Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Level Laser Therapy Stimulates Proliferation in Head and Neck Squamous Cell Carcinoma Cells

Objectives: Low-level laser therapy (LLLT) is a promising non-invasive treatment option for oropharyngeal mucositis, which is a common side effect of many oncological treatments. LLLT is known for its wound healing properties due to the stimulation of cellular processes, such as proliferation, migration and differentiation. Controversy exists on the possible stimulatory effect of LLLT on head and neck cancer (HNSCC) cells in patients treated with radiotherapy. The aim of this study was to evaluate the biostimulatory effect together with the underlying mechanisms of LLLT on HNSCC cancer cells and normal epithelial cells. Materials and methods: HNSCC cell lines (SCC154, SQD9, and SCC61) and human tonsil epithelial cells were exposed to a Gallium-Aluminum-Arsenide diode laser (830 nm, 150 mW) with energy densities of 0, 1, and 2 J/cm2. The proliferation potential of the cells was assessed by Sulforhodamine B assay, immunoblotting (mitogenic pathways), immunocytochemistry (Ki67), and flow cytometry (PI cell cycle staining). Results: Cell proliferation was increased in HNSCC cell lines after laser irradiation with 1 J/cm2, whereas no significant increase was seen after laser irradiation with 2 J/cm2. In contrast, no effect on cell proliferation was seen in the human tonsil epithelial cells after laser irradiation with any of the energy densities. The increased proliferation was associated with elevated levels of pAKT, pERK, and Ki67 protein expression and cell cycle progression. Conclusion: Our results show that LLLT increases cell proliferation in a dose-dependent manner in HNSCC cells but not in normal epithelial tonsil cells. These results suggest that LLLT has to be used with caution when treating oropharyngeal mucositis in HNSCC patients since tumor cells present in the LLLT irradiation field could be triggered by LLLT.
Exploring Recombination for Efficient Decoding of Neural Machine Translation

In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations. This means that partial hypotheses with different prefixes will be regarded differently no matter how similar they are. However, this might be inefficient since some partial hypotheses can contain only local differences that will not influence future predictions. In this work, we introduce recombination in NMT decoding based on the concept of the “equivalence” of partial hypotheses. Heuristically, we use a simple n-gram suffix based equivalence function and adapt it into beam search decoding. Through experiments on large-scale Chinese-to-English and English-to-Germen translation tasks, we show that the proposed method can obtain similar translation quality with a smaller beam size, making NMT decoding more efficient.
PATCONFDB: Design and Evaluation of Access Pattern Confidentiality-Preserving Indexes

When sensitive data is outsourced to an untrustworthy cloud storage provider, encryption techniques can be used to enforce data conﬁdentiality. Ideally, such encryption techniques should not only enforce the conﬁdentiality of data at rest but also the conﬁdentiality of data accesses, as database access patterns can leak information about the database’s contents. Oblivious RAM (ORAM) approaches were proposed to hide access patterns, but they currently support a very limited set of database query operations. In this paper 1 , we propose PATCONFDB that supports database query functionalities and hides query access patterns. PATCONFDB represents a construction based on ORAM schemes: when an index (B-tree) is outsourced, multiple ORAM instances are used to maintain access pattern conﬁdentiality. PATCONFDB can make use of up-to-date ORAM schemes, for example an implementation of Burst ORAM is used to signiﬁcantly boost the performance of accesses. We compare PATCONFDB with a shufﬂed B-tree protocol, provide a discussion on security properties, and give recommendations of which protocol to use in which usage scenario. We provide a rigorous efﬁciency evaluation to determine the storage and network overhead as well as query latency. In particular, we show that PATCONFDB with ORAM-based schemes like Burst ORAM only causes a marginal latency overhead when evaluating equality conditions on databases of up to 10 million records. However, the network overhead still remains a challenge.
An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems

ABSTRACT Dynamic optimisation problems (DOPs) have attracted a lot of research attention in recent years due to their practical applications and complexity. DOPs are more challenging than static optimisation problems because the problem information or data is either revealed or changed during the course of an ongoing optimisation process. This requires an optimisation algorithm that should be able to monitor the movement of the optimal point and the changes in the landscape solutions. In this paper, we proposed an Interleaved Artificial Bee Colony (I-ABC) algorithm for DOPs. Artificial Bee Colony (ABC) is a nature inspired algorithm which has been successfully used in various optimisation problems. The proposed I-ABC algorithm has two populations, called ABC1 and ABC2, which worked in an interleaved manner. While ABC1 focused on exploring the search space though using a probabilistic solution acceptance mechanism, ABC2 worked inside ABC1 and focused on the search around the current best solutions by using a greedy mechanism. The proposed algorithm was tested on the Moving Peak Benchmark. The experimental results indicated that the proposed algorithm achieved better results than the compared methods for 8 out of 11 scenarios.
Dorsal BNST α2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

Stress is a precipitating agent in neuropsychiatric disease and initiates relapse to drug-seeking behavior in addicted patients. Targeting the stress system in protracted abstinence from drugs of abuse with anxiolytics may be an effective treatment modality for substance use disorders. α2A-adrenergic receptors (α2A-ARs) in extended amygdala structures play key roles in dampening stress responses. Contrary to early thinking, α2A-ARs are expressed at non-noradrenergic sites in the brain. These non-noradrenergic α2A-ARs play important roles in stress responses, but their cellular mechanisms of action are unclear. In humans, the α2A-AR agonist guanfacine reduces overall craving and uncouples craving from stress, yet minimally affects relapse, potentially due to competing actions in the brain. Here, we show that heteroceptor α2A-ARs postsynaptically enhance dorsal bed nucleus of the stria terminalis (dBNST) neuronal activity in mice of both sexes. This effect is mediated by hyperpolarization-activated cyclic nucleotide-gated cation channels because inhibition of these channels is necessary and sufficient for excitatory actions. Finally, this excitatory action is mimicked by clozapine-N-oxide activation of the Gi-coupled DREADD hM4Di in dBNST neurons and its activation elicits anxiety-like behavior in the elevated plus maze. Together, these data provide a framework for elucidating cell-specific actions of GPCR signaling and provide a potential mechanism whereby competing anxiogenic and anxiolytic actions of guanfacine may affect its clinical utility in the treatment of addiction. SIGNIFICANCE STATEMENT Stress affects the development of neuropsychiatric disorders including anxiety and addiction. Guanfacine is an α2A-adrenergic receptor (α2A-AR) agonist with actions in the bed nucleus of the stria terminalis (BNST) that produces antidepressant actions and uncouples stress from reward-related behaviors. Here, we show that guanfacine increases dorsal BNST neuronal activity through actions at postsynaptic α2A-ARs via a mechanism that involves hyperpolarization-activated cyclic nucleotide gated cation channels. This action is mimicked by activation of the designer receptor hM4Di expressed in the BNST, which also induces anxiety-like behaviors. Together, these data suggest that postsynaptic α2A-ARs in BNST have excitatory actions on BNST neurons and that these actions can be phenocopied by the so-called “inhibitory” DREADDs, suggesting that care must be taken regarding interpretation of data obtained with these tools.
Energy efficient and fair resource allocation for LTE‐unlicensed uplink networks: A two‐sided matching approach with partial information

Long‐Term Evolution–unlicensed (LTE‐U) has recently attracted worldwide interest to meet the explosion in cellular traffic data. By using carrier aggregation, licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance. As there may exist other conventional unlicensed band users, such as WiFi users, LTE‐U users have to share the same unlicensed bands with them. Thus, an optimized resource allocation scheme to ensure the fairness between LTE‐U users and conventional unlicensed band users is critical for the deployment of LTE‐U networks. In this paper, we investigate an energy efficient resource allocation problem in LTE‐U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks. We formulate the problem as a multiobjective optimization problem and propose a semidistributed matching framework with a partial information‐based algorithm to solve it. We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered.
Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

Obtaining an optimum data representation is a challenging issue that arises in many intellectual data processing techniques such as data mining, pattern recognition, and gene clustering. Many existing methods formulate this problem as a nonnegative matrix factorization (NMF) approximation problem. The standard NMF uses the least square loss function, which is not robust to outlier points and noises and fails to utilize prior label information to enhance the discriminability of representations. In this study, we develop a novel matrix factorization method called robust semisupervised nonnegative local coordinate factorization by integrating robust NMF, a robust local coordinate constraint, and local spline regression into a unified framework. We use the l2,1 norm for the loss function of the NMF and a local coordinate constraint term to make our method insensitive to outlier points and noises. In addition, we exploit the local and global consistencies of sample labels to guarantee that data representation is compact and discriminative. An efficient multiplicative updating algorithm is deduced to solve the novel loss function, followed by a strict proof of the convergence. Several experiments conducted in this study on face and gene datasets clearly indicate that the proposed method is more effective and robust compared to the state-of-the-art methods.
Applications of Clinically Relevant Dissolution Testing: Workshop Summary Report

This publication summarizes the proceedings of day 3 of a 3-day workshop on “Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development. ” Specifically, this publication discusses the current approaches in building clinical relevance into drug product development for solid oral dosage forms, along with challenges that both industry and regulatory agencies are facing in setting clinically relevant drug product specifications (CRDPS) as presented at the workshop. The concept of clinical relevance is a multidisciplinary effort which implies an understanding of the relationship between the critical quality attributes (CQAs) and their impact on predetermined clinical outcomes. Developing this level of understanding, in many cases, requires introducing deliberate but meaningful variations into the critical material attributes (CMAs) and critical process parameters (CPPs) to establish a relationship between the resulting in vitro dissolution/release profiles and in vivo PK performance, a surrogate for clinical outcomes. Alternatively, with the intention of improving the efficiency of the drug product development process by limiting the burden of conducting in vivo studies, this understanding can be either built, or at least enhanced, through in silico efforts, such as IVIVC and physiologically based pharmacokinetic (PBPK) absorption modeling and simulation (M&S). These approaches enable dissolution testing to establish safe boundaries and reject drug product batches falling outside of the established safe range (e.g., due to inadequate in vivo performance) enabling the method to become clinically relevant. Ultimately, these efforts contribute towards patient-centric drug product development and allow regulatory flexibility throughout the lifecycle of the drug product.
Elastic Shape Analysis of Three-Dimensional Objects

Abstract Statistical analysis of shapes of 3D objects is an important problem with a wide range of applications. This analysis is difficult for many reasons, including the fact that objects differ in both geometry and topology. In this manuscript, we narrow the problem by focusing on objects with fixed topology, say objects that are diffeomorphic to unit spheres, and develop tools for analyzing their geometries. The main challenges in this problem are to register points across objects and to perform analysis while being invariant to certain shape-preserving transformations. We develop a comprehensive framework for analyzing shapes of spherical objects, i.e., objects that are embeddings of a unit sphere in ℝ, including tools for: quantifying shape differences, optimally deforming shapes into each other, summarizing shape samples, extracting principal modes of shape variability, and modeling shape variability associated with populations. An important strength of this framework is that it is elastic: it perf.
An examination of fitness costs of glyphosate resistance in the common morning glory, Ipomoea purpurea

Abstract Fitness costs are frequently invoked to explain the presence of genetic variation underlying plant defense across many types of damaging agents. Despite the expectation that costs of resistance are prevalent, however, they have been difficult to detect in nature. To examine the potential that resistance confers a fitness cost, we examined the survival and fitness of genetic lines of the common morning glory, Ipomoea purpurea, that diverged in the level of resistance to the herbicide glyphosate. We planted a large field experiment and assessed survival following herbicide application as well as fitness of the divergent selection lines in the absence of the herbicide. We found that genetic lines selected for increased resistance exhibited lower death compared to control and susceptible lines in the presence of the herbicide, but no evidence that resistant lines produced fewer seeds in the absence of herbicide. However, susceptible lines produced more viable seeds than resistant or control lines, providing some evidence of a fitness cost in terms of seed germination, and thus potential empirical support for the expectation of trait trade‐offs as a consequence of adaptation to novel environments.
Large-Deformation Image Registration of CT-TEE for Surgical Navigation of Congenital Heart Disease

The surgical treatment of congenital heart disease requires navigational assistance with transesophageal echocardiography (TEE); however, TEE images are often difficult to interpret and provide very limited anatomical information. Registering preoperative CT images to intraoperative TEE images provides surgeons with richer and more useful anatomical information. Yet, CT and TEE images differ substantially in terms of scale and geometry. In the present research, we propose a novel method for the registration of CT and TEE images for navigation during surgical repair of large defects in patients with congenital heart disease. Valve data was used for the coarse registration to determine the basic location. This was followed by the use of an enhanced probability model map to overcome gray-level differences between the two imaging modalities. Finally, the rapid optimization of mutual information was achieved by migrating parameters. This method was tested on a dataset of 240 images from 12 infant, children (≤ 3 years old), and adult patients with congenital heart disease. Compared to the “bronze standard” registration, the proposed method was more accurate with an average Dice coefficient of 0.91 and an average root mean square of target registration error of 1.2655 mm.
Advances in assessing Sabellaria spinulosa reefs for ongoing monitoring

Abstract Standardized and repeatable data acquisition and analyses are required to enable the mapping and condition monitoring of reefs within Marine Protected Areas (MPAs). Changes in habitat condition must be reliably identified and reported to best support evidence‐based management. Biogenic reefs in temperate waters, that is, hard matter created by living organisms and raised above the seabed, provide food and shelter for many plant and animal species. This article explores the feasibility of habitat mapping, using remote sensing datasets, as well as metrics for repeatable and suitable assessment of areas of Sabellaria spinulosa for their status as biogenic reef. Data were gathered within the North Norfolk Sandbanks and Saturn Reef candidate Special Area of Conservation/Site of Community Importance in the southern North Sea. Six study areas were identified as potential locations of biogenic reef using previously acquired data, and these were targeted for further investigation using a combination of high resolution multibeam echosounder and sidescan sonar. Where potential S. spinulosa was identified from the acoustic data, a drop‐down camera system was employed for visual verification. Areas of known and potential S. spinulosa reef were mapped successfully at two of the six study areas, although future approaches should take careful consideration of the seabed morphology and predominant habitat backdrop to successfully interpret such data. Camera tows from S. spinulosa reef areas were broken up into 5‐s segments, with each segment scored for (a) average tube elevation; (b) average percentage cover; and (c) for the presence or absence of S. spinulosa. These metrics were utilized to create summary statistics, including a value of patchiness derived from presence/absence data, that is recommended for application as part of future monitoring programs. The application of this methodology could benefit wider assessments of similar threated or declining habitats such as intertidal Mytilus edulis beds on mixed and sandy sediments, Maerl beds, Modioulus modiolus beds, Ostrea edulis beds, and Zostera beds where patchiness may also be considered of environmental importance.
Distinct Modulation of Spontaneous and GABA-Evoked Gating by Flurazepam Shapes Cross-Talk Between Agonist-Free and Liganded GABAA Receptor Activity

GABAA receptors (GABAARs) play a crucial inhibitory role in the CNS. Benzodiazepines (BDZs) are positive modulators of specific subtypes of GABAARs, but the underlying mechanism remains obscure. Early studies demonstrated the major impact of BDZs on binding and more recent investigations indicated gating, but it is unclear which transitions are affected. Moreover, the upregulation of GABAAR spontaneous activity by BDZs indicates their impact on receptor gating but the underlying mechanisms remain unknown. Herein, we investigated the effect of a BDZ (flurazepam) on the spontaneous and GABA-induced activity for wild-type (WT, α1β2γ2) and mutated (at the orthosteric binding site α1F64) GABAARs. Surprisingly, in spite of the localization at the binding site, these mutations increased the spontaneous activity. Flurazepam (FLU) upregulated this activity for mutants and WT receptors to a similar extent by affecting opening/closing transitions. Spontaneous activity affected GABA-evoked currents and is manifested as an overshoot after agonist removal that depended on the modulation by BDZs. We explain the mechanism of this phenomenon as a cross-desensitization of ligand-activated and spontaneously active receptors. Moreover, due to spontaneous activity, FLU-pretreatment and co-application (agonist + FLU) protocols yielded distinct results. We provide also the first evidence that GABAAR may enter the desensitized state in the absence of GABA in a FLU-dependent manner. Based on our data and model simulations, we propose that FLU affects agonist-induced gating by modifying primarily preactivation and desensitization. We conclude that the mechanisms of modulation of spontaneous and ligand-activated GABAAR activity concerns gating but distinct transitions are affected in spontaneous and agonist-evoked activity.
Magicscroll: a rollable display device with flexible screen real estate and gestural input

We present MagicScroll, a rollable tablet with 2 concatenated flexible multitouch displays, actuated scrollwheels and gestural input. When rolled up, MagicScroll can be used as a rolodex, smartphone, expressive messaging interface or gestural controller. When extended, it provides full access to its 7.5" high-resolution multitouch display, providing the display functionality of a tablet device. We believe that the cylindrical shape in the rolled-up configuration facilitates gestural interaction, while its shape changing and input capabilities allow the navigation of continuous information streams and provide focus plus context functionality. We investigated the gestural affordances of MagicScroll in its rolled-up configuration by means of an elicitation study.
Pacific Asia Conference on Information Systems ( PACIS ) 2014 ACADEMIC HOT-SPOT ANALYSIS ON INFORMATION SYSTEM BASED ON THE CO-TERM NETWORK

The amount of research literature is increasing so fast that the scholars are hard to clearly know the state of art about a certain research field. For IS scholars, understanding research hot-spots among numerous academic papers on IS field is always a significant and key task. In this paper, taking Information System field as example, an academic hot-spot analysis method is proposed to automatically find the research hot topic. Firstly, based on the key words of literatures, a co-term network is build, then fast greedy clustering method is used to find research topic, and hot degree of research topics are computed. After downloading the literature information about IS field from WEB OF SCIENCE, research hot topic during latest five years is identified. The result show that three general topics, respectively as GIS, Health-Care IS, Management & Internet, are important research direction. Then, the hot-spots analysis method, which decomposes the "Management & Internet" topic into 10 topic communities, generates and discussed the IS trends on top 5 academic topics of each year from 2009 to 2013 and the heat map of the IS hot-spots in 2013.
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

This paper investigates the problem of aerial vehicle recognition using a text-guided deep convolutional neural network classifier. The network receives an aerial image and a desired class, and makes a yes or no output by matching the image and the textual description of the desired class. We train and test our model on a synthetic aerial dataset and our desired classes consist of the combination of the class types and colors of the vehicles. This strategy helps when considering more classes in testing than in training.