Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advance of nanoparticle-based topical drug delivery to the posterior segment of the eye

ABSTRACT Introduction: Considering that the number of patients afflicted by posterior eye diseases is increasing, effective drug delivery is currently in high clinical demand. Topical administration has been identified as the preferred option, while sufferingfrom multiple barriers. The development of nanoparticle-based drug delivery system provides an option, which would enhance the drug permeability across the barriers and achieve the desired drug level in the targeted tissue. Areas covered: This review highlights the barrier to the posterior segment of the eye via topical administration. The up-to-date development of lipid nanoparticles, liposomes, emulsions, spanlastics, micelles, polymeric nanoparticles, layered double hydroxides (LDH), dendrimers, cyclodextrins(CDs), and prodrugs are summarized. Moreover, nanocarriers currently in clinical trials for posterior segment diseases have been discussed. Expert opinion: Topical nanoparticle-based drug delivery systems have demonstrated significant progress. An ideal formulation should prolong retention time on the surface, enhance drug permeability through the ocular tissues, and efficiently deliver drugs to the targeted site. To design the rational targeting nanoparticle-based drug delivery system, a better understanding of the distribution of transporters and receptors on the eye is required. Ultimately, there is an urgent need to develop targeting hybrid drug delivery systems with the combination of the advantages of several nanocarriers.
Transcriptomic analysis of longitudinal Burkholderia pseudomallei infecting the cystic fibrosis lung

The melioidosis bacterium, Burkholderia pseudomallei, is increasingly being recognized as a pathogen in patients with cystic fibrosis (CF). We have recently catalogued genome-wide variation of paired, isogenic B. pseudomallei isolates from seven Australasian CF cases, which were collected between four and 55 months apart. Here, we extend this investigation by documenting the transcriptomic changes in B. pseudomallei in five cases. Following growth in an artificial CF sputum medium, four of the five paired isolates exhibited significant differential gene expression (DE) that affected between 32 and 792 genes. The greatest number of DE events was observed between patient CF9 strains, consistent with the hypermutator status of the latter strain, which is deficient in the DNA mismatch repair protein MutS. Two patient isolates harbored duplications that concomitantly increased expression of the β-lactamase gene penA, and a 35kb deletion in another abolished expression of 29 genes. Convergent expression profiles in the chronically-adapted isolates identified two significantly downregulated and 17 significantly upregulated loci, including the antibiotic resistance-nodulation-division (RND) efflux pump BpeEF-OprC, the quorum-sensing hhqABCDE operon, and a cyanide- and pyocyanin-insensitive cytochrome bd quinol oxidase. These convergent pathoadaptations lead to increased expression of pathways that may suppress competing bacterial and fungal pathogens and that enhance survival in oxygen-restricted environments, the latter of which may render conventional antibiotics less effective in vivo. Treating chronically-adapted B. pseudomallei infections with antibiotics designed to target anaerobic infections, such as the nitroimidazole class of antibiotics, may significantly improve pathogen eradication attempts by exploiting this Achilles heel.
A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning

Topic structure analysis plays a pivotal role in dialogue understanding. We propose a reinforcement learning (RL) method for topic segmentation and labeling in goal-oriented dialogues, which aims to detect topic boundaries among dialogue utterances and assign topic labels to the utterances. We address three common issues in the goal-oriented customer service dialogues: informality, local topic continuity, and global topic structure. We explore the task in a weakly supervised setting and formulate it as a sequential decision problem. The proposed method consists of a state representation network to address the informality issue, and a policy network with rewards to model local topic continuity and global topic structure. To train the two networks and offer a warm-start to the policy, we firstly use some keywords to annotate the data automatically. We then pre-train the networks on noisy data. Henceforth, the method continues to refine the data labels using the current policy to learn better state representations on the refined data for obtaining a better policy. Results demonstrate that this weakly supervised method obtains substantial improvements over state-of-the-art baselines.
Effect of Cumulative Intravenous Voriconazole Dose on Renal Function in Hematological Patients

Intravenous voriconazole (VRC) is formulated by the incorporation of sulfobutylether-β-cyclodextrin (SBECD), which may accumulate to adversely affect renal function. However, the effect of long-term use of intravenous VRC on renal function is unclear. ABSTRACT Intravenous voriconazole (VRC) is formulated by the incorporation of sulfobutylether-β-cyclodextrin (SBECD), which may accumulate to adversely affect renal function. However, the effect of long-term use of intravenous VRC on renal function is unclear. Our retrospective analysis of data confirmed that worsening of renal function was significantly associated with a cumulative dose of intravenous VRC (≥400 mg/kg), suggesting that a higher cumulative dose of intravenous VRC is a risk factor for renal dysfunction.
Exploiting Graph Regularized Multi-dimensional Hawkes Processes for Modeling Events with Spatio-temporal Characteristics

Multi-dimensional Hawkes processes (MHP) has been widely used for modeling temporal events. However, when MHP was used for modeling events with spatio-temporal characteristics, the spatial information was often ignored despite its importance. In this paper, we introduce a framework to exploit MHP for modeling spatio-temporal events by considering both temporal and spatial information. Specifically, we design a graph regularization method to effectively integrate the prior spatial structure into MHP for learning influence matrix between different locations. Indeed, the prior spatial structure can be first represented as a connection graph. Then, a multi-view method is utilized for the alignment of the prior connection graph and influence matrix while preserving the sparsity and low-rank properties of the kernel matrix. Moreover, we develop an optimization scheme using an alternating direction method of multipliers to solve the resulting optimization problem. Finally, the experimental results show that we are able to learn the interaction patterns between different geographical areas more effectively with prior connection graph introduced for regularization.
The cost of deep sleep: Environmental influences on sleep regulation are greater for diurnal lemurs.

OBJECTIVES Primates spend almost half their lives asleep, yet we know little about how evolution has shaped variation in the duration or intensity of sleep (i.e., sleep regulation) across primate species. Our objective was to test hypotheses related to how sleeping site security influences sleep intensity in different lemur species. METHODS We used actigraphy and infrared videography to generate sleep measures in 100 individuals (males = 51, females = 49) of seven lemur species (genera: Eulemur, Lemur, Propithecus, and Varecia) at the Duke Lemur Center in Durham, NC. We also generated experimental data using sleep deprivation for 16 individuals. This experiment used a pair-wise design for two sets of paired lemurs from each genus, where the experimental pair experienced a sleep deprivation protocol while the control experienced normal sleeping conditions. We calculated a sleep depth composite metric from weighted z scores of three sleep intensity variables. RESULTS We found that, relative to cathemeral lemurs, diurnal Propithecus was characterized by the deepest sleep and exhibited the most disruptions to normal sleep-wake regulation when sleep deprived. In contrast, Eulemur mongoz was characterized by significantly lighter sleep than Propithecus, and E. mongoz showed the fewest disruptions to normal sleep-wake regulation when sleep deprived. Security of the sleeping site led to greater sleep depth, with access to outdoor housing linked to lighter sleep in all lemurs that were studied. CONCLUSIONS We propose that sleeping site security was an essential component of sleep regulation throughout primate evolution. This work suggests that sleeping site security may have been an important factor associated with the evolution of sleep in early and later hominins.
An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints.
Discussion of An Analysis of the Group Dynamics Surrounding Internal Control Assessment in Information Systems Audit and Assurance Domains

This paper presents a method for detecting scene changes in video sequences, in which the -test is modified by imposing weights according to NTSC standard. To automatically determine threshold values for scene change detection, the proposed method utilizes the frame differences that are obtained by the weighted -test. In the first step, the mean and the standard deviation of the difference values are calculated, and then, we subtract the mean difference value from each difference value. In the next step, the same process is performed on the remained difference values, mean-subtracted frame differences, until the stopping criterion is satisfied. Finally, the threshold value for scene change detection is determined by the proposed automatic threshold estimation algorithm. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method is reliably estimates the thresholds and detects scene changes.
Dynamic Resource Routing using Real-Time Dynamic Programming

Acquiring available resources in stochastic environments becomes more and more important to future mobility. For instance, cities like Melbourne, Canberra and San Francisco install sensors that detect in real-time whether a parking spot (resource) is available or not. In such environments, the current state of the resources may be fully observable, although the future development is stochastic. In order to reduce the traffic, such cities want to fully exploit parking spots, such that the amount of searching cars is minimized. Thus, we formulate a problem setting where the expected seek time for each driver is minimized. This problem can be modeled by a Markov Decision Process (MDP) and solved using standard algorithms. In this paper, we focus on the setting, where pre-computation is not possible and search policies have to be computed on the fly. Our approach is based on state-of-the-art Real-Time Dynamic Programming (RTDP) approaches. However, standard RTDP approaches do not perform well on this specific problem setting as shown in our experiments. We introduce adapted bounds and approximations that exploit the specific nature of the problem in order to improve the performance significantly.
Vitamin D’s Effect on the Proliferation and Inflammation of Human Intervertebral Disc Cells in Relation to the Functional Vitamin D Receptor Gene FokI Polymorphism

Vitamin D is known to have immunomodulatory effects, is involved in osteo-cartilaginous metabolism, and may have a role in human intervertebral disc pathophysiology. Although a link between vitamin D receptor (VDR) gene variants and disc degeneration-related pathologies has been observed, its functional contribution to pathologic processes has not been assessed yet. The aim of this study was to investigate the response of disc cells to vitamin D in terms of the regulation of proliferation, metabolism, and inflammatory processes, with a particular focus on the FokI VDR genotype. However, although it was found that vitamin D had a pro-apoptotic effect regardless of genotype, an up-regulation of IL-1Ra and downregulation of IL-6 was found to be evident only in Ff cells. Regarding the metabolic effects, in Ff cells, vitamin D promoted an upregulation of the aggrecan in inflammatory conditions but did not have an effect on the expression of collagen-related markers. Moreover, cells bearing the Ff genotype were the most responsive to vitamin D in the upregulation of catabolic markers. In addition, in contrast to the FF genotype, vitamin D downregulated the vitamin D-dependent signaling pathway in inflamed Ff cells, counteracting the inflammation-mediated catabolic effects. In conclusion, Ff cells were found to be more responsive to the anti-inflammatory and catabolic effects of vitamin D, which is likely to be related to matrix remodeling.
A Cloaking Mechanism to Mitigate Market Manipulation

We propose a cloaking mechanism to deter spoofing, a form of manipulation in financial markets. The mechanism works by symmetrically concealing a specified number of price levels from the inside of the order book. To study the effectiveness of cloaking, we simulate markets populated with background traders and an exploiter, who strategically spoofs to profit. The traders follow two representative bidding strategies: the non-spoofable zero intelligence and the manipulable heuristic belief learning. Through empirical game-theoretic analysis across parametrically different environments, we evaluate surplus accrued by traders, and characterize the conditions under which cloaking mitigates manipulation and benefits market welfare. We further design sophisticated spoofing strategies that probe to reveal cloaked information, and find that the effort and risk exceed the gains.
Solving Separable Nonsmooth Problems Using Frank-Wolfe with Uniform Affine Approximations

Frank-Wolfe methods (FW) have gained significant interest in the machine learning community due to their ability to efficiently solve large problems that admit a sparse structure (e.g. sparse vectors and low-rank matrices). However the performance of the existing FW method hinges on the quality of the linear approximation. This typically restricts FW to smooth functions for which the approximation quality, indicated by a global curvature measure, is reasonably good. In this paper, we propose a modified FW algorithm amenable to nonsmooth functions, subject to a separability assumption, by optimizing for approximation quality over all affine functions, given a neighborhood of interest. We analyze theoretical properties of the proposed algorithm and demonstrate that it overcomes many issues associated with existing methods in the context of nonsmooth low-rank matrix estimation.
High-Order Co-Clustering via Strictly Orthogonal and Symmetric L1-Norm Nonnegative Matrix Tri-Factorization

Different to traditional clustering methods that deal with one single type of data, High-Order Co- Clustering (HOCC) aims to cluster multiple types of data simultaneously by utilizing the inter- or/and intra-type relationships across different data types. In existing HOCC methods, data points routinely enter the objective functions with squared residual errors. As a result, outlying data samples can dominate the objective functions, which may lead to incorrect clustering results. Moreover, existing methods usually suffer from soft clustering, where the probabilities to different groups can be very close. In this paper, we propose an L1 -norm symmetric nonnegative matrix tri-factorization method to solve the HOCC problem. Due to the orthogonal constraints and the symmetric L1 -norm formulation in our new objective, conventional auxiliary function approach no longer works. Thus we derive the solution algorithm using the alternating direction method of multipliers. Extensive experiments have been conducted on a real world data set, in which promising empirical results, including less time consumption, strictly orthogonal membership matrix, lower local minima etc., have demonstrated the effectiveness of our proposed method.
Glass Ionomer Cements for the Restoration of Non-Carious Cervical Lesions in the Geriatric Patient

Background: The restoration of non-carious cervical lesions in geriatric patients is a demanding process. Glass ionomer cements can be promising materials for the management of these lesions in older adults. The aim of this literature review is to present the benefits of glass ionomers and how they can be used for the restoration of non-carious cervical lesions of older adults depending on the geriatric patient’s profile. Data sources: All available in vitro and in vivo studies from Google Scholar, PubMed and Scopus search engines corresponding to glass ionomer cements, geriatric dentistry, elderly patients, and non-carious lesions as key words were reviewed. Data synthesis: The advantages of glass ionomer cements, such as good retention and fluoride release, make them suitable for the restoration of non-carious cervical lesions. However, several factors related to the geriatric patient’s profile determine the most suitable material type. Conclusion: In general, the resin modified glass ionomer cements (RMGICs) appear to be preferred, but under certain circumstances the use of the conventional product is more appropriate, despite its poorer mechanical features. Further studies are required for more reliable data analysis and clinical interpretation of the relevant results.
Kadın Sağlığı Taramasında Güncel Durum

Kisa/uzun sureli yasanan saglik sorunlari ve kronik hastaliklar kisinin yasam aktivitelerini sinirlamakta ve yasam kalitesini etkileyebilmektedir. Bu nedenle kadinlar yasamlari suresince yasam kalitelerini etkileyecek/bozacak bir dizi hastaliklarla karsilasacaklarindan haberdar olmalidirlar. Tarama programlari ile herhangi bir hastaliga ozgu belirti ortaya cikmadan once saptanmasi mumkundur. Hastalik semptomlarina sahip bireylerin basvurmasini beklemek yerine, tarama programlari ile asemptomatik donemde hastalari yakalamak, gerek birey gerekse toplum acisindan cok onemlidir. Gunumuzde kadin sagligina yonelik bazi kanserler, jinekolojik sorunlar ve kronik hastaliklarla ilgili tarama yontemleri mevcuttur. Toplum ve bireyle surekli iletisim halinde olan saglik calisanlari, kadin sagligi acisindan onemli olan tarama programlari konusunda saglik danismanligi yaparak ve uygun veri toplayarak bireyleri dogru saglik aliskanliklari konusunda bilgilendirmeli, kendi sagliklarini korumada daha duyarli hale getirmelidir. Ayrica, saglik calisanlari da kendi sagliklarini koruma konusunda cok duyarli olmalidir. Bu nedenle bu makalede kadin sagligi taramasina yonelik bilgilerin, guncel literatur dogrultusunda paylasilmasi amaclanmistir.
Impact of Drosophila Models in the Study and Treatment of Friedreich’s Ataxia

Drosophila melanogaster has been for over a century the model of choice of several neurobiologists to decipher the formation and development of the nervous system as well as to mirror the pathophysiological conditions of many human neurodegenerative diseases. The rare disease Friedreich’s ataxia (FRDA) is not an exception. Since the isolation of the responsible gene more than two decades ago, the analysis of the fly orthologue has proven to be an excellent avenue to understand the development and progression of the disease, to unravel pivotal mechanisms underpinning the pathology and to identify genes and molecules that might well be either disease biomarkers or promising targets for therapeutic interventions. In this review, we aim to summarize the collection of findings provided by the Drosophila models but also to go one step beyond and propose the implications of these discoveries for the study and cure of this disorder. We will present the physiological, cellular and molecular phenotypes described in the fly, highlighting those that have given insight into the pathology and we will show how the ability of Drosophila to perform genetic and pharmacological screens has provided valuable information that is not easily within reach of other cellular or mammalian models.
Selecting representative and diverse spatio-textual posts over sliding windows

Thousands of posts are generated constantly by millions of users in social media, with an increasing portion of this content being geotagged. Keeping track of the whole stream of this spatio-textual content can easily become overwhelming for the user. In this paper, we address the problem of selecting a small, representative and diversified subset of posts, which is continuously updated over a sliding window. Each such subset can be considered as a concise summary of the stream's contents within the respective time interval, being dynamically updated every time the window slides to reflect newly arrived and expired posts. We define the criteria for selecting the contents of each summary, and we present several alternative strategies for summary construction and maintenance that provide different trade-offs between information quality and performance. Furthermore, we optimize the performance of our methods by partitioning the newly arriving posts spatio-textually and computing bounds for the coverage and diversity of the posts in each partition. The proposed methods are evaluated experimentally using real-world datasets containing geotagged tweets and photos.
Sparse Gene Coexpression Network Analysis Reveals EIF3J-AS1 as a Prognostic Marker for Breast Cancer

Predictive and prognostic biomarkers facilitate the selection of treatment strategies that can improve the survival of patients. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in cancer progression, with diagnostic and prognostic potential. However, few prognostic lncRNAs are reported for breast cancer, and little is known about their functions that contribute to cancer pathogenesis. In this paper, we used weighted correlation network analysis (WGCNA) to construct networks containing noncoding and protein-coding genes based on their expression in 1097 breast cancer patients. The differentially expressed genes were significantly overlapped with gene modules regulating cell cycle and cell adhesion. The cell cycle-related lncRNAs were consistently downregulated in breast cancer. One lncRNA, EIF3J-AS1, is significantly associated with clinicopathological characteristics, including tumor size, lymph node metastasis, estrogen receptor (ER), and progesterone receptor (PR) status. Kaplan–Meier survival analysis revealed that EIF3J-AS1, a downregulated lncRNA in breast tumor, is a potential prognostic marker for breast cancer. EIF3J-AS1 may function in an estrogen-independent manner and could be inhibited by the compound FDI-6. Therefore, integrating sparse gene coexpression network and clinicopathological features can accelerate identification and functional characterization of novel prognostic lncRNAs in breast cancer.