of what umview (or kmview) can accomplish.
Plans for the ARQMath Lab at CLEF 2020

The ARQMath Lab at CLEF 2020 considers the problem of ﬁnding answers to new mathematical questions among posted answers on a community question answering site (Math Stack Exchange). Queries are question postings held out from the test collection, each containing both text and at least one formula. We expect this to be a challenging task, as both math and text may be needed to ﬁnd relevant answer posts. While several models have been proposed for text question answering, math question answering is in an earlier stage of development. To advance math-aware search and mathematical question answering systems, we will create a standard test collection for researchers to use for benchmarking. ARQMath will also include a formula retrieval sub-task: individual formulas from question posts are used to locate formulas in earlier answer posts, with relevance determined by narrative ﬁelds created based on the original question. We will use these narrative ﬁelds to explore diverse information needs for formula search (e.g., alternative notation, applications in speciﬁc ﬁelds or deﬁnition).
CONSTRUCT AND INCREMENTAL VALIDITY OF THE SLOVAK VERSION OF TRAIT EMOTIONAL INTELLIGENCE QUESTIONNAIRE – ADOLESCENT ́S SHORT FORM

The study analyzes construct (convergent and discriminant) and incremental validity of trait emotional intelligence (EI) of the Slovak version of Trait Emotional Intelligence Questionnaire – Adolescent ́s Short Form (TEIQue-ASF) created by K.V. Petrides (2009) adapted to the Slovak conditions by Kaliská, Nábělková and Salbot (2015). The research sample consisted of 307 high school students (average age: 17.7 /SD=.46/). The validation tools: Ten-Item Personality Inventory – TIPI (Gosling, Rentfrow, & Swann, 2003), Type D-personality – DS14 (Denollet, 2005), State-Trait Anxiety Inventory – STAI (adapted by Muller, Ruisel, & Farkaš, 1980), Rosenberg ́s Self-esteem Scale – RSS (Rosenberg, 1965), Scale of Emotional Habitual Subjective Well-being – SEHP (Džuka & Dalbert, 2002) were used to examine trait EI construct and incremental validity by correlation and regression analyses. The results proved that high trait EI is positively correlated to personality and affect-related variables of self-confidence, emotional stability and extraversion and positive emotional state of mind (convergent validity) and negatively correlated to trait anxiety, type D-personality, self-depreciation and negative emotional state of mind (discriminant validity). Trait EI significantly predicts 2.8% of unique variance in trait anxiety after controlling for three variables supporting convergent validity and 1.2% of unique variance in trait anxiety after controlling for the variables supporting discriminant validity of trait EI in a sample of the Slovak high school adolescents.
Using Business Process Management Frameworks to Integrate Knowledge Management Services into Applications

In [Dav+12], David et al. present the Semantic Alliance (Sally) — a framework enabling integration of Knowledge Management services into applications typically used by knowledge workers (e.g. spreadsheet programs). While integrating new applications with the Sally framework did not pose serious challenges, adding new Knowledge Management (KM) services was getting ever more challenging with every newly integrated service. A closer look revealed that compared to other typical tools such as spreadsheet optimizers or rich visualization tools, Knowledge Management services typically have a very restricted set of software objects to which they can be applied. On the other hand, they can be applied in a much richer number of contexts. These requirements call for distinct integration strategies that this paper explores in more detail. A solution to accommodate these requirements was implemented based on frameworks developed in the Business Process Management research area.
Crossing the Boundaries: Language in Interaction

Cortical information flow (CIF) is a new framework for system identification in neuroscience. CIF models represent neural systems as coupled brain regions that each embody neural computations. These brain regions are coupled to observed data specific to that region. Neural computations are estimated via stochastic gradient descent. We show using a large-scale fMRI dataset that, in this manner, we can estimate models that learn meaningful neural computations. Our framework is general in the sense that it can be used in conjunction with any (combination of) neural recording techniques. It is also scalable, providing neuroscientists with a principled approach to make sense of the high-dimensional neural datasets.