Miracle-dz/UniFAR-FTUs huggingface dataset:
PD-1, a Follicular T-cell Marker Useful for Recognizing Nodular Lymphocyte-predominant Hodgkin Lymphoma

The nodularity and presence of T-cell rosettes surrounding the neoplastic cells has been described as a defining feature of nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL). We have explored the potential diagnostic value of a new marker (NAT105) that recognizes the antigen PD-1 in a series of 152 cases diagnosed as nodular sclerosis Hodgkin lymphoma, mixed cellularity Hodgkin lymphoma, lymphocyte-rich classic Hodgkin lymphoma, NLPHL, and T-cell/histiocyte-rich B-cell lymphoma (T/HRBCL). All the cases were immunostained with a panel of antibodies against CD10, bcl-6, CXCL13, CD57, and PD-1 (NAT-105). The series includes a set of cases diagnosed as NLPHL with diffuse areas, and a group of borderline cases with features between those of NLPHL and T/HRBCL. Results show that PD-1 (NAT-105) is an excellent immunomarker not only of follicular T-cell rosettes in NLPHL, but also of a subset of lymphocyte-rich classic Hodgkin lymphomas. However, it is not a unique and defining feature of NLPHL. The presence of PD-1–positive (NAT-105) T-cell rosettes seems to be an additional useful feature in the differential diagnosis of NLPHL and T/HRBCL, which is normally a controversial and difficult task. The standard T/HRBCL cases lack follicular T-cell rosettes, whereas most of the borderline cases between the 2 entities have follicular T-cell rosettes, thus suggesting a closer relation with NLPHL.
Evolution of ageing, costs of reproduction and the fecundity–longevity trade-off in eusocial insects

Eusocial insects provide special opportunities to elucidate the evolution of ageing as queens have apparently evaded costs of reproduction and reversed the fecundity–longevity trade-off generally observed in non-social organisms. But how reproduction affects longevity in eusocial insects has rarely been tested experimentally. In this study, we took advantage of the reproductive plasticity of workers to test the causal role of reproduction in determining longevity in eusocial insects. Using the eusocial bumblebee Bombus terrestris, we found that, in whole colonies, in which workers could freely ‘choose’ whether to become reproductive, workers' level of ovarian activation was significantly positively associated with longevity and ovary-active workers significantly outlived ovary-inactive workers. By contrast, when reproductivity was experimentally induced in randomly selected workers, thereby decoupling it from other traits, workers' level of ovarian activation was significantly negatively associated with longevity and ovary-active workers were significantly less long-lived than ovary-inactive workers. These findings show that workers experience costs of reproduction and suggest that intrinsically high-quality individuals can overcome these costs. They also raise the possibility that eusocial insect queens exhibit condition-dependent longevity and hence call into question whether eusociality entails a truly reversed fecundity–longevity trade-off involving a fundamental remodelling of conserved genetic and endocrine networks underpinning ageing.
How Different Input and Output Modalities Support Coding as a Problem-Solving Process for Children

Using coding education to promote computational thinking and nurture problem-solving skills in children has become an emerging global trend. However, how different input and output modalities in coding tools affect coding as a problem-solving process remains unclear. Of interest are the advantages and disadvantages of graphical and tangible interfaces for teaching coding to children. We conducted four kids coding workshops to study how different input and output methods in coding affected the problem-solving process and class dynamics. Results revealed that graphical input could keep children focused on problem solving better than tangible input, but it was less provocative for class discussion. Tangible output supported better schema construction and casual reasoning and promoted more active class engagement than graphical output but offered less affordance for analogical comparison among problems. We also derived insights for designing new tools and teaching methods for kids coding.
The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles

Many use cases of UAVs require beyond visual LOS communications. Mobile networks offer wide-area, high-speed, and secure wireless connectivity, which can enhance control and safety of UAV operations and enable beyond visual LOS use cases. In this article, we share some of our experience in LTE connectivity for low-altitude small UAVs. We first identify the typical airborne connectivity requirements and characteristics, highlight the different propagation conditions for UAVs and mobiles on the ground with measurement and ray tracing results, and present simulation results to shed light on the feasibility of providing LTE connectivity for UAVs. We also present several ideas on potential enhancements for improving LTE connectivity performance and identify fruitful avenues for future research.
Evaluation of IEEE 802.15.6 MAC user priorities with UWB PHY for medical applications

Wireless body area network (WBAN) is envisioned to became self-evident in future healthcare environments. One candidate to fulfill the reliably and efficiency requirements set by sending vital biomedical information is the novel IEEE 802.15. 6 WBAN standard. In this paper, we study the effect of user priority scheme defined for different types of data in the IEEE 802.15. 6 standard for medium access control (MAC) layer utilizing Slotted ALOHA channel access. A comparative performance analysis between the original IEEE 802.15. 6-based communication system utilizing user priority scheme and a system utilizing a traditional random backoff scheme is presented in wireless hospital environment. Based on the results it can be stated that the performance of the IEEE 802.15. 6 user priority scheme is approximately the same than the random backoff scheme in scenarios containing only high-priority medical data. However, in scenarios having both medical and low-priority nonmedical data transmissions the user priority scheme offers superior performance in terms of throughput and packet delivery ratio for medical applications.
Immobilization Techniques in the Fabrication of Nanomaterial-Based Electrochemical Biosensors: A Review

The evolution of 1st to 3rd generation electrochemical biosensors reflects a simplification and enhancement of the transduction pathway. However, in recent years, modification of the transducer with nanomaterials has become increasingly studied and imparts many advantages. The sensitivity and overall performance of enzymatic biosensors has improved tremendously as a result of incorporating nanomaterials in their fabrication. Given the unique and favorable qualities of gold nanoparticles, graphene and carbon nanotubes as applied to electrochemical biosensors, a consolidated survey of the different methods of nanomaterial immobilization on transducer surfaces and enzyme immobilization on these species is beneficial and timely. This review encompasses modification of enzymatic biosensors with gold nanoparticles, carbon nanotubes, and graphene.
Switchable Static and Dynamic Self-Assembly of Magnetic Droplets on Superhydrophobic Surfaces

Magnetic Self-Assembly During self-assembly, objects spontaneously assemble into larger ordered patterns as observed, for example, in the phase segregation of block copolymers or the assembly of micrometer-sized objects and components in electronics. In dynamic self-assembly, the ordered patterns require an external energy source, but still form because of intrinsic interactions within the system. Timonen et al. (p. 253; see the Perspective by Hermans et al.) studied the organization of magnetic droplets, in the form of a ferrofluid, placed on a low-friction surface. A time-varying magnetic field transformed the statically arranged droplets into a dynamic pattern. Magnetic droplets oscillate between static and dynamic self-assembly patterns in a magnetic field. [Also see Perspective by Hermans et al.] Self-assembly is a process in which interacting bodies are autonomously driven into ordered structures. Static structures such as crystals often form through simple energy minimization, whereas dynamic ones require continuous energy input to grow and sustain. Dynamic systems are ubiquitous in nature and biology but have proven challenging to understand and engineer. Here, we bridge the gap from static to dynamic self-assembly by introducing a model system based on ferrofluid droplets on superhydrophobic surfaces. The droplets self-assemble under a static external magnetic field into simple patterns that can be switched to complicated dynamic dissipative structures by applying a time-varying magnetic field. The transition between the static and dynamic patterns involves kinetic trapping and shows complexity that can be directly visualized.
Continuous Intrathecal Baclofen Treatment of Severe Spasms in Two Children with Spinal‐cord Injury

This study reports the use of intrathecal baclofen in two ventilator‐dependent children with severe spasms secondary to spinal‐cord injury. Baclofen was delivered via a subcutaneously implanted, programmable pump. The children were followed for 12 and 24 months. Baclofen dramatically reduced spasms, resulting in more stable ventilation, improved ease of care, reduced distress and better integration into the community. Although effective, intrathecal baclofen represents a significant intervention; careful consideration must be given to potential complications and the need for long‐term management. Full effectiveness was dependent on free CSF flow.
A Useful Tool As a Medical Checkup in a General Population—Bioelectrical Impedance Analysis

Accumulation of visceral fat leads to metabolic syndrome and increases risks of cerebro-cardiovascular diseases, which should be recognized and improved at the early stage in general population. Accurate measurement of visceral fat area (VFA) is commonly performed by the abdominal cross-sectional image measured by computed tomography scan, which is, however, limited due to the radiation exposure. The bioelectrical impedance analysis (OMRON, HDS-2000 DUALSCANR) has been recently developed to measure VFA, which is more easily accessible modality. In the present study, we investigated the clinical usefulness of DUALSCANR in 226 subjects who received health examination, including blood chemistries, electrocardiography, cardio, and carotid ultrasonography. VFA was measured within only just 5 min. Average of VFA was 83.5 ± 36.3 cm2 in men, and 64.8 ± 28.0 cm2 in women, which was correlated to weight (r = 0.7404, p < 0.0001), body mass index (BMI) (r = 0.7320, p < 0.0001), and waist circumstance (r = 0.7393, p < 0.0001). In multivariate analyses, VFA was significantly associated with weight (p < 0.0001), BMI (p < 0.0001), and waist circumstance (p < 0.0001). Compared to the group of smaller waist and normal BMI, VFA was significantly increased (p < 0.0001) in the group of larger waist and obese subjects. In conclusion, these results indicated that DUALSCANR is useful to measure VFA easily in general population, even in a large number of subjects.
Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients

Objective: This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with two genomic biomarkers, namely protein expression of the excision repair cross-complementing 1 genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of stage I nonsmall-cell lung cancer (NSCLC) patients after surgery. Methods: By using chest computed tomography images, we developed a computer-aided detection scheme to segment lung tumors and computed tumor-related image features. After feature selection, we trained a Naïve Bayesian network-based classifier using eight image features and a multilayer perceptron classifier using two genomic biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and tested using a dataset with 79 stage I NSCLC cases, a synthetic minority oversampling technique and a leave-one-case-out validation method. A fusion method was also applied to combine prediction scores of two classifiers. Results: Areas under ROC curves (AUC) values are 0.78 ± 0.06 and 0.68 ± 0.07 when using the image feature and genomic biomarker-based classifiers, respectively. AUC value significantly increased to 0.84 ± 0.05 (p <; 0.05) when fusion of two classifier-generated prediction scores using an equal weighting factor. Conclusion: A quantitative image feature-based classifier yielded significantly higher discriminatory power than a genomic biomarker-based classifier in predicting cancer recurrence risk. Fusion of prediction scores generated by the two classifiers further improved prediction performance. Significance: We demonstrated a new approach that has potential to assist clinicians in more effectively managing stage I NSCLC patients to reduce cancer recurrence risk.
The "Concept Cloud": Supporting Collaborative Knowledge Construction Based on Semantic Extraction from Learner-Generated Artefacts

Explicit visual representations of domain knowledge have the potential to support students engaged in scientific inquiry learning activities on an epistemic level. This can be facilitated using computational methods for the extraction of concepts from student generated knowledge artefacts such as hypotheses, concept maps, or wiki articles. We propose an application of this approach in the context of inquiry learning with online science laboratories. As a cognitive awareness tool, the "concept cloud" presents domain concepts and key phrases to the learners in order to help them reflect on their own learning and knowledge building. As part of a learning analytics toolset, the concept cloud also supports teachers in supervising their students' knowledge building from an epistemic perspective. The approach has been tested in a classroom scenario with 84 secondary high school students.
Control of the 1D continuous version of the Cucker-Smale model*

The well-known Cucker-Smale model is a microscopic system reproducing the alignment of velocities in a group of autonomous agents. Here, we focus on its mean-field limit, which we call the continuous Cucker-Smale model. It is a transport partial differential equation with nonlocal terms. For some choices of the parameters in the Cucker-Smale model (and the continuous one), alignment is not ensured for some initial configurations, therefore it is natural to study the enforcing of alignment via an external force. We provide a control strategy enforcing alignment for every initial data and acting only on a small portion of the crowd at each time. This is an adapted version of the sparse control for a finite number of agent, that is the constraint of acting on a small number of agents at each time.
Interprocedural Path Pro ling

In path pro ling, a program is instrumented with code that counts the number of times particular path fragments of the program are executed. This paper extends the intraprocedural path-pro ling technique of Ball and Larus to collect information about interprocedural paths (i.e., paths that may cross procedure boundaries).
iotechnological potential of respiring Zymomonas mobilis : A toichiometric analysis of its central metabolism gris Pentjussa

The active, yet energetically inefficient electron transport chain of the ethanologenic bacterium Zymomonas mobilis could be used in metabolic engineering for redox-balancing purposes during synthesis of certain products. Although several reconstructions of Z. mobilis metabolism have been published, important aspects of redox balance and aerobic catabolism have not previously been considered. Here, annotated genome sequences and metabolic reconstructions have been combined with existing biochemical evidence to yield a medium-scale model of Z. mobilis central metabolism in the form of COBRA Toolbox model files for flux balance analysis (FBA). The stoichiometric analysis presented here suggests the feasibility of several metabolic engineering strategies for obtaining high-value products, such as
SEGMENTATION OF AUTOMATICALLY TRANSCRIBED BROADCAST

Expertise in the automatic transcription of broadcast speech has progressed to the point of being able to use the resulting transcripts for information retrieval purposes. In this paper, we describe the Segmentation system used by Dragon Systems in the Segmentation task of the 1998 TDT evaluation, highlighting improvements made since the September 1998 dryrun. Segmentation of closed-caption and human transcripts of news is contrasted with the results of segmenting the ASR transcripts. This will be followed by a discussion of the metric, in particular how the value of the segmentation metric relates to the value of the tracking metric, when the these latter two tasks are performed on automatically segmented ASR text, rather than the ASR text with correctly marked boundaries.
A Survey: Clustering Ensemble Selection

Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area,it has been known that selective classifier ensembles can always achieve better solutions.Following the selective classifier ensembles,the question of clustering ensemble is defined as clustering ensemble selection.The paper introduces the concept of clustering ensemble selection and gives the survey of clustering ensemble selection algorithms.
Change We Can Believe In

People are motivated to defend and rationalize the status quo, a phenomenon known as system justification. We propose the existence of a second, countervailing system-level motivation: system-change motivation, which is concerned with bettering the status quo over time. The opportunity to receive diagnostic information about the status quo pits the two system-level motives against each other. Whereas system justification promotes a preference for positive information about the status quo, system-change motivation promotes a preference for negative information about the status quo. In three experiments, we found that people preferred negative over positive feedback about the status quo when it was presented as being changeable. Our findings are the first to suggest the operation of a system-change motive.
Automatic Representation and Segmentation of Video Sequences via a Novel Framework Based on the nD-EVM and Kohonen Networks

Recently in the Computer Vision field, a subject of interest, at least in almost every video application based on scene content, is video segmentation. Some of these applications are indexing, surveillance, medical imaging, event analysis, and computer-guided surgery, for naming some of them. To achieve their goals, these applications need meaningful information about a video sequence, in order to understand the events in its corresponding scene. Therefore, we need semantic information which can be obtained from objects of interest that are present in the scene. In order to recognize objects we need to compute features which aid the finding of similarities and dissimilarities, among other characteristics. For this reason, one of the most important tasks for video and image processing is segmentation. The segmentation process consists in separating data into groups that share similar features. Based on this, in this work we propose a novel framework for video representation and segmentation. The main workflow of this framework is given by the processing of an input frame sequence in order to obtain, as output, a segmented version. For video representation we use the Extreme Vertices Model in the -Dimensional Space while we use the Discrete Compactness descriptor as feature and Kohonen Self-Organizing Maps for segmentation purposes.
Neural Encoding of Acupuncture Needling Sensations: Evidence from a fMRI Study

Deqi response, a psychophysical response characterized by a spectrum of different needling sensations, is essential for Chinese acupuncture clinical efficacy. Previous neuroimaging research works have investigated the neural correlates of an overall deqi response by summating the scores of different needling sensations. However, the roles of individual sensations in brain activity and how they interact with each other remain to be clarified. In this study, we applied fMRI to investigate the neural correlates of individual components of deqi during acupuncture on the right LV3 (Taichong) acupoint. We selected a subset of deqi responses, namely, pressure, heaviness, fullness, numbness, and tingling. Using the individual components of deqi of different subjects as covariates in the analysis of percentage change of bold signal, pressure was found to be a striking sensation, contributing to most of negative activation of a limbic-paralimbic-neocortical network (LPNN). The similar or opposite neural activity in the heavily overlapping regions is found to be responding to different needling sensations, including bilateral LPNN, right orbitofrontal cortex, and bilateral posterior parietal cortex. These findings provide the neuroimaging evidence of how the individual needle sensations interact in the brain, showing that the modulatory effects of different needling sensations contribute to acupuncture modulations of LPNN network.
HADES: Hierarchical Approximate Decoding for Structured Prediction

Many fundamental tasks in machine learning require predicting complex objects rather than a simple yes-no answer or a number. Structured Output Prediction deals with learning such complex objects, which model an inherent structure between interdependent variables. Recently, large margin methods like Structured SVMs (SSVM) have gained popularity to solve this task due to efficient and generalized optimization techniques. These optimization algorithms typically rely on solving an inference or decoding sub-problem every iteration, which is computationally expensive. Moreover, little is known on learning a structured model by solving this sub-problem approximately. To address these issues, this thesis introduces a generalized technique to learn from a series of coarse-to-fine approximate candidates based on the recent Block-Coordinate Frank-Wolfe algorithm for Structured SVMs. The core observation we use is that one can learn to a reasonable degree, even from approximate solutions. This technique is presented in the context of a popular Computer Vision problem – Semantic Image Segmentation. We pose the decoding sub-problem to that of solving a series of increasingly complex surrogate Conditional Random Fields (CRF) in search for a candidate which meets the required approximation quality. We evaluate our technique in the context of natural scene image segmentation on the MSRC-21 dataset. Our experiments indicate that even extremely approximate solutions, which are 50x faster to decode, contribute to learning using our strategy. We achieve the same accuracy as our baseline and in addition, we reach a reasonable accuracy 1.5x-4x as quickly.