Miracle-dz/UniFAR-FTUs huggingface dataset:
RINT1 Loss Impairs Retinogenesis Through TRP53-Mediated Apoptosis

Genomic instability in the central nervous system (CNS) is associated with defective neurodevelopment and neurodegeneration. Congenital human syndromes that affect the CNS development originate from mutations in genes of the DNA damage response (DDR) pathways. RINT1 (Rad50-interacting protein 1) is a partner of RAD50, that participates in the cellular responses to DNA double-strand breaks (DSB). Recently, we showed that Rint1 regulates cell survival in the developing brain and its loss led to premature lethality associated with genomic stability. To bypass the lethality of Rint1 inactivation in the embryonic brain and better understand the roles of RINT1 in CNS development, we conditionally inactivated Rint1 in retinal progenitor cells (RPCs) during embryogenesis. Rint1 loss led to accumulation of endogenous DNA damage, but RINT1 was not necessary for the cell cycle checkpoint activation in these neural progenitor cells. As a consequence, proliferating progenitors and postmitotic neurons underwent apoptosis causing defective neurogenesis of retinal ganglion cells, malformation of the optic nerve and blindness. Notably, inactivation of Trp53 prevented apoptosis of the RPCs and rescued the generation of retinal neurons and vision loss. Together, these results revealed an essential role for TRP53-mediated apoptosis in the malformations of the visual system caused by RINT1 loss and suggests that defective responses to DNA damage drive retinal malformations.
The Antitumor Effect of Gene-Engineered Exosomes in the Treatment of Brain Metastasis of Breast Cancer

Strategies for treating brain metastases of breast cancer have demonstrated limited efficacy due to the blood–brain barrier (BBB). Gene therapy could improve the efficacy of chemotherapeutic drugs. Exosomes derived from the mesenchymal stem cells (MSCs) are small membrane-based gene vectors that can pass through the BBB. CXCR4 is the most commonly found chemokine receptor in human cancer cells. Furthermore, the SDF-1/CXCR4 axis plays an important role in the homing of MSCs for tumor cell diffusion and metastasis. TRAIL can selectively induce apoptosis in transformed cells without significant toxic side effects in normal tissues. In this study, exosomes were isolated from MSCCXCR4+TRAIL transduced with CXCR4 and TRAIL using a lentiviral vector. Synergistic antitumor study showed that exosomeCXCR4+TRAIL exerted significant activity as a cooperative agent with carboplatin in an MDA-MB-231Br SCID mouse model, potentially engendering a novel strategy for advancing the treatment of brain metastases of breast cancer. Based on this study, further investigation of the effect of the vector on BBB and inducing apoptosis of brain tumors is warranted. In addition, the safety of the vector in animals during the treatment needs to be evaluated.
IT-Application Behaviour Analysis: Predicting Critical System States on OpenStack using Monitoring Performance Data and Log Files

: Recent studies have proposed several ways to optimize the stability of IT-services with an extensive portfolio of processual, reactive or proactive approaches. The goal of this paper is to combine monitored performance data, such as CPU utilization, with discrete data from log files in a joint model to predict critical system states. We propose a systematic method to derive mathematical prediction models, which we experimentally test using a downsized clone of a real life contract management system as a testbed. First, this testbed is used for data acquisition under variable and fully controllable system loads. Next, based on the monitored performance metrics and log file data, we train models (logistic regression and decision trees) that unify both, numeric and textual, data types in a single incident forecasting model. We focus on 1) investigating different cases to identify an appropriate prediction time window, allowing to prepare countermeasures by considering prediction accuracy and 2) identifying variables that appear more likely than others in the predictive models.
Assessing the Geometric Structure of a White Clover ( Trifolium repens L.) Canopy using3-D Digitising

Abstract A method was developed for assessing the three dimensional (3-D) geometric structure of white clover canopies. 3-D co-ordinates of pre-defined points on leaves, petioles and stolons were measured using a Polhemus Fastrak electromagnetic 3-D digitiser. Digitising progressed downwards from the top of the canopy and plant parts were removed after they have been digitised. Leaflets were treated as four quarter-ellipses, and petiole and stolons were treated as cylinders. Leaf dimensions and areas calculated from 3-D co-ordinates were within about 5% and 20% of direct measurements made with a ruler and a planimeter, respectively. Special software and freeware POV-Ray were used to reconstruct a virtual canopy from digitiser records and to calculate canopy characteristics such as leaf area index (LAI), petiole intersection area, and profiles of leaflet areas and inclinations with height. It took between 3 and 7 h to digitise 10 × 10 cm stands of clover and the resulting information was considerably more comprehensive and accurate than could have been obtained by the alternative ‘point quadrat’ or ‘stratified clipping’ methods.
Kikuyu-based pasture for dairy production: a review

Abstract. The amount of pasture grown and converted to animal product is closely linked with the profitability of pasture-based systems. Kikuyu (Pennisetum clandestinum Hochst. ex Chiov.) is the predominant C4 grass in coastal Australian beef and dairy systems. These kikuyu-based production systems face several key challenges to achieving high levels of productivity. In this review, we bring together the literature to highlight the opportunities for closing the gap between current and potential utilisation and for increasing dairy production from kikuyu-based pastures. More specifically, we highlight the significant gains that can be made on kikuyu-based commercial farms based on a conceptual model to show where the main losses originate, namely input and grazing management. The physical limitations associated with kikuyu for dairy systems are also presented, such as the relatively higher content of cell wall and lower content of water-soluble carbohydrates, together with nutrient imbalances relative to other grass species. Together, these limitations clearly indicate the need of supplying cows with supplements (particularly grain-based concentrates) to achieve moderate to high milk yield per cow. To achieve this without compromising pasture utilisation, dairy producers farming on kikuyu-based pastures need to use relatively greater stocking rates to generate enough demand of feed that can be used to align rate of pasture intake with rate of pasture growth, creating enough deficit of feed per cow to justify the addition of supplementary feed without impinging on pasture utilisation. The variability that exists between cows in kikuyu dry matter and neutral detergent fibre intake is also highlighted in this review, opening up new avenues of research that may allow significant productivity gains for kikuyu-based dairy farming in the future.
Avoiding Network and Host Detection using Packet Bit-masking

: Current host and network intrusion detection and prevention systems mainly use deep packet inspection, signature analysis and behavior analytics on trafﬁc and relevant software to detect and prevent malicious activity. Solutions are applied on both system and network level. We present an evasion attack to remotely control a shell and/or exﬁltrate sensitive data that manages to avoid most popular host and network intrusion techniques. The idea is to use legitimate trafﬁc and victim-generated packets that belong to different contexts and reuse it to communicate malicious content without tampering their payload or other information (except destination IP). We name the technique “bit-masking”. The attack seems able to exﬁltrate any amount of data and execution time does not seem to affect detection rates. For proof, we develop the “Leaky-Faucet” software that allows us to (i) remotely control a reverse shell and (ii) transfer data unnoticed. The validation scope for the presented attack includes evading 5 popular NIDS, 8 of the most popular integrated end-point protection solutions and a Data Leakage Prevention system (DLP); both on the network and host session level. We present three different variations of the attack able to transfer (i) shell commands, (ii) large chunks of data, and (iii) malicious code to a remote command and control (CnC) center. During experiments, we also detected an NPcap library bug that allows resent packets to avoid logging from network analysis tools for Windows that use the Npcap library.
Producing locality: Space, houses and public culture in a Hindu festival in Malaysia

Victor Turner's concepts of liminality and communitas have left an indelible mark on anthropological studies on ritual. Basically, Turner argued that there is a dialectic between the mediacy of social structure (characterised as a 'closed society' or 'status system') and the immediacy of communitas (an 'open society'). This article argues for a more fluid understanding of these kinds of social processes, drawing from Arjun Appadurai's char acterisation of a 'locality'as a 'complex phenomenological quality, constituted by a series of links between the sense of social immediacy, the technologies of interactivity and the relativity of contexts'. This is illustrated through the ethnographic description and analy sis of a local annual Hindu festival in an urban squatter settlement in Malaysia. While the mythic territoriality of the female deity primarily engenders symbolic boundary-making and life-sustaining activities, it also constitutes other layers of social spaces for organ isers and participants alike. Individual and corporate agendas overlap and criss-cross one another. Local knowledge is both parochial and constituted within the wider religious, social and political landscapes. Altogether, these kinds of activities contribute towards a 'public culture' of Hinduism in Malaysia, characterised both by differentiation and the semblance of communitas.
Integrative Analysis Revealing Human Heart-Specific Genes and Consolidating Heart-Related Phenotypes

Elucidating expression patterns of heart-specific genes is crucial for understanding developmental, physiological, and pathological processes of the heart. The aim of the present study is to identify functionally and pathologically important heart-specific genes by performing the Ingenuity Pathway Analysis (IPA). Through a median-based analysis of tissue-specific gene expression based on the Genotype-Tissue Expression (GTEx) data, we identified 56 genes with heart-specific or elevated expressions in the heart (heart-specific/enhanced), among which three common heart-specific/enhanced genes and four atrial appendage-specific/enhanced genes were unreported regarding the heart. Differential expression analysis further revealed 225 differentially expressed genes (DEGs) between atrial appendage and left ventricle. Our integrative analyses of those heart-specific/enhanced genes and DEGs with IPA revealed enriched heart-related traits and diseases, consolidating evidence of relationships between these genes and heart function. Our reports on comprehensive identification of heart-specific/enhanced genes and DEGs and their relation to pathways associated with heart-related traits and diseases provided molecular insights into essential regulators of cardiac physiology and pathophysiology and potential new therapeutic targets for heart diseases.
Development of Genomic Resources and Identification of Genetic Diversity and Genetic Structure of the Domestic Bactrian Camel in China by RAD Sequencing

The domestic Bactrian camel is indispensable to agricultural production in the desertification area of China owning to its endurance to hunger and thirst, cold resistance, drought resistance, and good long-distance transportation. Therefore, it is necessary to investigate the genetic diversity, genetic structure, and genes with important roles in the evolution of this species. In this study, 1,568,087 SNPs were identified in 47 domestic Bactrian camels inhabiting four regions of China, namely Inner Mongolia, Gansu, Qinghai, and Xinjiang, by restriction site associated DNA sequencing (RAD-seq). The SNP data were used for nucleotide diversity analysis (π) and linkage disequilibrium (LD) attenuation analysis to elucidate the genetic diversity of the domestic Bactrian camel in the four regions studied. Results showed that Xinjiang camels had the highest nucleotide diversity and the fastest decay rate of the LD coefficient; therefore, Xinjiang camels had the highest genetic diversity. Structure analysis, principal component analysis (PCA), and phylogenetic tree construction by the neighbor-joining (NJ) method showed that Qinghai camels clustered separately, at a larger phylogenetic distance from camels in the other regions. Through analyses of selection signals, it was found that the number of selected genes shared by Inner Mongolia camels, Qinghai camels, Xinjiang camels, and Gansu camels was 7, 24, 25, and 113, respectively. The shared selected genes of the domestic Bactrian camel in the four regions were further analyzed, and three shared genes (GRIA3, XIAP, and THOC2) of the domestic Bactrian camel in China were identified. Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the shared selected genes of the domestic Bactrian camel in all four regions studied. Across all regions, genes involved in the cellular process were the most abundant subcategory under biological process. Cell and cell part represented the main proportion of genes under cellular component. Binding represented the main molecular function. In addition, the shared selected genes of the domestic Bactrian camel in the four regions of China were significantly enriched in the long-term depression pathway. The research should enable further study of the genetic resources of the domestic Bactrian camel, as well as the conservation of these resources.
Ensuring the Integrity of Outsourced Web Scripts

Dynamic web browsing, supported by web scripting languages such as JavaScript, has quickly conquered the Internet. In spite of the obvious advantages they offer, they have also opened many security flaws for the user browsing. The browser starts by retrieving some external scripts, potentially distributed over many servers. In terms of security, this process is extremely sensitive, therefore many solutions have been introduced to secure web browsing. Unfortunately, they mostly rely on server side actions. Hence, a malicious server is able to compromise the client by modifying the security policy and the scripts sent. We propose an efficient solution, which does not require any trust in the servers, to ensure the integrity of distributed web scripts. Our protocols rely on simple cryptographic tools, such as digital signature schemes and hash functions. In the end, we provide a proven secure, user-friendly and easy-to-deploy solution which only adds a small latency in the
From Social Trust and Happiness to Government Trust: The Moderating Role of Political Systems and Governance in the Philippines

There are frequent calls to enhance citizens' trust in government to pave the way towards a new paradigm of participatory governance and strong citizen support for government. In various realms, citizens may directly or indirectly engage with the government through various available mediums, even though, despite the availability of various policies and services provided by the government, citizens are generally passive and adamant in trusting the public sector. While many studies have explored a set of determinants that influence citizens' trust in government (i.e., central government, local government, parliament, and the legal system), few studies have ascertained the relationship and the role of social trust, happiness, governance, and political systems. These are critical factors that may influence trust in government. To address this gap, this study draws on the theoretical lens of social capital theory, proposing that cognitive social trust and citizen happiness—environment and performance—are the most likely predictors of citizen trust in government. This study assumes that citizens' perceptions of governance and political systems will moderate the effect of social trust and happiness on trust in government. Using data from the Asia Barometer Survey 2007, and focusing on data collected from the Philippines, this study tests a latent model employing the structural equation modelling technique. It finds that happiness negatively predicts trust in the central government and the legal system, while all other predictors do not have a significant effect. The findings also show that the political system moderates the impact of social trust and happiness on trust in government. Finally, this article points out its theoretical, empirical, and practical implications and provides directions for future research.
A simple burn wound severity assessment classifier based on spatial frequency domain imaging (SFDI) and machine learning

Assessment of burn severity is critical for wound treatment. Spatial frequency domain imaging (SFDI) has been previously used to characterize burns based on the relationships between histology and tissue optical properties. Recently, multispectral and hyperspectral imaging optical features have been combined with machine learning to classify burn severity. Here, we investigated the use of SFDI reflectance data at multiple wavelengths and spatial frequencies, with a support vector machine (SVM), to predict severity in a porcine model of graded burns. Burn severity predictions using SVM were compared to burn grade determined using histology techniques. Results suggest that the combination of spatial frequency data with machine learning models has the potential for accurately predicting burn severity at the 24 hr postburn time point.
Real-Time Cleaning and Refinement of Facial Animation Signals

With the increasing demand for real-time animated 3D content in the entertainment industry and beyond, performance-based animation has garnered interest among both academic and industrial communities. While recent solutions for motion-capture animation have achieved impressive results, handmade postprocessing is often needed, as the generated animations often contain artifacts. Existing real-time motion capture solutions have opted for standard signal processing methods to strengthen temporal coherence of the resulting animations and remove inaccuracies. While these methods produce smooth results, they inherently filter-out part of the dynamics of facial motion, such as high frequency transient movements. In this work, we propose a real-time animation refining system that preserves -or even restores- the natural dynamics of facial motions. To do so, we leverage an off-the-shelf recurrent neural network architecture that learns proper facial dynamics patterns on clean animation data. We parametrize our system using the temporal derivatives of the signal, enabling our network to process animations at any framerate. Qualitative results show that our system is able to retrieve natural motion signals from noisy or degraded input animation.
Speeding Up Deep Convolutional Neural Networks Based on Tucker-CP Decomposition

Convolutional neural networks (CNNs) have made great success in computer vision tasks. But the computational complexity of CNNs is huge, which makes CNNs run slowly especially when computational resources are limited. In this paper, we propose a scheme based on tensor decomposition to accelerate CNNs. Firstly, Tucker method is used to decompose the convolution kernel into a small core tensor with key information and two factor matrices reflecting the linear relationship in the third dimension and fourth dimension of the convolution kernel respectively. Then CP (CANDECOMP/PARAFAC) method is used to decompose the core tensor into several rank-1 tensors. This scheme can remove the linear redundancy in convolution kernels and greatly speed up CNNs while maintaining the high classification accuracy. The scheme is used to decompose all the convolutional layers in AlexNet, and the accelerated model is trained and tested on ImageNet. The results show that our scheme achieves a whole-model speedup of 4 x with merely a 1.9% increase in top-5 error for AlexNet.
Hybrid Context-awareness Modelling and Reasoning Approach for Microgrid's Intelligent Control

: Modern microgrids are promoting the integration of the information and communication technologies (ICT) in order to enhance the emerging advanced power management functionalities such as the integration of renewable energy sources, distributed storage optimization, demand-response strategies, electric vehicles charging, power generation rate forecasting and scheduling, etc. For this, sophisticated sensing and smart metering infrastructures are incorporated in the used equipment as well as in the involved subsystems. Hence many contextual data are become more and more available and its taking into consideration in the control tasks are likely to provide promising results. However, making the microgrid control system understand the data and take the proper decisions based on the identiﬁed context is not an easy task to perform. In fact, recognizing the relations and meanings of the sensed data is difﬁcult and complex due to the heterogeneity and intricacy of the involved parts. Hence, providing context-aware modelling and reasoning mechanisms for microgrids becomes necessary. In this context, this paper contributes with two main solutions. First, a microgrid’s for-malized design providing an easy and understandable view of the system is provided. This deﬁnition respects the separation of concerns principle in order to tame the complexity of the complicated system. Second, an ontology-based context modelling and a rule-based context reasoning in the framework of microgrids are provided. To show the suitability of the proposed processes, a formal case study is carried out. The proposed processes are proved to be less resources consuming compared to some of the existing works.
Simu5G: A System-level Simulator for 5G Networks

: This paper presents Simu5G, a new OMNeT++-based system-level simulator of 5G networks. Simu5G is built starting from the SimuLTE simulation library, which models 4G (i.e., LTE/LTE-A) networks, and is compatible with the latter, thus allowing the simulation of 4G-5G coexistence and transition scenarios. We discuss the modelling of the protocol layers, network entities and functions, and validate our abstraction of the physical layer using 3GPP-based scenarios. Moreover, we report profiling results related to Simu5G execution, and we describe how it can be employed to evaluate Radio Access Network configurations, as well as end-to-end scenarios involving communication and computation, e.g., with Multi-access Edge Computing applications.
Genetic Algorithm for Hybrid on-Chip Memory

In an era of computation, speed is a major criterion. With the advent of chip multiprocessor (CMP) systems, it’s exigent for an innovative strategy to bypass the improficiency in present memory system & architecture. In accordance to the above, frequent on chip memory access have increased analytical challenges in delivering high memory access performance with compact power and latency. The generalized concept of Scratch Pad Memory (SPM) can be configured from SRAM, MRAM & Z-RAM to evolve a heterogeneous SPM architecture. In this paper, we focus on uplifting latency & reducing power consumption. We have used, Adaptive Genetic Algorithm for Data Allocation (AGADA) for allocating data to above mentioned memory units forming the architecture along with test results.