Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the Preparation and Chemical Structure Characterization of Melanin from Boletus griseus

In this study, melanin (BgM) was obtained from Boletus griseus. The chemical composition and structure of BgM were characterized by UV-visible absorption spectrum, Fourier transform infrared spectrum, elemental analysis, nuclear magnetic resonance, pyrolysis gas chromatography mass spectrometry, and ultra-performance liquid chromatography–high resolution mass spectrometry. The percentage contents of C, H, N, S and O elements were 56.38%, 5.86%, 6.17%, 2.44%, and 28.04%, and the S/N and C/N ratios were 0.17 and 10.66, respectively. The UV-vis spectrum of BgM showed a maximum absorption peak at 214 nm. Characteristic absorption peaks were observed at 3426, 1600 and 1105 cm−1, and BgM contained phenolic hydroxyl, amidogen, carbonyl, methylene, and methyl groups. Moreover, BgM is an eumelanin, and its main skeleton has both a benzene ring and an indole, and the branched chain mainly consists of alkanes, alcohols, and fatty acids. BgM was hydrolyzed by H2O2 and four compounds were tentatively analyzed from the UPLC-MS/MS profile. The chemical structure of BgM was characterized as 5,6-dihydroxyindole eumelanin, and the condensed molecular formula is [C28(OR1)4(OR2)3H11O6N4]n.
Mjolnir: deformable image registration using feature diffusion

Image registration is the process of aligning separate images into a common reference frame so that they can be compared visually or statistically. In order for this alignment to be accurate and correct it is important to identify the correct anatomical correspondences between different subjects. We propose a new approach for a feature-based, inter-subject deformable image registration method using a novel displacement field interpolation. Among the top deformable registration algorithms in the literature today is the work of Shen et al. called HAMMER. This is a feature-based, hierarchical registration algorithm, which introduces the novel idea of fusing feature and intensity matching. The algorithm presented in this paper is an implementation of that method, where significant improvements of some important aspects have been made. A new approach to the algorithm will be introduced as well as clarification of some key features of the work of Shen et al. which have not been elaborated in previous publications. The new algorithm, which is referred to as Mjolnir (Thor's hammer), was validated on both synthesized and real T1 weighted MR brain images. The results were compared with results generated by HAMMER and show significant improvements in accuracy with reduction in computation time.
Towards the design of smart video-surveillance system

Security and monitoring systems are increasingly demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The aim of this study is to identify some limiting factors in the existing video-surveillance systems and to propose a set of best practices for developing a smart platform for a security monitoring system incorporating advanced techniques for video processing and analysis. In this work, we focus on the effect of the video quality on the biometric part of the video-surveillance systems for public security. In such systems, face detection and recognition from video sequences acquired from surveillance cameras, are challenging tasks, due to presence of strong illumination variations, noise, and changes in facial expressions. In this paper, we mainly focus on the illumination issue occurred in video surveillance. The low light video data is processed using a perceptual based approach, namely multi-scale Retinex method, to improve the video quality, followed by face detection. The experimental results demonstrate significant performance improvement in face detection and recognition, by improving the illumination of video sequences over the unprocessed video data.
CP-Less OFDM With Alignment Signals for Enhancing Spectral Efficiency, Reducing Latency, and Improving PHY Security of 5G Services

Although orthogonal frequency-division multiplexing (OFDM) is a widely accepted waveform in many standards and is expected to keep its dominance in future 5G systems with various types of parameterized waveforms, its performance in terms of spectral efficiency as well as transmission latency is usually degraded due to the excessive usage of cyclic prefix (CP). Particularly, in highly dispersive channels, CP rate might be very large in order to maintain the low-complex frequency-domain equalization. In this paper, we propose a novel method that can fit the low latency and high spectral efficiency requirements of future 5G wireless services by eliminating the need for inserting CP between successive OFDM symbols while keeping the whole detection process the same at the receiver side. In order to achieve that, we utilize specially designed alignment signals that can cancel the interference of one symbol on the other and add an additional signal component that makes the signal circularly convolved with the channel at the receiver side. Simulation results prove the superiority of the proposed scheme in terms of enhancing spectral and power efficiency, reducing latency, and improving physical-layer security against eavesdropping while using a low-complexity one-tap frequency-domain equalizer. These numerous, simultaneous, and desirable advantages have the potential to make the proposed technique a suitable fit for future 5G wireless services and applications including Internet of Things-based massive machine-type communication, ultra-reliable and low-latency communication, and enhanced mobile broadband.
Outlier detection in the medical Questionnaire Rising and Sitting Down (QR\&S)

Outlier detection in item scores from questionnaires for the measurement of medical concepts has to deal with highly discrete data. In this study, two outlier scores are used which both indicate the degree of inconsistency of a subject's itemscore vector with the remainder of the data. In two studies, simulated data are used to investigate the error rates and the sensitivity of four statistical tests that are used to decide whether an outlier score is discordant. In the third study, the out lier scores and the discordancy tests are applied to real data obtained by means of the medical Questionnaire Rising and Sitting Down (QR&S)*.
A Typology of Social Networks and Its Relationship to Psychological Well-Being in Korean Adults

The purposes of this study were to (a) develop an empirical typology of the social networks in Korean adults aged 18 years or older and (b) examine the relation of network types on depressive symptoms and satisfaction with life. Data for this study were drawn from the survey with 1,017 community-dwelling adults aged 18 years or older in South Korea representing three life stages: young adults, middle-aged adults, and older adults. Latent profile analysis was conducted based on eight social network-related variables: marital status, living arrangement, number of family confidants, number of friend confidants, frequency of contact with friends using phone or social media, frequency of participation in social groups, frequency of conversation with neighbors, and perceived closeness of family. The identified typologies were then regressed on depressive symptoms and satisfaction with life. A model with four network types was identified as being the most optimal, and the groups were labeled as: diverse-family, diverse-friend, solo-restricted, and coresident-restricted. Regression analyses showed that in comparison with the coresident-restricted group referent, diverse-friend and solo-restricted groups exhibited elevated levels of depressive symptoms; diverse-family and diverse-friend type had higher levels of satisfaction with life. Implications of the study findings were discussed in line with current research and intervention efforts.
Overview of Collateral Meridian Therapy in Pain Management: A Modified Structured Formulated Chinese Acupressure

Collateral meridian therapy (CMT) is a recent developed technique according to the traditional Chinese medicine (TCM) experiences; it differs from traditional Chinese acupuncture (TCA) in many ways. In addition to TCA, CMT offers an alternative for different types of pain management. This article introduces the theory of CMT, acupoints localization, general treatment protocol principle, and some published reports. CMT is developed by Dr. Shan-Chih Ko, he combined the TCM and Western medicine, by using two-point acupressure technique, manipulating the control C-point and treatment functional F-point. CMT differs from TCA in three major points. CMT follows a standard formulated treatment protocols, instead of individual experiences in TCA. Secondly, CMT is based on a two-point acupressure maneuver on the C-point and F-point rather than manipulation of a single point as advocated by TCA. The third, CMT provides a standard set of 108 acupoints, instead of 361 in TCA. It allows redistribution of body energy, ”Qi”, from one meridian to another in order to augment or remove the unbalanced ”Qi”. CMT has been used effectively in our pain clinic to treat musculoskeletal pain and even intractable pains. Dr. Ko simplifies the theory and technique that makes CMT easy to learn and perform, by using English letters, it can be used world-widely. The role of CMT in pain management is promising for both acute and chronic pains. Randomized controlled trials are needed to strengthen its scientific evidence.
Objective Food Intake in Night and Day Shift Workers: A Laboratory Study

Night shift work is associated with risk of overweight and obesity. In night shift workers, short sleep duration combined with circadian misalignment may contribute to altered food intake regulation, favoring positive energy balance and weight gain. Prior work investigating food intake in shift workers has suffered methodologically due to reliance on subjective self-report for dietary assessment. No study has yet been done to examine the impact of night shift work on food intake in real-life shift workers using objective measures. Female day (n = 12) and night (n = 12) shift workers from a hospital setting participated in a laboratory-based objective food intake assessment. Participants entered the laboratory in the fasted state after awakening from the sleep episode following a final work shift, and underwent an ad libitum 14-item test meal buffet to objectively quantify food choice/intake. Sleep duration (measured via wrist-accelerometry) during the sleep episode before laboratory assessment was significantly longer in day vs. night workers (373.9 ± 127.5 vs. 260.6 ± 102.9 min, p = 0.03). No significant group difference was observed in calories consumed during the test meal (943.08 ± 469.55 vs. 878.58 ± 442.68 kcal, p = 0.74). When expressed as percent of energy consumed, day workers had higher protein consumption vs. night workers (16.03 ± 5.69 vs. 11.82 ± 4.05%; p = 0.05). To our knowledge, this is the first laboratory-based behavioral assessment of food choice/intake in actual night and day shift workers. Although not studied here, work by others has linked protein intake to satiety. This may be a potential pathway placing shift workers at risk for overweight and obesity.
An Efficient Web Usage Mining Approach Using Chaos Optimization and Particle Swarm Optimization Algorithm Based on Optimal Feedback Model

The dynamic nature of information resources as well as the continuous changes in the information demands of the users has made it very difficult to provide effective methods for data mining and document ranking. This paper proposes an efficient particle swarm chaos optimization mining algorithm based on chaos optimization and particle swarm optimization by using feedback model of user to provide a listing of best-matching webpages for user. The proposed algorithm starts with an initial population of many particles moving around in a D-dimensional search space where each particle vector corresponds to a potential solution of the underlying problem, which is formed by subsets of webpages. Experimental results show that our approach significantly outperforms other algorithms in the aspects of response time, execution time, precision, and recall.
Externalities in Socially-Based Resource Sharing Network

This paper investigates the impact of link formation between a pair of agents on resource availability of other agents in a social cloud network, which is a special case of socially-based resource sharing systems. Specifically, we study the correlation between externalities, network size, and network density. We first conjecture and experimentally support that if an agent experiences positive externalities, then its closeness (harmonic centrality measure) should increase. Next, we show the following for ring networks: in less populated networks no agent experiences positive externalities; in more populated networks a set of agents experience positive externalities, and larger the distance between agents forming a link, more the number of beneficiaries; and the number of beneficiaries is always less than the number of non-beneficiaries. Finally, we show that network density is inversely proportional to positive externalities, and further, it plays a crucial role in determining the kind of externalities.
Reasoning with inconsistent ontologies in possibilistic defeasible logic programming

We present a preliminary framework for reasoning with possibly inconsistent Description Logic ontologies in Possibilistic Defeasible Logic Programming. A case study is presented where we show how the proposed approach works. The proposal presented is apt for being used in the context of Semantic Web ontologies as it can be applied to the Web Ontology Language OWL.
LGLG-WPCA: An Effective Texture-based Method for Face Recognition

In this paper, we proposed an effective face feature extraction method by Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component Analysis (WPCA), called LGLG-WPCA. The proposed method learns face features from the embedded multivariate Gaussian in Gabor wavelet domain; it has the robust performance to adverse conditions such as varying poses, skin aging and uneven illumination. Because the space of Gaussian is a Riemannian manifold and it is difficult to incorporate learning mechanism in the model. To address this issue, we use L2EMG to map the multidimensional Gaussian model to the linear space, and then use WPCA to learn face features. We also implemented the key-point-based version of LGLG-WPCA, called LGLG(KP)-WPCA. Experiments show the proposed methods are effective and promising for face texture feature extraction and the combination of the feature of the proposed methods and the features of Deep Convolutional Network (DCNN) achieved the best recognition accuracies on FERET database compared to the state-of-the-art methods. In the next version of this paper, we will test the performance of the proposed methods on the large-varying pose databases.
Temperament and Early Intentions to Retire: A Northern Finland Birth Cohort 1966 Study

Objective: To examine associations between temperament traits and early retirement intentions. Methods: In the Northern Finland Birth Cohort 1966, we analyzed early retirement intentions at the age 46 according to Cloninger temperament traits at the age 46 (n = 4409) and at the age 31 (n = 3226). Ordinal logistic regression analyses were used. Adjustments were made for marital status, education, work type, job satisfaction, perceived health, and mental health problems. Results: 11% of men and 9% of women had strong retirement intentions. Especially higher harm avoidance was associated with higher early retirement intentions in both sexes at the age of 46. A similar pattern of results, although with weaker associations, was found for temperament assessed at the age of 31. Conclusions: Temperament and especially harm avoidance is related to early retirement intentions.
All-in-Focus Sweep Imaging Based on Wigner Distribution Function

To extend the depth of field, the all-in-focus image can be achieved via the computational imaging method by designing the data acquisition process under the condition of maintaining the signal-to-noise ratio. In this paper, the data acquisition of all-in-focus sweep imaging is described by the Wigner distribution function (WDF); then, the WDF-based all-in-focus imaging model and the computational imaging method are established. In the all-in-focus imaging model, the WDF-based light propagation model is utilized to describe the imaging process. As a consequence, the point spread function (PSF) of the imaging system is derived via the WDF-based light propagation model. By analyzing the PSF of the imaging model, the approximate 3-D spatial invariance of the PSF is achieved, and the all-in-focus sweep data can be expressed as a convolution of the all-in-focus image and the PSF. As a result, the all-in-focus image can be reconstructed by utilizing the deconvolution in the computational imaging. In the simulated experiments, the computational imaging results in different object planes are analyzed based on the structural similarity index to verify the all-in-focus sweep imaging method. In the real data experiments, the resolution targets placed in two different depths can be imaged with extended depth of field via the all-in-focus imaging method proposed in this paper.
Computational Study of Hodgkin-Huxley Type Calcium-Dependent Potassium Current in Urinary Bladder Over Activity

Detrusor smooth muscle (DSM) instability is a common symptom of over active bladder (OAB) which effects quality of millions of lives. The enhanced spontaneous contraction due to generation of more frequent spontaneous action potentials (AP) is the fundamental cause of DSM instability. Ca2+ dependent potassium channels are abundantly distributed in DSM cells and regulate the membrane excitability by modulating AP shape and the resting membrane potential. Quantitative studies of these channels will help to predict the underlying mechanism of electrical signaling in DSM cells. Here, we present a large conductance Ca2+ dependent potassium (BK) channel based upon Hodgkin-Huxley (HH) formalism which is incorporated into a published single DSM cell model to investigate several modulating effects. This simple BK ion channel model is constructed with biophysical details from documented electrophysiological studies. As it reflects several modulating properties found in DSM cell electrophysiology, further investigation can shed light in genesis of OB.
Conducting a Data Interview

Michael Witt & Jake Carlson, Purdue University Libraries, West Lafayette, Indiana, USA INTRODUCTION Librarians at Purdue University are beginning to identify the scienti�ic datasets that are being generated by our faculty and researchers as information assets to be collected, preserved, and made accessible as a function of the library’s collection development. These librarians are subjectarea specialists, and many have advanced degrees in their respective disciplines in addition to a degree in library science. They have all been trained in collection management; however, much of this training was related to traditional formats such as monographs and serials and not datasets. In our experience, one of the most effective tactics for eliciting datasets for the collection is a simple librarian-researcher interview. In this poster, we share a set of ten questions that a librarian can use as a starting point for such a “data interview”. It is not a comprehensive strategy but instead a practical tool to draw out information that needs to be considered in order to evaluate the suitability of a dataset for the collection and the requirements for the infrastructure and services that will be needed for data curation. #2 What form and format are the data in? What computing environments (e.g., software) are required to use the data? If the data are in proprietary structures, you may consider reformatting them into agnostic formats or ones that can be more easily re-versioned. Is there any existing metadata, either external to the data or description that could be extracted from it? Ideally the data could be described to be discoverable by researchers from another discipline.
Test-retest reliability and responsiveness of the shortened disability arm shoulder hand (QuickDASH) questionnaire: a pilot study

viii CHAPTER 1: INTRODUCTION 1 1.1 Purpose 3 CHAPTER 2: REVIEW OF LITERATURE 4 2.1 Methodology 4 2.2 Results 4 2.3 Discussion 5 2.3.1 Assessing Upper Extremity Injuries 5 2.3.1.1 Objective Assessment 5 2.3.1.2 Functional Outcome Assessment 6 2.3.2 Health Outcome Measurement Frameworks 6 2.3.3 What are Functional Outcomes? 7 2.3.3.1 Generic Health Profiles 7 2.3.3.2 Disease specific Instruments 8 2.3.3. 3 Whole Limb Outcome Measures 9 2.3.3.4 Patient Generated Instruments 10 2.3.4 Components of a Functional Outcome Measure 10
Direct coupling analysis of epistasis in allosteric materials

In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such al-losteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well mutation costs but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in agreement with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects. Author summary Allostery in proteins is the property of highly specific responses to ligand binding at a distant site. To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations. In this work we consider allosteric architectures artificially evolved to optimize the cooperativity of binding at allosteric and active site. We first characterize the emergent pattern of epistasis as well as the underlying mechanical phenomena, finding four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range. The numerical evolution of these allosteric architectures allows us to benchmark Direct Coupling Analysis, a method which relies on co-evolution in sequence data to infer direct evolutionary couplings, in connection to allostery. We show that Direct Coupling Analysis predicts quantitatively mutation costs but underestimates strong long-range epistasis. We provide an argument, based on a simplified model, illustrating the reasons for this discrepancy and we propose neural networks as more promising tool to measure epistasis.