Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Pulsed Electromagnetic Fields to Promote Bone Responses to Biomaterials In Vitro and In Vivo

Implantable biomaterials are extensively used to promote bone regeneration or support endosseous prosthesis in orthopedics and dentistry. Their use, however, would benefit from additional strategies to improve bone responses. Pulsed Electromagnetic Fields (PEMFs) have long been known to act on osteoblasts and bone, affecting their metabolism, in spite of our poor understanding of the underlying mechanisms. Hence, we have the hypothesis that PEMFs may also ameliorate cell responses to biomaterials, improving their growth, differentiation, and the expression of a mature phenotype and therefore increasing the tissue integration of the implanted devices and their clinical success. A broad range of settings used for PEMFs stimulation still represents a hurdle to better define treatment protocols and extensive research is needed to overcome this issue. The present review includes studies that investigated the effects of PEMFs on the response of bone cells to different classes of biomaterials and the reports that focused on in vivo investigations of biomaterials implanted in bone.
A recap of RNA recapping

The N7‐methylguanosine cap is a hallmark of the 5′ end of eukaryotic mRNAs and is required for gene expression. Loss of the cap was believed to lead irreversibly to decay. However, nearly a decade ago, it was discovered that mammalian cells contain enzymes in the cytoplasm that are capable of restoring caps onto uncapped RNAs. In this review, we summarize recent advances in our understanding of cytoplasmic RNA recapping and discuss the biochemistry of this process and its impact on regulating and diversifying the transcriptome. Although most studies focus on mammalian RNA recapping, we also highlight new observations for recapping in disparate eukaryotic organisms, with the trypanosome recapping system appearing to be a fascinating example of convergent evolution. We conclude with emerging insights into the biological significance of RNA recapping and prospects for the future of this evolving area of study.
Masking a fish's detection of environmental stimuli: application to improving downstream migration at river infrastructure

According to Signal Detection Theory, the ability to detect a stimulus (discriminability, d') is inversely related to the magnitude of internal and external noise. In this study, downstream moving brown trout Salmo trutta were used to investigate whether external hydrodynamic noise (in this case turbulence) could mask a signal that induced an unwanted response, such as rejecting accelerating velocity gradients commonly encountered at entrances to fish bypass channels. S. trutta behaviour was quantified in the absence (control) or presence of an accelerating velocity gradient created by an unconstricted or constricted channel, respectively, under two levels (low and high) of background turbulent kinetic energy (hydrodynamic noise). Experiments were conducted in an indoor recirculating flume in the dark and a range of passage metrics were quantified. Under the control condition, most (ca. 91%) S. trutta passed, usually oriented downstream (67%), with minimal delay (median 0.13 min). In comparison, fewer S. trutta (ca. 43%) passed under constricted conditions, they tended to orient facing into the flow (ca. 64%) and delay was greater (median > 20 min). When viewed from a coarse‐scale perspective, discriminability of the velocity gradient was lower when turbulence was high suggesting masking of the signal occurred. However, the resulting increase in the percentage of fish that passed, decrease in time to pass and reduction in the distance at which S. trutta reacted (switched orientation) was subtle and non‐significant. Despite the mixed results obtained, the use of masking to manipulate an animal's perception of environmental stimuli as a fisheries management tool is conceptually valid and the results of this experiment present a useful stepping stone for future research.
Fraud Proofs: Maximising Light Client Security and Scaling Blockchains with Dishonest Majorities

Light clients, also known as Simple Payment Verification (SPV) clients, are nodes which only download a small portion of the data in a blockchain, and use indirect means to verify that a given chain is valid. Typically, instead of validating block data, they assume that the chain favoured by the blockchain’s consensus algorithm only contains valid blocks, and that the majority of block producers are honest. By allowing such clients to receive fraud proofs generated by fully validating nodes that show that a block violates the protocol rules, and combining this with probabilistic sampling techniques to verify that all of the data in a block actually is available to be downloaded, we can eliminate the honest-majority assumption, and instead make much weaker assumptions about a minimum number of honest nodes that rebroadcast data. Fraud and data availability proofs are key to enabling on-chain scaling of blockchains (e.g., via sharding or bigger blocks) while maintaining a strong assurance that on-chain data is available and valid. We present, implement, and evaluate a novel fraud and data availability proof system.
Shift-based Primitives for Efficient Convolutional Neural Networks

We propose a collection of three shift-based primitives for building efficient compact CNN-based networks. These three primitives (channel shift, address shift, shortcut shift) can reduce the inference time on GPU while maintains the prediction accuracy. These shift-based primitives only moves the pointer but avoids memory copy, thus very fast. For example, the channel shift operation is 12.7x faster compared to channel shuffle in ShuffleNet but achieves the same accuracy. The address shift and channel shift can be merged into the point-wise group convolution and invokes only a single kernel call, taking little time to perform spatial convolution and channel shift. Shortcut shift requires no time to realize residual connection through allocating space in advance. We blend these shift-based primitives with point-wise group convolution and built two inference-efficient CNN architectures named AddressNet and Enhanced AddressNet. Experiments on CIFAR100 and ImageNet datasets show that our models are faster and achieve comparable or better accuracy.
Image Saliency Detection Algorithm Based on Spatial and Frequency Domain

Detection of visually salient image regions is important for object segmentation, adaptive compression and object recognition. Because of the complex colour, complex surface texture and changeable background in the image, the quality of traditional image saliency detection methods is not high enough to exactly reflect the saliency information of the image. The test results of traditional methods cannot clearly point out the outline of the salient object. Meanwhile, neither do they highlight the most prominent object, nor do they uniformly highlight the entire object. These shortcomings make the saliency map unreliable and inefficient in various applications. To solve these problems, we introduce a method for salient region detection that outputs high resolution saliency maps with high brightness salient objects. Firstly, the image is filtered based on contrast sensitivity function to suppress the redundant image information. Then the saliency map is extracted in both spatial and frequency domains. Finally, the two saliency maps are weighted together to obtain the final saliency map.
Human Endogenous Retrovirus-K HML-2 integration within RASGRF2 is associated with intravenous drug abuse and modulates transcription in a cell-line model

Significance The human genome is “littered” with remnants of ancient retrovirus infections that invaded the germ line of our ancestors. Only one of these may still be proliferating, named HERV-K HML-2 (HK2). Not all humans have the same HK2 viruses in their genomes. Here we show that one specific uncommon HK2, which lies close to a gene involved in dopaminergic activity in the brain, is more frequently found in drug addicts and thus is significantly associated with addiction. We experimentally show that HK2 can manipulate nearby genes. Our study provides strong evidence that uncommon HK2 can be responsible for unappreciated pathogenic burden, and thus underlines the health importance of exploring the phenotypic roles of young, insertionally polymorphic HK2 integrations in human populations. HERV-K HML-2 (HK2) has been proliferating in the germ line of humans at least as recently as 250,000 years ago, with some integrations that remain polymorphic in the modern human population. One of the solitary HK2 LTR polymorphic integrations lies between exons 17 and 18 of RASGRF2, a gene that affects dopaminergic activity and is thus related to addiction. Here we show that this antisense HK2 integration (namely RASGRF2-int) is found more frequently in persons who inject drugs compared with the general population. In a Greek HIV-1–positive population (n = 202), we found RASGRF2-int 2.5 times (14 versus 6%) more frequently in patients infected through i.v. drug use compared with other transmission route controls (P = 0.03). Independently, in a United Kingdom-based hepatitis C virus-positive population (n = 184), we found RASGRF2-int 3.6 times (34 versus 9.5%) more frequently in patients infected during chronic drug abuse compared with controls (P < 0.001). We then tested whether RASGRF2-int could be mechanistically responsible for this association by modulating transcription of RASGRF2. We show that the CRISPR/Cas9-mediated insertion of HK2 in HEK293 cells in the exact RASGRF2 intronic position found in the population resulted in significant transcriptional and phenotypic changes. We also explored mechanistic features of other intronic HK2 integrations and show that HK2 LTRs can be responsible for generation of cis-natural antisense transcripts, which could interfere with the transcription of nearby genes. Our findings suggest that RASGRF2-int is a strong candidate for dopaminergic manipulation, and emphasize the importance of accurate mapping of neglected HERV polymorphisms in human genomic studies.
Upregulation of long noncoding RNA CCAT1-L promotes epithelial–mesenchymal transition in gastric adenocarcinoma

Objective In this study, we aimed to investigate the role of a long-chain noncoding RNA, colorectal cancer-associated transcript 1-long (CCAT1-L) in gastric adenocarcinoma. Patients and methods Expressions of CCAT1-L and c-MYC mRNA and MYC protein in gastric adenocarcinoma tissue and adjacent normal tissues of 60 patients were analyzed using quantitative real-time polymerase chain reaction and Western blot, respectively. The CCAT1-L levels in the normal gastric epithelial cell line, GES1, and human gastric adenocarcinoma cell lines, MGC803, MKN-28, SGC7901, and BGC823 were analyzed by quantitative real-time polymerase chain reaction. CCAT1-L knockdown in MGC803 and MKN28 cells was performed using RNA interference, followed by evaluating cell proliferation, invasion, and migration with soft agar colony formation assay, scratch wound assay, and transwell assay. Twenty BALB/C-nu-nu nude mice were inoculated with gastric tumor xenografts and treated with CCAT1-L small-interfering RNA (siRNA), followed by monitoring survival and tumor growth. Western blot was also used to analyze the expression of epithelial–mesenchymal transition-related proteins, including MYC, RAS, T-ERK, P-ERK, E-cadherin, and vimentin, in gastric adenocarcinoma MKN-28 cells. Results The expression of CCAT1-L and MYC in tumor tissue was significantly higher than that in adjacent normal tissues (P<0.001). There was a positive correlation between the expression level of CCAT1-L mRNA and c-MYC mRNA (r=0.863, P<0.001). CCAT1-L expression was also significantly higher in gastric adenocarcinoma cell lines than that in normal cell lines (P<0.01). Knockdown of CCAT1-L in MGC803 and MKN-28 cells markedly reduced the cell proliferation, migration, and invasion (P<0.001). CCAT1-L knockdown also evidently inhibited tumor growth and improved survival in nude mice (P<0.001). Expressions of MYC, RAS, and vimentin, and the phosphorylation of ERK protein were dramatically decreased, while the expression of E-cadherin protein was increased by CCAT1-L knockdown in MKN-28 cell. Conclusion CCAT1-L is a pro-oncogenic marker in gastric adenocarcinoma. CCAT1-L knockdown inhibits epithelial–mesenchymal transition of gastric adenocarcinoma cells and thus suppresses the gastric adenocarcinoma metastasis.
Emerging PARP inhibitors for treating breast cancer

ABSTRACT Introduction: Some breast cancers harbor defects in DNA repair pathways, including BRCA1 and BRCA2 mutations, leading to a genomic instability. Compromised DNA-damage repair response is found in 11 to 42% of triple negative breast cancers, with a frequency varying according to family history and ethnicity. The oral PARP inhibitors are a promising strategy in breast cancer exploiting Homologous Deficient Recombination deficiency (HRD) by a synthetic lethal approach. Several PARP inhibitors have currently reached early phase trials with studies on going in the adjuvant, neoadjuvant and metastatic setting. Area covered: Here, we review completed and ongoing trials with PARP inhibitors as well as their mechanisms of activity and acquired resistance. Expert opinion: PARP inhibitors show promising results in breast cancer. However, several issues are raised including the identification of biomarkers to predict treatment response and strategies to counteract emerging resistance.
mytune: webvisualization technologies for irish traditional music archives

Online digital music libraries have become important repositories for music for all enthusiasts of Irish traditional music. These libraries have certainly facilitated the discovery of new material and enabled extensive exploration of musical variations on well-known favorites. Current web interfaces to these repositories, offer a combination of text and notation-based information as well as providing audio and midi listening formats. However, these interfaces could take advantage of more recently available technologies offering graph-based, interactive, visual displays and more useful data analytic content. Drawing on the state-of the-art data visualization web libraries, the objective of this work is to present a prototype of a new digital library for Irish traditional music. The design and architecture is detailed using standard Unified Modelling Language (UML) notation. Evaluation is carried out by a usability survey to determine the effectiveness of the enhancements.
Effect of Acute Exercise Mode on Serum Brain-Derived Neurotrophic Factor (BDNF) and Task Switching Performance

Previous studies have consistently reported a positive effect of acute exercise on cognition, particularly on executive function. However, most studies have focused on aerobic and resistant forms of exercise. The purpose of this study was to compare the effect of ‘open-skill’ with ‘closed-skill’ exercise (defined in terms of the predictability of the performing environment) on brain-derived neurotrophic factor (BDNF) production and task switching performance. Twenty young adult males participated in both closed (running) and open (badminton) skill exercise sessions in a counterbalanced order on separate days. The exercise sessions consisted of 5 min of warm up exercises followed by 30 min of running or badminton. The exercise intensity was set at 60% (±5%) of the heart rate reserve level (HRR) with HR being monitored by a wireless heart rate monitor. Blood samples were taken and participation in a task-switching paradigm occurred before and after each exercise session. Results showed no differences in serum BDNF or task-switching performance at the pre-test stage, however, badminton exercise resulted in significantly higher serum BDNF levels (a proxy for levels of BDNF in the brain) and near significant smaller global switching costs relative to running. This study has provided preliminary evidence in support the relative benefits of open-skills exercises on BDNF and executive function.
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps

This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions. In a supervised learning scenario, the quality of predictions is intrinsically related to the training labels, which guide the optimization process. For indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to provide dense, albeit short-range, depth maps. On the other hand, for outdoor scenes, LiDARs are considered the standard sensor, which comparatively provides much sparser measurements, especially in areas further away. Rather than modifying the neural network architecture to deal with sparse depth maps, this article introduces a novel densification method for depth maps, using the Hilbert Maps framework. A continuous occupancy map is produced based on 3D points from LiDAR scans, and the resulting reconstructed surface is projected into a 2D depth map with arbitrary resolution. Experiments conducted with various subsets of the KITTI dataset show a significant improvement produced by the proposed Sparse-to-Continuous technique, without the introduction of extra information into the training stage.
FCHD: A fast and accurate head detector

In this paper, we propose FCHD-Fully Convolutional Head Detector, which is an end-to-end trainable head detection model, which runs at 5 fps and with 0.70 average precision (AP), on a very modest GPU. Recent head detection techniques have avoided using anchors as a starting point for detection especially in the cases where the detection has to happen in the wild. The reason is poor performance of anchor-based techniques under scenarios where the object size is small. We argue that a good AP can be obtained with carefully designed anchors, where the anchor design choices are made based on the receptive field size of the hidden layers. Our contribution is two folds. 1) A simple fully convolutional anchor based model which is end-to-end trainable and has a very low inference time. 2) Carefully chosen anchor sizes which play a key role in getting good average precision. Our model achieves comparable results than many other baselines on challenging head detection dataset like BRAINWASH. Along with accuracy, our model has least runtime among all the baselines along with modest hardware requirements which makes it suitable for edge deployments in surveillance applications. The code is made open-source at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector.
Evaluating the impact of a home-based rehabilitation service on older people and their caregivers: a matched-control quasi-experimental study

Introduction Previous studies evaluating home-based rehabilitation service (HBRS) merely focused on the period immediately after the patients’ discharge from hospitals. The present study focuses on HBRS that covers clients who have not been recently hospitalized. HBRS aims to meet older clients’ rehabilitation needs and support their caregivers in the community. This study intended to evaluate the impact of HBRS on the older clients’ health outcomes and hospital services utilization, and caregivers’ strain in providing care for clients. Methods This study used a matched-control quasi-experimental design with a 3-month follow-up to evaluate HBRS. The health outcome measures used for the older clients included Elderly Mobility Scale, Timed Up and Go test, Modified Barthel Index, Lawton’s Instrumental Activities of Daily Living Scale, Mini-Mental State Examination, and World Health Organization Quality of Life Scale, Short Form, Hong Kong version (WHOQOL-BREF [HK]). Meanwhile, the Caregiver Strain Index was used to measure the caregivers’ caregiving strain. Data on clients’ hospital services utilization 3 and 6 months before and after the study were also collected and evaluated. Results The final sample consisted of 122 pairs of older clients and caregivers who live in a community in Hong Kong. In the follow-up after 3 months, the intervention group showed immensely substantial improvements across all the health outcome measures compared with the control group. The intervention group also demonstrated substantial reduction in the clients’ hospital services utilization compared with the control group. However, no significant differences in the clients’ hospital services utilization exist between the two groups in the follow-up after 6 months. Conclusion HBRS of this study is an effective intervention service to improve health outcomes and reduce hospital services utilization among older people living in the community. Moreover, HBRS of this study was effective in reducing the caregivers’ caregiving strain.
Learning schema mappings

A schema mapping is a high-level specification of the relationship between a source schema and a target schema. Recently, a line of research has emerged that aims at deriving schema mappings automatically or semi-automatically with the help of data examples, that is, pairs consisting of a source instance and a target instance that depict, in some precise sense, the intended behavior of the schema mapping. Several different uses of data examples for deriving, refining, or illustrating a schema mapping have already been proposed and studied. In this article, we use the lens of computational learning theory to systematically investigate the problem of obtaining algorithmically a schema mapping from data examples. Our aim is to leverage the rich body of work on learning theory in order to develop a framework for exploring the power and the limitations of the various algorithmic methods for obtaining schema mappings from data examples. We focus on GAV schema mappings, that is, schema mappings specified by GAV (Global-As-View) constraints. GAV constraints are the most basic and the most widely supported language for specifying schema mappings. We present an efficient algorithm for learning GAV schema mappings using Angluin's model of exact learning with membership and equivalence queries. This is optimal, since we show that neither membership queries nor equivalence queries suffice, unless the source schema consists of unary relations only. We also obtain results concerning the learnability of schema mappings in the context of Valiant's well-known PAC (Probably-Approximately-Correct) learning model, and concerning the learnability of restricted classes of GAV schema mappings. Finally, as a byproduct of our work, we show that there is no efficient algorithm for approximating the shortest GAV schema mapping fitting a given set of examples, unless the source schema consists of unary relations only.
Query Understanding via Entity Attribute Identification

Understanding searchers' queries is an essential component of semantic search systems. In many cases, search queries involve specific attributes of an entity in a knowledge base (KB), which can be further used to find query answers. In this study, we aim to move forward the understanding of queries by identifying their related entity attributes from a knowledge base. To this end, we introduce the task of entity attribute identification and propose two methods to address it: (i) a model based on Markov Random Field, and (ii) a learning to rank model. We develop a human annotated test collection and show that our proposed methods can bring significant improvements over the baseline methods.