Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging

Abstract. Near-infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response in patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In this study, we introduce our recently developed ultrasound-guided DOT system, which has been improved in system compactness, robustness, and user-friendliness by custom-designed electronics, automated data preprocessing, and implementation of a new two-step reconstruction algorithm. The system performance has been tested with several sets of solid and blood phantoms and the results show accuracy in reconstructed absorption coefficients as well as blood oxygen saturation. A clinical example of a breast cancer patient, who was undergoing neoadjuvant chemotherapy, is given to demonstrate the system performance.
Image-Based Profiling of Patient-Derived Pancreatic Tumor–Stromal Cell Interactions Within a Micropatterned Tumor Model

Pancreatic cancer is one of the most aggressive cancers with a 5-year patient survival rate of 8.2% and limited availability of therapeutic agents to target metastatic disease. Pancreatic cancer is characterized by a dense stromal cell population with unknown contribution to the progression or suppression of tumor growth. In this study, we describe a microengineered tumor stromal assay of patient-derived pancreatic cancer cells to study the heterotypic interactions of patient pancreatic cancer cells with different types of stromal fibroblasts under basal and drug-treated conditions. The population dynamics of tumor cells in terms of migration and viability were visualized as a functional end point. Coculture with cancer-associated fibroblasts increased the migration of cancer cells when compared to dermal fibroblasts. Finally, we imaged the response of a bromodomain and extraterminal inhibitor on the viability of pancreatic cancer clusters surrounding by stroma in microengineered tumor stromal assay. We visualized a codynamic reduction in both cancer and stromal cells with bromodomain and extraterminal treatment compared to the dimethyl sulfoxide-treated group. This study demonstrates the ability to engineer tumor–stromal assays with patient-derived cells, study the role of diverse types of stromal cells on cancer progression, and precisely visualize a coculture during the screening of therapeutic compounds.
Performance Analysis of Routing Protocol for Ad Hoc UAV Network

This paper provides an analysis of the performance of routing protocols in the paradigm of a FANET network. The protocols studied, AODV, OLSR and DSDV, were created based on the paradigm of MANET networks. Therefore, it becames necessary to verify the viability of the protocols in a FANET network. They are tested in several scenarios, where some important parameters are changed. In the final, it is concluded that proactive protocols have better performance on networks with high dynamic topology.
Moodle Moods?: A User Experience Study of a Small Private Online Course for Higher Teacher Education

The aim of this paper is to present a usability and user experience study of a blended learning, hybrid small private online course, which aim is to introduce university teachers to digital teaching and learning. The e-learning platform Moodle is used to host the online part of the course. The goal of the study conducted at the first phase of a user-centred design process, was to collect end users experiences in order to inform design choices. User experience tests were conducted with targeted end users at the beginning of the course (n=6), and at the end (n=8). A mixed-methods approach was applied, including video observation, think aloud, task success rate, semi-structured interviews, screen-recordings and questionnaires. Quantitative data was analysed by descriptive statistics and the qualitative data was subjected to qualitative content analysis. The findings of the study highlighted positive and negative user experiences related to the following aspects: pragmatic aspects, hedonic aspects, pedagogical aspects, and social aspects. The findings also revealed a change in user perceptions of pragmatic quality and hedonic quality over time.
Extracting Figures and Captions from Scientific Publications

Figures and captions convey essential information in scientific publications. As such, there is a growing interest in mining published figures and in utilizing their respective captions as a source of knowledge. There is also much interest in image captioning systems that can automatically generate captions for images, whose training requires large datasets of image-caption pairs. Notably, the first fundamental step of obtaining figures and captions from publications is neither well-studied nor yet well-addressed. In this paper, we introduce a new and effective system for figure and caption extraction, PDFigCapX. Unlike current methods that extract figures by handling raw encoded contents of PDF documents, we separate text from graphical contents and utilize layout information to detect and disambiguate figures and captions. Files containing the figures and their associated captions are then produced as output to the end-user. We test PDFigCapX on both a previously used generic dataset and on two new sets of publications within the biomedical domain. Our experiments and results show a significant improvement in performance compared to the state-of-the-art, and demonstrate the effectiveness of our approach. Our system will be available for use at: https://www.eecis. udel.edu/~compbio/PDFigCapX.
Refocusing Supports of Panorama Light-Field Images in Head-Mounted Virtual Reality

Virtual Reality (VR) has been deeply researched in recent years, although the novel technology delivers some astonishing experience, it may also cause people the sense of imbalance, dizziness, and vomiting when wearing head-mounted displays (HMDs) after a long period of time. In this paper, we combine the light field technology into a novel panorama viewing system implemented on Unity to provide dynamic depth of field and natural focus effect for better user experience. The system is also coped with FOVE, an HMD with eye-tracking technology, to better track the eye movements and collect the gazing parameters, e.g., gazing point, for better user experience.With such a design, a more immersive panorama image/video viewing experience is delivered. An evaluation is held for the user experience, which shows that our proposed system reduces the refocusing time by up to 319 times and increases the subjective Mean Opinion Score (MOS) by 19%.
Conditional Expression Synthesis with Face Parsing Transformation

Facial expression synthesis with various intensities is a challenging synthesis task due to large identity appearance variations and a paucity of efficient means for intensity measurement. This paper advances the expression synthesis domain by the introduction of a Couple-Agent Face Parsing based Generative Adversarial Network (CAFP-GAN) that unites the knowledge of facial semantic regions and controllable expression signals. Specially, we employ a face parsing map as a controllable condition to guide facial texture generation with a special expression, which can provide a semantic representation of every pixel of facial regions. Our method consists of two sub-networks: face parsing prediction network (FPPN) uses controllable labels (expression and intensity) to generate a face parsing map transformation that corresponds to the labels from the input neutral face, and facial expression synthesis network (FESN) makes the pretrained FPPN as a part of it to provide the face parsing map as a guidance for expression synthesis. To enhance the reality of results, couple-agent discriminators are served to distinguish fake-real pairs in both two sub-nets. Moreover, we only need the neutral face and the labels to synthesize the unknown expression with different intensities. Experimental results on three popular facial expression databases show that our method has the compelling ability on continuous expression synthesis.
Microfluidization trends in the development of nanodelivery systems and applications in chronic disease treatments

Plant bioactive compounds are known for their extensive health benefits and therefore have been used for generations in traditional and modern medicine to improve the health of humans. Processing and storage instabilities of the plant bioactive compounds, however, limit their bioavailability and bioaccessibility and thus lead researchers in search of novel encapsulation systems with enhanced stability, bioavailability, and bioaccessibility of encapsulated plant bioactive compounds. Recently many varieties of encapsulation methods have been used; among them, microfluidization has emerged as a novel method used for the development of delivery systems including solid lipid nanocarriers, nanoemulsions, liposomes, and so on with enhanced stability and bioavailability of encapsulated plant bioactive compounds. Therefore, the nanodelivery systems developed using microfluidization techniques have received much attention from the medical industry for their ability to facilitate controlled delivery with enhanced health benefits in the treatment of various chronic diseases. Many researchers have focused on plant bioactive compound-based delivery systems using microfluidization to enhance the bioavailability and bioaccessibility of encapsulated bioactive compounds in the treatment of various chronic diseases. This review focuses on various nanodelivery systems developed using microfluidization techniques and applications in various chronic disease treatments.
IT and Knowledge in Teams: A Longitudinal Study

The literature on information systems development (ISD) appears to be fragmented into two main streams: one that uses a knowledge management perspective and another one emphasizes the role of information technology to enhance ISD. Such fragmentation can be detrimental to establish a common ground for future research. In this paper, we synthesize the prior literature on information systems development and propose a theoretical model for ISD performance based on knowledge-oriented factors and IT. We argue the effects of knowledge-oriented factors on ISD team performance and how IT can impact such effects overtime. This study aims to understand how IT facilitates the stream of knowledge within teams to enhance ISD team performance over the course of IS projects implementation.
Dest-ResNet: A Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction

With the ever-increasing urbanization process, the traffic jam has become a common problem in the metropolises around the world, making the traffic speed prediction a crucial and fundamental task. This task is difficult due to the dynamic and intrinsic complexity of the traffic environment in urban cities, yet the emergence of crowd map query data sheds new light on it. In general, a burst of crowd map queries for the same destination in a short duration (called "hotspot'') could lead to traffic congestion. For example, queries of the Capital Gym burst on weekend evenings lead to traffic jams around the gym. However, unleashing the power of crowd map queries is challenging due to the innate spatiotemporal characteristics of the crowd queries. To bridge the gap, this paper firstly discovers hotspots underlying crowd map queries. These discovered hotspots address the spatiotemporal variations. Then Dest-ResNet (Deep spatiotemporal Residual Network) is proposed for hotspot traffic speed prediction. Dest-ResNet is a sequence learning framework that jointly deals with two sequences in different modalities, i.e., the traffic speed sequence and the query sequence. The main idea of Dest-ResNet is to learn to explain and amend the errors caused when the unimodal information is applied individually. In this way, Dest-ResNet addresses the temporal causal correlation between queries and the traffic speed. As a result, Dest-ResNet shows a 30% relative boost over the state-of-the-art methods on real-world datasets from Baidu Map.
Sports Video Captioning by Attentive Motion Representation based Hierarchical Recurrent Neural Networks

Sports video captioning is a task of automatically generating a textual description for sports events (e.g. football, basketball or volleyball games). Although previous works have shown promising performance in producing the coarse and general description of a video, it is still quite challenging to caption a sports video with multiple fine-grained player's actions and complex group relationship among players. In this paper, we present a novel hierarchical recurrent neural network (RNN) based framework with an attention mechanism for sports video captioning. A motion representation module is proposed to extract individual pose attribute and group-level trajectory cluster information. Moreover, we introduce a new dataset called Sports Video Captioning Dataset-Volleyball for evaluation. We evaluate our proposed model over two public datasets and our new dataset, and the experimental results demonstrate that our method outperforms the state-of-the-art methods.
iHuman3D: Intelligent Human Body 3D Reconstruction using a Single Flying Camera

Aiming at autonomous, adaptive and real-time human body reconstruction technique, this paper presents iHuman3D: an intelligent human body 3D reconstruction system using a single aerial robot integrated with an RGB-D camera. Specifically, we propose a real-time and active view planning strategy based on a highly efficient ray casting algorithm in GPU and a novel information gain formulation directly in TSDF. We also propose the human body reconstruction module by revising the traditional volumetric fusion pipeline with a compactly-designed non-rigid deformation for slight motion of the human target. We unify both the active view planning and human body reconstruction in the same TSDF volume-based representation. Quantitative and qualitative experiments are conducted to validate that the proposed iHuman3D system effectively removes the constraint of extra manual labor, enabling real-time and autonomous reconstruction of human body.
The Definition, Current Situation and Development Trend of Latent Aspect Rating Analysis in Text Mining

The field of latent aspect rating analysis has been developed in the last few years. Firstly, we introduce the background and definition of latent aspect rating analysis in text mining. Secondly, we have collected literature on the latent aspect rating analysis of the research in recent years and summarized the development status of this field. Finally, the future development trend and expectation of this field are put forward according to relevant literature. Furthermore, the main contribution of this paper is to describe the field and analyze its development trend according to the author's research work.
Association between Sarcopenic Obesity, Type 2 Diabetes, and Hypertension in Overweight and Obese Treatment-Seeking Adult Women

The last decade has seen a new condition that describes the coexistence of obesity and sarcopenia, termed sarcopenic obesity (SO). We aimed to assess the prevalence of SO in overweight and obese treatment-seeking adult women and the association with type 2 diabetes, hypertension, and dyslipidemia. A body composition assessment was conducted with an InBody bioimpedance analyser in 154 overweight and obese women referred to the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (BAU) in Lebanon, and 30 normal-weight participants of similar age. The overweight and obese patients were then categorized as being with or without sarcopenia. Thirty-one out of the 154 overweight or obese participants met the criteria for SO and displayed a significantly higher prevalence of type 2 diabetes and hypertension than those without SO. Logistic regression analysis showed that SO increases the odds of having type 2 diabetes and hypertension by nearly 550% (odds ratio = 5.42, 95% confidence interval = 1.37–21.40, p < 0.05) after adjusting for central fat, eating habits, level of physical activity, and smoking. SO affects nearly 20% of treatment-seeking overweight and obese adult women. Moreover, SO seems to be strongly associated with type 2 diabetes and hypertension.
AniDance: Real-Time Dance Motion Synthesize to the Song

In this paper, we present a demo named AniDance that can synthesize dance motions with melody in real-time. When users sing a song or play one in their phone to AniDance, their melody will drive the 3D-space character to dance to create a lively dance animation. In practice, we conduct a music oriented 3D-space dance motion dataset by capturing real dance performances, using LSTM-autoencoder to identify the relation between music and dance. Based on these technologies, users can create valid choreographies that capable of musical expression, witch can promote their learning ability and interest in dance and music.
Automatic Smile Detection of Infants in Mother-Infant Interaction via CNN-based Feature Learning

Smile is one of the most frequently appeared facial expressions of human beings. It is an explicit social signal to speculate on humans' emotion. Interpretation of infants smile behavior has received much attention in the field of psychology and pediatric medicine, i.e. Autism Spectral Disorder (ASD). Due to low efficiency of manual emotion encoding of infant facial images, automatic smile detection system has drawn many researchers' attention. However, lack of large amount of labeled data hinders the research progress in this field. In this paper, a novel dataset named RCLA&NBH\_Smile Dataset was presented. Thirty four infants facial expression videos were recorded during mother-infant interaction and over 77,000 frames were manually labeled. For developing an automatic smile analysis system, we proposed a Convolutional Neural Network (CNN) based method for smile detection. We firstly evaluated our proposed method on two public datasets, GENKI-4K and CelebA, and it achieved satisfactory result of mean accuracy of 94.55% and 92.60%, respectively. Experiments on RCLA&NBH\_Smile dataset show that our proposed approach can achieve the mean accuracy of 87.16% and F1-score of 62.54%, respectively.
Efficacy and safety of chemopreventive agents on colorectal cancer incidence and mortality: systematic review and network meta-analysis

Background Various interventions have been tested as primary prevention of colorectal cancers (CRC), but comprehensive evidence comparing them is absent. We examined the effects of various chemopreventive agents (CPAs) on CRC incidence and mortality. Methods We did a network meta-analysis based on a systematic review of randomized controlled trials (RCTs) that compared at least one CPA (aspirin, antioxidants, folic acid, vitamin B6, vitamin B12, calcium, vitamin D, alone or in combination) to placebo or other CPA in persons without history of CRC. Several databases were searched from inception up to March 2017. Primary outcomes were early and long-term CRC incidence and mortality. Results Twenty-one RCTs comprising 281,063 participants, 9 RCTS comprising 160,101 participants, and 7 RCTs comprising 24,001 participants were included in the network meta-analysis for early risk of CRC incidence, long-term risk of CRC incidence and mortality, respectively. For early CRC incidence, no CPAs were found to be effective. For long-term CRC incidence and mortality, aspirin was the only intervention that showed protective effects with potential dose-dependent effects (risk ratio [RR], 0.74 [95% CI, 0.57–0.97] for high-dose [≥325 mg/day] and RR, 0.81 [95% CI, 0.67–0.98] for very-low-dose [≤100 mg/day]). Similar trend was found for mortality (RR, 0.43 [95% CI, 0.23–0.81] for low-dose [>100–325 mg/day] and RR, 0.65 [95% CI, 0.45–0.94] for very-low-dose). However, in net clinical benefit analysis, when combining risk estimates on mortality from CRC, cardiovascular disease, and pooled risk estimates of major gastrointestinal bleeding, low-dose aspirin provided the highest net survival gain (%) of 1.736 [95% CI, 1.010–2.434]. Conclusion Aspirin at the dose range of 75–325 mg/day is a safe and effective primary prevention for long-term CRC among people at average risk. None of the other CPAs were found to be effective. There may potentially be differential effects among various doses of aspirin that needs further investigation.
Multi-Scale Context Attention Network for Image Retrieval

Recent attempts on the Convolutional Neural Network (CNN) based image retrieval usually adopt the output of a specific convolutional or fully connected layer as feature representation. Though superior representation capability has yielded better retrieval performance, the scale variation and clutter distracting remain to be two challenging problems in CNN based image retrieval. In this work, we propose a Multi-Scale Context Attention Network (MSCAN) to generate global descriptors, which is able to selectively focus on the informative regions with the assistance of multi-scale context information. We model the multi-scale context information by an improved Long Short-Term Memory (LSTM) network across different layers. As such, the proposed global descriptor is equipped with the scale aware attention capability. Experimental results show that our proposed method can effectively capture the informative regions in images and retain reliable attention responses when encountering scale variation and clutter distracting. Moreover, we compare the performance of the proposed scheme with the state-of-the-art global descriptors, and extensive results verify that the proposed MSCAN can achieve superior performance on several image retrieval benchmarks.
Needle-based confocal laser endomicroscopy of pancreatic cystic lesions: a prospective multicenter validation study in patients with definite diagnosis

Abstract Background Needle-based confocal laser endomicroscopy (nCLE) enables observation of the inner wall of pancreatic cystic lesions (PCLs) during an endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA). This study prospectively evaluated the diagnostic performance of nCLE for large, single, noncommunicating PCLs using surgical histopathology or EUS-FNA cytohistopathology as a reference diagnosis. Methods From April 2013 to March 2016, consecutive patients referred for EUS-FNA of indeterminate PCLs without evidence of malignancy or chronic pancreatitis were prospectively enrolled at five centers. EUS-FNA and nCLE were performed and cystic fluid was aspirated for cytohistopathological and carcinoembryonic antigen (CEA) analysis. The diagnostic performance of nCLE was assessed against the reference standard and compared with that of EUS and CEA. This study was registered on ClinicalTrials.gov (NCT01563133). Results 206 patients underwent nCLE and 78 PCLs (mean size 40 mm, range 20 – 110 mm) had reference diagnoses (53 premalignant and 25 benign PCLs). Post-procedure pancreatitis occurred in 1.3 % of the patients. nCLE was conclusive in 71 of the 78 cases (91 %). The sensitivies and specifities of nCLE for the diagnosis of serous cystadenoma, mucinous PCL, and premalignant PCL were all ≥ 0.95 (with 95 % confidence interval from 0.85 to 1.0). The AUROC was significantly larger for nCLE than for CEA or EUS. Conclusions nCLE had excellent diagnostic performance that surpassed that of CEA and EUS for the diagnosis of large, single, noncommunicating PCLs. The nCLE procedure should be considered in patients with indeterminate PCLs to ensure a more specific diagnosis.