Miracle-dz/UniFAR-FTUs huggingface dataset:
Vaccinia virus infection induces concurrent alterations in host chromatin architecture, accessibility, and gene expression

Genomic DNA folds into complex configurations that produce particular local and global structures thought to profoundly impact genome function. To understand the dynamic nature of this relationship, we investigated the extent of host chromatin structural and functional changes in response to a viral agent. We performed comprehensive assessments of host architecture (Hi-C), accessibility (ATAC-seq), and gene expression (RNA-seq) in a paired manner in response to attenuated vaccinia (smallpox) virus. Over time, infection significantly increased long-range intra-chromosomal interactions and decreased chromatin accessibility. Fine-scale accessibility changes were independent of broad-scale chromatin compartment exchange, which increased (up to 12% of the genome) over time, underscoring potential independent mechanisms for global and local chromatin reorganization. The majority of differentially expressed genes, including those downregulated in immune responses, had concurrent alterations in local accessibility and loop domain restructuring. Increased B compartmentalization, intra-chromosomal interactions, and decreased inter-chromosomal interactions and chromatin accessibility together indicate that infection converts the host genome into a more condensed state with nearly equal bidirectional differential gene expression. These changes in host chromatin features may have implications for developing efficacious anti-viral countermeasures. Overall, our empirical data provides evidence of orchestrated concurrent alterations in chromatin architecture, accessibility, and gene expression in response to infection, further reinforcing the notion of coordinated structure-function dynamics of the genome.
Porphyry metallogenic system of Geza arc in the Sanjiang region, southwestern China

Gaza arc is an important tectonic unit formed by evolution of Tethys in the Sanjiang region, Southwest China. The island arc has complex tectonic structures, strong magmatisms, active ore forming fluids, complicated and multiple metallogenics which formed a number of large-super large scale deposits with complete deposit types. Pervasive traces of multi-episode tectonic-magmatic-hydrothermal activities make the island arc a natural laboratory for studies of tectonic evolution and multi-episode metallogenics since Tethys period in the Sanjiang region. Based on discoveries of large-super large scale deposits in this area and achievements on systematic studies of magmatisms and metallogenics since Indosinian, the authors divided the Gaza island arc metallogenics into three metallogenic systems based on three-stage magmatisms: Indosinian porphyry Cu-polymetallic metallogenic system, Yanshanian porphyry Mo-Cu-polymetallic metallogenic system and Himalayan alkali-rich porphyry Au-Mo-Cu polymetallic metallogenic system. This paper discussed and concluded the mineralization ages, source of ore-forming materials, ore-forming fluid systems and metallogenic dynamics backgrounds of the three-stage porphyry metallogenic systems of Gaza island metallogenic system.
Time-Sensitive Data Processing Strategy for Enhancing the Performance of BFT Consensus Mechanism in IoT Edge Computing Environment

Applying the BFT consensus mechanism in the IoT edge computing environment effectively solves the problem of data consistency and trustworthiness. However, the existing BFT consensus mechanism needs to pay attention to the impact of the data processing flow on consensus performance, and the repetitive data processing operations lead to the waste of computing resources of consensus nodes. Therefore, in this paper, we analyze the data processing flow of the BFT consensus mechanism, propose a time-sensitive data processing strategy from the idea of reducing the number of consensus nodes involved in data processing, and select data processing nodes by taking data processing time as the optimization target to improve the resource utilization of consensus nodes while ensuring consensus reaching. Through simulation experiments, our proposed time-sensitive data processing strategy achieves the best latency and throughput performance in a simulated IoT edge computing environment, demonstrating that the time-sensitive data processing strategy can effectively improve the performance of the BFT consensus mechanism.
SoK: “Plug & Pray” Today – Understanding USB Insecurity in Versions 1 through C

—USB-based attacks have increased in complexity in recent years. Modern attacks now incorporate a wide range of attack vectors, from social engineering to signal injection. To address these security challenges, the security community has responded with a growing set of fragmented defenses. In this work, we survey and categorize USB attacks and defenses, unifying observations from peer-reviewed research and industry solutions. Our systematization extracts offensive and defensive primitives that operate across layers of communication within the USB ecosystem. Based on our taxonomy, we discover that USB attacks often abuse the Trust-by-Default nature in the ecosystem, and transcend different layers within a software stack; none of the existing defenses provides a complete solution, and solutions expanding multiple layers are most effective. We then turn to the ﬁrst formal veriﬁcation of the recently released USB Type-C Authentication speciﬁcation, and uncover fundamental ﬂaws in the speciﬁcation’s design. We further evaluate the spec using ﬁndings from our systematization, and ﬁnd that while the spec has successfully pinpointed an urgent need to solve the USB security problem, those ﬂaws render its goals in vain. We conclude by outlining future research directions to ensure a safer computing experience with USB.
CLUSTERING LONG-DISTANCE RUNNERS BASED ON THEIR TECHNIQUE AT ONE SINGLE SPEED DOES NOT GENERALISE TO MULTIPLE SPEEDS

The aim of this study was to assess whether clustering runners based on their technique resulted in consistent group allocations across multiple speeds. Eighty-four runners (34 females) completed four 4-minute running stages at 10, 11, 12 and 13 km/h. For each stage, running technique was characterised using a set of continuous variables in the sagittal plane and discrete stride-based variables. An autoencoder neural network was used for dimensionality reduction and agglomerative hierarchical clustering was applied to identify groups of runners with a similar technique. Two clusters for each speed were selected and the clustering partitions at different incremental speeds were compared. Our results showed that partitions were inconsistent across speeds, and therefore clustering results at one single speed do not generalise to the range of speeds an athlete typically runs at. Single speed clustering may be limited to drive the design of cluster-specific running training interventions and different clustering approaches are needed to better capture runners’ technique at their typical speeds.
Applying Communities of Practice to the Learning of Police

This paper presents an argument for the application of Wenger’s (1998) theory of a community of practice to develop understanding of the learning that occurs in early-career police. The focus of this research is on the ways that early career police learn the craft of policing. Early career police are defined as police in their first three years of service. This definition is formed from the general requirements of the New South Wales Police for the completion of one year probation and two further years as a general duties constable prior to application to transfer or specialise. During the probation year police officers continue to undertake further formal university-based study. Police learning experiences will be investigated using Lave and Wenger’s social theory of learning and the notion of communities of practice, applied within a para-military police culture. The argument is that a community of practice can be used to explore, and better understand, the impact of field experience on the learning of police post their compulsory recruit training. At present there is only a few studies of this issue, such as Chan et al. (2003), Fielding (1988) and van Maanen (1978); these studies tend to cease at the conclusion of the probation year, are not set within the recent context of a university provided education, as is the case in New South Wales, and there exists no research applying community of practice theory in this context. It is intended that in exploring the learning and socialisation process, this research will contribute to the understanding and realisation of professionalism in policing, facilitate the improved design of the Associate Degree in Policing Practice (ADPP) and lead to better understanding of how police learn to be police, thus contributing to the design of training and education that can target their development.
eCommons@AKU eCommons@AKU

Background: There remains scarcity of literature regarding the patient ’ s health status post-COVID-19 infection. This study analyzes the prevalence of residual symptoms and quality of life (QoL) after COVID-19. Methods: An anonymous online survey was administrated in Pakistan from November 2020 to April 2021 in COVID-19 survivors. The questionnaire used the 12-Item Short Form Health Survey (SF-12) to assess mental and physical QoL. Multivariate linear regression was used to explore factors associated with mental and physical QoL scores. Results: A total of 331 COVID-19 survivors participated in our survey. Around 42.0% of the cohort reported within 1 – 3 months of diagnosis of COVID-19. The common residual symptoms were body aches (39.9%), low mood (32.6%), and cough (30.2%). Better physical QoL was associated with being male (adjusted beta: 3.328) and having no residual symptoms (6.955). However, suffering from nausea/vomiting during initial COVID-19 infection ( (cid:0) 4.026), being admitted to the ICU during COVID-19 infection ( (cid:0) 9.164), and suffering from residual body aches ( (cid:0) 5.209) and low mood ( (cid:0) 2.959) was associated with poorer QoL. Better mental QoL was associated with being asymptomatic during initial COVID-19 infection (6.149) and post-COVID (6.685), while experiencing low mood post-COVID was associated with poorer mental QoL ( (cid:0) 8.253 [-10.914, (cid:0) 5.592]). Conclusion: Despite presumed “ recovery ” from COVID-19, patients still face a wide range of residual symptoms months after initial infection, which contributes towards poorer QoL. Healthcare professionals must remain alert to the long-lasting effects of COVID-19 infection and aim to address them appropriately to improve patients ’ QoL.
scat-A Motion-Based Approach to Abdominal Clutter Reduction

— In ultrasound images, clutter is a noise artifact most easily observed in anechoic or hypoechoic regions. It appears as diffuse echoes overlying anatomical structures of diagnostic importance, obscuring tissue borders and reducing image contrast. A novel clutter reduction method for abdominal images is proposed, wherein the abdominal wall is displaced during successive-frame image acquisitions. A region of clutter distal to the abdominal wall was observed to move with the abdominal wall, and finite impulse response (FIR) and blind source separation (BSS) motion filters were implemented to reduce this clutter. The proposed clutter reduction method was tested in simulated and phantom data and applied to fundamental and harmonic in vivo bladder and liver images from 2 volunteers. Results show clutter reductions ranging from 0 to 18 dB in FIR-filtered images and 9 to 27 dB in BSS-filtered images. The contrast-to-noise ratio was improved by 21 to 68% and 44 to 108% in FIR-and BSS-filtered images, respectively. Improvements in contrast ranged from 4 to 12 dB. The method shows promise for reducing clutter in other abdominal images.
Public Attitudes Towards Non-Criminal Preventive Detention as a Public Attitudes Towards Non-Criminal Preventive Detention as a Function of the COVID-19 Pandemic Function of the COVID-19 Pandemic

Non-criminal preventive detention is justified by both the state’s parens patriae and police power roles. Individuals with mental illness and individuals with a highly communicable, potentially lethal disease can be involuntarily detained. Modern applications of quarantine have led to higher court decisions that address the balance between liberty and public health and safety. The inherent tensions that underlie quarantine law – individual liberty versus public safety – are apparent in our contemporary, COVID-19 America. Consequently, much of the current discussion appears to have political overtones. In order to empirically address this issue, in the current study we examine attitudes towards preventive detention within the context of resisting quarantine due to the COVID-19 pandemic. In addition, we investigate whether participants report pre-and post-COVID differences in their tolerance for ambiguity, perceived vulnerability to disease and endorsement of procedural justice. Finally, to test the presence of political overtones, we examine the predictive ability of political ideology on participants’ endorsement of preventive detention. We observed a significant association between participants identified as Right Wing and support for non-criminal preventive detention. Our findings add to evidence that assessment of and response to the COVID-19 pandemic is sharply divided along political ideologies.
Behavioural Brain Research

Contextual regularities help us make sense of our visual environment. In scenes, semantically consistent objects are typically better recognized than inconsistent ones (e.g., a toaster vs. printer in a kitchen). What is the role of object and scene orientation in this so-called scene consistency e ﬀ ect? We presented consistent and inconsistent objects either upright (Experiment 1) or inverted (rotated 180°; Experiment 2) on upright, inverted, and scrambled background scenes. In Experiment 1, on upright scenes, consistent objects were recognized with higher accuracy than inconsistent ones, and we observed N300/N400 event-related potentials (ERPs) re ﬂ ecting object-scene semantic processing. No such e ﬀ ects were observed for inverted or scrambled scenes. In Experiment 2, on both upright and inverted scenes, consistent objects were recognized with higher accuracy than inconsistent ones. Moreover, inconsistent objects on upright scenes triggered N300/N400 responses. Interestingly, no N300 but only an N400 de ﬂ ection was found for inconsistent objects on inverted scenes. No e ﬀ ects were observed for scrambled scenes. These data suggest that while upright scenes modulate recognition irrespective of object orientation, inverted scenes only modulate the recognition of inverted objects. In ERPs, we found further evidence that inverted scenes can a ﬀ ect semantic object processing, with contextual in ﬂ uences occurring later in time, possibly driven by delayed or impaired scene gist processing. Mere object inversion does not seem to explain the later emergence of contextual in ﬂ uences. Taken together, the results suggest that the orientation of objects and scenes as well as their relationship to each other can in ﬂ uence ongoing object identi ﬁ cation.
HoTT Operads

Internalising distinct classes of datatypes has been a longstanding pursuit in type theory. For example, the theory of containers captures strictly positive types, while combinatorial species capture finitely labelled structures. To date, however, there has been no similar attempt to internalise distinct classes of operations on datatypes. In this paper we show how the theory of operads, which extend species with a well-behaved notion of composition, provide a natural approach to internalising finitary operations. We present an internalisation of operads in homotopy type theory, which provides a generic framework for capturing and reasoning about operations with particular algebraic properties. All our results are formalised in Cubical Agda.
Time Expression Normalization with Meta Time Information

—Time expression (a.k.a., timex) normalization is a fundamental task for many downstream researches and applications. Previous researches mainly developed deterministic rules and machine-learning methods for the end-to-end task of timex recognition and normalization (TERN). However, deterministic rules heavily depend on speciﬁc domains while machine-learning methods are somewhat unexplainable. To better understand the task, we analyze three diverse benchmark datasets for the characteristics of timex types and values. According to these characteristics, we propose a rule-based method termed MetaTime 1 with three kinds of meta time information to normalize timexes into standard type and value formats. MetaTime is independent of speciﬁc domains and textual types. Experimental results on three diverse benchmark datasets demonstrate that MetaTime outperforms four representative state-of-the-art methods.
The E  ects o  Grounding on Meditation Quality: Preliminary Study Report—A Case Series

Background: Published research shows that grounding the human body to Earth (also called “earthing”) produces multiple health bene  ts. An earlier study documented immediate and abrupt changes in the le  t hemi - sphere o  the brain upon grounding, an indi - cation o  improved brain  unction (Chevalier et al., 2006). The  ndings suggest that ground - ing might improve the practice o  meditation, an activity that has gained widespread popularity throughout the world. Objective: To explore possible added bene  ts  rom meditat - ing indoors while grounded, an experiment was set up involving 10 longtime meditators. Methods: Brain mapping with electroencepha - lographic (EEG) electrodes applied to the head was used to measure brain  unction during grounding vs. non-grounding periods of the meditation. The participants were monitored separately while meditating in a special conductive recliner chair. They were grounded  or 40 minutes in the middle o  their medita - tion session. Grounding was accomplished by using conductive cords to connect the chair, as well as patches applied to the palms of the hands, to the grounding system o  the build - ing. For the  rst 15 minutes o  the meditation and the last 10 minutes, participants were disconnected, that is, not grounded. They were blind to when they were and were not grounded. Results: Both objectively and participatively, a deeper meditation was documented during the period of the meditation when participants were grounded compared to when they were not grounded. About hal  o  the participants showed evidence through brain mapping o  improvements in brain  unction. The principal characteristics of the meditation improvements during grounding were: (a) high Alpha in the  rontal lobes (increased top-down control regu - lation and emotional control), (b) high Theta (increase in internal  ocus, spiritual awareness, and meditation), and (c) tendencies toward brain disorders disappeared during grounding. Conclusion: This small pilot project presents evidence that meditating indoors while grounded o  ers bene  ts beyond the meditation itsel  and replicates traditional practices in which individuals meditated while sitting on the ground. These results warrant more research with more participants and a control group.
Modeli g the role of salie ce i the allocatio of overt visual atte tio

A biologically motivated computatio al model of bottom-up visual selective atte tio was used to exami e the degree to which stimulus salie ce guides the allocatio of atte tio . Huma eye moveme ts were recorded while participa ts viewed a series of digitized images of complex atural a d artiﬁcial sce es. Stimulus depe de ce of atte tio , as measured by the correlatio betwee computed stimulus salie ce a d ﬁxatio locatio s, was fou d to be sig iﬁca tly greater tha that expected by cha ce alo e a d furthermore was greatest for eye moveme ts that immediately follow stimulus o set. The ability to guide atte tio of three modeled stimulus features (color, i te sity a d orie tatio ) was exami ed a d fou d to vary with image type. Additio ally, the eﬀect of the drop i visual se sitivity as a fu ctio of ecce tricity o stimulus salie ce was exami ed, modeled, a d show to be a importa t
Optimization of a breath analysis methodology to potentially diagnose transplanted kidney rejection

Chronic kidney disease may result in end stage renal disorder and increased mortality rate. Up to date, kidney transplantation represents the only de�nitive treatment to restore normal life expectancy. Nevertheless, there is a high risk of organ rejection in the short-medium term after surgery. This paper proposes a new ReCIVA®-GC-MS technology to potentially predict the rejection of the transplanted kidney during the �rst year after surgery by analyzing human breath. Twenty VOCs, recognized by literature as targets or as representative of the major classes of molecules essential to the identi�cation of chronic kidney disease affected patients and/or healthy subjects, were selected and employed for this methodology optimization. The breath pro�le of healthy subjects was considered as target in case of restored kidney function after transplantation. Calibration curves, linearity concentration range, limit of detection and quanti�cation of selected molecules were estimated as well as the intra-day and inter-day reproducibility of the method. To test the applicability of the GC-MS developed methodology, the breath of healthy subjects and chronic kidney disease affected patients was ReCIVA® sampled, and then analyzed. Sixty-seven molecules were identi�ed, and between these, thirteen of twenty selected compounds were quanti�ed, con�rming the robustness of the optimized protocol.
A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson’s Disease

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson’s disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.
Following Afﬁrmative and Negated Rules

Rules are often stated in a negated manner (“no trespassing”) rather than in an afﬁrmative manner (“stay in your lane”). Here, we build on classic research on negation processing and, using a ﬁnger-tracking design on a touchscreen, we show that following negated rather than afﬁrmative rules is harder as indicated by multiple performance measures. Moreover, our results indicate that practice has a surprisingly limited effect on negated rules, which are implemented more quickly with training, but this effect comes at the expense of reduced efﬁciency. Only afﬁrmative rules are thus put into action efﬁ-ciently, highlighting the importance of tailoring how rules are communicated to the peculiarities of the human mind.