Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey-based Study of Pharmacist Acceptance and Resistance to Health Information Technology.

Purpose Because user acceptance and resistance to the use of health information technology (HIT) affects system utilization and previous studies in this area have typically excluded pharmacists, this study specifically addresses the response of institutional pharmacists to HIT. Methods A survey investigating pharmacists' responses to electronic medical record (EMR) system use was developed using questions modified from previously validated research. The survey was distributed electronically to the mailing list for pharmacy preceptors for the University of Tennessee College of Pharmacy. Descriptive statistics and univariate and multivariate analyses were used to analyze the collected data based on a previously validated dual-factor model. Results Of the 96 responses from institutional pharmacists, 64 responses (66.7 percent) were complete and usable. Of the acceptance and resistance constructs evaluated, only attitude and perceived behavior control were found to be significantly associated with acceptance of use (p = .036 and p = .025, respectively), and only transition cost was found to be significantly associated with resistance to use (p = .018). System vendor and interface integration were also significantly associated with acceptance of use. These findings suggest that attitude, perceived behavior control, and transition costs may have the most impact on pharmacists' responses to the use of EMR systems. Conclusion It is reasonable for hospitals to focus efforts on specific factors influencing acceptance of and resistance to EMR use and, before a system is selected, to consider the effects of vendor selection and level of interface integration on acceptance of use.
Properties of sets of Subspaces with Constant Intersection Dimension

A $(k,k-t)$-SCID (set of Subspaces with Constant Intersection Dimension) is a set of $k$-dimensional vector spaces that have pairwise intersections of dimension $k-t$. Let $\mathcal{C}=\{\pi_1,\ldots,\pi_n\}$ be a $(k,k-t)$-SCID. Define $S:=\langle \pi_1, \ldots, \pi_n \rangle$ and $I:=\langle \pi_i \cap \pi_j \mid 1 \leq i < j \leq n \rangle$. We establish several upper bounds for $\dim S + \dim I$ in different situations. We give a spectrum result for the case $(n-1)(k-t)\leq k$ and for the case $n\leq\frac{q^{t(n-\eta)}-1}{q^t-1}$, giving examples of $(k,k-t)$-SCIDs reaching a large interval of values for $\dim S + \dim I$.
Evaluation of wound fluid biomarkers to determine healing in adults with venous leg ulcers: A prospective study

Clinical practice guidelines recommend using repeated wound surface area measurements to determine if a chronic ulcer is healing. This results in delays in determining the healing status. This study aimed to evaluate whether any of a panel of biomarkers can determine the healing status of chronic venous leg ulcers. Forty‐two patients with chronic venous leg ulcers had their wound measured and wound fluid collected at weekly time points for 13 weeks. Wound fluid was analyzed using multiplex enzyme‐linked immunosorbent assay to determine the concentration of biomarkers in the wound fluid at each weekly time point. Healing status was determined by examining the change in wound size at the previous and subsequent weeks. Predictive accuracy with 95% confidence intervals (CI) is reported. Of 42 patients, 105 evaluable weekly time points were obtained, with 32 classified as healing, 27 as nonhealing, and 46 as indeterminate. Thirteen biomarkers significantly differed between healing and nonhealing wounds (p < 0.1) and were included in a multivariate logistic regression model. Granulocyte macrophage‐colony stimulating factor (p < 0.001) and matrix metalloprotease‐13 (p = 0.004) were the best predictors of wound healing. Receiver operating characteristic curves indicated 92% accuracy (95% CI: 85%,100%) for granulocyte macrophage‐colony stimulating factor, and 78% accuracy (95% CI: 65%,90%) for matrix metalloprotease‐13 in discriminating between healing and nonhealing wounds. This study found that two biomarkers from wound fluid can predict healing status in chronic venous leg ulcers. These findings may lead to the ability to determine the future trajectory of a wound and the ability to modify treatment accordingly.
Effect of sound pressure level on contralateral inhibition underlying duration-tuned neurons in the mammalian inferior colliculus.

Duration tuning in the mammalian inferior colliculus (IC) is created by the interaction of excitatory and inhibitory synaptic inputs. We used extracellular recordings and paired tone stimulation to measure the strength and time-course of the contralateral inhibition underlying duration-tuned neurons (DTNs) in the IC of the awake bat. The onset time of a short, best duration (BD), excitatory probe tone set to +10 dB (re threshold) was varied relative to the onset of a longer-duration, non-excitatory (NE) suppressor tone whose sound pressure level (SPL) was varied. Spikes evoked by the roving BD tone were suppressed when the stationary NE tone amplitude was at or above the BD tone threshold. When the NE tone was increased from 0 to +10 dB the inhibitory latency became shorter than the excitatory first spike latency and the duration of inhibition increased, but no further changes occurred at +20 dB (re BD tone threshold). We used the effective duration of inhibition as a function of the NE tone amplitude to obtain suppression-level functions that were used to estimate the inhibitory half-maximum SPL (ISPL50). We also measured rate-level functions of DTNs with single BD tones varied in SPL and modeled the excitatory half-maximum SPL (ESPL50). There was a correlation between the ESPL50and ISPL50, and the dynamic range of excitation and inhibition were similar. We conclude that the strength of inhibition changes in proportion to excitation as a function of SPL, and this feature likely contributes to the amplitude tolerance of the responses of DTNs.
The Pros and Cons of Mesenchymal Stem Cell-Based Therapies

The need to search for new, alternative treatments for various diseases has prompted scientists and physicians to focus their attention on regenerative medicine and broadly understood cell therapies. Currently, stem cells are being investigated for their potentially widespread use in therapies for many untreatable diseases. Nowadays modern treatment strategies willingly use mesenchymal stem cells (MSCs) derived from different sources. Researchers are increasingly aware of the nature of MSCs and new possibilities for their use. Due to their properties, especially their ability to self-regenerate, differentiate into several cell lineages and participate in immunomodulation, MSCs have become a promising tool in developing modern and efficient future treatment strategies. The great potential and availability of MSCs allow for their various clinical applications in the treatment of many incurable diseases. In addition to their many advantages and benefits, there are still questions about the use of MSCs. What are the mechanisms of action of MSCs? How do they reach their destination? Is the clinical use of MSCs safe? These are the main questions that arise regarding MSCs when they are considered as therapeutic tools. The diversity of MSCs, their different clinical applications, and their many traits that have not yet been thoroughly investigated are sources of discussions and controversial opinions about these cells. Here, we reviewed the current knowledge about MSCs in terms of their therapeutic potential, clinical effects and safety in clinical applications.
Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease

Neurodegenerative diseases (NDs) are becoming increasingly prevalent in the world, with an aging population. In the last few decades, due to the devastating nature of these diseases, the research of biomarkers has become crucial to enable adequate treatments and to monitor the progress of disease. Currently, gene mutations, CSF and blood protein markers together with the neuroimaging techniques are the most used diagnostic approaches. However, despite the efforts in the research, conflicting data still exist, highlighting the need to explore new classes of biomarkers, particularly at early stages. Small non-coding RNAs (MicroRNA, Small nuclear RNA, Small nucleolar RNA, tRNA derived small RNA and Piwi-interacting RNA) can be considered a “relatively” new class of molecule that have already proved to be differentially regulated in many NDs, hence they represent a new potential class of biomarkers to be explored. In addition, understanding their involvement in disease development could depict the underlying pathogenesis of particular NDs, so novel treatment methods that act earlier in disease progression can be developed. This review aims to describe the involvement of small non-coding RNAs as biomarkers of NDs and their potential role in future clinical applications.
Monetary Policy, Capital Inflows and the Housing Boom

A range of hypotheses have been put forward to explain the boom in house prices that occurred in the United States from the mid-1990s to 2007. This paper considers the relative importance of two of these hypotheses. First, global imbalances increased liquidity in the US financial system, driving down long-term real interest rates. Second, the Federal Reserve kept interest rates low in the first half of the 2000s. Both factors reduced the cost of borrowing and may have encouraged the boom in house prices. This paper develops an empirical framework to separate the relative contributions of these two factors to the US housing market. The results suggest that capital inflows to the United States played a bigger role in generating the increase in house prices than monetary policy loosening. Using VAR methods, we find that compared to monetary policy, the effect of a capital inflows shock on US house prices and residential investment is about twice as large and substantially more persistent. Results from variance decompositions suggest that, at a forecast horizon of 20 quarters, capital flows shocks explain 15% of the variation in real house prices, while monetary policy shocks explain only 5%. In a simple counterfactual exercise, we find that if the ratio of the current account deficit to GDP had remained constant since the end of 1998, real house prices by the end of 2007 would have been 13% lower. Similar exercises with constant policy rates and the path of policy rates implied by the Taylor rule deliver smaller effects.
ExpandNet: Training Compact Networks by Linear Expansion

While very deep networks can achieve great performance, they are ill-suited to applications in resource-constrained environments. In this paper, we introduce a novel approach to training a given compact network from scratch. We propose to expand each linear layer of the compact network into multiple linear layers, without adding any nonlinearity. As such, the resulting expanded network can be compressed back to the compact one algebraically, but, as evidenced by our experiments, consistently outperforms it. In this context, we introduce several expansion strategies, together with an initialization scheme, and demonstrate the benefits of our ExpandNets on several tasks, including image classification on ImageNet, object detection on PASCAL VOC, and semantic segmentation on Cityscapes.
Robust Intelligent Malware Detection Using Deep Learning

Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks, malware detection continues to be a hot research topic. Current malware detection solutions that adopt the static and dynamic analysis of malware signatures and behavior patterns are time consuming and have proven to be ineffective in identifying unknown malwares in real-time. Recent malwares use polymorphic, metamorphic, and other evasive techniques to change the malware behaviors quickly and to generate a large number of new malwares. Such new malwares are predominantly variants of existing malwares, and machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. However, such approaches are time consuming as they require extensive feature engineering, feature learning, and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Recently reported research studies in this direction show the performance of their algorithms with a biased training data, which limits their practical use in real-time situations. There is a compelling need to mitigate bias and evaluate these methods independently in order to arrive at a new enhanced method for effective zero-day malware detection. To fill the gap in the literature, this paper, first, evaluates the classical MLAs and deep learning architectures for malware detection, classification, and categorization using different public and private datasets. Second, we remove all the dataset bias removed in the experimental analysis by having different splits of the public and private datasets to train and test the model in a disjoint way using different timescales. Third, our major contribution is in proposing a novel image processing technique with optimal parameters for MLAs and deep learning architectures to arrive at an effective zero-day malware detection model. A comprehensive comparative study of our model demonstrates that our proposed deep learning architectures outperform classical MLAs. Our novelty in combining visualization and deep learning architectures for static, dynamic, and image processing-based hybrid approach applied in a big data environment is the first of its kind toward achieving robust intelligent zero-day malware detection. Overall, this paper paves way for an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments.
Exploring Cycle-to-Cycle and Device-to-Device Variation Tolerance in MLC Storage-Based Neural Network Training

A multilevel cell (MLC) memristor that provides high-density on-chip memory has become a promising solution for energy-efficient artificial neural networks (ANNs). However, MLC storage that stores multiple bits per cell is prone to device variation. In this paper, the device variation tolerance of ANN training is investigated based on our cell-specific variation modeling method, which focuses on characterizing realistic cell-level variation. The parameters of cycle-to-cycle variation (CCV) and device-to-device variation (DDV) are extracted separately from the experimental data of a 39-nm, 1-Gb phase-change random access memory (PCRAM) array. A quantized neural network designed for low bit-width (≥6-bit) training is used for simulations to demonstrate the potential of MLC storage. Our results demonstrate that training is more vulnerable to DDV than CCV, and CCV can even compensate for accuracy degradation caused by severe DDV. As a result, for a multilayer perceptron (MLP) on Modified National Institute of Standards and Technology (MNIST) database, 95% accuracy can be achieved with three MLC PCRAM devices per weight, which is a 40% reduction in the number of cells compared with using conventional single-level cells (SLCs). If the size of DDV is reduced by half, then only two cells, that is 60% fewer cells than using SLC, are needed.
Value of Content-Based Deception Detection Methods

Literaturę abounds with examples of various m ethods serving detection of deception in testim onies. They can be divided both according to the m ethods used depending on the tactics o f witness interrogation (Gruza, 2009) and the psychological m odel o f analysing testim ony veracity (M arten, 2012). Functioning currently is also a division of m ethods of detecting deception based on the channel o f com m unication analysed (Vrij, 2008): m ethods based on the analysis of the so-called non-verbal and vocal detection of deception (DePaulo et al., 2003), m ethods based on psychophysiological analyses (polygraph, EEG, fM RI, and therm ography exam inations), and m ethods that analyse the contents o f the testim ony (e.g. Content Based C riteria Analysis CBCA ,
Salmonella invasion is controlled through the secondary structure of the hilD transcript

Virulence functions of bacterial pathogens are often energetically costly and thus are subjected to intricate regulatory mechanisms. In Salmonella, invasion of the intestinal epithelium, an essential early step in virulence, requires the production of a multi-protein type III secretion apparatus. The pathogen mitigates the overall cost of invasion by inducing it in only a fraction of its population. This constitutes a successful virulence strategy as invasion by a small number is sufficient to promote the proliferation of the non-invading majority. Such a system suggests the existence of a sensitive triggering mechanism that permits only a minority of Salmonella to reach a threshold of invasion-gene induction. We show here that the secondary structure of the invasion regulator hilD message provides such a trigger. The 5’ end of the hilD mRNA is predicted to contain two mutually exclusive stem-loop structures, the first of which (SL1) overlaps the ribosome-binding site and the ORF start codon. Changes that reduce its stability enhance invasion gene expression, while those that increase stability reduce invasion. Conversely, disrupting the second stem-loop (SL2) represses invasion genes. Although SL2 is the energetically more favorable, repression through SL1 is enhanced by binding of the global regulator CsrA. This system thus alters the levels of hilD mRNA and is so sensitive that changing a single base pair within SL1, predicted to augment its stability, eliminates expression of invasion genes and significantly reduces Salmonella virulence in mice. This system thus provides a possible means to rapidly and finely tune an essential virulence function.
Improper Gaussian Signaling for Broadcast Interference Networks

For a multi-user multi-cell network, which suffers both intra-cell and inter-cell interference, this letter considers improper Gaussian signaling (IGS) as a means to improve the achievable rate. The problem of interest is designing of improper Gaussian signals’ augmented covariance matrices to maximize the users’ minimum rate subject to transmit power constraints. This problem is seen as a nonconvex matrix optimization problem, which cannot be solved by conventional techniques, such as weighted minimum mean square error minimization or alternating optimization. A path-following algorithm, which iterates a sequence of improved feasible points, is proposed for its computation. The provided simulation results for three cells serving 18 users show that IGS offers a much better max–min rate compared with that achieved by conventional proper Gaussian signaling. Another problem of maximizing the energy efficiency in IGS is also considered.
Photo Actuation Performance of Nanotube Sheet Incorporated Azobenzene Crosslinked Liquid Crystalline Polymer Nanocomposite

Crosslinked liquid crystalline polymers (CLCPs) containing azobenzene (AZO-CLCPs) are a type of promising material due to their significance in the design of light-driven smart actuators. Developing AZO-CLCP composites by incorporating AZO-CLCPs with other materials is an effective way of enhancing their practicability. Herein, we report an AZO-CLCP/CNT nanocomposite prepared by the in situ polymerization of diacrylates containing azobenzene chromophores on carbon nanotube (CNT) sheets. The liquid crystal phase structure of CLCP matrix was evidenced by the two-dimensional X-ray scattering. The prepared pure AZO-CLCP films and AZO-CLCP/CNT nanocomposite films demonstrated strong reversible photo-triggered deformation under the irradiation of UV light at 366 nm of wavelength, as a result of photo-induced isomerization of azobenzene moieties in the polymer network. But compared to pure AZO-CLCP films, the AZO-CLCP/CNT nanocomposite films could much more rapidly return to their initial shapes after the UV light irradiation was removed due to the elasticity effect of CNT sheets. The deformation behavior of AZO-CLCP/CNT nanocomposite films under the light irradiation was also different from that of the pure AZO-CLCP films due to the interfacial interaction between a polymer network and CNT sheet. Furthermore, incorporation of a CNT sheet remarkably increased the mechanical strength and robustness of the material. We also used this AZO-CLCP/CNT nanocomposite as a microvalve membrane actuator, which can be controlled by light, for a conceptual device of a microfluidic system. The results showed that this AZO-CLCP/CNT nanocomposite may have great potential in smart actuator applications for biological engineering, medical treatment, environment detection and microelectromechanical systems (MEMS), etc.
A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Latent fingerprint identification is attracting increasing interest because of its important role in law enforcement. Although the use of various fingerprint features might be required for successful latent fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform other methods. However, as many fingerprint feature representations exist, we sought to determine if the selection of feature representation has an impact on the performance of automated fingerprint identification systems. In this paper, we review the most prominent fingerprint feature representations reported in the literature, identify trends in fingerprint feature representation, and observe that representations designed for verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of the most popular fingerprint feature representations over a common latent fingerprint database. Therefore, we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than 10% for Rank−1 and 24% for Rank−100 comparing the minutiae in the database NIST SD27, illustrating the need of new minutia descriptors for latent fingerprint identification.