Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusion++: Volumetric Object-Level SLAM

We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions with object size-dependent resolutions and a novel 3D foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation. Objects are incrementally refined via depth fusion, and are used for tracking, relocalisation and loop closure detection. Loop closures cause adjustments in the relative pose estimates of object instances, but no intra-object warping. Each object also carries semantic information which is refined over time and an existence probability to account for spurious instance predictions. We demonstrate our approach on a hand-held RGB-D sequence from a cluttered office scene with a large number and variety of object instances, highlighting how the system closes loops and makes good use of existing objects on repeated loops. We quantitatively evaluate the trajectory error of our system against a baseline approach on the RGB-D SLAM benchmark, and qualitatively compare reconstruction quality of discovered objects on the YCB video dataset. Performance evaluation shows our approach is highly memory efficient and runs online at 4-8Hz (excluding relocalisation) despite not being optimised at the software level.
Achieving Maximum Distance Separable Private Information Retrieval Capacity With Linear Codes

We propose three private information retrieval (PIR) protocols for distributed storage systems (DSSs), where data is stored using an arbitrary linear code. The first two protocols, named Protocol 1 and Protocol 2, achieve privacy for the scenario with noncolluding nodes. Protocol 1 requires a file size that is exponential in the number of files in the system, while Protocol 2 requires a file size that is independent of the number of files and is hence simpler. We prove that, for certain linear codes, Protocol 1 achieves the maximum distance separable (MDS) PIR capacity, i.e., the maximum PIR rate (the ratio of the amount of retrieved stored data per unit of downloaded data) for a DSS that uses an MDS code to store any given (finite and infinite) number of files, and Protocol 2 achieves the asymptotic MDS-PIR capacity (with infinitely large number of files in the DSS). In particular, we provide a necessary and a sufficient condition for a code to achieve the MDS-PIR capacity with Protocols 1 and 2 and prove that cyclic codes, Reed–Muller (RM) codes, and a class of distance-optimal local reconstruction codes achieve both the finite MDS-PIR capacity (i.e., with any given number of files) and the asymptotic MDS-PIR capacity with Protocols 1 and 2, respectively. Furthermore, we present a third protocol, Protocol 3, for the scenario with multiple colluding nodes, which can be seen as an improvement of a protocol recently introduced by Freij-Hollanti et al. Similar to the noncolluding case, we provide a necessary and a sufficient condition to achieve the maximum possible PIR rate of Protocol 3. Moreover, we provide a particular class of codes that is suitable for this protocol and show that RM codes achieve the maximum possible PIR rate for the protocol. For all three protocols, we present an algorithm to optimize their PIR rates.
Therapeutic Drug Monitoring of Voriconazole in Children from a Tertiary Care Center in China

Voriconazole is a broad-spectrum triazole antifungal and the first-line treatment for invasive aspergillosis (IA). The aim of this research was to study the dose adjustments of voriconazole as well as the affecting factors influencing voriconazole trough concentrations in Asian children to optimize its daily administration. ABSTRACT Voriconazole is a broad-spectrum triazole antifungal and the first-line treatment for invasive aspergillosis (IA). The aim of this research was to study the dose adjustments of voriconazole as well as the affecting factors influencing voriconazole trough concentrations in Asian children to optimize its daily administration. Clinical data were analyzed of inpatients 2 to 14 years old who were subjected to voriconazole trough concentration monitoring from 1 June 2015 to 1 December 2017. A total of 138 voriconazole trough concentrations from 42 pediatric patients were included. Voriconazole trough concentrations at steady state ranged from 0.02 to 9.35 mg/liter, with high inter- and intraindividual variability. Only 50.0% of children achieved the target range (1.0 to 5.5 mg/liter) at initial dosing, while 35.7% of children were subtherapeutic, and 14.3% of children were supratherapeutic at initial dosing. There was no correlation between initial trough concentrations and initial dosing. A total of 28.6% of children (12/42) received an adjusted dose according to trough concentrations. Children <6, 6 to 12, and >12 years old required a median oral maintenance dose to achieve the target range of 11.1, 7.2, and 5.3 mg/kg twice daily, respectively (P = 0.043). The average doses required to achieved the target range were 7.7 mg/kg and 5.6 mg/kg, respectively, and were lower than the recommended dosage (P = 0.033 and 0.003, respectively). Affecting factors such as administration routes and coadministration with proton pump inhibitors (PPIs) explained 55.3% of the variability in voriconazole exposure. Therapeutic drug monitoring (TDM) of voriconazole could help to individualize antifungal therapy for children and provide guidelines for TDM and dosing optimization in Asian children.
Event Detection with Neural Networks: A Rigorous Empirical Evaluation

Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset.
Points and Stripes: A Novel Technique for Masking Biological Motion Point-Light Stimuli

Human articulated motion can be readily recognized robustly even from impoverished so-called point-light displays. Such sequence information is processed by separate visual processing channels recruiting different stages at low and intermediate levels of the cortical visual processing hierarchy. The different contributions that motion and form information make to form articulated, or biological, motion perception are still under investigation. Here we investigate experimentally whether and how specific spatio-temporal features, such as extrema in the motion energy or maximum limb expansion, indicated by the lateral and longitudinal extension, constrain the formation of the representations of articulated body motion. In order to isolate the relevant stimulus properties we suggest a novel masking technique, which allows to selectively impair the ankle information of the body configuration while keeping the motion of the point-light locations intact. Our results provide evidence that maxima in feature channel representations, e.g., the lateral or longitudinal extension, define elemental features to specify key poses of biological motion patterns. These findings provide support for models which aim at automatically building visual representations for the cortical processing of articulated motion by identifying temporally localized events in a continuous input stream.
Towards Developing Bioresponsive, Self-Assembled Peptide Materials: Dynamic Morphology and Fractal Nature of Nanostructured Matrices

(Arginine-alanine-aspartic acid-alanine)4 ((RADA)4) nanoscaffolds are excellent candidates for use as peptide delivery vehicles: they are relatively easy to synthesize with custom bio-functionality, and assemble in situ to allow a focal point of release. This enables (RADA)4 to be utilized in multiple release strategies by embedding a variety of bioactive molecules in an all-in-one “construct”. One novel strategy focuses on the local, on-demand release of peptides triggered via proteolysis of tethered peptide sequences. However, the spatial-temporal morphology of self-assembling nanoscaffolds may greatly influence the ability of enzymes to both diffuse into as well as actively cleave substrates. Fine structure and its impact on the overall effect on peptide release is poorly understood. In addition, fractal networks observed in nanoscaffolds are linked to the fractal nature of diffusion in these systems. Therefore, matrix morphology and fractal dimension of virgin (RADA)4 and mixtures of (RADA)4 and matrix metalloproteinase 2 (MMP-2) cleavable substrate modified (RADA)4 were characterized over time. Sites of high (glycine-proline-glutamine-glycine+isoleucine-alanine-serine-glutamine (GPQG+IASQ), CP1) and low (glycine-proline-glutamine-glycine+proline-alanine-glycine-glutamine (GPQG+PAGQ), CP2) cleavage activity were chosen. Fine structure was visualized using transmission electron microscopy. After 2 h of incubation, nanofiber networks showed an established fractal nature; however, nanofibers continued to bundle in all cases as incubation times increased. It was observed that despite extensive nanofiber bundling after 24 h of incubation time, the CP1 and CP2 nanoscaffolds were susceptible to MMP-2 cleavage. The properties of these engineered nanoscaffolds characterized herein illustrate that they are an excellent candidate as an enzymatically initiated peptide delivery platform.
LncRNA SNHG4 promotes tumour growth by sponging miR‐224‐3p and predicts poor survival and recurrence in human osteosarcoma

Accumulating data show that dysregulation of long noncoding RNAs (lncRNAs) acts a critical role in a variety of malignancies. Among these lncRNAs, small nucleolar RNA host genes (SNHGs) are associated with tumour growth and progression. But, the molecular mechanisms by which SNHG4 contributes to osteosarcoma remain undocumented.
Event‐related deep brain stimulation of the subthalamic nucleus affects conflict processing

Many lines of evidence suggest that response conflict recruits brain regions in the cortical–basal ganglia system. Within the basal ganglia, deep brain recordings from the subthalamic nucleus (STN) have shown that conflict triggers a transient increase in low‐frequency oscillations (LFOs; 2–8Hz). Here, we deployed a new method of delivering short trains of event‐related deep brain stimulation (DBS) to the STN to test the causal role of the STN and its associated circuits in conflict‐related processing.
Architecture of cell–cell adhesion mediated by sidekicks

Significance Cell adhesion is important for the survival of living organisms and is mediated by the adhesion molecules that bridge the membranes of adjacent cells. Cell adhesion molecules usually have long flexible ectodomains, and their structures have been studied extensively in the past decades. However, due to technical limitations, how these long molecules are assembled between membranes and what are the mechanisms of forming cell adhesion interfaces have not been well understood. Here, we combine electron microscopy with other biophysical methods to investigate the structure of cell–cell adhesion mediated by Sdk molecules and generate three-dimensional views of the adhesion interfaces in situ, thus revealing the architecture and the potential mechanism of Sdk-mediated cell adhesion at molecular level. Cell–cell adhesion is important for cell growth, tissue development, and neural network formation. Structures of cell adhesion molecules have been widely studied by crystallography, revealing the molecular details of adhesion interfaces. However, due to technical limitations, the overall structure and organization of adhesion molecules at cell adhesion interfaces has not been fully investigated. Here, we combine electron microscopy and other biophysical methods to characterize the structure of cell–cell adhesion mediated by the cell adhesion molecule Sidekick (Sidekick-1 and Sidekick-2) and obtain 3D views of the Sidekick-mediated adhesion interfaces as well as the organization of Sidekick molecules between cell membranes by electron tomography. The results suggest that the Ig-like domains and the fibronectin III (FnIII) domains of Sidekicks play different roles in cell adhesion. The Ig-like domains mediate the homophilic transinteractions bridging adjacent cells, while the FnIII domains interact with membranes, resulting in a tight adhesion interface between cells that may contribute to the specificity and plasticity of cell–cell contacts during cell growth and neural development.
Induction of Expression of CD271 and CD34 in Mesenchymal Stromal Cells Cultured as Spheroids

Cultured mesenchymal stromal cells (MSCs) are cells that can be used for tissue engineering or cell therapies owing to their multipotency and ability to secrete immunomodulatory and trophic molecules. Several studies suggest that MSCs can become pericytes when cocultured with endothelial cells (ECs) but failed to use pericyte markers not already expressed by MSCs. We hypothesized ECs could instruct MSCs to express the molecules CD271 or CD34, which are expressed by pericytes in situ but not by MSCs. CD271 is a marker of especial interest because it is associated with multipotency, a characteristic that wanes in MSCs as they are culture expanded. Consequently, surface expression of CD271 and CD34 was detected in roughly half of the MSCs cocultured with ECs as spheroids in the presence of insulin-like growth factor 1 (IGF-1). Conversely, expression of CD271 and CD34 was detected in a similar proportion of MSCs cultured under these conditions without ECs, and expression of these markers was low or absent when no IGF-1 was added. These findings indicate that specific culture conditions including IGF-1 can endow cultured MSCs with expression of CD271 and CD34, which may enhance the multipotency of these cells when they are used for therapeutic purposes.
The relationship between nomophobia and the distraction associated with smartphone use among nursing students in their clinical practicum

Background The increasing concern about the adverse effects of overuse of smartphones during clinical practicum implies the need for policies restricting smartphone use while attending to patients. It is important to educate health personnel about the potential risks that can arise from the associated distraction. Objective The aim of this study was to analyze the relationship between the level of nomophobia and the distraction associated with smartphone use among nursing students during their clinical practicum. Methods A cross-sectional study was carried out on 304 nursing students. The nomophobia questionnaire (NMP-Q) and a questionnaire about smartphone use, the distraction associated with it, and opinions about phone restriction policies in hospitals were used. Results A positive correlation between the use of smartphones and the total score of nomophobia was found. In the same way, there was a positive correlation between opinion about smartphone restriction polices with each of the dimensions of nomophobia and the total score of the questionnaire. Conclusions Nursing students who show high levels of nomophobia also regularly use their smartphones during their clinical practicum, although they also believe that the implementation of policies restricting smartphone use while working is necessary.
The psychological appeal of owning private land for conservation

Continued threats to global biodiversity have stimulated interest in the private purchase of land for conservation. Though not a new phenomenon, private land conservation appears to be on the rise, and its ambiguous position between philanthropy and financial investment leads to questions about the nature of landowner motives. To examine these motives, we used grounded theory techniques to analyze transcripts of narrative interviews with the owners of privately conserved areas (PCAs) and assessed what landowners had in common across a variety of cultural and policy contexts. The result was a model of PCA engagement in which landowners became personally invested in the management of PCAs and in the nature they protected. We found that PCAs can be conceptualized not merely as philanthropic endeavors or investments, but also as meaningful projects in which their owners engage. We integrated our findings with literature indicating that fundamental psychological drives for autonomy, efficacy, and social connection facilitate the engagement process, regardless of whether conservation motivation is intrinsic or extrinsic. Our findings suggest that land conservation programs might benefit from allowing landowner autonomy to the maximum extent possible, developing best practice standards against which landowners can assess their efficacy, and facilitating the development of landowner networks.
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set

Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as Arabic dialect identification or native language identification. In this paper, we apply two simple yet effective transductive learning approaches to further improve the results of string kernels. The first approach is based on interpreting the pairwise string kernel similarities between samples in the training set and samples in the test set as features. Our second approach is a simple self-training method based on two learning iterations. In the first iteration, a classifier is trained on the training set and tested on the test set, as usual. In the second iteration, a number of test samples (to which the classifier associated higher confidence scores) are added to the training set for another round of training. However, the ground-truth labels of the added test samples are not necessary. Instead, we use the labels predicted by the classifier in the first training iteration. By adapting string kernels to the test set, we report significantly better accuracy rates in English polarity classification and Arabic dialect identification.
Molecular dynamics simulations reveal the role of ceramicine B as novel PPAR{\gamma} partial agonist against type 2 diabetes

Peroxisome proliferator-activated receptors gamma (PPAR{\gamma}) are ligand-activated controllers of various metabolic actions and insulin sensitivity. PPAR{\gamma} is thus considered as an important target to treat type 2 diabetes. Available PPAR{\gamma} drugs (full agonists) have robust insulin-sensitizing properties but are accompanied by severe side effects leading to complicated health problems. Here, we have used molecular docking and a molecular dynamics simulation study to find a novel PPAR{\gamma} ligand from a natural product. Our study suggests that the inhibition of ceramicine B in the PPAR{\gamma} ligand-binding domain (LBD) could act as a partial agonist and block cdk5-mediated phosphorylation. This result may provide an opportunity for the development of new anti-diabetic drugs by targeting PPAR{\gamma} while avoiding the side effects associated with full agonists.
Painting Outside the Box: Image Outpainting with GANs

The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion). Accordingly, we present a deep learning approach based on Iizuka et al. for adversarially training a network to hallucinate past image boundaries. We use a three-phase training schedule to stably train a DCGAN architecture on a subset of the Places365 dataset. In line with Iizuka et al., we also use local discriminators to enhance the quality of our output. Once trained, our model is able to outpaint $128 \times 128$ color images relatively realistically, thus allowing for recursive outpainting. Our results show that deep learning approaches to image outpainting are both feasible and promising.
The Effect of Usual Source of Care on the Association of Annual Healthcare Expenditure with Patients’ Age and Chronic Disease Duration

Along with rapid population aging, the importance of chronic disease management increases with high growth of national healthcare expenditures, and efficient spending on healthcare is required to reduce unnecessary utilizations. For that reason, this study examined the association of annual healthcare expenditure with age and disease duration of chronic patients. Furthermore, the study investigated the effect of usual source of care (USOC) to suggest directions for preventive management of chronic disease. Using Korean Health Panel Study data, this study selected 1481 outpatients, who had out-of-pocket costs for hypertension or diabetes, and their total healthcare and chronic disease management (CDM) costs were examined. With patient aging, CDM cost decreased while the total healthcare cost increased, but longer duration of hypertension or diabetes resulted in increases in both CDM and total healthcare costs. In addition, the moderating effect of USOC indicated that elderly patients had increased CDM costs when they had a regular site for healthcare. In contrast, patients with longer duration had reductions in both CDM and total healthcare costs while having a regular doctor increased CDM cost. The results of this study could be an evidence for future policies to suggest proper preventive management plans for specific subjects.