Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Analysis of the Concentration of Trifluridine in Tumor Hypoxic Regions Using a Novel Platform Combining Functional Endoscopy and Mass Spectrometry

Hypoxic regions in solid tumors are highly resistant to drugs and thus represents an obstacle in drug discovery. Currently, however, there are technical barriers in sampling human hypoxic tumors and examining drug delivery with high sensitivity and accuracy. Herein, we present a new platform combining functional endoscopy and highly sensitive liquid chromatography‐mass spectrometry (LC–MS) to assess drug delivery to hypoxic regions. Because oxygen saturation endoscopic imaging (OXEI), a functional endoscopy, can evaluate lesions and hypoxia in real‐time by simultaneously acquiring a pseudocolor map of oxygen saturation and conventional endoscopic images, this platform can be used to evaluate drug delivery with human samples from hypoxic regions. As the first clinical application of this platform, the relationship between hypoxic regions and the concentration of trifluridine (FTD) incorporated into DNA was evaluated in patients with advanced gastric cancer treated with FTD/tipiracil (FTD/TPI; n = 13) by obtaining and analysis of tissue samples by OXEI and LC–MS and vascular maturity index by CD31/α‐SMA staining ex vivo. The results showed that the concentration of FTD was significantly higher in the normoxic region than in the hypoxic region (P < 0.05) and there were significantly more immature vessels in hypoxic regions than in normoxic regions (P < 0.05). These results indicate that the platform was sufficiently sensitive to evaluate differences in drug anabolism in different oxygenic regions of human tumor tissue. This new platform allows quantitative drug analysis in hypoxic regions and is expected to initiate a new era of drug discovery and development.
Characteristics of Excess Pore Water Pressure of Nanjing Saturated Fine Sand Under the Interaction of Wave and Earthquake Loading

A stress path with continuous rotation of the principal stress direction and continuous alteration of amplitude of deviatoric stress difference under the interaction of wave and earthquake loading was proposed based on the characteristics of the stress path under wave and earthquake loading, respectively. Using a GDS dynamic hollow cylinder apparatus, a series of cyclic triaxial-torsional coupling shear tests were performed on Nanjing saturated fine sand via the stress path mentioned previously under different relative densities, effective initial confining pressures, plastic fines contents, and loading frequencies to study the development of excess pore water pressure (EPWP) of saturated sand under the interaction of wave and earthquake loading. It was found that the development of EPWP follows the trend of fast-steady-mutative-drastic, which is different from that under the cyclic triaxial test or wave loading. The number of cycles causing initial liquefaction (NL) of saturated sand increases remarkably with relative densities. However, the relationships between NL and effective initial confining pressures, plastic fines content, or loading frequencies are more complex.
Researching ecological impacts

Wildlife tourism seeks to increase people’s chances of exhilarating close encounters with wildlife in a context that also protects the animals. The methodologies developed by wildlife biologists to study the behaviour, ecology and management of wildlife are integral in achieving this aim and informing management of the best options to ensure the sustainability of wildlife tourism operations. An emphasis on testing predictions derived from hypotheses may be the most important approach wildlife biology can give to applied studies of the impacts of wildlife tourism. The experience of over 50 years of wildlife biology crystallised in the 1980s and 1990s into the view that research should collect experimental or observational data to test explicit predictions about the system under study. A concise formulation of this position is: . specific questions about nature. ordinarily stated as hypotheses, which are statements about how someone thinks that nature works. In other words, they contain implied predictions, and confirmation of those predictions is the most powerful means available to demonstrate their accuracy of our understanding of the world around us. (Hairston, 1989: 12) Hypotheses that do not yield testable predictions only describe events rather than explain them, so an emphasis on predictions links cause and effect explicitly and offers unambiguous advice to management (e.g. Bergerud, 1974; Romesberg, 1985; Caughley & Gunn, 1996; Ratti & Garton, 1996; Ford, 2000). In keeping with this philosophy, this chapter has five aims: • to outline a hypothesis-testing framework for answering questions about the impacts of wildlife tourism; • to demonstrate how testable predictions can be developed for assessing putative impacts of wildlife tourism; • to facilitate choice of study techniques by providing concise descriptions of the major research approaches and field techniques relevant to elucidating the impacts of wildlife tourism, with an indication of key references which expand these points more fully; • to outline key issues in data analysis, drawing conclusions and publishing the results of studies of the impacts of wildlife tourism; • to show how the hypothesis-testing framework can be integrated into adaptive management of wildlife tourism.
10Be measured in a GRIP snow pit and modeled using the ECHAM5‐HAM general circulation model

10Be measured in a Greenland Ice Core Project (GRIP) snow pit (1986–1990) with a seasonal resolution is compared with the ECHAM5–HAM GCM run. The mean modeled 10Be concentration in ice (1.0·104 atoms/g) agrees well with the measured value (1.2·104 atoms/g). The measured 10Be deposition flux (88 atoms/m2/s) also agrees well with the modeled flux (69 atoms/m2/s) and the measured precipitation rate (0.67 mm/day) agrees with the modeled rate (0.61 mm/day). The mean surface temperature of −31°C estimated from δ18O is lower than the temperature measured at a near‐by weather station (−29°C) and the modeled temperature (−26°C). During the 5‐year period the concentrations and deposition fluxes, both measured and modeled, show a decreasing trend consistent with the increase in the solar activity. The variability of the measured and modeled concentrations and deposition fluxes is very similar suggesting that the variability is linked to a variability in production rather than the local meteorology.
Probing Tissue Viscoelasticity With STL Ultrasound Shearwave Spectroscopy Using Cole–Cole Diagrams

Objective: Viscoelasticity is mapped by dispersion in shearwave elastography. Incomplete spectral information of shearwaves is therefore used to estimate mechanical stiffness. We propose capturing the “full-waveform-information” of the shear wave spectra to better resolve complex shear modulus <inline-formula><tex-math notation="LaTeX">$\mu ^{*} (\omega)$</tex-math></inline-formula>. Approach is validated on phantom models, animal tissues, and feasibility demonstrated on human post-delivery placenta. Methods: We captured robust estimates of <inline-formula><tex-math notation="LaTeX">$\mu ^{*}$</tex-math></inline-formula> in <italic>ex-vivo</italic> livers subjected to water bath ablation, glutaraldehyde exposure and in the placenta. Results: Complex modulus at 200 Hz is more reflective of tissue stiffness than cross-correlation estimate. Bias increased in phantoms with higher gelatin (G) (0.65: 6% G) and oil (O) (0.58: 6% G and 40% O) concentration, compared to elastic phantoms with low stiffness (0.33: 3% G). Actual tissues also reported higher bias in cross-correlation estimate (rabbit liver: 0.61, porcine liver: 2.20, and human placenta: 0.63). Stiffness is sensitive to ablation temperature, where the overall modulus changed from 3.02 KPa at 16 °C to 2.75 KPa at 56 °C in water bath. With exposure to Glutaraldehyde, the overall modulus increased from 2.37 to 9.03 KPa. Reconstruction errors in the loss modulus decreased by 68% with the power law compared to a Maxwell model in porcine livers with Cole-Cole inverse fitting. Conclusion: Omitting Shear wave attenuation leads to bias. Reconstruction of rheological response with a model is sensitive to its architecture and also the framework. Significance: We use “full spectral information” in ultrasound shear wave elastography to better map <inline-formula><tex-math notation="LaTeX">$\mu ^{*}(\omega)$</tex-math></inline-formula> changes in viscoelastic tissues.
The effective enhancement of information in 3D small-world networks of biological neuronal cells

The cardiovascular system, the kidney, or the brain, are examples of complex systems - where the properties of the systems arise because of the layout of cells in those systems. One way to characterize systems is using networks, where elements and interactions of the systems are represented as nodes and links of a graph. Network’s topology can be, in turn, measured by the small-world coefficient. Small world networks feature increased clustering and shorter paths compared to random or periodic networks of the same size. This suggests that systems with small world attributes can also efficiently transport signals, nutrients, or information within their body. While several reports in literature have illustrated that real biological systems are small-world, yet little is known about how information varies as a function of the small-world-ness ( sw ) of three dimensional graphs. Here, we used a model of the brain to estimate quantitatively the information processed in 3D networks. In the model, nodes of the graph are neuronal units capable to receive, integrate and transmit signals to other neurons of the system in parallel. The information encoded in the signals was then extracted using the techniques of information theory. In simulations where the topology of networks of 400 nodes was varied over large intervals, we found that in the 0−9 sw range information scales linearly with the small world coefficient, with a five-fold increase. Results of the paper and review of the existing literature on model organisms suggest that a small-world architecture may offer an evolutionary advantage to biological systems.
Antibiotic resistance in low- and middle-income countries: current practices and its global implications

ABSTRACT Introduction Antibiotic consumption rates are high in both high and low-income countries, but addressing antibiotic practices in low- and middle-income countries (LMICs) is crucial to combatting antibiotic resistance (AR). Area covered This editorial examines the current landscape of antibiotic practices in LMICs, investigate the factors driving such trends including lack of resource equity and shared global responsibility, and explore (AR) global implications, with particular emphasis placed on swift measures to combat its spread. Expert opinion LMICs face three key obstacles that exacerbate AR: inadequate WASH services, climate factors, and misuse of antibiotics. A lack of sanitation and clean water promotes infections, while poor hygiene exacerbates resistant pathogen spread. Global strategies should go beyond simply educating LMICs about antibiotic misuse; they must also understand its repercussions, such as prolonged illnesses and drug-resistant bacteria like MRSA. Addressing this challenge requires multifaceted strategies, including improving WASH services, acknowledging climate impacts, and tightening regulations. Prioritizing WASH requires significant funding, community participation, technology solutions, and partnerships with NGOs. Utilizing social media influencers can boost AR awareness. Antibiotic regulation reforms, manufacturing quality, and seeking antibiotic alternatives are vital. Antimicrobial Stewardship Programs and AI’s potential in managing resistant infections are notable.
Epitope Mapping of BmpA and BBK32 Borrelia burgdorferi Sensu Stricto Antigens for the Design of Chimeric Proteins with Potential Diagnostic Value

Lyme disease is a tick-borne zoonosis caused by Gram-negative bacteria belonging to the Borrelia burgdorferi sensu lato (s.l.) group. In this study, IgM- and IgG-specific linear epitopes of two B. burgdorferi sensu stricto (s.s.) antigens BmpA and BBK32 were mapped using a polypeptide array. Subsequently, two chimeric proteins BmpA-BBK32-M and BmpA-BBK32-G were designed to validate the construction of chimeras using the identified epitopes for the detection of IgM and IgG, respectively, by ELISA. IgG-ELISA based on the BmpA-BBK32-G antigen showed 71% sensitivity and 95% specificity, whereas a slightly lower diagnostic utility was obtained for IgM-ELISA based on BmpA-BBK32-M, where the sensitivity was also 71% but the specificity decreased to 89%. The reactivity of chimeric proteins with nondedicated antibodies was much lower. These results suggest that the identified epitopes may be useful in the design of new forms of antigens to increase the effectiveness of Lyme disease serodiagnosis. It has also been proven that appropriate selection of epitopes enables the construction of chimeric proteins exhibiting reactivity with a specific antibody isotype.
Measuring health-related stigma: Exploring challenges and research priorities to improve assessment

ABSTRACT Despite the advances in stigma research, measuring health-related stigma continues to be challenging and knowledge gaps remain. This study gained insight into challenges and research priorities related to the assessment of health-related stigma. Interviews were conducted with 14 stigma researchers, followed by a survey that was completed by 36 respondents. The findings showed a diverse range of research priorities. Among the top ranked priorities were the need for robust measurement properties of existing scales (content validity, responsiveness, validation across settings), exploration and assessment of subtle changes in stigma, and investigation on ways to assess actual behaviour and discrimination. Various challenges with the cross-cultural use of measures were identified, along with a research opportunity to shorten the cross-cultural validation process. Other identified research priorities related to: studying multi-level intersectional stigma; focusing on positive features that counter stigma; rephrasing negative and offending scale items; developing generic measures; and, the further development of practical tools to support researchers with scale implementation. The defined research priorities can guide future studies to advance stigma measurements and, as our ability to measure is critical for our understanding, enhance our knowledge about the complex stigma processes.
Emerging roles of RNA binding proteins in intervertebral disc degeneration and osteoarthritis

The etiology of intervertebral disc degeneration (IDD) and osteoarthritis (OA) is complex and multifactorial. Both predisposing genes and environmental factors are involved in the pathogenesis of IDD and OA. Moreover, epigenetic modifications affect the development of IDD and OA. Dysregulated phenotypes of nucleus pulposus (NP) cells and OA chondrocytes, including apoptosis, extracellular matrix disruption, inflammation, and angiogenesis, are involved at all developmental stages of IDD and OA. RNA binding proteins (RBPs) have recently been recognized as essential post‐transcriptional regulators of gene expression. RBPs are implicated in many cellular processes, such as proliferation, differentiation, and apoptosis. Recently, several RBPs have been reported to be associated with the pathogenesis of IDD and OA. This review briefly summarizes the current knowledge on the RNA‐regulatory networks controlled by RBPs and their potential roles in the pathogenesis of IDD and OA. These initial findings support the idea that specific modulation of RBPs represents a promising approach for managing IDD and OA.
GENEA Workshop 2023: The 4th Workshop on Generation and Evaluation of Non-verbal Behaviour for Embodied Agents

Non-verbal behavior is advantageous for embodied agents when interacting with humans. Despite many years of research on the generation of non-verbal behavior, there is no established benchmarking practice in the field. Most researchers do not compare their results to prior work, and if they do, they often do so in a manner that is not compatible with other approaches. The GENEA Workshop 2023 seeks to bring the community together to discuss the major challenges and solutions, and to identify the best ways to progress the field.
Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction

For machines to effectively assist humans in challenging visual search tasks, they must differentiate whether a human is simply glancing into a scene (navigational intent) or searching for a target object (informational intent). Previous research proposed combining electroencephalography (EEG) and eye-tracking measurements to recognize such search intents implicitly, i.e., without explicit user input. However, the applicability of these approaches to real-world scenarios suffers from two key limitations. First, previous work used fixed search times in the informational intent condition - a stark contrast to visual search, which naturally terminates when the target is found. Second, methods incorporating EEG measurements addressed prediction scenarios that require ground truth training data from the target user, which is impractical in many use cases. We address these limitations by making the first publicly available EEG and eye-tracking dataset for navigational vs. informational intent recognition, where the user determines search times. We present the first method for cross-user prediction of search intents from EEG and eye-tracking recordings and reach accuracy in leave-one-user-out evaluations - comparable to within-user prediction accuracy () but offering much greater flexibility.
SolareSkin: Self-powered Visible Light Sensing Through a Solar Cell E-Skin

SolareSkin is a self-powered and ubiquitous electronic skin equipped with ultraflexible organic solar cells for visible light sensing and energy harvesting. This dual-functional system captures light signals, transforms them into electrical impulses and enables multi-class gesture and activity recognition. Its design employs a photocurrent model that allows solar cells to serve as energy harvesters and visible light sensors simultaneously. The solar cells demonstrate a decent conversion rate of incident light into electricity, supporting an efficient, sustainable operation. Additionally, the system incorporates advanced system integration with a low-powered data collection board embedded with wireless transmission modules and an intuitive user interface. An algorithm is employed for signal analysis with data pre-processing methods and several machine learning models. The data pre-processing methods comprise filtering, scaling, normalization, segmentation, and downsampling of raw sensor data to reduce noise and increase prediction accuracy. The machine learning model evaluation focuses on three algorithms: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest, due to their efficiency with high-dimensional, nonlinear data. The experimental results suggest the excellent performance of SVM in recognizing 7-class finger gestures and 7-class body activities, with accuracies of 97.3% and 96.7%, respectively. This advancement in electronic skin technology is promising for ubiquitous human-centric sensing, enabling various applications such as healthcare monitoring, human-computer interaction, and smart homes.
GSR Based Generic Stress Prediction System

Stress detection is important for ensuring overall mental well-being of an individual. Literature suggests several approaches for prediction or classification of stress. However, the performance of these approaches varies a lot across subjects and tasks. Moreover, perception of stress is highly subjective and hence it is difficult to create a generic model/devices for prediction of stress. In this study, we have proposed an approach for creating a generic stress prediction model by combining the knowledge and variety from multiple public datasets containing galvanic skin response (GSR) data recorded during different context and activities. Most significant features are selected from these recorded signals and a voting based approach was finally adopted to develop a model for predicting mental stress. Proposed model has been validated using test data as well as a set of completely unseen data collected in our lab. We achieved an average classification accuracy of 89% (F-score 0.87) for test data and similar performance for completely unseen data as well. Results show that the proposed model outperforms the training models created using individual datasets. In addition, our model is created using skin response data recorded using off-the-shelf devices. Thus, our proposed model with selected feature set can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.
Influence of hand representation on a grasping task in augmented reality

Research has shown that modifying the aspect of the virtual hand in immersive virtual reality can convey objects properties to users. Whether we can achieve the same results in augmented reality is still to be determined since the user’s real hand is visible through the headset. Although displaying a virtual hand in augmented reality is usually not recommended, it could positively impact the user effectiveness or appreciation of the application. For this purpose, we propose an algorithm to compute virtual hand shape in AR, based on inverse kinematics and physical constraints. It allows to naturally grasp virtual objects while keeping the virtual hand on their surface. We compare the influence of this hand representation on performance and user experience in a grasping task in AR, with two control conditions: a simple virtual hand that follows the real hand and a baseline condition without a virtual hand. Results on 48 participants show that all virtual hand conditions decreased user performance, but enhanced the satisfaction with the task.
Analyzing Synergetic Functional Spectrum from Head Movements and Facial Expressions in Conversations

A framework, synergetic functional spectrum analysis (sFSA), is proposed to reveal how multimodal nonverbal behaviors such as head movements and facial expressions cooperatively perform communicative functions in conversations. We first introduce a functional spectrum to represent the functional multiplicity and ambiguity in nonverbal behaviors, e.g., a nod could imply listening, agreement, or both. More specifically, the functional spectrum is defined as the distribution of perceptual intensities of multiple functions across multiple modalities, which are based on multiple raters’ judgments. Next, the functional spectrum is decomposed into a small basis set called the synergetic functional basis, which can characterize primary and distinctive multimodal functionalities and span a synergetic functional space. Using these bases, the input spectrum is approximated as a linear combination of the bases and corresponding coefficients, which represent the coordinate in the functional space. To that purpose, this paper proposes semi-orthogonal nonnegative matrix factorization (SO-NMF) and discovers some essential multimodal synergies in the listener’s back-channel, thinking, positive responses, and speaker’s thinking and addressing. Furthermore, we proposes regression models based on convolutional neural networks (CNNs) to estimate the functional space coordinates from head movements and facial action units, and confirm the potential of the sFSA.