Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructured hybrid nanocomposite flexible piezoresistive sensor and its sensitivity analysis by mechanical finite-element simulation

The flexible piezoresistive sensor is the key component used to obtain human biological information via wearable electronic systems, and forms the basis of this technology. In order to improve the sensitivity, stability, and simplicity of the flexible piezoresistive sensor, this study innovatively prepared multi-walled carbon nanotube/silver nanowire/polyurethane (MWNT/AgNW/PU) films whose surface had a one-dimensional cube microstructure by using a mixed solution template method, and integrated the resulting microstructured hybrid nanocomposite films (MNFs) with a single-sided interdigital copper electrode. Meanwhile, mechanical finite-element models of MNF were constructed to study the influence of the structure parameters on the response sensitivity. The piezoresistive experiment results show that, within a pressure range of 0–2 kPa, the sensitivity of the sensor is as high as 42.6 kPa−1. Moreover, with the hybridization of MWNTs and AgNW, the mechanical stability, linearity, and hysteresis of the sensor have been significantly improved. The hysteresis error and nonlinear error of sensors with 5 wt% MWNTs and 5 wt% AgNWs can be as low as ±4.15% and ±12.5%, respectively, which is 93.24% and 78.93% lower compared with MWNTs/PU. The simulation experiments show that the contact area of the MNF and the single-sided electrode has a negative correlation with the piezoresistive sensitivity of the sensors. Furthermore, the prepared flexible piezoresistive sensor is used for human body physiological signal monitoring. It provides the possibility of expanding and optimizing the application of flexible sensors in electronic skin, wearable health monitor devices, smart textiles, clothing, and so on.
Lentinan up-regulates microRNA-340 to promote apoptosis and autophagy of human osteosarcoma cells.

BACKGROUND Osteosarcoma (OS) is a common tumor of bone, and the high incidence and poor prognosis of OS call for novel therapeutic strategies. We aimed to explore the functional role of lentinan (LNT) in human OS MG63 cells as well as the underlying mechanisms. METHODS Cell viability of MG63 cells under LNT stimulation was measured by CCK-8 assay to explore the adequate concentration of LNT. Cell proliferation, apoptosis and expression of microRNA (miR)-340 in MG63 cells after LNT treatments were assayed by BrdU incorporation assay, flow cytometry assay and quantitative reverse transcription PCR, respectively. Expression of proteins associated with cell cycle, apoptosis, and autophagy were determined by western blot analysis. Subsequently, whether LNT affected MG63 cells through miR-340 as well as the related signaling pathway was explored. RESULTS Cell viability was reduced by 5-100 mg/mL of LNT. Percentage of BrdU-positive cells was reduced while that of apoptotic cells was enhanced by LNT treatment. LNT decreased cyclin D1 level but increased levels of active caspase-3 and caspase-9. After treatment, LNT enhanced LC3B-II/LC3B-I and Beclin-1 levels but reduced the p62 level. The miR-340 level was up-regulated by LNT, and further experiments showed LNT promoted apoptosis and autophagy through up-regulating miR-340. Moreover, LNT reduced the phosphorylated levels of MAPK and ERK through up-regulating miR-340. CONCLUSION LNT reduced proliferation and induced apoptosis and autophagy by up-regulating miR-340 in MG63 cells, along with inhibition of the MAPK/ERK pathway.
SLC43A3 is a Biomarker of Sensitivity to the Telomeric DNA Damage Mediator 6-Thio-2'-Deoxyguanosine.

Cell membrane transporters facilitate the passage of nucleobases and nucleosides for nucleotide synthesis and metabolism, and are important for the delivery of nucleoside analogues used in anti-cancer drug therapy. Here, we investigated if cell membrane transporters are involved in the cellular uptake of the nucleoside analogue DNA damage mediator 6-thio-2'-deoxyguanosine (6-thio-dG). A large panel of non-small cell lung cancer (NSCLC) cell lines (73 of 77) were sensitive to 6-thio-dG; only 4 NSCLC lines were resistant to 6-thio-dG . When analyzed by microarray and RNA sequencing, the resistant NSCLC cell lines clustered together, providing a molecular signature for patients that may not respond to 6-thio-dG. Significant downregulation of solute carrier family 43 A3 (SLC43A3), an equilibrative nucleobase transporter, was identified as a candidate in this molecular resistance signature. High levels of SLC43A3 mRNA predicted sensitivity to 6-thio-dG and therefore SLC43A3 could serve as a promising biomarker for 6-thio-dG sensitivity in NSCLC patients.
Optimized implementation of the conjugate gradient algorithm for FPGA-based platforms using the Dirac-Wilson operator as an example

It is now a noticeable trend in High Performance Computing that the systems are becoming more and more heterogeneous. Compute nodes with a host CPU are being equipped with accelerators, the latter being a GPU or FPGA cards or both. In many cases at the heart of scientific applications running on such systems are iterative linear solvers. In this work we present a software package which includes an FPGA implementation of the Conjugate Gradient algorithm using a particular problem of the Dirac-Wilson operator as encountered in numerical simulations of Quantum Chromodynamics. The software is written in OpenCL and C++ and is optimized for maximal performance. Our framework allows for a simple implementation of other linear operators, while keeping the data transport mechanisms unaltered. Hence, our software can serve as a backbone for many applications which are expected to gain a significant boost factor on FPGA accelerators. As such systems are expected to become more and more widespread, the need for highly performant FPGA implementations of the Conjugate Gradient algorithm and its variants will certainly increase and the porting investment can be greatly facilitated by the attached code.
Understanding the relationship between traumatic suffering, posttraumatic growth, and prosocial variables

Abstract Objective We investigated facets of posttraumatic growth (PTG) theory, including the nature of the index event, the relationship between suffering and PTG, and prosocial correlates of PTG. Methods Undergraduate students (N = 253) completed measures of PTG and trauma-related, psychopathology/affect, and prosocial correlates. Results Differences emerged in PTG according to event severity, with greater PTG for those who experienced a trauma compared to a stressor. PTG was associated with nearly all trauma-related, select psychopathology/affect (e.g., anxiety, positive affect) and prosocial (e.g., empathy, volunteerism) variables. In a multivariable model, race/ethnicity, helpfulness, perceived chronicity of distress, and positive affect were associated with PTG. Curvilinear trends demonstrated that moderate chronicity of distress and current emotional distress from trauma were associated with the greatest PTG. Conclusions Findings highlight factors associated with growth, including a moderately distressing trauma, positive affect, and prosocial behavior. Results clarify PTG theory and contribute to understanding conflicting evidence in prior PTG literature.
Curcumin Can Decrease Tissue Inflammation and the Severity of HSV-2 Infection in the Female Reproductive Mucosa

Herpes Simplex Virus Type 2 (HSV-2) is one of the most prevalent sexually transmitted viruses and is a known risk factor for HIV acquisition in the Female Genital Tract (FGT). Previously, we found that curcumin can block HSV-2 infection and abrogate the production of inflammatory cytokines and chemokines by genital epithelial cells in vitro. In this study, we investigated whether curcumin, encapsulated in nanoparticles and delivered by various in vivo routes, could minimize inflammation and prevent or reduce HSV-2 infection in the FGT. Female mice were pre-treated with curcumin nanoparticles through oral, intraperitoneal and intravaginal routes, and then exposed intravaginally to the tissue inflammation stimulant CpG-oligodeoxynucleotide (ODN). Local intravaginal delivery of curcumin nanoparticles, but not intraperitoneal or oral delivery, reduced CpG-mediated inflammatory histopathology and decreased production of pro-inflammatory cytokines Interleukin (IL)-6, Tumor Necrosis Factor Alpha (TNF-α) and Monocyte Chemoattractant Protein-1 (MCP-1) in the FGT. However, curcumin nanoparticles did not demonstrate anti-viral activity nor reduce tissue pathology when administered prior to intravaginal HSV-2 infection. In an alternative approach, intravaginal pre-treatment with crude curcumin or solid dispersion formulations of curcumin demonstrated increased survival and delayed pathology following HSV-2 infection. Our results suggest that curcumin nanoparticle delivery in the vaginal tract could reduce local tissue inflammation. The anti-inflammatory properties of curcumin delivered to the vaginal tract could potentially reduce the severity of HSV-2 infection and decrease the risk of HIV acquisition in the FGT of women.
The variability of bacterial communities in both the endosphere and ectosphere of different niches in Chinese chives (Allium tuberosum)

Deciphering the various types of interactions between plants and their microbiomes is a hot topic for research in ecology as well as in plant sciences and agronomy. To analyse and compare the differences in microbial communities in different compartments of Chinese chives, high-throughput sequencing technology was employed to amplify and sequence the V5-V6 region of the 16S rDNA of microorganisms in the leaves, phylloplanes, stems, roots and rhizospheres of Chinese chives. The sequences were clustered by operational taxonomic units (OTUs), and the community composition of bacteria between the endosphere (inner tissues) and ectosphere (outer surfaces) of Chinese chives was analysed based on the OTU. Overall, the results indicated that the endophytic bacteria in Chinese chives mainly include Proteobacteria, Actinobacteria, and Actinomycetes. Alpha diversity index analysis and OTU number analysis showed that the bacterial diversity and richness of the underground plant compartments were higher than those of the above-ground parts. PCoA based on the OTU level showed that the vertical stratification structure of plants and compartments had significant effects on the bacterial community structure. The richness of endophytic bacteria also varied greatly among the different varieties of Chinese chive. A considerable number of endophytic bacteria form symbiotic and mutually beneficial relationships with host plants, which play an important role in regulating host growth, metabolism and stress resistance. Further investigations are needed to uncover the evolution of interactions between plants and endophytes.
A Small Ship Target Detection Method Based on Polarimetric SAR

The detection of small fishing ships is very important for maritime fishery supervision. However, it is difficult to detect small ships using synthetic aperture radar (SAR), due to the weak target scattering and very small number of pixels. Polarimetric synthetic aperture radar (PolSAR) has been widely used in maritime ship detection due to its abundant target scattering information. In the present paper, a new ship detector, named ΛM, is developed based on the analysis of polarization scattering differences between ship and sea, then combined with the two-parameter constant false alarm rate method (TP-CFAR) algorithm to conduct ship detection. The goals of the detector construction are to fully consider the ship’s depolarization effect, and further amplify it through sliding window processing. First, the signal-to-clutter ratio (SCR) enhancement performance of ΛM for ships with different lengths ranging from 8 to 230 m under 90 different combinations of windows are analyzed in detail using three set of RADARSAT-2 quad-polarization data, then the appropriate window size is determined. In addition, the SCR enlargement between ΛM and some typical polarization features is compared. Among these, for ships of length greater than 35 m, the average contrast of ΛM is 33.7 dB, which is 20 dB greater than that of the HV channel. For small vessels of length less than 16 m, the average contrast of ΛM is 16 dB higher than that of HV channel on average. Finally, the RADARSAT-2 data including nonmetallic small vessels are used to perform ship detection tests, and the detection ability for conventional and small ships of some classic algorithms are compared and analyzed. For large vessels of length greater than 35 m, the method proposed in this paper is able to obtain a superior detection result, maintain the ship contour well, and suppress false alarms caused by the cross side lobe in the SAR image. For small vessels of length less than 16 m, the method proposed in this paper can reduce the number of missed targets, while also obtaining superior detection results, especially for small nonmetallic vessels.
Unifying Training and Inference for Panoptic Segmentation

We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things". In contrast to recent works, our network exploits a parametrised, yet lightweight panoptic segmentation submodule, powered by an end-to-end learnt dense instance affinity, to capture the probability that any pair of pixels belong to the same instance. This panoptic submodule gives rise to a novel propagation mechanism for panoptic logits and enables the network to output a coherent panoptic segmentation map for both "stuff" and "thing" classes, without any post-processing. Reaping the benefits of end-to-end training, our full system sets new records on the popular street scene dataset, Cityscapes, achieving 61.4 PQ with a ResNet-50 backbone using only the fine annotations. On the challenging COCO dataset, our ResNet-50-based network also delivers state-of-the-art accuracy of 43.4 PQ. Moreover, our network flexibly works with and without object mask cues, performing competitively under both settings, which is of interest for applications with computation budgets.
Wi2Vi: Generating Video Frames From WiFi CSI Samples

Objects in an environment affect electromagnetic waves. While this effect varies across frequencies, there exists a correlation between them, and a model with enough capacity can capture this correlation between the measurements in different frequencies. In this paper, we propose the Wi2Vi model for associating variations in the WiFi channel state information with video frames. The proposed Wi2Vi system can generate video frames entirely using CSI measurements. The produced video frames by the Wi2Vi provide auxiliary information to the conventional surveillance system in critical circumstances. Our implementation of the Wi2Vi system confirms the feasibility of constructing a system capable of deriving the correlations between measurements in different frequency spectrums.
Curb Your Enthusiasm: Definitions, Adaptation, and Expectations for Quality of Life in ICU Survivorship.

Curb Your Enthusiasm: Definitions, Adaptation, and Expectations for Quality of Life in ICU Survivorship Alison E. Turnbull, Michael S. Hurley, Ian M. Oppenheim, Megan M. Hosey, and Ann M. Parker Division of Pulmonary and Critical Care Medicine, and Department of Physical Medicine and Rehabilitation, School of Medicine, Department of Epidemiology, Bloomberg School of Public Health, and Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University, Baltimore, Maryland; and Las Vegas, Nevada
EEV Dataset: Predicting Expressions Evoked by Diverse Videos

When we watch videos, the visual and auditory information we experience can evoke a range of affective responses. The ability to automatically predict evoked affect from videos can help recommendation systems and social machines better interact with their users. Here, we introduce the Evoked Expressions in Videos (EEV) dataset, a large-scale dataset for studying viewer responses to videos based on their facial expressions. The dataset consists of a total of 4.8 million annotations of viewer facial reactions to 18,541 videos. We use a publicly available video corpus to obtain a diverse set of video content. The training split is fully machine-annotated, while the validation and test splits have both human and machine annotations. We verify the performance of our machine annotations with human raters to have an average precision of 73.3%. We establish baseline performance on the EEV dataset using an existing multimodal recurrent model. Our results show that affective information can be learned from EEV, but with a MAP of 20.32%, there is potential for improvement. This gap motivates the need for new approaches for understanding affective content. Our transfer learning experiments show an improvement in performance on the LIRIS-ACCEDE video dataset when pre-trained on EEV. We hope that the size and diversity of the EEV dataset will encourage further explorations in video understanding and affective computing.
Insulin Enhances the In Vitro Osteogenic Capacity of Flexor Tendon-Derived Progenitor Cells

There is increased incidence of tendon disorders and decreased tendon healing capacity in people with diabetes mellitus (DM). Recent studies have also suggested pathological ossification in repair tendon of people with DM. Therefore, the objective of this study is to investigate the effects of insulin supplementation, an important pathophysiologic stimulus of DM, on tendon progenitor cell (TPC) proliferation and in vitro osteogenic capacity. Passage 3 TPCs were isolated from collagenase-digested, equine superficial digital flexor tendons. TPC proliferation was measured via MTT assay after 3 days of monolayer culture in medium supplemented with 0, 0.007, 0.07, and 0.7 nM insulin. In vitro osteogenic capacity of TPCs (Alizarin Red staining, osteogenic mRNA expression, and alkaline phosphatase bioactivity) was assessed with 0, 0.07, and 0.7 nM insulin-supplemented osteogenic induction medium. Insulin (0.7 nM) significantly increased TPC proliferation after 3 days of monolayer culture. Alizarin Red staining of insulin-treated TPC osteogenic cultures demonstrated robust cell aggregation and mineralized matrix secretion, in a dose-dependent manner. Runx2, alkaline phosphatase, and Osteonectin mRNA and alkaline phosphatase bioactivity of insulin-treated TPC cultures were significantly higher at day 14 of osteogenesis compared to untreated controls. Addition of picropodophyllin (PPP), a selective IGF-I receptor inhibitor, mitigated the increased osteogenic capacity of TPCs, indicating that IGF-I signaling plays an important role. Our findings indicate that hyperinsulinemia may alter TPC phenotype and subsequently impact the quality of repair tendon tissue.
Graphene-Based Biosensors for Detection of Biomarkers

The development of biosensors with high sensitivity and low-detection limits provides a new direction for medical and personal care. Graphene and graphene derivatives have been used to prepare various types of biosensors due to their excellent sensing performance (e.g., high specific surface area, extraordinary electronic properties, electron transport capabilities and ultrahigh flexibility). This perspective review focuses on graphene-based biosensors for quantitative detection of cancer-related biomarkers such as DNA, miRNA, small molecules and proteins by integrating with different signal outputting approaches including fluorescent, electrochemistry, surface plasmon resonance, surface enhanced Raman scattering, etc. The article also discussed their challenges and potential solutions along with future prospects.
VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection

Topic modeling has found wide application in many problems where latent structures of the data are crucial for typical inference tasks. When applying a topic model, a relatively standard pre-processing step is to first build a vocabulary of frequent words. Such a general pre-processing step is often independent of the topic modeling stage, and thus there is no guarantee that the pre-generated vocabulary can support the inference of some optimal (or even meaningful) topic models appropriate for a given task, especially for computer vision applications involving "visual words". In this paper, we propose a new approach to topic modeling, termed Vocabulary-Selection-Embedded Correspondence-LDA (VSEC-LDA), which learns the latent model while simultaneously selecting most relevant words. The selection of words is driven by an entropy-based metric that measures the relative contribution of the words to the underlying model, and is done dynamically while the model is learned. We present three variants of VSEC-LDA and evaluate the proposed approach with experiments on both synthetic and real databases from different applications. The results demonstrate the effectiveness of built-in vocabulary selection and its importance in improving the performance of topic modeling.
Shedding Light on the Antimicrobial Peptide Arsenal of Terrestrial Isopods: Focus on Armadillidins, a New Crustacean AMP Family

In crustaceans, antimicrobial peptides (AMPs) are clustered into four major groups according to their amino acid composition and structure: (1) single-domain peptides containing cysteine residues such as anti-lipopolysaccharide-factor (ALF), (2) multi-domain or chimeric AMPs such as crustins, (3) non-conventional AMPs, and (4) linear single-domain AMPs. The majority of AMPs has been described in commercially exploited crustaceans, particularly decapods living in aquatic environments (crab, shrimp, lobster, and crayfish). Here, we aimed at establishing the AMPs repertoire of terrestrial isopods (Oniscidea), an original suborder of crustaceans adapted to life outside of the aquatic environment. Using transcriptomic data from 21 species, we identified 110 ALF and 73 crustin sequences. We also characterized the full-length sequence of armadillidins from 17 species, similar to the AMP previously described in the terrestrial isopod Armadillidium vulgare. Furthermore, we tested the antimicrobial activity of three armadillidin peptides characterized from three distantly related species. This analysis revealed similar activity spectra against pathogens, despite extensive structural variation among the tested peptides. In addition to conventional crustacean AMPs, our work highlights armadillidins as a new and independent family of AMPs specific to the Oniscidea, thus opening new perspectives concerning the study of the immune system of terrestrial isopods.