Miracle-dz/UniFAR-FTUs huggingface dataset:
Leigh Syndrome: A Tale of Two Genomes

Leigh syndrome is a rare, complex, and incurable early onset (typically infant or early childhood) mitochondrial disorder with both phenotypic and genetic heterogeneity. The heterogeneous nature of this disorder, based in part on the complexity of mitochondrial genetics, and the significant interactions between the nuclear and mitochondrial genomes has made it particularly challenging to research and develop therapies. This review article discusses some of the advances that have been made in the field to date. While the prognosis is poor with no current substantial treatment options, multiple studies are underway to understand the etiology, pathogenesis, and pathophysiology of Leigh syndrome. With advances in available research tools leading to a better understanding of the mitochondria in health and disease, there is hope for novel treatment options in the future.
Moderating Multiple Mediation Model of the Impact of Inclusive Leadership on Employee Innovative Behavior

Leadership is an important antecedent variable of employee innovative behavior. In previous studies, the influence of inclusive leadership on employee innovative behavior mainly focus on one mediating variable, which may lead to a deviation in parameter estimation due to the omission of other mediating variables. According to the social cognitive theory and motivation theory and from the perspective of cognitive–motivation integration, this study establishes a moderating multi-mediation model to understand the impact of inclusive leadership on employee innovative behavior. Psychological safety and creative self-efficacy are used as mediating variables, and innovation rewards are used as moderating variables. The data are collected from 418 employees of the manufacturing industry in China. The results show that, at first, inclusive leadership has a positive effect on employee innovative behavior. Secondly, both psychological safety and creative self-efficacy play partial mediating effects between inclusive leadership and employee innovative behavior, with the effect of the former being significantly smaller than that of the latter. Thirdly, innovation rewards positively moderate the relationships of “psychological safety—employee innovative behavior” and of “creative self-efficacy—employee innovative behavior. ” Fourthly, innovation rewards positively moderate the indirect effect of inclusive leadership on employee innovative behavior through psychological safety and creative self-efficacy. These findings are not only helpful to expand how inclusive leadership influences the innovative behavior of employees but also provides some suggestions for enterprise innovation development.
Know Yourself: Physical and Psychological Self-Awareness With Lifelog

Self-awareness is an essential concept in physiology and psychology. Accurate overall self-awareness benefits the development and well being of an individual. The previous research studies on self-awareness mainly collect and analyze data in the laboratory environment through questionnaires, user study, or field research study. However, these methods are usually not real-time and unavailable for daily life applications. Therefore, we propose a new direction of utilizing lifelog for self-awareness. Lifelog records about daily activities are used for analysis, prediction, and intervention on individual physical and psychological status, which can be automatically processed in real-time. With the help of lifelog, ordinary people are able to understand their condition more precisely, get effective personal advice about health, and even discover physical and mental abnormalities at an early stage. As the first step on using lifelog for self-awareness, we learn from the traditional machine learning problems, and summarize a schema on data collection, feature extraction, label tagging, and model learning in the lifelog scenario. The schema provides a flexible and privacy-protected method for lifelog applications. Following the schema, four topics were conducted: sleep quality prediction, personality detection, mood detection and prediction, and depression detection. Experiments on real datasets show encouraging results on these topics, revealing the significant relation between daily activity records and physical and psychological self-awareness. In the end, we discuss the experiment results and limitations in detail and propose an application, Lifelog Recorder, for multi-dimensional self-awareness lifelog data collection.
Performance Metrics for Fluidic Soft Robot Rotational Actuators

The field of soft robotics is continuing to grow as more researchers see the potential for robots that can safely interact in unmodeled, unstructured, and uncertain environments. However, in order for the design, integration, and control of soft robotic actuators to develop into a full engineering methodology, a set of metrics and standards need to be established. This paper attempts to lay the groundwork for that process by proposing six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. Additionally we provide a simple case study as an example of how these metrics can be used to evaluate soft robot actuators for a designated task. While this paper does not claim to present a comprehensive list of all possible soft robot actuator metrics, the metrics presented can 1) be used to initiate the development and comparison of soft robot actuators in an engineering framework and 2) start a broader discussion of which metrics should be standardized in future soft robot actuator research.
No Evidence of Unexpected Transgenic Insertions in T1190 – A Transgenic Apple Used in Rapid Cycle Breeding – Following Whole Genome Sequencing

Rapid cycle breeding uses transgenic early flowering plants as crossbreed parents to facilitate the shortening of breeding programs for perennial crops with long-lasting juvenility. Rapid cycle breeding in apple was established using the transgenic genotype T1190 expressing the BpMADS4 gene of silver birch. In this study, the genomes of T1190 and its non-transgenic wild-type PinS (F1-offspring of ‘Pinova’ and ‘Idared’) were sequenced by Illumina short-read sequencing in two separate experiments resulting in a mean sequencing depth of 182× for T1190 and 167× for PinS. The sequencing revealed 8,450 reads, which contain sequences of ≥20 bp identical to the plant transformation vector. These reads were assembled into 125 contigs, which were examined to see whether they contained transgenic insertions or if they are not using a five-step procedure. The sequence of one contig represents the known T-DNA insertion on chromosome 4 of T1190. The sequences of the remaining contigs were either equally present in T1190 and PinS, their part with sequence identity to the vector was equally present in apple reference genomes, or they seem to result from endophytic contaminations rather than from additional transgenic insertions. Therefore, we conclude that the transgenic apple plant T1190 contains only one transgenic insertion, located on chromosome 4, and shows no further partial insertions of the transformation vector. Accession Numbers: JQ974028.1.
Differential and Prognostic Significance of HOXB7 in Gliomas

Diffuse glioma is the most common primary tumor of the central nervous system. The prognosis of the individual tumor is heavily dependent on its grade and subtype. Homeobox B7 (HOXB7), a member of the homeobox family, is abnormally overexpressed in a variety of tumors. However, its function in glioma is unclear. In this study, HOXB7 mRNA and protein expression levels were analyzed in 401 gliomas from the CGGA RNA-seq database (325 cases) and our hospital (76 cases). HOXB7 expression, at both mRNA and protein levels, were upregulated in glioblastoma (GBM) and isocitrate dehydrogenase 1 (IDH1) wild-type glioma tissues. Kaplan–Meier with log-rank test showed that patients with high HOXB7 expression had a poor prognosis (p < 0.0001). Moreover, HOXB7 protein was deleted in 90.9% (20/22) of oligodendrogliomas and 13.0% (3/23) of astrocytomas. The sensitivity and specificity of HOXB7 protein deletion in oligodendroglioma were 90.9% (20/22) and 87.0% (20/23), respectively. To verify the reliability of using HOXB7 in differentiating oligodendroglioma, we used 1p/19q fluorescence in situ hybridization (FISH) testing as a positive control. The Cohen’s kappa coefficient of HOXB7 immunohistochemistry staining and 1p/19q FISH testing was 0.778 (95% CI: 0.594–0.962, p < 0.001). In conclusion, HOXB7 is an independent predictor of poor prognosis in all grade gliomas. Additionally, HOXB7 is also a highly sensitive and specific indicator to differentiate oligodendroglioma from astrocytoma.
Geochemistry and Weathering Indices of Yedoma and Alas Deposits beneath Thermokarst Lakes in Central Yakutia

Ice- and organic-rich deposits of late Pleistocene age, known as Yedoma Ice Complex (IC), are widespread across large permafrost regions in Northeast Siberia. To reconstruct Yedoma IC formation in Central Yakutia, we analyzed the geochemistry, sedimentology, and stratigraphy of thawed and frozen deposits below two thermokarst lakes in different evolutionary stages (a mature alas lake and a initial Yedoma lake) from the Yukechi site in the Lena-Aldan interfluve. We focused on inorganic geochemical characteristics and mineral weathering in two ∼17 m long sediment cores to trace syngenetic permafrost aggradation and degradation over time. Geochemical properties, element ratios, and specific weathering indices reflect varying sedimentation processes and seasonal thaw depths under variable environmental conditions. Deeper thaw during the interstadial Marine Isotope Stage (MIS) 3 enabled increasing mineral weathering and initial thermokarst processes. Sedimentological proxies reflect high transport energy and short transport paths and mainly terrestrial sediment supply. The Yedoma formation resulted from fluvial, alluvial and aeolian processes. Low mean TOC contents in both cores contrast with Yedoma deposits elsewhere. Likely, this is a result of the very low organic matter content of the source material of the Yukechi Yedoma. Pronounced cryostructures and strongly depleted pore water stable isotopes show a perennially frozen state and preserved organic matter for the lower part of the Yedoma lake core, while changing permafrost conditions, conditions promoting weathering, and strong organic matter decomposition are suggested by our proxies for its middle and upper parts. For the alas lake core, less depleted water stable isotopes reflect the influence of recent precipitation, i.e. the infiltration of rain and lake water into the unfrozen ground. The FENG, MIA(R), and ICV weathering indices have proven to be promising proxies for the identification of conditions that promote mineral weathering to different degrees in the stratigraphy of the thawed and frozen Yedoma deposits, for which we assume a rather homogeneous chemical composition of the parent material. Our study highlights that the understanding of environmental conditions during Yedoma formation and degradation processes by specific geochemical proxies is crucial for assessing the potential decomposition and preservation of the frozen and unfrozen Yedoma inventories.
Association Analysis Revealed That TaPYL4 Genes Are Linked to Plant Growth Related Traits in Multiple Environment

Abscisic acid (ABA), one of phytohormones, plays an important regulatory role in plant growth and development. ABA receptor PYL4 (pyrabactin resistance 1-like 4) was previously detected to be involved in plant response to a variety of stresses. TaPYL4 overexpression could enhance wheat (Triticum aestivum) drought resistance. In order to further investigate TaPYL4’s role in regulating development of other major agronomic traits in wheat, genes of TaPYL4-2A, TaPYL4-2B, and TaPYL4-2D were cloned from wheat, respectively. Polymorphism analysis on TaPYL4 sequences revealed that encoding regions of the three genes were highly conserved, without any SNP (single nucleotide polymorphism) presence. However, nine SNPs and four SNPs were identified in the promoter regions of TaPYL4-2A and TaPYL4-2B, respectively. Functional molecular markers were developed based on these polymorphisms, which were then used to scan a natural population of 323 common wheat accessions for correlation analysis between genotype and the target phenotypic traits. Both TaPYL4-2A and TaPYL4-2B markers were significantly correlated with plant growth-related traits under multiple environments (well-watered, drought and heat stress treatments). The additive effects of TaPYL4-2A and TaPYL4-2B were verified by the combinational haplotype (Hap-AB1∼Hap-AB4) effects determined from field data. Cis-acting elements were analyzed in the promoters of TaPYL4-2A and TaPYL4-2B, showing that a TGA-element bound by ARFs (auxin response factors) existed only in Hap-2A-1 of TaPYL4-2A. Gene expression assays indicated that TaPYL4-2A was constitutively expressed in various tissues, with higher expression in Hap-2A-1 genotypes than in Hap-2A-2 materials. Notably, TaARF4 could act as TaPYL4-2A transcription activator in Hap-2A-1 materials, but not in Hap-2A-2 genotypes. Analysis of geographic distribution and temporal frequency of haplotypes indicated that Hap-AB1 was positively selected in wheat breeding in China. Therefore, TaPYL4-2A and TaPYL4-2B could be a valuable target gene in wheat genetic improvement to develop the ideal plant architecture.
Climate Change Impacts on Tropical Reptiles: Likely Effects and Future Research Needs Based on Sri Lankan Perspectives

The tropical island nation of Sri Lanka has a rich terrestrial and aquatic reptilian fauna. However, like most other tropical countries, the threat of climate change to its reptile diversity has not been adequately addressed, in order to manage and mitigate the extinction threats that climate change poses. To address this shortfall, a review of the international literature regarding climate change impacts on reptiles was undertaken with specific reference to national requirements, focusing on predicted changes in air temperature, rainfall, water temperature, and sea level. This global information base was then used to specify a national program of research and environmental management for tropical countries, which is urgently needed to address the shortcomings in policy-relevant data, its availability and access so that the risks of extinction to reptiles can be clarified and mitigated. Specifically, after highlighting how climate change affects the various eco-physiological features of reptiles, we propose research gaps and various recommendations to address them. It is envisaged that these assessments will also be relevant to the conservation of reptilian biodiversity in other countries with tropical and subtropical climatic regimes
Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.
Programming Exercises Interoperability: The Case of a Non-Picky Consumer

Problem-solving is considered one of the most important skills to retain in the coming decades for building a modern and proactive society. In this realm, computer programming learning is vital to enrich those skills. Practicing in this area boils down to solve programming exercises. In order to foster this practice, it is necessary to provide students with the best of the breed automated tools and a good set of exercises in a fair quantity covering the curricula of a typical programming course. Despite the increasing appearance of automated tools such as program evaluators, gamification engines and sophisticated web environments, access to exercises remains problematic. In fact, although the existence of several code repositories (most for feed computer programming contests), the majority of them store the exercises in proprietary formats and without any access facilities hindering their use. This leaves no other option to teachers but to manually create programming exercises which is time-consuming and error prone, or simply, reuse the same exercises, from previous years, which is considered as a detrimental and limiting approach to enhance multi-faceted and creative programmers. The article surveys the current interoperability efforts on programming exercises, more precisely, in terms of serialization formats and communication protocols. This study will sustain the selection of an API to feed a code playground called LearnJS with random programming exercises.
Permission Sensitivity-Based Malicious Application Detection for Android

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.
A Study on the Relationship between Public Derivative Big Data and Industrial Policymaking: Taking Bike Sharing as an Example

Smart government is an important part of the smart world. The use of big data analysis technology can effectively improve the government’s ability of fine management. Taking China’s bike-sharing industry as the research object, we study the relationship between public-derived big data and industrial policy. First, a feature-enhanced short text clustering method is proposed to perform topic clustering on publicly derived big data. Second, keyword extraction based on word frequency is used to quantify the text of industrial policy. Finally, time is taken as the main line to analyze the co-occurrence of clustering topics and keywords. The results show that (1) the feature enhancement method we proposed can effectively improve the clustering effect. (2) There is a great correlation between the industrial policy and the information mined by Weibo, but there is an obvious lag. Rational use of public-derived big data will effectively help the industrial policy to be released in a better and faster way.
A Lightweight and Secure Anonymous User Authentication Protocol for Wireless Body Area Networks

The recent development of wireless body area network (WBAN) technology plays a significant role in the modern healthcare system for patient health monitoring. However, owing to the open nature of the wireless channel and the sensitivity of the transmitted messages, the data security and privacy threats in WBAN have been widely discussed and must be solved. In recent years, many authentication protocols had been proposed to provide security and privacy protection in WBANs. However, many of these schemes are not computationally efficient in the authentication process. Inspired by these studies, a lightweight and secure anonymous authentication protocol is presented to provide data security and privacy for WBANs. The proposed scheme adopts a random value and hash function to provide user anonymity. Besides, the proposed protocol can provide user authentication without a trusted third party, which makes the proposed scheme have no computational bottleneck in terms of architecture. Finally, the security and performance analyses demonstrate that the proposed scheme can meet security requirements with low computational and communication costs.
NER in Archival Finding Aids

At the moment, the vast majority of Portuguese archives with an online presence use a software solution to manage their finding aids: e.g. Digitarq or Archeevo . Most of these finding aids are written in natural language without any annotation that would enable a machine to identify named entities, geographical locations or even some dates. That would allow the machine to create smart browsing tools on top of those record contents like entity linking and record linking. In this work we have created a set of datasets to train Machine Learning algorithms to find those named entities and geographical locations. After training several algorithms we tested them in several datasets and registered their precision and accuracy. These results enabled us to achieve some conclusions about what kind of precision we can achieve with this approach in this context and what to do with the results: do we have enough precision and accuracy to create toponymic and anthroponomic indexes for archival finding aids? Is this approach suitable in this context? These are some of the questions we intend to answer along this paper. 2012