Miracle-dz/UniFAR-FTUs huggingface dataset:
Superion: Grammar-Aware Greybox Fuzzing

In recent years, coverage-based greybox fuzzing has proven itself to be one of the most effective techniques for finding security bugs in practice. Particularly, American Fuzzy Lop (AFL for short) is deemed to be a great success in fuzzing relatively simple test inputs. Unfortunately, when it meets structured test inputs such as XML and JavaScript, those grammar-blind trimming and mutation strategies in AFL hinder the effectiveness and efficiency. To this end, we propose a grammar-aware coverage-based greybox fuzzing approach to fuzz programs that process structured inputs. Given the grammar (which is often publicly available) of test inputs, we introduce a grammar-aware trimming strategy to trim test inputs at the tree level using the abstract syntax trees (ASTs) of parsed test inputs. Further, we introduce two grammar-aware mutation strategies (i.e., enhanced dictionary-based mutation and tree-based mutation). Specifically, tree-based mutation works via replacing subtrees using the ASTs of parsed test inputs. Equipped with grammar-awareness, our approach can carry the fuzzing exploration into width and depth. We implemented our approach as an extension to AFL, named Superion; and evaluated the effectiveness of Superion using large- scale programs (i.e., an XML engine libplist and three JavaScript engines WebKit, Jerryscript and ChakraCore). Our results have demonstrated that Superion can improve the code coverage (i.e., 16.7% and 8.8% in line and function coverage) and bug-finding capability (i.e., 34 new bugs, among which we discovered 22 new vulnerabilities with 19 CVEs assigned and 3.2K USD bug bounty rewards received) over AFL and jsfunfuzz.
Interprofessional education model for geriatric falls risk assessment and prevention

Background One in three people over the age of 65 fall every year, with 1/3 sustaining at least moderate injury. Falls risk reduction requires an interprofessional health team approach. The literature is lacking in effective models to teach students how to work collaboratively in interprofessional teams for geriatric falls prevention. The purpose of this paper is to describe the development, administration and outcome measures of an education programme to teach principles of interprofessional care for older adults in the context of falls prevention. Methods Students from three academic institutions representing 12 health disciplines took part in the education programme over 18 months (n=237). A mixed method one-group pretest and post-test experimental design was implemented to measure the impact of a multistep education model on progression in interprofessional collaboration competencies and satisfaction. Results Paired t-tests of pre-education to posteducation measures of Interprofessional Socialization and Valuing Scale scores (n=136) demonstrated statistically significant increase in subscales and total scores (p<0.001). Qualitative satisfaction results were strongly positive. Discussion Results of this study indicate that active interprofessional education can result in positive student attitude regarding interprofessional team-based care, and satisfaction with learning. Lessons learnt in a rapid cycle plan-do-study-act approach are shared to guide replication efforts for other educators. Conclusion Effective models to teach falls prevention interventions and interprofessional practice are not yet established. This education model is easily replicable and can be used to teach interprofessional teamwork competency skills in falls and other geriatric syndromes.
Practical Integrity Protection with Oblivious Hashing

Oblivious hashing (OH) is an integrity protection technique that checks the (side) effects resulting from the executed code, in contrast to checking the code itself as done by self-checking (SC). SC introduces atypical behavior in the program logic, like reading the code section loaded in memory. Since such atypical behavior can be detected by attackers, OH is more appealing to be employed in practice than SC. However, OH is incapable of protecting a presumable majority of program instructions, those that depend on nondeterministic (input) data or branches, which have to be manually identified and subsequently skipped. In this paper, we extend OH into a practical protection scheme by proposing i) a technique for automatic segregation of deterministic instructions, and ii) a novel extension, Short Range Oblivious Hashing (SROH), for OH to cover control-flow instructions dependent on nondeterministic data. Our SROH technique increases the range of instructions that OH can protect to nondeterministic branches. Moreover, we intertwine OH with SC to cover (nondeterministic) data dependent instructions and enhance the resilience against tampering attacks. We evaluate the performance overhead as well as the security of our scheme using the MiBench dataset and 3 open source games. Our experiments show that the proposed technique yields a 20-fold increase in the median number of protected instructions and, on non-CPU-intensive programs, imposes an overhead of 52%.
A Study of Techniques to Increase Instruction Level Parallelisms

Instruction Level Parallelism (ILP) is the number of instructions that can be executed in simultaneously a program in a clock cycle. The microprocessors exploit ILP by means of several techniques that have been implemented in the last decades and according to the advances that have been obtained in hardware, this survey presents the different techniques that have been used successfully in the execution of multiple instructions of a single program in a single clock cycle.
Cannabidiol's Upregulation of N-acyl Ethanolamines in the Central Nervous System Requires N-acyl Phosphatidyl Ethanolamine-Specific Phospholipase D

Abstract Introduction: Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are bioactive cannabinoids. We recently showed that acute THC administration drives region-dependent changes in the mouse brain lipidome. This study tested the hypothesis that cell lines representing cell types present in the central nervous system (CNS), neurons (N18 cells), astrocytes (C6 glioma cells), and microglia (BV2 cells) would respond differently to THC, CBD, or their combination. This experimental strategy also allowed us to test the hypothesis that THC and CBD are metabolized differently if presented in combination and to test the hypothesis that responses to CBD are not like the fatty acid amide hydrolase (FAAH) inhibitor URB597. Finally, we tested the hypothesis that CBD's CNS effects would differ in the N-acyl phosphatidyl ethanolamine-specific phospholipase D (NAPE-PLD) knockout (KO) compared to wild-type (WT) mice. Methods: N18, C6, and BV2 cells were stimulated with 1 μM THC, 1 μM CBD, 1 μM THC:CBD, 1 μM URB597, or vehicle for 2 h and lipids extracted. Adult female WT and NAPE-PLD KO mice were injected with 3 mg/kg CBD or vehicle i.p., brains collected 2 h later, eight brain regions dissected, and lipids extracted. Extracted lipids were characterized and quantified using high-pressure liquid chromatography coupled with tandem mass spectrometry (HPLC/MS/MS). Results: Lipid levels in each cell type were differentially affected by THC, CBD, or THC:CBD with a few exceptions. In all cell lines, THC increased levels of arachidonic acid and CBD increased levels of N-acyl ethanolamines (NAEs), including N-arachidonoyl ethanolamine. More THC remained when cells were coincubated with CBD; however, levels of THC metabolites were cell-type dependent. CBD and URB597 caused very different lipid profiles in the cell-based assays with the primary similarity being increases in NAEs. CBD increased levels of NAEs in the WT hippocampus, cerebellum, thalamus, cortex, midbrain, and brainstem; however, NAEs did not increase in any brain region after CBD in NAPE-PLD KO mice. Conclusions: CBD and THC differentially modify the lipidome of the brain and CNS-type cell lines. Increases in NAEs observed after CBD treatment had previously been attributed to FAAH inhibition; however, data here suggest the alternative hypothesis that CBD is activating NAPE-PLD to increase NAE levels.
Data‐driven pedestrian re‐identification based on hierarchical semantic representation

Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re‐identification to be a difficult task. Besides, applications of pedestrian re‐identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data‐driven pedestrian re‐identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid‐level ‘attributes’. Firstly, CNNs, well‐trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of ‘attributes‐classes mapping relations’, final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i‐LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state‐of‐the‐art in terms of accuracy and semanteme.
Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina

Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities.
A retrospective observational study of EEG findings and antiepileptic drug use among children referred for EEG to Zambia's University Teaching Hospital

Despite the heavy burden of epilepsy in Sub‐Saharan Africa, there remains a relative paucity of neurophysiology services and limited published data on electroencephalography (EEG) features among African children. The aim of this study was to describe clinical characteristics, EEG findings, and antiepileptic drug (AED) use among children referred for EEG to the University Teaching Hospital in Zambia.
vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases

Blockchains have recently been under the spotlight due to the boom of cryptocurrencies and decentralized applications. There is an increasing demand for querying the data stored in a blockchain database. To ensure query integrity, the user can maintain the entire blockchain database and query the data locally. However, this approach is not economic, if not infeasible, because of the blockchain's huge data size and considerable maintenance costs. In this paper, we take the first step toward investigating the problem of verifiable query processing over blockchain databases. We propose a novel framework, called vChain, that alleviates the storage and computing costs of the user and employs verifiable queries to guarantee the results' integrity. To support verifiable Boolean range queries, we propose an accumulator-based authenticated data structure that enables dynamic aggregation over arbitrary query attributes. Two new indexes are further developed to aggregate intra-block and inter-block data records for efficient query verification. We also propose an inverted prefix tree structure to accelerate the processing of a large number of subscription queries simultaneously. Security analysis and empirical study validate the robustness and practicality of the proposed techniques.
Exploiting Impacts of Intercell Interference on SWIPT-Assisted Non-Orthogonal Multiple Access

In this paper, we examine the influence of intercell interference (ICI) on the system outage behavior with important derived results in the proposed model of simultaneous wireless information and power transfer (SWIPT) together with the nonorthogonal multiple access (NOMA) using the amplify-and-forward protocol. We derive the closed-form expression of coverage probability for two NOMA users as a function of the signal-to-interference-plus-noise ratio (SINR). To fully take into account the effect of ICI, we adopt more practical parameters to evaluate the optimal power splitting coefficient regarding energy harvesting system performance analysis. Furthermore, to consider a more practical scenario, based on the fact that the number of ICI sources can affect wireless powered relays, we investigate the average outage probability by considering impacts of the reasonable number of participating ICI.
On-Chip Isoniazid Exposure of Mycobacterium smegmatis Penicillin-Binding Protein (PBP) Mutant Using Time-Lapse Fluorescent Microscopy

Antibiotic resistance has been one of the biggest threats to global health. Despite the available prevention and control strategies and efforts in developing new antibiotics, the need remains for effective approaches against antibiotic resistance. Efficient strategies to cope with antimicrobial resistance require a quantitative and deeper understanding of microbial behavior, which can be obtained using different techniques to provide the missing pieces of the current antibiotic-resistance puzzle. Microfluidic-microscopy techniques are among the most promising methods that contribute modernization of traditional assays in microbiology. They provide monitoring and manipulation of cells at micro-scale volumes. Here, we combined population-level, culture-based assays with single-cell resolution, microfluidic-microscopy systems to investigate isoniazid response of Mycobacterium smegmatis penicillin-binding protein (PBP) mutant. This mutant exhibited normal growth in plain medium and sensitivity to stress responses when treated with thermal stress (45 °C), detergent stress (0.1% sodium dodecyl sulfate), acid stress (pH 4.5), and nutrient starvation (1XPBS). The impact of msm0031 transposon insertion on drug-mediated killing was determined for isoniazid (INH, 50 µg/mL), rifampicin (RIF, 200 µg/mL), ethionamide (ETH, 200 µg/mL), and ethambutol (EMB, 5 µg/mL). The PBP mutant demonstrated remarkable isoniazid-killing phenotype in batch culture. Therefore, we hypothesized that single-cell analysis will show increased lysis kinetics and fewer intact cells after drug treatment. However, the single-cell analysis data showed that upon isoniazid exposure, the percentage of the intact PBP mutant cells was 24%, while the percentage of the intact wild-type cells was 4.6%. The PBP mutant cells exhibited decreased cell-lysis profile. Therefore, the traditional culture-based assays were not sufficient to provide insights about the subpopulation of viable but non-culture cells. Consequently, we need more adequate tools to be able to comprehend and fight the antibiotic resistance of bacteria.
Treatment of multiple myeloma with renal involvement: the nephrologist’s view

ABSTRACT Renal injury is a common complication in multiple myeloma (MM). In fact, as many as 10% of patients with MM develop dialysis-dependent acute kidney injury related to increased free light chain (FLC) production by a plasma cell clone. Myeloma cast nephropathy (MCN) is the most prevalent pathologic diagnosis associated with renal injury, followed by light chain deposition disease and light chain amyloidosis. Several FLC removal techniques have been explored to improve kidney disease in MM but their impact on renal clinical outcomes remains unclear. According to the evidence, high cut-off haemodialysis should be restricted to MM patients on chemotherapy with histological diagnosis of MCN and haemodialysis requirements. From our perspective, more efforts are needed to improve kidney outcomes in patients with MM and renal failure.
Global temporal changes in the proportion of children with advanced disease at the start of combination antiretroviral therapy in an era of changing criteria for treatment initiation

The CD4 cell count and percent at initiation of combination antiretroviral therapy (cART) are measures of advanced HIV disease and thus are important indicators of programme performance for children living with HIV. In particular, World Health Organization (WHO) 2017 guidelines on advanced HIV disease noted that >80% of children aged <5 years started cART with WHO Stage 3 or 4 disease or severe immune suppression. We compared temporal trends in CD4 measures at cART start in children from low‐, middle‐ and high‐income countries, and examined the effect of WHO treatment initiation guidelines on reducing the proportion of children initiating cART with advanced disease.
Oriented Carbon Nanostructures by Plasma Processing: Recent Advances and Future Challenges

Carbon, one of the most abundant materials, is very attractive for many applications because it exists in a variety of forms based on dimensions, such as zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and-three dimensional (3D). Carbon nanowall (CNW) is a vertically-oriented 2D form of a graphene-like structure with open boundaries, sharp edges, nonstacking morphology, large interlayer spacing, and a huge surface area. Plasma-enhanced chemical vapor deposition (PECVD) is widely used for the large-scale synthesis and functionalization of carbon nanowalls (CNWs) with different types of plasma activation. Plasma-enhanced techniques open up possibilities to improve the structure and morphology of CNWs by controlling the plasma discharge parameters. Plasma-assisted surface treatment on CNWs improves their stability against structural degradation and surface chemistry with enhanced electrical and chemical properties. These advantages broaden the applications of CNWs in electrochemical energy storage devices, catalysis, and electronic devices and sensing devices to extremely thin black body coatings. However, the controlled growth of CNWs for specific applications remains a challenge. In these aspects, this review discusses the growth of CNWs using different plasma activation, the influence of various plasma-discharge parameters, and plasma-assisted surface treatment techniques for tailoring the properties of CNWs. The challenges and possibilities of CNW-related research are also discussed.
A Short Note on the Jensen-Shannon Divergence between Simple Mixture Distributions

This short note presents results about the symmetric Jensen-Shannon divergence between two discrete mixture distributions $p_1$ and $p_2$. Specifically, for $i=1,2$, $p_i$ is the mixture of a common distribution $q$ and a distribution $\tilde{p}_i$ with mixture proportion $\lambda_i$. In general, $\tilde{p}_1\neq \tilde{p}_2$ and $\lambda_1\neq\lambda_2$. We provide experimental and theoretical insight to the behavior of the symmetric Jensen-Shannon divergence between $p_1$ and $p_2$ as the mixture proportions or the divergence between $\tilde{p}_1$ and $\tilde{p}_2$ change. We also provide insight into scenarios where the supports of the distributions $\tilde{p}_1$, $\tilde{p}_2$, and $q$ do not coincide.
Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis

Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. The complexity of such big data repositories offer new opportunities and pose new challenges to investigate brain, cognition, and disease. Canonical correlation analysis (CCA) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. This doubly-multivariate tool can simultaneously consider two variable sets from different modalities to uncover essential hidden associations. Our primer discusses the rationale, promises, and pitfalls of CCA in biomedicine.
Genomic Takeover by Transposable Elements in the Strawberry Poison Frog

Abstract We sequenced the genome of the strawberry poison frog, Oophaga pumilio, at a depth of 127.5× using variable insert size libraries. The total genome size is estimated to be 6.76 Gb, of which 4.76 Gb are from high copy number repetitive elements with low differentiation across copies. These repeats encompass DNA transposons, RNA transposons, and LTR retrotransposons, including at least 0.4 and 1.0 Gb of Mariner/Tc1 and Gypsy elements, respectively. Expression data indicate high levels of gypsy and Mariner/Tc1 expression in ova of O. pumilio compared with Xenopus laevis. We further observe phylogenetic evidence for horizontal transfer (HT) of Mariner elements, possibly between fish and frogs. The elements affected by HT are present in high copy number and are highly expressed, suggesting ongoing proliferation after HT. Our results suggest that the large amphibian genome sizes, at least partially, can be explained by a process of repeated invasion of new transposable elements that are not yet suppressed in the germline. We also find changes in the spliceosome that we hypothesize are related to permissiveness of O. pumilio to increases in intron length due to transposon proliferation. Finally, we identify the complement of ion channels in the first genomic sequenced poison frog and discuss its relation to the evolution of autoresistance to toxins sequestered in the skin.
Intraocular Pressure After Corneal Refractive Surgery

Introduction: Laser in situ keratomileusis (LASIK), a refractive surgery procedure is being performed in a large number among people with refractive errors. In all the people undergoing the procedure, there is a potential risk to misdiagnose the glaucoma disease due to changes in central corneal thickness (CCT). In subjects who have undergone laser refractive interventions, intraocular tension may be lower and underestimated, and this can lead to later detection of glaucoma. Aim: The objective of this study was to analyze the intraocular pressure (IOP) after LASIK in patients with myopia. Methods: Thirty-seven patients underwent LASIK intervention to treat myopia. In total, 74 eyes were treated. Before the intervention, all patients underwent complete ophthalmologic examination, including the measurement of central corneal thickness (CCT) and measurement of IOP with non-contact tonometer. The IOP was also measured on days 1, 3 and 30 after the surgery. The mean IOP was taken for statistical analysis. Results: Seventy-four eyes of 37 patients (mean age 31.6) underwent LASIK intervention to treat myopia. Mean CCT before the intervention was 551.9 ±31. 9µm, while mean postoperative CCT was 469.8 ±45. 3µm (p<0.0001). Mean preoperative IOP was 16.4mmHg while mean postoperative IOP was 11.0mmHg (p<0.0001). The average spherical equivalent was -5.9 diopters. Conclusions: The reduction of IOP after LASIK refractive surgery is significant. This reduction is about 1mmHg per 1 diopter. This should be taken into account in the future in these patients because, due to the underestimation of the IOP, the glaucoma disease may be overlooked.