Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of [11C]BIIB104, an α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic-Acid-Positive Allosteric Modulator, and Evaluation of the Bio-Distribution in Non-Human Primate Brains Using Positron Emission Tomography

The aim of this study was to measure the brain penetrance and kinetics of BIIB104, a first-in-class AMPA receptor potentiator developed for cognitive impairment associated with schizophrenia. It was recently halted in phase 2 clinical development, and there are a lack of tools to directly measure AMPA receptor engagement. To achieve this, the drug candidate was radiolabeled with carbon-11, and its brain penetrance and kinetics were measured in non-human primates via dynamic PET scans. Radiolabeling was achieved through a three-step nucleophilic [11C]cyanation reaction in one pot, resulting in the high radioactivity and radiochemical purity (>99%) of [11C]BIIB104. The study found that [11C]BIIB104 entered the non-human primate brains at 4–5% ID at peak, with a homogeneous distribution. However, a mild regional heterogeneity was observed in the thalamus. The lack of conclusive evidence for a change in regional values after BIIB104 dosing suggests that any specific binding component of BIIB104 is negligible compared to the free and non-specific components in the living brain. Overall, the study demonstrated high brain uptake with minor variability in [11C]BIIB104 distribution across various brain regions, its kinetics were consistent with those of passive diffusion, and the dominating components were the free concentration and non-specific binding. This information is valuable for understanding the potential effects and mechanisms of BIIB104 in the brain.
Photoplethysmography for Assessing Microcirculation in Hypertensive Patients After Taking Antihypertensive Drugs: A Review

Abstract High blood pressure (BP) is a common disease and is associated with many chronic diseases. Measuring BP is essential for the treatment and management of many diseases, and therefore there is a growing need for a non-invasive, sleeveless and continuous BP monitoring device. With the development of technology, pulse waveform analysis using photoplethysmography (PPG) has become more feasible for evaluating BP. This study aimed to evaluate the changes of vascular elasticity and blood volume over time by using the characteristic parameters extracted by PPG. We reviewed the latest progress and literature on the observation of capillary network characteristics in hypertensive and non-hypertensive patients by PPG, the influence of different drugs on microcirculation characteristics in hypertensive patients with PPG, and further explored the key relationship between microcirculation and hypertension. We found that the PPG waveform produced by the fingertips of hypertensive patients is very different from that of healthy people, and the PPG waveform changes significantly during diastolic period after antihypertensive treatment. With the rapid development of medical technology, people can get more intuitive microcirculation image data, which provides beneficial help for the comprehensive understanding of hypertension.
PortBlocker: Detection and Mitigation of Hardware Trojan through Re-routing and Bypassing

The ever-evolving semiconductor industry demands swift adaptation to meet dynamic market requirements. Companies that do not have expertise in the entire supply chain process of realizing semiconductor integrated circuits often rely on third-party intellectual property (IP) to cater to the market demands. The dependence on external vendors exposes chips to vulnerabilities like Hardware Trojans (HT) that compromise the system’s performance. Network On Chip (NoC), the backbone of modern multicore processors is prone to such HT attacks due to the extensive use of third-party IPs. In this paper, we model a packet misrouting HT for a NoC framework and study its impact at the core, cache, and interconnect levels. We also detect the presence of such HTs using traffic profiling. Our mitigation technique called PortBlocker ensures packets reach their destination through an alternate path bypassing the HT router. Experimental analysis using SPEC CPU 2006 benchmark workloads shows the effectiveness of our approach for various packet categories.
Saving the Day for Users in Web Platforms: A Chatbot-based Solution for Privacy

In the rapidly evolving online digital landscape, privacy is an issue of great importance for individuals, while the use of web platforms handling personal data has reached significant levels. Regulations like the European Union General Data Protection Regulation (GDPR) enforce systems to integrate an array of privacy features allowing users to exercise their privacy rights and shield their personal data. However, users tend to find themselves overwhelmed or even ignorant, unsure of how to locate and use them. Research also showed that users find privacy policies hard to read and tend to skip them. This work introduces a novel approach to addressing these privacy concerns by presenting a user-centric solution to enhancing user privacy in web platforms and empowering users in handling their own personal data: an easy-to-use chatbot-based solution. The tool aims to provide a user-friendly and intuitive all-in-one interface, allowing users to easily navigate and access privacy features and manage their personal data while retrieving information about privacy aspects effortlessly from one location. An admin panel enables the customisation of important privacy parameters. We present the design and development process, evaluation and results.
Finding Potential Research Collaborations from Social Networks Derived from Topic Models

Community detection is a valuable tool for analyzing social networks given its potential for identifying groups with common characteristics and common interests. In this work, we focused on detecting scholarly communities based on researchers’ publication data to discover interdisciplinary collaboration recommendations (researchers working on different domains). Specifically, instead of using any physical or direct relationship between researchers, we utilized a topic model to obtain the topic-based similarity between researchers to construct the social network graph. Next, we employed an edge-weakening procedure to alter the initially constructed network to uncover a more refined community structure. Two community detection algorithms, Louvain and Spectral clustering, were utilized to find the community structures in the modified network. The results of our experiments revealed the ability to discover possible research communities for both algorithms that were comparable, which suggests that our method has the potential for identifying hidden interdisciplinary research collaboration recommendations using topical relationships as the basis for building and analyzing the social network graph.
The Important Factors Influence the Interest of Tourists to Revisit Tourism Destinations at the District Level in Indonesia

Several important factors supporting regional tourist destinations need to be developed so that tourists are interested in visiting or revisiting a tourist attraction. The local government has done this, but the construction and development carried out has not fully met the wishes of tourists. This research aims to see what important factors need to be developed in accordance with tourists’ expectations so that they are interested in visiting or returning. The method used is a study of several regional regulatory documents and related journals and SEM PLS is used to process data and examine important factors that need attention and priority scale. By paying attention to the point of view of tourists who have visited Gunungkidul tourism, the supporting factors that need attention from the local government are infrastructure, views and attractions, information, security, accommodation, and transportation. Two important factors that have an influence but do not satisfy tourists are transportation, especially between tourist destinations, while in terms of information, regions are still not taking advantage of digital promotions and services.
TEXT CLASSIFICATION USING INDOBERT FINE-TUNING MODELING WITH CONVOLUTIONAL NEURAL NETWORK AND BI-LSTM

The technological advancements in goods delivery facilities have been increasing year by year in tandem with the growing online trade, which necessitates delivery services to fulfill the transactional process between sellers and buyers. Since 2000, top brand awards have often conducted official survey analyses to provide comparisons of goods or services, one of which includes delivery services. However, the survey rankings based on public opinion are less accurate due to users of delivery services and service companies being unaware of the specific success factors and weaknesses in their services. The aim of this research is to analyze the comparison of text mining using the Indonesian language transformation method, IndoBert. The algorithm utilized to demonstrate analysis performance employs Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). This method is utilized to determine the impact of opinion data from Twitter on the J&T Express expedition delivery service, incorporating both text preprocessing and data without text preprocessing. The IndoBert parameters vary in the learning rate section based on four factors: price, time, returns, and others. The research data consisted of 2525 comments from Twitter users regarding the delivery service spanning from January 1, 2021, to March 31, 2023. The testing showed that Bi-LSTM with text preprocessing performed 2% higher, achieving 79% at a learning rate of 1x10-6, compared to without text preprocessing at the same learning rate, which reached 77%. Additionally, CNN outperformed by 3% with a rate of 83%, compared to 80% without text preprocessing at a learning rate of 1x10-5. The highest accuracy, reaching 83%, was obtained by CNN with parameters set at 1x10-5, and the preprocessing technique was considered superior to Bi-LSTM.
High‐resolution NO2 observations from the Airborne Compact Atmospheric Mapper: Retrieval and validation

Nitrogen dioxide (NO2) is a short‐lived atmospheric pollutant that serves as an air quality indicator and is itself a health concern. The Airborne Compact Atmospheric Mapper (ACAM) was flown on board the NASA UC‐12 aircraft during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality Maryland field campaign in July 2011. The instrument collected hyperspectral remote sensing measurements in the 304–910 nm range, allowing daytime observations of several tropospheric pollutants, including nitrogen dioxide (NO2), at an unprecedented spatial resolution of 1.5 × 1.1 km2. Retrievals of slant column abundance are based on the differential optical absorption spectroscopy method. For the air mass factor computations needed to convert these retrievals to vertical column abundance, we include high‐resolution information for the surface reflectivity by using bidirectional reflectance distribution function data from the Moderate Resolution Imaging Spectroradiometer. We use high‐resolution simulated vertical distributions of NO2 from the Community Multiscale Air Quality and Global Modeling Initiative models to account for the temporal variation in atmospheric NO2 to retrieve middle and lower tropospheric NO2 columns (NO2 below the aircraft). We compare NO2 derived from ACAM measurements with in situ observations from NASA's P‐3B research aircraft, total column observations from the ground‐based Pandora spectrometers, and tropospheric column observations from the space‐based Ozone Monitoring Instrument. The high‐resolution ACAM measurements not only give new insights into our understanding of atmospheric composition and chemistry through observation of subsampling variability in typical satellite and model resolutions, but they also provide opportunities for testing algorithm improvements for forthcoming geostationary air quality missions.
The impact of insect egg deposition on Pinus sylvestris transcriptomic and phytohormonal responses to larval herbivory

Abstract Plants can improve their resistance to feeding damage by insects if they have perceived insect egg deposition prior to larval feeding. Molecular analyses of these egg-mediated defence mechanisms have until now focused on angiosperm species. It is unknown how the transcriptome of a gymnosperm species responds to insect eggs and subsequent larval feeding. Scots pine (Pinus sylvestris L.) is known to improve its defences against larvae of the herbivorous sawfly Diprion pini L. if it has previously received sawfly eggs. Here, we analysed the transcriptomic and phytohormonal responses of Scots pine needles to D. pini eggs (E-pine), larval feeding (F-pine) and to both eggs and larval feeding (EF-pine). Pine showed strong transcriptomic responses to sawfly eggs and—as expected—to larval feeding. Many egg-responsive genes were also differentially expressed in response to feeding damage, and these genes play an important role in biological processes related to cell wall modification, cell death and jasmonic acid signalling. EF-pine showed fewer transcriptomic changes than F-pine, whereas EF-treated angiosperm species studied so far showed more transcriptional changes to the initial phase of larval feeding than only feeding-damaged F-angiosperms. However, as with responses of EF-angiosperms, EF-pine showed higher salicylic acid concentrations than F-pine. Based on the considerable overlap of the transcriptomes of E- and F-pine, we suggest that the weaker transcriptomic response of EF-pine than F-pine to larval feeding damage is compensated by the strong, egg-induced response, which might result in maintained pine defences against larval feeding.
Physiological correlates of stress induced by virtual humans in a naturalistic virtual reality scenario

The ability to induce various levels of stress is central to understanding and modeling stress related responses. Virtual reality holds much promise for eliciting stress in a real-life yet controllable environment. ; this requires compelling, virtual social environments with interactive virtual humans. In this study we use semi-automated virtual humans to simulate a realistic stressful event from everyday life – a job interview. Furthermore, using this environment we derive a dynamic physiological index of ongoing stress. Twenty-seven participants underwent an interactive VR interview while their electrocardiogram, skin conductance, respiration, and head motion were recorded. Our goal was to determine whether these signals were predictive of participants’ momentary stress level, which were reported in a post experiment session. For that aim, we extracted 21 physiological features and tested their correlation with post-hoc reported stress per event. Our analysis indicated that the most informative feature was the magnitude of heart rate deceleration, which positively correlated with the reported stress level (r=0.61, p=0.008, averaged over all participants). We discuss the implications of our findings for applications based on the combination of interactive virtual humans in VR with real time physiology.
Fostering Parent-Child Interactions through Behavioral Understanding of Synchrony

High-quality, social interactions between parents and their children are crucial for young children’s development. In order to develop affective, intelligent interactions between parents and children, it is important to understand how the parent’s nonverbal behavior influences the child’s affective state. In this paper, we explore the role of a parent’s nonverbal cues on a child’s engagement during an educational, dialogic reading interaction. We specifically focus on using synchrony in the parent and child’s body pose features and affective states to better inform a child’s engagement during the task. To understand the moments when children lose engagement in a task, we qualitatively assessed video clips and found that those moments are associated with either the parent or the child exhibiting actions unrelated to reading the stories, such as yawning or the parent checking their cellphone. Finally, we provide an overview of design implications for parent-child facilitator AI systems as well as future research directions in this area.
Grounding the Evaluation of Affect Recognition Models Beyond Dataset-Based Ground Truths

We propose a new methodology to evaluate zero-shot visual affect recognition models based on large vision-language foundation models. The motivation for this new evaluation methodology is to be able of evaluating models beyond assessing their performance on a closed emotion dataset. Concretely, we introduce the concept of Partial Credit Matrix, which allows to reassess the performance of the models by rebalancing the results under the lens of different psychology theories. In this way, the evaluation of models incorporates more semantic variability, and is less limited by the reduced emotion taxonomies used in closed datasets. The proposed evaluation methodology also incorporates variability into the input of the models to study the stability of the results. Our experiments on CLIP, ALIGN, and LAION show different patterns on the performance of each model. Our code is available at https://github.com/ccivit/partial_credit_evaluation.
Weightbearing Imaging Assessment of Midfoot Instability in Patients with Confirmed Hallux Valgus Deformity: A Systematic Review of the Literature

Hallux valgus deformity (HVD) involves subluxation of the first metatarsophalangeal joint. While HVD is primarily considered a forefoot condition, midfoot instability may play a significant role in its development and severity. However, very few studies have placed a heavy emphasis on studying this phenomenon. Therefore, this review had a particular focus on understanding midfoot instability based on weightbearing imaging assessments of the TMT joint. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and searched five databases for studies on midfoot instability in HVD patients. The severity of HVD was defined by hallux valgus angle (HVA) and distal metatarsal articular angle (DMAA). Data was extracted, and articles were graded using the Methodological Index for Non-Randomized Studies (MINORS). Of 547 initially retrieved articles, 23 met the inclusion criteria. Patients with HVD showed higher HVA and DMAA on weightbearing radiographs (WBRG) and weightbearing computed tomography (WBCT) compared to healthy individuals. Midfoot instability was assessed through intermetatarsal angle (IMA) and tarsometatarsal angle (TMT angle). Patients with HVD exhibited greater IMA and TMT angles on both WBRG and WBCT. This review highlights the importance of weightbearing imaging assessments for midfoot instability in HVD. IMA and TMT angles can differentiate between healthy individuals and HVD patients, emphasizing the significance of midfoot assessment in understanding HVD pathology. These findings validate the limited evidence thus far in the literature pertaining to consistent midfoot instability in HVD patients and are able to provide ample reasoning for physicians to place a larger emphasis on midfoot imaging when assessing HVD in its entirety.
Site-specific Probabilistic Seismic Hazard Map of Himachal Pradesh, India. Part II. Hazard Estimation

This article presents site-specific probable seismic hazard of the Himachal Pradesh province, situated in a seismically active region of northwest Himalaya, using the ground motion relations presented in a companion article. Seismic recurrence parameters for all the documented probable sources are established from an updated earthquake catalogue. The contour maps of probable spectral acceleration at 0, 0.2, and 1 s (5% damping) are presented for 475 and 2475 years return periods. Also, the hazard curves and uniform hazard response spectrums are presented for all the important cities in this province. Results indicate that the present codal provision underestimates the seismic hazard at cities of Bilaspur, Shimla, Hamirpur, Chamba, Mandi, and Solan. In addition, regions near Bilaspur and Chamba exhibit higher hazard levels than what is reported in literature.
Critical Thinking. Undeveloped Competence in Teachers? A Review

Purpose: In a framework of the notoriety of the lack of critical thinking competence in teachers, it was intended to give approximations that allow visualizing the formative failures that lead to the precariousness of developing this thinking in young students. Method: An exhaustive systematic review of 27 scientific articles was made using the PRISMA methodology and the Atlas ti® tool to identify and code subcategories. Results and conclusion: The results showed that this competence was not developed in many teachers and, therefore, the promotion of this competence in the students of these teachers is not applied either, having as a good example to follow the action-research interventions that some authors carried out. Research implications: Likewise, the educational systems where the studies were registered were described as a barrier instead of an incentive. University educational management should give greater consideration to the systematization and transmission of critical thinking promotion strategies in teachers, designing a mechanism for recurrent support to achieve greater effectiveness in implementation with students. Originality/value:That situation led to the reflection that the curricular plans of university students must be reviewed to improve the development and strengthening of this thought, starting with teachers. However, it should be noted that very few contributed practical strategies that could be implemented for this purpose.
A Novel Endmember Bundle Extraction Framework for Capturing Endmember Variability by Dynamic Optimization

The spectral variability problem is a big challenge in hyperspectral unmixing. Endmember bundles have been used to address the spectral variability problem by adopting a bundle of endmember spectra to represent one kind of endmember class. Existing endmember bundle extraction (EBE) algorithms mainly rely on the convex geometry assumption and integrate endmembers from image subsets as endmember bundles. On the one hand, they suffer from high risk of bad performance for real hyperspectral scene where the convex geometry assumption is not satisfied. On the other hand, endmember variabilities within image subsets are neglected, which may lose representative endmembers. In this article, we propose a novel EBE framework to capture endmember variability by introducing a dynamic optimization mechanism. Endmember bundles are obtained by dynamically minimizing the root-mean-square error (RMSE) between original pixels and reconstructed pixels through an iteration process, and a particle swarm optimization (PSO) method is introduced to find the optimal endmember combination in each iteration. The proposed EBE framework imposes no assumption on the hyperspectral data distribution and has great potential to be used in complex hyperspectral scenes. Experimental results on two real hyperspectral datasets demonstrate that the proposed algorithm is able to obtain endmember bundles that well express the spectral variability, and the performance of the proposed algorithm is competitive with the state-of-the-art algorithms.