Miracle-dz/UniFAR-FTUs huggingface dataset:
Extremely imbalanced subarachnoid hemorrhage detection based on DenseNet-LSTM network with class-balanced loss and transfer learning

Subarachnoid Hemorrhage (SAH) detection is a critical, severe problem that confused clinical residents for a long time. With the rise of deep learning technologies, SAH detection made a significant breakthrough in recent ten years. Whereas, the performances are significantly degraded on imbalanced data, makes deep learning models have always suffered criticism. In this study, we present a DenseNet-LSTM network with Class-Balanced Loss and the transfer learning strategy to solve the SAH detection problem on an extremely imbalanced dataset. Compared to the previous works, the proposed framework not merely effectively integrate greyscale features the and spatial information from the consecutive CT scans, but also employ Class-Balanced loss and transfer learning to alleviate the adverse effects and broaden feature diversity respectively on an extreme SAH cases scarcity dataset, mimicking the actual situation of emergency departments. Comprehensive experiments are conducted on a dataset, consisted of 2,519 cases without hemorrhage cases and only 33 cases with SAH. Experimental results demonstrate the F-measure score of SAH detection achieved a remarkable improvement, the backbone DenseNet121 gained around 33% promotion after transfer learning, and on this basis, importing the Class-Balanced Loss and the LSTM structure, the F-measure score further increased 6.1% and 2.7% sequentially.
Construction of a multi-phase contrast computed tomography kidney atlas

The Human BioMolecular Atlas Program (HuBMAP) seeks to create a molecular atlas at the cellular level of the human body to spur interdisciplinary innovations across spatial and temporal scales. While the preponderance of effort is allocated towards cellular and molecular scale mapping, differentiating and contextualizing findings within tissues, organs and systems are essential for the HuBMAP efforts. The kidney is an initial organ target of HuBMAP, and constructing a framework (or atlas) for integrating information across scales is needed for visualizing and integrating information. However, there is no abdominal atlas currently available in the public domain. Substantial variation in healthy kidneys exists with sex, body size, and imaging protocols. With the integration of clinical archives for secondary research use, we are able to build atlases based on a diverse population and clinically relevant protocols. In this study, we created a computed tomography (CT) phase-specific atlas for the abdomen, which is optimized for the kidney organ. A two-stage registration pipeline was used by registering extracted abdominal volume of interest from body part regression, to a high-resolution CT. Affine and non-rigid registration were performed to all scans hierarchically. To generate and evaluate the atlas, multiphase CT scans of 500 control subjects (age: 15 - 50, 250 males, 250 females) are registered to the atlas target through the complete pipeline. The abdominal body and kidney registration are shown to be stable with the variance map computed from the result average template. Both left and right kidneys are substantially localized in the high-resolution target space, which successfully demonstrated the sharp details of its anatomical characteristics across each phase. We illustrated the applicability of the atlas template for integrating across normal kidney variation from 64 cm3 to 302 cm3 .
Machine-learning-based clinical plaque detection using a synthetic plaque lesion model for coronary CTA

Coronary computed tomography angiography (coronary CTA) is a robust and well-established non-invasive diagnostic tool to detect and assess coronary artery disease (CAD). The accurate detection, quantification and characterization of the coronary plaque burden has become an important part of this imaging modality. The quality and performance of modern machine-learning-based data-driven learning approaches is often impacted by either insufficient or inconsistently-labeled training data and is further subject to additional bias from human annotators. To address these shortcomings for coronary plaque characterization, we have developed a synthetic lesion generating framework for CTA applications, which can produce accurate and high-quality labeled training data for data-driven learning approaches. This approach can help to ease the manual annotation burden, which is often the limiting factor in data-driven learning algorithms and instead provides reliable ground truth data for modern deep learning approaches. Furthermore, this framework can easily be used to create custom tailored training data that can be used for pre- or post-training steps of already existing machine learning approaches for CTA applications. We tested this data generation framework by inserting synthetic lesions in 11 clinical CTA scans of healthy patients resulting in a data set of ~7000 annotated 2D slices. With this data we performed several plaque detection experiments using a data-driven machine learning approach with a neural encoder architecture. In this plaque classification task we first demonstrate that the synthetic lesion generation module can consistently perform well in recognizing unseen synthetic test data with an overall classification accuracy of 93%. Next we apply the synthetic lesion framework in a transfer learning experiment, where we demonstrate the feasibility to learn to classify real clinical plaque lesions with a purely synthetic model (overall classification accuracy 84%) that never saw real clinical lesions during model training. Second, we show that using synthetically data for pre-training with a subsequent training on clinical data can enhance the overall classification accuracy (from 91% to 92%) while strongly increasing the true positive count. We conclude that the synthetic plaque lesions model faithfully covers many important image characteristics of real plaque lesions in coronary CTA imaging and can thus help reduce the annotation burden for data-driven predictive vascular systems in this domain. This allows the creation of exhaustively annotated and site-specific customizable training data with a computationally fast forward model.
Troubling Dialogue and Digital Media: A Subaltern Critique

Over the past several decades, scholars have explored dialogue and digital media. While this scholarship has advanced strategic communication theory, it lacks a critical focus on how marginalized groups have been written out of these theories and practices. We bring a critical lens to dialogue, employing a subaltern critique to elevate the experiences and voices of members of an activist group working on behalf of low-income, minority women. Advancing theoretical and empirical work on dialogue and social media, our study approaches activist communication and dialogue through a co-optation orientation, to consider how advocacy groups are co-opted or erased through dialogic methods entailed in dominant discourses and how these groups exert agency and resistance. While social media may not always help activists penetrate the walls upheld by powerful social actors, they offer connective and transformative possibilities.
Segmentation of retinal detachment and retinoschisis in OCT images based on improved U-shaped network with cross-fusion global feature module

Retinal detachment (RD) refers to the separation of the retinal neuroepithelium layer (RNE) and retinal pigment epithelium (RPE), and retinoschisis (RS) is characterized by the RNE splitting into multiple layers. Retinal detachment and retinoschisis are the main complications leading to vision loss in high myopia. Optical coherence tomography (OCT) is the main imaging method for observing retinal detachment and retinoschisis. This paper proposes a U-shaped convolutional neural network with a cross-fusion global feature module (CFCNN) to achieve automatic segmentation of retinal detachment and retinoschisis. Main contributions include: (1) A new cross-fusion global feature module (CFGF) is proposed. (2) The residual block is integrated into the encoder of the U-Net network to enhance the extraction of semantic information. The method was tested on a dataset consisting of 540 OCT B-scans. With the proposed CFCNN method, the mean Dice similarity coefficient of retinal detachment and retinoschisis segmentation reached 94.33% and 90.29% and were better than some existing advanced segmentation networks.
Improved segmentation by adversarial U-Net

Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U–Net that consists of an encoder-decoder based architecture. Many variations exist to the U–Net architecture. In this work, we present a new training procedure that combines U–Net with an adversarial training we refer to as Adversarial U–Net. We show that Adversarial U–Net outperformes the conventional U–Net in three versatile domains that differ in the acquisition method as well as the physical characteristics and yields smooth and improved segmentation maps.
Improving T1w MRI-based brain tumor segmentation using cross-modal distillation

Although multi-modal imaging tends to improve the segmentation and classification performance in the field of medical image processing, lacking certain modalities at test time limits its clinical applicability. In this paper, we explored the ability of cross-modal distillation for increasing the performance of T1w MRI-based brain tumor segmentation. More specifically, we considered having high resolution T1w and T2w MRI sequences available for training while having only a high resolution T1w MRI sequence available at test time. We investigated the efficacy of the proposed method to improve the whole tumor segmentation using the BRATS 2018 dataset. Both cross-modal knowledge distillation and cross-modal feature distillation approaches were confirmed to enrich the representation of the T1w MRI sequence by learning from the representation of the more informative T2w MRI sequence during training, thereby improving the mean Dice scores by 6.14 % and 7.02 %, respectively.
Towards high-throughput polymer replication of transparent fused silica glass

Microstructured fused silica glass is of high demand for many applications in microsystems engineering, microfluidics and microoptics. However, structuring of fused silica glass is extremely difficult needing either very high temperatures for melt processing, hazardous chemicals for wet etching or time-consuming mechanical post-processing steps. The lack of feasible high-throughput manufacturing techniques prevent the usage of fused silica for many applications so far. Recently, soft replication and 3D printing of fused silica were introduced using silica nanocomposites that are converted into fused silica glass in a subsequent heat treatment. While these processes allow facile structuring of silica glasses on the laboratory scale, it is not suited for high-throughput manufacturing. In this work, we present a process towards rapid manufacturing of microstructured fused silica glass by hot embossing a thermoplastic silica nanocomposite. The structured nanocomposite is converted to high quality fused silica glass by subsequent debinding and sintering at 1320°C. We show hot embossing of microoptical structures as well as microfluidic channels with an aspect ratio of up to 6. Further, we have developed facile solvent and thermal bonding procedures allowing the fabrication of embedded and fully functional microfluidic chips in fused silica.
Video-based infant monitoring using a CNN-LSTM scheme

Infants are sensitive to pain and discomfort in their daily lives and frequent discomfort and pain can even lead to abnormal brain development, resulting in long-term adverse neurodevelopmental outcomes. With the help of video-based monitoring, a contactless method is considered to be promising for detecting discomfort automatically. In this study, a method for distinguishing infant discomfort status from comfort is proposed. We first extract two-dimensional (2D) features from video frames using pretrained Convolutional Neural Networks (CNNs), which is followed by two different Long Short-term Memory (LSTM) networks (uni- and bi-directional LSTMs) for the comfort/discomfort classification task. The methods are evaluated using videos acquired from 23 infants. Experimental results show the best AUC of 0.89 is achieved when using the bi-directional LSTM model based on the 2D features extracted by the VGG16 network. The high detection score indicates that the proposed method is promising for clinical use.
Enhanced mesenchymal stem cells growth on antibacterial microgrooved electrospun zinc chloride/polycaprolactone conduits for peripheral nerve regeneration

In this study, we have investigated the effect of adding zinc chloride (ZnCl2) on polycaprolactone (PCL) before and after electrospinning. The rheological properties and conductivity of ZnCl2/PCL solutions were measured prior to the electrospinning process. The resultant electrospun mats were characterized by SEM, contact angle, FTIR, XRD, mechanical properties, as well as its antibacterial and stem cell proliferation assessment were tested. It was found that the fibers became finer by increasing the zinc salt content. Moreover, stability increased slightly up to 5% Zn-PCL and also the hydrophilicity has been enhanced by 52%. By adding ZnCl2, the degradation rate and mechanical properties were significantly increased. Also, the resultant mats have shown antibacterial properties against S. aureus than E. coli. From the stem cells proliferation study, it can be observed that by increasing ZnCl2, the stem cells proliferation was significantly increased. Grooved multichannel nerve conduits were successfully fabricated by rolling the electrospun mats produced on corn husks which has shown better cell alignment and attachment. Hence, adding zinc chloride is a facile biocompatible enhancement to polycaprolactone nanofibers to be used in nerve regeneration.
Optical constants and band structure of trigonal NiO

Using spectroscopic ellipsometry and transmission measurements, the authors determined the optical constants (absorption coefficient, complex refractive index, and dielectric function) of bulk trigonal NiO from 0.08 to 6.5 eV. By careful discussion of the data, elastic scattering by oxygen bubbles was ruled out and the effects of surface roughness were removed numerically to obtain an accurate dielectric function of NiO. A direct band gap of 0.85 eV was found from transmission and assigned to direct interband transitions from the Ni-O hybrid valence band states to the Ni 4s conduction band at the center of the Brillouin zone. At 4 eV, the authors find the well-known charge transfer gap from the lower to the upper Hubbard band. Several intermediate sharp peaks were also found. The temperature dependence of the NiO charge transfer gap is similar to the E1 gap of Si between 100 and 700 K. At higher temperatures, heating NiO in vacuum leads to sublimation, which has drastic irreversible consequences for the p.
Understanding the psychology of trust between patients and their community pharmacists

Background: Pharmacists need patients to trust them in order to support best possible health outcomes. There has been little empirical work to test the widely stated claim that pharmacists are the “most trusted” health care professional. This study was undertaken to characterize the factors that shape public trust of individual pharmacists and the profession as a whole. Methods: An exploratory qualitative study was undertaken. Semistructured interviews with 13 patients from 5 different community pharmacies were completed. Interview data were transcribed, coded and categorized to identify trust-enhancing and trust-diminishing factors influencing patients’ perceptions of pharmacists. Results: Four trust-diminishing factors were identified, including the business context within which community pharmacy is practised, lack of transparency regarding pharmacists’ remuneration, lack of awareness of how pharmacists qualify and are regulated and inconsistent previous experiences with pharmacists. Four trust-enhancing factors were identified, including accessibility, affability, acknowledgement and respect. Discussion: This study illustrates that trust-diminishing factors appear to be somewhat outside the day-to-day control of individual community pharmacists, while trust-enhancing factors are elements that pharmacists may have greater personal control over. Further research is required to better understand these factors and to develop a more generalizable understanding of how patients develop trust in their pharmacists. Can Pharm J (Ott) 2021;154:xx-xx.
Antivirulence DsbA inhibitors attenuate Salmonella enterica serovar Typhimurium fitness without detectable resistance

Inhibition of the DiSulfide Bond (DSB) oxidative protein folding machinery, a major facilitator of virulence in Gram‐negative bacteria, represents a promising antivirulence strategy. We previously developed small molecule inhibitors of DsbA from Escherichia coli K‐12 (EcDsbA) and showed that they attenuate virulence of Gram‐negative pathogens by directly inhibiting multiple diverse DsbA homologues. Here we tested the evolutionary robustness of DsbA inhibitors as antivirulence antimicrobials against Salmonella enterica serovar Typhimurium under pathophysiological conditions in vitro. We show that phenylthiophene DsbA inhibitors slow S. Typhimurium growth in minimal media, phenocopying S. Typhimurium isogenic dsbA null mutants. Through passaging experiments, we found that DsbA inhibitor resistance was not induced under conditions that rapidly induced resistance to ciprofloxacin, an antibiotic commonly used to treat Salmonella infections. Furthermore, no mutations were identified in the dsbA gene of inhibitor‐treated S. Typhimurium, and S. Typhimurium virulence remained susceptible to DsbA inhibitors. Our work demonstrates that under in vitro pathophysiological conditions, DsbA inhibitors can have both antivirulence and antibiotic action. Importantly, our finding that DsbA inhibitors appear to be evolutionarily robust offers promise for their further development as next‐generation antimicrobials against Gram‐negative pathogens.
Effectiveness of different lures for attracting Ips acuminatus (Coleoptera: Curculionidae: Scolytinae)

Ips acuminatus (the sharp‐toothed bark beetle, STBB) is currently considered to be one of the most serious pests of Scots pine in many European countries. STBB management is among the most challenging tasks in pine forests; the development of methods for monitoring, predicting and managing outbreaks of this bark beetle is therefore crucial. Pheromone‐baited traps have been widely recommended as a valuable tool for the monitoring and mass trapping of bark beetles. Although different suppliers offer a variety of STBB lures, their effectiveness has rarely, if ever, been evaluated under natural conditions. We evaluated the attractiveness of three commercially available and five experimental synthetic lures by comparing the numbers of STBBs captured in white, six‐funnel traps. The studies were conducted in 2017–2019 in Poland, in Scots pine‐dominated forests affected by STBB outbreaks. Our study demonstrated significant differences in the effectiveness of the lures. The experimental lure produced by the Witasek company (Austria) and the recently marketed lure Acumodor Micro from Chemipan (Poland) were the most attractive to STBB. Among the least effective were two commercial lures (Acuwit and Acumodor), hitherto used in Central Europe. The results will be useful in developing methods for the monitoring and management of STBB populations.
RECENT RESEARCH TOPICS AND A BIBLIOMETRIC ANALYSIS OF JOCEC

ABSTRACT This paper investigates research from the Journal of Organizational Computing and Electronic Commerce over the time frame 2016–2020 and captures the primary topics of those most cited papers in order to better understand those research topics of greatest interest. In addition, this paper performs a bibliometric analysis of those papers, analyzing citations, views, and other factors associated with those papers. As part of that analysis, this paper finds that the number of views of a paper, is related to Altmetric activity and whether or not a paper is available on open access. The paper also finds that the number of Google, Dimensions and Crossref citations are correlated and positively related to the number of Views and the amount of Altmetric activity.
Palladium nanoparticles encapsulated in porous silica shells: an efficient and highly stable catalyst for CO oxidation

Porous silica-supported metallic Pd catalysts were prepared in three steps: (1) synthesis of oleylamine-capped Pd nanoparticles, (2) silica polymerization around the oleylamine-capped Pd in a water-in-oil microemulsion system and (3) removal of the capping agent through calcination. With the characterization of transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction and N2 physisorption, the resultant 3 nm Pd nanoparticles were identified to be homogenously encapsulated within 10 nm-thick porous silica shells. During the oxidation of CO at 443 K, the as-prepared Pd@SiO2-673 delivered a turnover frequency up to 33 times greater than that associated with the reference Pd/SiO2-673 catalyst prepared by a conventional immobilization method. Meanwhile, the as-prepared Pd@SiO2-673 also exhibited significantly improved stability for a continuous reaction of 576 h than the control catalyst. The remarkable enhancement of the catalytic performance was found to depend on the core-shell configuration. The Pd particle cores have a small size (3 nm), contributing to weakening the strength of CO adsorption and thus enhancing catalytic activity. On the other hand, the porous silica shells allow the reactants to penetrate into the core-shell structured Pd@SiO2 composite and thus increase the accessibility of the Pd cores. Furthermore, they are separate Pd nanoparticles and therefore improve the thermal stability.
On Latency of E-Commerce Platforms

ABSTRACT For e-commerce retailers, even small increases in waiting times have been found to lead to user dissatisfaction and losses in sales. This paper investigates how contextual and behavioral factors influence the impact of latency on the website performance of a real e-commerce platform. The results show an increased impact of latency for users with faster navigation speed and mobile users. They further show a long-term effect of latency on customer returns and confirm previous findings that show a lower impact of latency for users that are familiar with a website. These results provide a case for real-time behavioral analytics by classifying customers based on navigational speed. It further points out two ways to increase revenue: (i) by prioritization of customers and (ii) by optimization of web-pages toward lower latency.
A step toward sustainability, dignity, and good health through Noraa

ABSTRACT Sustainable entrepreneurs can play an important role in the 2030 Agenda for Sustainable Development, adopted by the United Nations in 2015, by promoting inclusive economic development, generating income and employment opportunities, and protecting our planet. The sustainable enterprises that are categorized as micro-, small, and medium-sized enterprises (MSMEs) may be small in size, but their impact is crucial for the local areas where they operate. This article discusses the case of Noraa, a micro-enterprise in India, which is making an important contribution to the achievement of the Sustainable Development Goals by taking care of both people and the planet. Noraa is a sustainable enterprise run by two young entrepreneurs. It provides a range of organic and biodegradable sanitary pads and is committed to the cause of the environment and menstrual hygiene.