Miracle-dz/UniFAR-FTUs huggingface dataset:
How can a measure improve assessment and management of symptoms and concerns for people with dementia in care homes? A mixed-methods feasibility and process evaluation of IPOS-Dem

Background Assessment of people with dementia is challenging; with undetected and under treated symptoms and concerns resulting in avoidable distress, and few evidence-based interventions to support this. We aimed to understand the mechanisms of action of a measure to support comprehensive assessment of people with dementia in care homes; and its acceptability, feasibility, and implementation requirements. Methods A qualitative study with an embedded quantitative component in three residential care homes, underpinned by an initial theoretical model of mechanisms of action. The measure, the Integrated Palliative care Outcome Scale for Dementia (IPOS-Dem), was introduced into the care of residents with dementia for 12 weeks. Qualitative data comprised focus groups and semi-structured interviews with family, care home staff, general practitioners and district nurses; and non-participant observations. Quantitative data comprised IPOS-Dem data. Directed content analysis for qualitative data, and descriptive statistics were used for quantitative data. Findings Key mechanisms of action were: improved observation and awareness of residents, collaborative assessment, comprehensive ‘picture of the person’, systematic record keeping, improved review and monitoring, care planning and changes to care provision, and facilitated multi-agency communication. Potential benefit included improved symptom management, improved comprehensive care, and increased family empowerment and engagement. IPOS-Dem was found to be acceptable and feasible. It was perceived as quick and easy to use, with proportion of overall missing data decreasing from 2.1% to 1.1% from baseline to final time points. ‘Trust’ in the measure was important; and leadership essential to ensure integration into care processes. Conclusions In a population with complex care needs, with challenges to assessment and barriers to multi-agency working, a measure introduced into routine care is feasible and acceptable, and supports assessment and management of symptoms and concerns. A refined theoretical model demonstrating the likely mechanisms of action was developed. Further evaluation is required to test its effectiveness.
Autophagy and Rheumatoid Arthritis: Current Knowledges and Future Perspectives

Autophagy is a degradation mechanism by which cells recycle cytoplasmic components to generate energy. By influencing lymphocyte development, survival, and proliferation, autophagy regulates the immune responses against self and non-self antigens. Deregulation of autophagic pathway has recently been implicated in the pathogenesis of several autoimmune diseases, including rheumatoid arthritis (RA). Indeed, autophagy seems to be involved in the generation of citrullinated peptides, and also in apoptosis resistance in RA. In this review, we summarize the current knowledge on the role of autophagy in RA and discuss the possibility of a clinical application of autophagy modulation in this disease.
GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

Abstract ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2—Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks

We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, how to construct a robust representation that easily captures the spatial-temporal evolutions of motions from skeleton sequences. Second, how to design D-CNNs capable of learning discriminative features from the new representation in a effective manner. To address these tasks, a skeleton-based representation, namely, SPMF (Skeleton Pose-Motion Feature) is proposed. The SPMFs are built from two of the most important properties of a human action: postures and their motions. Therefore, they are able to effectively represent complex actions. For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs. Our method is evaluated on two challenging datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated that the proposed method surpasses state-of-the-art methods whilst requiring less computation.
Magnesium-Rich Nanometric Layer in the Skeleton of Pocillopora damicornis With Possible Involvement in Fibrous Aragonite Deposition

The complex structures and morphologies of coral skeletons make it difficult to study the construction of individual skeleton components. This is especially true regarding micro-structures deposited during different stages of skeletogenesis. As such structures serve as the basis for subsequent skeleton development, they are often obscured by additional skeletal deposition and growth. Recently we described a new model system, coral-on-a-chip, for studying coral biology and skeletogenesis. This system utilizes micropropagates of the Indo-Pacific coral Pocillopora damicornis maintained within a microfluidic environment, allowing us to follow early stages of skeletogenesis over time and under controlled environmental conditions. Our findings reveal that, following settlement onto glass slides, the micropropagates initially form a thin, almost two dimensional skeleton, which subsequently develops into a robust three-dimensional form with features resembling those of the mother colony. Studying the early stages of skeleton accretion in our micropropagates using high-resolution scanning electron microscopy and Energy Dispersive X-ray Spectroscopy (EDS) revealed a magnesium-rich layer deposited directly on the glass surface. This layer, which has a typical Mg/Ca ratio of 1.43±0.78, forms a dense lattice with a typical pore size of 100 nm. Microscopic observations indicate that this lattice serves as the basis for subsequent growth of fibrous aragonite. Examination of the underside of a skeleton from a small P. damicornis colony growing on a glass surface revealed a similar high-magnesium lattice at the interface between the glass and aragonitic skeleton in association with fibrous aragonite deposition. These observations suggest a role for this magnesium-rich lattice in the deposition of the fibrous aragonite forming the bulk of the coral skeleton.
Deep neural network ensemble by data augmentation and bagging for skin lesion classification

This work summarizes our submission for the Task 3: Disease Classification of ISIC 2018 challenge in Skin Lesion Analysis Towards Melanoma Detection. We use a novel deep neural network (DNN) ensemble architecture introduced by us that can effectively classify skin lesions by using data-augmentation and bagging to address paucity of data and prevent over-fitting. The ensemble is composed of two DNN architectures: Inception-v4 and Inception-Resnet-v2. The DNN architectures are combined in to an ensemble by using a $1\times1$ convolution for fusion in a meta-learning layer.
Secrecy Rate Maximization for Untrusted Relay Networks With Nonorthogonal Cooperative Transmission Protocols

In this paper, a relay based network is considered where the source transmits confidential message to the destination via a nonregenerative relay. Due to the fact that the relay is unidentified, and thus may wiretap the relayed message from source to destination. In this regard, two new half-duplex nonorthogonal amplify-and-forward (NAF) secure communication protocols are proposed, with different assumptions on the transmission ability of the destination, i.e., NAF-I for destination without transmission ability and NAF-II for destination with transmission ability. We separately derive the sufficient and necessary conditions to yield positive secrecy rate for these protocols under fixed source/destination transmission power, and discuss their joint power allocation that maximizes the system secrecy rate. Despite the nonconvexity of the considered power allocation problems, alternative optimization approach is proved useful in dealing with these problems, and the corresponding algorithms are proposed. We further prove the convergence of the proposed algorithms and analyze their complexity. The discussion is also extended to the orthogonal amplify-and-forward (OAF) based protocols, i.e., OAF-I or OAF-II, for comparison purpose. Our numerical results demonstrate the gain of the secrecy rate performance of the proposed NAF protocols over existing transmission protocols, and also verify the effectiveness of the designed alternative optimization algorithms.
The effect of HIF-1α and PKM1 expression on acquisition of chemoresistance

Background In patients with gastric cancer, one of the greatest obstacles to effective chemotherapy is the development of chemoresistance. It has been previously reported that hypoxia-inducible factor-1 alpha (HIF-1α) is associated with acquisition of chemoresistance, and more recent studies have also noted an association of pyruvate kinase muscle 1 (PKM1) and chemoresistance. The purpose of this study was to identify the effect of HIF-1α and PKM1 expression on the development of acquired chemoresistance using a paclitaxel (PTX)-resistant gastric cancer cell line. Materials and methods A cancer cell line resistant to PTX was established from MKN45 cells by stepwise exposure to drug (rMKN45-PTX). The expressions of HIF-1α, apoptosis, vascular endothelial growth factor (VEGF), multidrug transporters and glycolytic enzyme were examined by Western blotting, enzyme-linked immunosorbent assay and immunohistochemistry. We also assessed the tumor proliferation by subcutaneous tumor and peritoneal dissemination of mouse xenograft model. Results The resistance index was 6.1 by determining as the ratio of the 50% growth inhibition (IC50) of rMKN45-PTX/IC50 of MKN45. Expression of nuclear factor kappa B and HIF-1α was increased in rMKN45-PTX cells compared with the parent cells. Expression of Bax and caspase-3 was significantly downregulated, whereas expression of Bcl-xL, P-glycoprotein, multidrug resistance-associated protein and VEGF was increased in rMKN45-PTX. The expression level of PKM1 was upregulated in rMKN45-PTX, leading to an increase in the PKM1/PKM2 ratio. Using xenograft models, we demonstrated that mouse subcutaneous tumors derived from rMKN45-PTX were significantly larger than those derived from MKN45 cells. Conclusion Under the stress of chemotherapeutic agent exposure, high expression of HIF-1α affects various downstream genes. Although the underlying mechanism is unknown, our data suggest that PKM1 is also a molecular target for gastric cancer treatment.
Phylogenomic history of enigmatic pygmy perches: implications for biogeography, taxonomy and conservation

Pygmy perches (Percichthyidae) are a group of poorly dispersing freshwater fishes that have a puzzling biogeographic disjunction across southern Australia. Current understanding of pygmy perch phylogenetic relationships suggests past east–west migrations across a vast expanse of now arid habitat in central southern Australia, a region lacking contemporary rivers. Pygmy perches also represent a threatened group with confusing taxonomy and potentially cryptic species diversity. Here, we present the first study of the evolutionary history of pygmy perches based on genome-wide information. Data from 13 991 ddRAD loci and a concatenated sequence of 1 075 734 bp were generated for all currently described and potentially cryptic species. Phylogenetic relationships, biogeographic history and cryptic diversification were inferred using a framework that combines phylogenomics, species delimitation and estimation of divergence times. The genome-wide phylogeny clarified the biogeographic history of pygmy perches, demonstrating multiple east–west events of divergence within the group across the Australian continent. These results also resolved discordance between nuclear and mitochondrial data from a previous study. In addition, we propose three cryptic species within a southwestern species complex. The finding of potentially new species demonstrates that pygmy perches may be even more susceptible to ecological and demographic threats than previously thought. Our results have substantial implications for improving conservation legislation of pygmy perch lineages, especially in southwestern Western Australia.
Modeling and Trade-Off for Mobile Communication, Computing and Caching Networks

This paper considers a new mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing- enabled mobile devices via a shared wireless link. Each mobile device can pre-store the input or output data of a task and also execute a task locally. We aim to investigate the impact of local caching and computing at mobile devices as well as content-centric multicast transmission on the saving of required bandwidth on the wireless link. To this end, we first formulate a joint caching and computing decision optimization problem to minimize the required transmission bandwidth subject to latency, caching and energy constraints at each mobile device in the general case. The joint policy optimization problem is shown to be NP-hard. Based on equivalent transformation and exact penalization of the problem, a stationary point is obtained via concave convex procedure. In the special case where all the computation tasks are symmetric and user requests are uniform, we obtain the closed- form expressions for the local caching gain, local computing gain, and multicasting gain. Our results indicate that exploiting the computing and caching resources at mobile devices can provide significant bandwidth savings.
Recurrence risk after preoperative biopsy in patients with resected early-stage non-small-cell lung cancer: a retrospective study

Background Tumor cell dissemination after needle biopsy has been reported in a variety of malignancies, including non-small-cell lung cancer (NSCLC). However, there is little clinical evidence in regard to whether preoperative biopsy increases the risk of recurrence in completely resected NSCLC. Patients and methods A total of 322 patients diagnosed as pathological stage I NSCLC using intraoperative biopsy (IOB) (control group), preoperative percutaneous needle biopsy (PNB) or bronchoscopic biopsy were included in this study. Baseline characteristics were collected and compared. The disease-free survival (DFS) of patients was analyzed using Kaplan–Meier method. Subgroup analysis and Cox regression were performed to evaluate the effect of preoperative biopsy on recurrence risk with adjustment for potential confounders. Results Among these patients, 202 (63%) underwent IOB, 66 (20%) underwent PNB, and 54 (17%) underwent bronchoscopic biopsy. DFS of patients who had preoperative PNB or bronchoscopic biopsy was similar to those who had IOB (P=0.514 and 0.869). Neither preoperative PNB nor transbronchial biopsy significantly affected recurrence incidence across all the relevant subgroups. Furthermore, multivariate analysis showed that preoperative biopsy was not associated with increased recurrence risk in NSCLC patients with adjustment for confounders, while squamous cell carcinoma and adjuvant chemotherapy were associated with prolonged DFS. Conclusion Neither preoperative PNB nor bronchoscopic biopsy increased the recurrence risk in patients with resected stage I NSCLC, indicating that these procedures could be safely used for diagnosis of early-stage NSCLC.
Beyond Data and Model Parallelism for Deep Neural Networks

The computational requirements for training deep neural networks (DNNs) have grown to the point that it is now standard practice to parallelize training. Existing deep learning systems commonly use data or model parallelism, but unfortunately, these strategies often result in suboptimal parallelization performance. In this paper, we define a more comprehensive search space of parallelization strategies for DNNs called SOAP, which includes strategies to parallelize a DNN in the Sample, Operation, Attribute, and Parameter dimensions. We also propose FlexFlow, a deep learning framework that uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy's performance and is three orders of magnitude faster than prior approaches that have to execute each strategy. We evaluate FlexFlow with six real-world DNN benchmarks on two GPU clusters and show that FlexFlow can increase training throughput by up to 3.8x over state-of-the-art approaches, even when including its search time, and also improves scalability.
Pangloss: Fast Entity Linking in Noisy Text Environments

Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph construction, and question answering. Traditionally, this work has focused on text that has been well-formed, like news articles, but in common real world datasets such as messaging, resumes, or short-form social media, non-grammatical, loosely-structured text adds a new dimension to this problem. This paper presents Pangloss, a production system for entity disambiguation on noisy text. Pangloss combines a probabilistic linear-time key phrase identification algorithm with a semantic similarity engine based on context-dependent document embeddings to achieve better than state-of-the-art results (>5% in F1) compared to other research or commercially available systems. In addition, Pangloss leverages a local embedded database with a tiered architecture to house its statistics and metadata, which allows rapid disambiguation in streaming contexts and on-device disambiguation in low-memory environments such as mobile phones.
Triphenylphosphonium and D-α-tocopheryl polyethylene glycol 1000 succinate-modified, tanshinone IIA-loaded lipid-polymeric nanocarriers for the targeted therapy of myocardial infarction

Background Cardiovascular diseases (CVDs) are the leading causes of mortality worldwide. Currently, the best treatment options for myocardial infarction focus on the restoration of blood flow as soon as possible, which include reperfusion therapy, percutaneous coronary intervention, and therapeutic thrombolytic drugs. Materials and methods In the present study, we report the development of lipid-polymeric nanocarriers (LPNs) for mitochondria-targeted delivery of tanshinone IIA (TN). D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) was linked to the triphenylphosphonium (TPP) cation. The LPNs were fabricated by nanoprecipitation method. LPNs were evaluated in vitro and in vivo in comparison with free drugs and other similar nanocarriers. Results The mean diameter of TN/nanoparticles (NPs) was 89.6 nm, while that of TN/LPNs was 121.3 nm. The zeta potential of TN/NPs and TN/LPNs was −33.6 and −22.3 mV, respectively. Compared with free TN and TN/NPs, TN/LPNs exhibited significantly improved compatibility and therapeutic efficiency. In addition, the in vivo pharmacokinetics, biodistribution, and infarct therapy studies in Sprague Dawley rats showed that TPP-TPGS/TN/LPNs had better efficiency than their nonmodified TN/LPNs counterparts in all respects. Conclusion These results indicated that the TPP-TPGS/TN/LPNs were promising nanocarriers for efficient delivery of cardiovascular drugs and other therapeutic agents for the treatment of CVDs.
WPNets and PWNets: From the Perspective of Channel Fusion

The performance and parameters of neural networks have a positive correlation, and there are a lot of parameter redundancies in the existing neural network architectures. By exploring the channels relationship of the whole and part of the neural network, the architectures of the convolution network with the tradeoff between the parameters and the performance are obtained. Two network architectures are implemented by dividing the convolution kernels of one layer into multiple groups, thus ensuring that the network has more connections and fewer parameters. In these two network architectures, the information of one network flows from the whole to the part, which is called whole-to-part connected networks (WPNets), and the information of the other network flows from the part to the whole, which is called part-to-whole connected networks (PWNets). WPNets use the whole channel information to enhance partial channel information, and the PWNets use partial channel information to generate or enhance the whole channel information. We evaluate the proposed architectures on three competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet), and our models obtain comparable results even with far fewer parameters compared to many state of the arts. Our network architecture code is available at github.
The Chicago School Readiness Project: Examining the long-term impacts of an early childhood intervention

The current paper reports long-term treatment impact estimates for a randomized evaluation of an early childhood intervention designed to promote children's developmental outcomes and improve the quality of Head Start centers serving high-violence and high-crime areas in inner-city Chicago. Initial evaluations of end-of-preschool data reported that the program led to reductions in child behavioral problems and gains in measures of executive function and academic achievement. For this report, we analyzed adolescent follow-up data taken 10 to 11 years after program completion. We found evidence that the program had positive long-term effects on students’ executive function and grades, though effects were somewhat imprecise and dependent on the inclusion of baseline covariates. Results also indicated that treated children had heightened sensitivity to emotional stimuli, and we found no evidence of long-run effects on measures of behavioral problems. These findings raise the possibility that developing programs that improve on the Head Start model could carry long-run benefits for affected children.