Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel Versatile Peroxidase from Lentinus squarrosulus (MH172167)towards enhanced delignification and digestibility of crop residues

Scarcity of quality feed is a major constraint concerning livestock productivity with recalcitrant lignin hindering utilization of crop residues as quality animal feed. Degradation of lignin in nature is contributed by white-rot fungi through their enriched ligninolytic system. Versatile Peroxidase plays a key role in ligninolysis through its capability to oxidize diverse class of aromatics without mediators. In this study, wild isolates of wood rotting fungi were screened for potential peroxidases oxidizing manganese and aromatic compounds. The strain identified as Lentinus squarrosulus (TAMI004, BankIt2098576 MH172167) was monitored for enzyme activity in solid state and submerged fermentation. L. squarrosulus demonstrated predominant Versatile Peroxidase activity amongst the screened wild isolates displaying hybrid characteristic of manganese oxidation and manganese independent reactions on aromatic compounds. The manganese oxidizing peroxidase activity evidenced in submerged fermentation was 12 IU/L whereas in solid state fermentation it was 131 IU/L. This ability to act through manganese mediated and independent reactions on phenolics reveals its biotechnological and industrial significance. Treatment of common crop residues with crude extract of L. squarrosulus rich in Versatile Peroxidase obtained from both Solid state and submerged fermentations showed a decrease in their Neutral Detergent Fiber, Acid Detergent Fiber and Acid Detergent Lignin content showing biodegradation, substantiating the ligninolytic ability and more prominently increase in their digestibility. To the best of our knowledge, this is the first report describing Versatile Peroxidase from Lentinus squarrosulus with potential to augment the ruminant digestibility of crop residues. Importance Versatile Peroxidase of White-rot fungi, a relatively less studied lignolytic enzyme, is very efficient in depolymerization of lignin macromolecule through its multivalent catalytic sites. Lignin degradation is very appealing from the application perspective as attack on lignin exposes the energy affluent polysaccharides for utilization in extensive biotechnological applications. Reports on relevance of Versatile Peroxidase for these purposes are still emerging, however the role of ligninolytic enzymes especially Versatile Peroxidase in enriching ruminant feed is yet unturned. Here, this work demonstrates the potential of Versatile Peroxidase from a novel species Lentinus squarrosulus in delignification thereby upgrading the digestibility and nutritive value of crop residues. The observations validate the importance of the enzyme in improvement of crop residues for feeding ruminants in the current scenario where, livestock productivity is severely impacted by lack of quality feed and demand for alternate feed resources is intensifying.
Functional Interplay Between Posterior Parietal Cortex and Hippocampus During Detection of Memory Targets and Non-targets

Posterior parietal cortex is frequently activated during episodic memory retrieval but its role during retrieval and its interactions with the hippocampus are not yet clear. In this fMRI study, we investigated the neural bases of recognition memory when study repetitions and retrieval goals were manipulated. During encoding participants studied words either once or three times, and during retrieval they were rewarded more to detect either studied words or new words. We found that (1) dorsal parietal cortex (DPC) was more engaged during detection of items studied once compared to three times, whereas regions in the ventral parietal cortex (VPC) responded more to items studied multiple times; (2) DPC, within a network of brain regions functionally connected to the anterior hippocampus, responded more to items consistent with retrieval goals (associated with high reward); (3) VPC, within a network of brain regions functionally connected to the posterior hippocampus, responded more to items not aligned with retrieval goals (i.e., unexpected). These findings support the hypothesis that DPC and VPC regions contribute differentially to top-down vs. bottom-up attention to memory. Moreover, they reveal a dissociation in the functional profile of the anterior and posterior hippocampi.
A sequential sampling model of pedestrian road crossing choice

Urban street space is often contested with competition and coordination between multiple road users and transport modes. Understanding how pedestrians decide to enter the carriageway can help inform how to plan and manage street space and is increasingly relevant as autonomous vehicles mature. In this paper, we present a description of pedestrian road crossing behaviour using a novel sequential sampling modelling approach implemented in a spatial agent-based model. The model explicitly represents the gradual process of deliberation between discrete road crossing choice alternatives resulting in a choice of crossing location. The model reproduces some characteristic pedestrian road crossing behaviours such as trade offs between vehicle exposure and journey time, non-compliant crossing (jaywalking), and dependence of crossing choice on the proximity of crossing alternatives.
MPEG1/Perforin-2 Haploinsufficiency Associated Polymicrobial Skin Infections and Considerations for Interferon-γ Therapy

Introduction Macrophage expressed gene 1 (MPEG1) is highly expressed in macrophages and other phagocytes. The gene encodes a bactericidal pore-forming protein, dubbed Perforin-2. Structural-, animal-, and cell-based studies have established that perforin-2 facilitates the destruction of phagocytosed microbes upon its activation within acidic phagosomes. Relative to wild-type controls, Mpeg1 knockout mice suffer significantly higher mortality rates when challenged with gram-negative or -positive pathogens. Only four variants of MPEG1 have been functionally characterized, each in association with pulmonary infections. Here we report a new MPEG1 non-sense variant in a patient with the a newly described association with persistent polymicrobial infections of the skin and soft tissue. Case Description A young adult female patient was evaluated for recurrent abscesses and cellulitis of the breast and demonstrated a heterozygous, rare variant in MPEG1 p.Tyr430*. Multiple courses of broad-spectrum antimicrobials and surgical incision and drainage failed to resolve the infection. Functional studies revealed that the truncation variant resulted in significantly reduced capacity of the patient’s phagocytes to kill intracellular bacteria. Patient-derived macrophages responded to interferon gamma (IFN-γ) by significantly increasing the expression of MPEG1. IFN-γ treatment supported perforin-2 dependent bactericidal activity and wound healing. Conclusions This case expands the phenotype of MPEG1 deficiency to include severe skin and soft tissue infection. We showed that haploinsufficiency of perforin-2 reduced the bactericidal capacity of human phagocytes. Interferon-gamma therapy increases expression of perforin-2, which may compensate for such variants. Thus, treatment with IFN-γ could help prevent infections.
Moving Target Defense Considerations in Real-Time Safety- and Mission-Critical Systems

Moving-target defenses (MTDs) have been widely studied for common general-purpose and enterprise-computing applications. Indeed, such work has produced highly effective, low-overhead defenses that are now commonly deployed in many systems today. One application space that has seen comparatively little focus is that of safety- and mission-critical systems, which are often real-time systems (RTS) with temporal requirements. Furthermore, such systems are increasingly being targeted by attackers, such as in industrial control systems (ICS), including power grids. The strict timing requirements of these systems presents a different design objective than is common in general-purpose applications -- systems should be designed around the worst-case performance, rather than the average case. Perhaps in part due to these alternative design considerations, many real-time systems have not benefited from much of the work on software security that common general-purpose and enterprise applications have, despite the ubiquity of real-time systems that actively control so many applications we as a society have come to rely on, from power generation and distribution, to automotive and avionic applications, and many others. This paper explores the application of moving-target defenses in the context of real-time systems. In particular, the worst-case performance of several address-space randomization defenses are evaluated to study the implications of such designs in real-time applications. These results suggest that current moving-target defenses, while performant in the average case, can exhibit significant tail latencies, which can be problematic in real-time applications, especially if such overheads are not considered in the design and analysis of the system. These results inform future research directions for moving-target defenses in real-time applications.
Continuous and Multiregional Monitoring of Malicious Hosts

The number of cybersecurity threats has been increasing, and these threats have become more sophisticated year after year. Malicious hosts play a large role in modern cyberattacks, e.g., as a launcher of remote-control attacks or as a receiver of stolen information. In such circumstances, continuous monitoring of malicious hosts (URL/IP addresses) is indispensable to reveal cyberattack activities, and many studies have been conducted on that. However, many of them have limitations: they help only in the short-term or they help only a few regions and/or a few organizations. Therefore, we cannot effectively monitor attacks that are active for only a short time or that change their behavior depending on where the victims are from (e.g., country/organization). In this paper, we propose Stargazer, a program that monitors malicious hosts from multiple points on a long-term basis. Multiregional monitoring sensors and inter-organizational collaboration are conducted to achieve this surveillance. In this paper, we describe an implementation of the Stargazer prototype and how monitoring was carried out using multiregional sensors starting in Dec. 2018 of 1,050 malicious hosts; 10,929,418 measurements were obtained. Case studies on (1) revived hosts, (2) hosts that only respond to specific regions, and (3) the behavior of attack preparation were created.
Novel Information Discovery and Collaborative Filtering to Support Group Creativity

Teams that innovate encounter challenges in divergent and convergent thinking processes related to the need to: 1) leverage diverse internal and external knowledge, and 2) produce something that is both novel and valuable. Integrating the extant literature, we describe these challenges and propose a new approach to solving issues related to divergent and convergent thinking in groups in an innovation context. Specifically, we design group processes to support divergent and convergent thinking, including the use of several information technology (IT) tools to support them: 1) a novel-information discovery tool to foster computer-supported divergent thinking and sensemaking, and 2) a collaborative-filtering tool to foster computer-supported convergent thinking and sensegiving. Findings indicate that the novel-information discovery tool increases efficiency and effectiveness in the divergent thinking process and that the collaborative-filtering tool supports convergent thinking by focusing the group's attention on ideas that might otherwise be neglected. Combining these two IT tools with group processes for divergent and convergent thinking has important implications for both research and practice.
Was Vector a Companion during Shutdown?: Insights from an Ethnographic Study in Austria

The COVID-19 pandemic led to shutdowns in several countries all over the world, including Austria (March to April 2020). In February 2020 we initiated an ethnographic study to explore the adoption of the commercially available robot Anki Vector in eight households. The fourth household visit of this study happened right during the shutdown, and was thus conducted online. We were particularly curious to see if being isolated at home increased (or changed) peoples' engagement with Vector (as we expected people to have more time with the robot and a higher need for entertainment and companionship). Our interviews revealed that novel use cases emerged during the shutdown: a single-person household found a way to integrate Vector in their Yoga routine, and video calls with family and friends. However, we did not find a qualitative indication that engagement with Vector increased during the shutdown period. This goes in line with findings from fellow researchers suggesting that commercially available service robots fail to engage their users in the longer term.
Apathy Classification Based on Doppler Radar Image for the Elderly Person

Apathy is a disease characterized by diminished motivation not attributable to a diminished level of consciousness, cognitive impairment, or emotional distress. It is a serious problem facing the elderly in today's society. The diagnosis of apathy needs to be done at a clinic, which is particularly inconvenient and difficult for elderly patients. In this work, we examine the possibility of using doppler radar imaging for the classification of apathy in the elderly. We recruited 178 elderly participants to help create a dataset by having them fill out a questionnaire and submit to doppler radar imaging while performing a walking action. We selected walking because it is one of the most common actions in daily life and potentially contains a variety of useful health information. We used radar imaging rather than an RGB camera due to the greater privacy protection it affords. Seven machine learning models, including our proposed one, which uses a neural network, were applied to apathy classification using the walking doppler radar images of the elderly. Before classification, we perform a simple image pre-processing for feature extraction. This pre-processing separates every walking doppler radar image into four parts on the vertical and horizontal axes and the number of feature points is then counted in every separated part after binarization to create eight features. In this binarization, the optimized threshold is obtained by experimentally sliding the threshold. We found that our proposed neural network achieved an accuracy of more than 75% in apathy classification. This accuracy is not as high as that of other object classification methods in current use, but as an initial research in this area, it demonstrates the potential of apathy classification using doppler radar images for the elderly. We will examine ways of increasing the accuracy in future work.
The Social Engineer: An Immersive Virtual Reality Educational Game to Raise Social Engineering Awareness

As system infrastructures are becoming more secure against technical attacks, it is more difficult for attackers to overcome them with technical means. Social engineering instead exploits the human factor of information security and can have a significant impact on organizations. The lack of awareness about social engineering favors the successful realization of social engineering attacks, as employees do not recognize them as such early enough, resulting in high costs for the affected company. Current training approaches and awareness courses are limited in their versatility and create little motivation for employees to deal with the topic. The high immersion of virtual reality can improve learning in this context. We created The Social Engineer, an immersive educational game in virtual reality, to raise awareness and to sensitize players about social engineering. The player impersonates a penetration tester and conducts security audits in a virtually simulated company. The game consists of a detailed game world containing three distinct missions that require the player to apply different social engineering attack methods. Our concept enables the game to be highly extensible and flexible regarding different playable scenarios and settings. The Social Engineer can potentially benefit companies as an immersive self-training tool for their employees, support security experts in teaching social engineering awareness as part of a comprehensive training course, and entertain interested individuals by leveraging fun and innovative gameplay mechanics.
Development and Evaluation of Agent's Adaptive Gaze Behaviors

The purpose of this study is to develop gaze behaviors of a virtual agent that adapt to the gaze behavior of a human conversant so that both introverts and extroverts feel comfortable with. The proposed adaptive gaze model changes the agent's gaze percentage to the user according to the user's gaze percentage to the agent. The results of the evaluation experiment suggest the adaptive gaze model enhances perceived friendliness of the agent and lowers stress toward conversation for introverts.
PDiff: Semantic-based Patch Presence Testing for Downstream Kernels

Open-source kernels have been adopted by massive downstream vendors on billions of devices. However, these vendors often omit or delay the adoption of patches released in the mainstream version. Even worse, many vendors are not publicizing the patching progress or even disclosing misleading information. However, patching status is critical for groups (e.g., governments and enterprise users) that are keen to security threats. Such a practice motivates the need for reliable patch presence testing for downstream kernels. Currently, the best means of patch presence testing is to examine the existence of a patch in the target kernel by using the code signature match. However, such an approach cannot address the key challenges in practice. Specifically, downstream vendors widely customize the mainstream code and use non-standard building configurations, which often change the code around the patching sites such that the code signatures are ineffective. In this work, we propose PDiff, a system to perform highly reliable patch presence testing with downstream kernel images. Technically speaking, PDiff generates summaries carrying the semantics related to a target patch. Based on the semantic summaries, PDiff compares the target kernel with its mainstream version before and after the adoption of the patch, preferring the closer reference version to determine the patching status. Unlike previous research on patch presence testing, our approach examines similarity based on the semantics of patches and therefore, provides high tolerance to code-level variations. Our test with 398 kernel images corresponding to 51 patches shows that PDiff can achieve high accuracy with an extremely low rate of false negatives and zero false positives. This significantly outperforms the state-of-the-art tool. More importantly, PDiff demonstrates consistently high effectiveness when code customization and non-standard building configurations occur.
Identification of Ginger (Zingiber officinale Roscoe) Reference Genes for Gene Expression Analysis

Quantitative real-time PCR (qRT-PCR) is widely used in the detection of gene expression level. However, there is no suitable ginger reference gene for qPCR analysis. Therefore, it is the primary task to select and validate the appropriate ginger reference gene to normalize the expression of target genes. In this study, 14 candidate reference genes were selected and analyzed in different tissues (leaf, and rhizome), different development stages, different varieties, and abiotic stress (ABA and salt stress). Expression stability was calculated using geNorm and NormFinder, Bestkeeper, and RefFinder. For abiotic stress and total conditions, 28S and COX were identified as the most stable genes. In addition, RPII was the most stable in the different development stages and different varieties. TEF2 and RPL2 were the least stably expressed in the tissue and all the conditions. In order to verify the feasibility of these genes as reference genes, we used the most stable and least stable reference genes to normalize the expression levels of ZoSPS genes under different conditions. This work can provide theoretical support for future research on ginger gene expression.
On the Use of TMS to Investigate the Pathophysiology of Neurodegenerative Diseases

Neurodegenerative diseases are a collection of disorders that result in the progressive degeneration and death of neurons. They are clinically heterogenous and can present as deficits in movement, cognition, executive function, memory, visuospatial awareness and language. Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation tool that allows for the assessment of cortical function in vivo. We review how TMS has been used for the investigation of three neurodegenerative diseases that differ in their neuroanatomical axes: (1) Motor cortex—corticospinal tract (motor neuron diseases), (2) Non-motor cortical areas (dementias), and (3) Subcortical structures (parkinsonisms). We also make four recommendations that we hope will benefit the use of TMS in neurodegenerative diseases. Firstly, TMS has traditionally been limited by the lack of an objective output and so has been confined to stimulation of the motor cortex; this limitation can be overcome by the use of concurrent neuroimaging methods such as EEG. Given that neurodegenerative diseases progress over time, TMS measures should aim to track longitudinal changes, especially when the aim of the study is to look at disease progression and symptomatology. The lack of gold-standard diagnostic confirmation undermines the validity of findings in clinical populations. Consequently, diagnostic certainty should be maximized through a variety of methods including multiple, independent clinical assessments, imaging and fluids biomarkers, and post-mortem pathological confirmation where possible. There is great interest in understanding the mechanisms by which symptoms arise in neurodegenerative disorders. However, TMS assessments in patients are usually carried out during resting conditions, when the brain network engaged during these symptoms is not expressed. Rather, a context-appropriate form of TMS would be more suitable in probing the physiology driving clinical symptoms. In all, we hope that the recommendations made here will help to further understand the pathophysiology of neurodegenerative diseases.
Benchmarking Label Dynamics of VirusTotal Engines

VirusTotal is the largest online anti-malware scanning service. It is widely used by security researchers for labeling malware data or serving as a comparison baseline. However, several important challenges of using VirusTotal are left unaddressed (e.g., whether VirusTotal labels are already stable, when VirusTotal labels can be trusted), severely harming the correctness of research projects depending on VirusTotal. In this paper, we present VTSet, which contains daily VirusTotal labels on more than 14,000 files over one year. VTSet can be used to build and evaluate various tools to tackle the existing challenges and facilitate the usage of VirusTotal. Besides the data, VTSet also provides a demonstration tool to display many measurement results and a query tool to ease the access of its data. A video demonstration of VTSet is located at the following link: https://youtu.be/aSVaUGHxFi4.