Miracle-dz/UniFAR-FTUs huggingface dataset:
The NANOGrav 11-year Data Set: High-precision Timing of 45 Millisecond Pulsars

We present high-precision timing data over time spans of up to 11 years for 45 millisecond pulsars observed as part of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) project, aimed at detecting and characterizing low-frequency gravitational waves. The pulsars were observed with the Arecibo Observatory and/or the Green Bank Telescope at frequencies ranging from 327 MHz to 2.3 GHz. Most pulsars were observed with approximately monthly cadence, and six high-timing-precision pulsars were observed weekly. All were observed at widely separated frequencies at each observing epoch in order to fit for time-variable dispersion delays. We describe our methods for data processing, time-of-arrival (TOA) calculation, and the implementation of a new, automated method for removing outlier TOAs. We fit a timing model for each pulsar that includes spin, astrometric, and (for binary pulsars) orbital parameters; time-variable dispersion delays; and parameters that quantify pulse-profile evolution with frequency. The timing solutions provide three new parallax measurements, two new Shapiro delay measurements, and two new measurements of significant orbital-period variations. We fit models that characterize sources of noise for each pulsar. We find that 11 pulsars show significant red noise, with generally smaller spectral indices than typically measured for non-recycled pulsars, possibly suggesting a different origin. A companion paper uses these data to constrain the strength of the gravitational-wave background.
Paper-based electrochemiluminescence sensor for highly sensitive detection of amyloid-β oligomerization: Toward potential diagnosis of Alzheimer's disease

Development of a rapid and sensitive method for Aβ(1-42) aggregation detection is of great importance to overcome the limitations of conventional techniques. In this study, we developed a label-free paper-based electrochemiluminescence sensor for amyloid-β aggregation detection toward potential diagnosis of Alzheimer's disease (AD). The paper-based chip used in the system serves as a low-cost and disposable detection method. In this detection platform, the bonding of [Ru(phen)2dppz]2+ to Aβ(1-42) aggregates results in enhanced electrochemiluminescence due to the change in the polarity of the microenvironment when [Ru(phen)2dppz]2+ intercalated into the β-sheets during oligomerization. The oligomerization process of Aβ(1-42) can be monitored in real time by the novel method, and as low as 100 pM equivalent monomer concentration of Aβ(1-42) could be detected simultaneously. In addition, the cerebrospinal fluid of transgenic AD model mice was tested by this method, which is highly consistent with genetic identification. In addition, we demonstrated that this detection platform could be a potential new method for the screening of Aβ(1-42) aggregation inhibitors, highlighting the practical application capacity of this platform. The platform is label free, low cost and sensitive. Therefore, the proposed platform holds great promise for the diagnosis of AD.
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring

Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performed face detection, face recognition, facial action unit detection, head pose detection, facial expression recognition, posture recognition, actigraphy analysis, sound pressure and light level detection, and visitation frequency detection. We were able to detect patient's face (Mean average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their postures (F1=0.94). We also found that all facial expressions, 11 activity features, visitation frequency during the day, visitation frequency during the night, light levels, and sound pressure levels during the night were significantly different between delirious and non-delirious patients (p-value<0.05). In summary, we showed that granular and autonomous monitoring of critically ill patients and their environment is feasible and can be used for characterizing critical care conditions and related environment factors.
Visual Concepts and Compositional Voting

It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.
Communicating Forgiveness within Adult Sibling Relationships

vi LIST OF TABLES . x CHAPTER
Progress in Clinical Neurosciences: Stroke Recovery and Rehabilitation

ABSTRACT: Background: Recent literature has provided new insights into the role of rehabilitation in neurological recovery post-stroke. The present review combines results of animal and clinical research to provide a summary of published information regarding the mechanisms of neural recovery and impact of rehabilitation. Methods: Plasticity of the uninjured and post-stroke brain is examined to provide a background for the examination of brain reorganization and recovery following stroke. Summary and Conclusions: Recent research has confirmed many of the basic underpinnings of rehabilitation and provided new insight into the role of rehabilitation in neurological recovery. Recovery post stroke is dependent upon cortical reorganization, and therefore, upon the presence of intact cortex, especially in areas adjacent to the infarct. Exposure to stimulating and complex environments and involvement in tasks or activities that are meaningful to the individual with stroke serves to increase cortical reorganization and enhance functional recovery. Additional factors associated with neurological recovery include size of stroke lesion, and the timing and intensity of therapy.
Integration of the Pokeweed miRNA and mRNA Transcriptomes Reveals Targeting of Jasmonic Acid-Responsive Genes

The American pokeweed plant, Phytolacca americana, displays broad-spectrum resistance to plant viruses and is a heavy metal hyperaccumulator. However, little is known about the regulation of biotic and abiotic stress responses in this non-model plant. To investigate the control of miRNAs in gene expression, we sequenced the small RNA transcriptome of pokeweed treated with jasmonic acid (JA), a hormone that mediates pathogen defense and stress tolerance. We predicted 145 miRNAs responsive to JA, most of which were unique to pokeweed. These miRNAs were low in abundance and condition-specific, with discrete expression change. Integration of paired mRNA-Seq expression data enabled us to identify correlated, novel JA-responsive targets that mediate hormone biosynthesis, signal transduction, and pathogen defense. The expression of approximately half the pairs was positively correlated, an uncommon finding that we functionally validated by mRNA cleavage. Importantly, we report that a pokeweed-specific miRNA targets the transcript of OPR3, novel evidence that a miRNA regulates a JA biosynthesis enzyme. This first large-scale small RNA study of a Phytolaccaceae family member shows that miRNA-mediated control is a significant component of the JA response, associated with widespread changes in expression of genes required for stress adaptation.
Effect of cognitive behavioural therapy for insomnia on sleep architecture and sleep EEG power spectra in psychophysiological insomnia

There is now an overwhelming preponderance of evidence that cognitive behavioural therapy for insomnia (CBT‐I) is effective, as effective as sedative hypnotics during acute treatment (4–8 weeks), and is more effective in long term (following treatment). Although the efficacy of CBT‐I in the treatment of chronic insomnia is well known, however there is little objective data on the effects of CBT‐I on sleep architecture and sleep EEG power densities. The present study evaluated, first, subjective change in sleep quality and quantity, and secondly the modifications occurring in polysomnography and EEG power densities during sleep after 8 weeks of CBT‐I. Nine free drug patients with psychophysiological insomnia, aged 33–62 years (mean age 47 ± 9.7 years), seven female and two male participated in the study. Self‐report questionnaires were administered 1 week before and 1 week after CBT‐I, a sleep diary was completed each day 1 week before CBT‐I, during CBT‐I and 1 week after CBT‐I. Subjects underwent two consecutive polysomnographic nights before and after CBT‐I. Spectral analysis was performed the second night following 16 h of controlled wakefulness. After CBT‐I, only scales assessing insomnia were significantly decreased, stages 2, REM sleep and SWS durations were significantly increased. Slow wave activity (SWA) was increased and the SWA decay shortened, beta and sigma activity were reduced. In conclusion CBT‐I improves both subjective and objective sleep quality of sleep. CBT‐I may enhance sleep pressure and improve homeostatic sleep regulation.
Generalized Adaptive A*

Agents often have to solve series of similar search problems. Adaptive A* is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A* because it updates the h-values using information from previous searches. It basically transforms consistent h-values into more informed consistent h-values. This allows it to find shortest paths in state spaces where the action costs can increase over time since consistent h-values remain consistent after action cost increases. However, it is not guaranteed to find shortest paths in state spaces where the action costs can decrease over time because consistent h-values do not necessarily remain consistent after action cost decreases. Thus, the h-values need to get corrected after action cost decreases. In this paper, we show how to do that, resulting in Generalized Adaptive A* (GAA*) that finds shortest paths in state spaces where the action costs can increase or decrease over time. Our experiments demonstrate that Generalized Adaptive A* outperforms breadth-first search, A* and D* Lite for moving-target search, where D* Lite is an alternative state-of-the-art incremental heuristic search algorithm that finds shortest paths in state spaces where the action costs can increase or decrease over time.
Conflict Resolution Using Transactional Analysis and Aikido

Centering yourself, connecting with the aggressor, channeling the attack so that neither you nor your aggressor gets hurt, and safely concluding the interaction constitute four essential steps to reaching a peaceful conflict resolution–even in the face of conflict potentially involving physical harm. The author uses these steps as the basis for what he calls “the 4Cs model. ” Transactional analysis provides the operational concepts underlying the 4Cs and reveals how they work, while the Japanese martial art of aikido, which focuses on stopping conflict before it starts and on establishing harmony, offers practical techniques and a unifying metaphor. The 4Cs are described in detail, and aikido classes for children are used as a case study to discuss how to teach autonomy “through the body. ”
Motivating the Relevance Approach to Conditionals

The aim is to motivate theoretically a relevance approach to (indicative) conditionals in a comparative discussion of the main alternatives. In particular, it will be argued that a relevance approach to conditionals is better motivated than the suppositional theory currently enjoying wide endorsement. In the course of this discussion, an argument will be presented for why failures of the epistemic relevance of the antecedent for the consequent should be counted as genuine semantic defects (as opposed to be relegated to pragmatics). Furthermore, strategies for dealing with compositionality and the perceived objective purport of indicative conditionals will be put forward.
Genetic Diversity and Antimicrobial Susceptibility of Campylobacter jejuni Isolates Associated with Sheep Abortion in the United States and Great Britain

ABSTRACT Campylobacter infection is a leading cause of ovine abortion worldwide. Historically, genetically diverse Campylobacter fetus and Campylobacter jejuni strains have been implicated in such infections, but since 2003 a highly pathogenic, tetracycline-resistant C. jejuni clone (named SA) has become the predominant cause of sheep abortions in the United States. Whether clone SA was present in earlier U.S. abortion isolates (before 2000) and is associated with sheep abortions outside the United States are unknown. Here, we analyzed 54 C. jejuni isolates collected from U.S. sheep abortions at different time periods and compared them with 42 C. jejuni isolates associated with sheep abortion during 2002 to 2008 in Great Britain, using multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE), and array-based comparative genomic hybridization (CGH). Although clone SA (ST-8) was present in the early U.S. isolates, it was not as tetracycline resistant (19% versus 100%) or predominant (66% versus 91%) as it was in the late U.S isolates. In contrast, C. jejuni isolates from Great Britain were genetically diverse, comprising 19 STs and lacking ST-8. PFGE and CGH analyses of representative strains further confirmed the population structure of the abortion isolates. Notably, the Great Britain isolates were essentially susceptible to most tested antibiotics, including tetracycline, while the late U.S. isolates were universally resistant to this antibiotic, which could be explained by the common use of tetracyclines for control of sheep abortions in the United States but not in Great Britain. These results suggest that the dominance of clone SA in sheep abortions is unique to the United States, and the use of tetracyclines may have facilitated selection of this highly pathogenic clone.
Development of Activity in the Mouse Visual Cortex

A comprehensive developmental timeline of activity in the mouse cortex in vivo is lacking. Understanding the activity changes that accompany synapse and circuit formation is important to understand the mechanisms by which activity molds circuits and would help to identify critical checkpoints for normal development. To identify key principles of cortical activity maturation, we systematically tracked spontaneous and sensory-evoked activity with extracellular recordings of primary visual cortex (V1) in nonanesthetized mice. During the first postnatal week (postnatal days P4–P7), V1 was not visually responsive and exhibited long (>10 s) periods of network silence. Activation consisted exclusively of “slow-activity transients,” 2–10 s periods of 6–10 Hz “spindle-burst' oscillations; the response to spontaneous retinal waves. By tracking daily changes in this activity, two key components of spontaneous activity maturation were revealed: (1) spindle-burst frequency acceleration (eventually becoming the 20–50 Hz broadband activity caused by the asynchronous state) and (2) “filling-in” of silent periods with low-frequency (2–4 Hz) activity (beginning on P10 and complete by P13). These two changes are sufficient to create the adult-like pattern of continuous activity, alternation between fast-asynchronous and slow-synchronous activity, by eye opening. Visual responses emerged on P8 as evoked spindle-bursts and neuronal firing with a signal-to-noise ratio higher than adult. Both were eliminated by eye opening, leaving only the mature, short-latency response. These results identify the developmental origins of mature cortical activity and implicate the period before eye opening as a critical checkpoint. By providing a systematic description of electrical activity development, we establish the murine visual cortex as a model for the electroencephalographic development of fetal humans. SIGNIFICANCE STATEMENT Cortical activity is an important indicator of long-term health and survival in preterm infants and molds circuit formation, but gaps remain in our understanding of the origin and normal progression of this activity in the developing cortex. We aimed to rectify this by monitoring daily changes in cortical activity in the nonanesthetized mouse, an important preclinical model of disease and development. At ages approximately equivalent to normal human term birth, mouse cortex exhibits primarily network silence, with spontaneous “spindle bursts” as the only form of activity. In contrast, mature cortex is noisy, alternating between asynchronous/discontinuous and synchronous/continuous states. This work identifies the key processes that produce this maturation and provides a normative reference for murine-based studies of cortical circuit development.
Salivary cytokine levels in early gingival inflammation

ABSTRACT Salivary protein levels have been studied in periodontitis. However, there is lack of information on salivary cytokine levels in early gingival inflammation. The aim of this study was to determine salivary levels of vascular endothelial growth factor (VEGF), interleukin (IL)-8, monocyte chemoattractant protein (MCP)-1, IL-1β, and IL-1 receptor antagonist (IL-1Ra) in gingival inflammation. Twenty-eight systemically and orally healthy nonsmokers abstained from oral hygiene protocols for 10 days. After that, self-performed cleaning was resumed for 14 days. Plaque and gingival indexes were measured, and saliva samples were collected at days 1, 4, 7, 10, and 24. Salivary cytokines were detected with Luminex®-xMAP™. Salivary IL-1β, IL-1Ra, and VEGF levels decreased after 10 days’ development of experimental gingivitis and reached baseline levels at the end of the 2-week resolution period. Salivary IL-8 levels decreased and remained low during development and resolution of experimental gingivitis. Initial inflammation in gingival tissues is associated with a decrease in inflammatory cytokines in saliva. Further studies are needed to evaluate if inflammatory cytokines bind to their functional receptors within the gingival tissue during early gingivitis, which may limits their spillover to the gingival crevice and ultimately saliva.
From Grace to Grass: Kenyan soccer players’ career transition and experiences in retirement

The purpose of this study was to investigate the main factors that compel Kenyan soccer players to retire from active participation and problems that they experienced after retirement. It was predicted that the above factors would not significantly vary in relation to their age categories, levels of education and playing experience. Data were collected using a self-administered questionnaire from 130 retired soccer players (ages ranged from 21 to more than 60 years). Descriptive statistics (means, standard deviations and frequencies) were used to analyse the data; and One-way ANOVA was used to test the null hypotheses of the study. The main findings of the study indicated that majority of the retired players had retired voluntarily due to lack of attractive incentives (x =3.48±1.50), diminishing physical ability (x =2.76±1.44), lack of encouragement from significant others (x =2.76±1.44) and de-selection or declined soccer performance (x =2.75±2.75). They experienced problems of loss of identity (x =2.97±1.51), desperation for employment (x =2.43±1.44), depression (x =2.38±1.46), idleness (x =2.31±1.37) and alcoholism (x =2.15±1.40). The ANOVA results show that problems experienced did not significantly (p≥0.05) differ based on their age categories and their playing experience with the exception of marital break-ups (x =1.88±1.17; F4,129 =2.5, p < 0.04). As such, providing incentives, insurance and medical schemes, pre and post-retirement programmes to soccer stakeholders such as soccer clubs, government agencies and coaches may reverse the trends.
Reflective leadership and team learning: an exploratory study

Purpose Although the relationship between leadership behaviors and team reflexivity has been investigated in prior research, little is known about the dimensions of reflective leadership or leadership behaviors that facilitate reflection in a group or organization. This paper aims to examine the interrelated characteristics of reflective leadership behaviors that promote team learning using survey data. Design/methodology/approach Because this research was exploratory in nature, a research model was developed based on the preliminary research. The research model was tested using a quantitative study of 507 employees in 98 teams. Findings Results of regression analyses of a group-level study revealed three types of reflective leadership – open reflection, problem-based reflection and goal-based reflection – and found that only the open reflection and goal-based reflection were positively related to team learning. Research limitations/implications As reflective leadership has not been examined in previous studies, the research model and the measurement scales were developed based on preliminary research in the present research. Practical implications Team leaders need to understand that goal-based reflection plays a central role in reflecting on their work because it may provide members with criteria to recognize whether they are on the right track. Originality/value The present research revealed three dimensions of reflective leadership that had not been adequately investigated previously.