Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascaded Semantic Fractionation for identifying a domain in social media

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes.
Low Profile Orthomode Full-Duplex Antenna for Wearable Biomedical Communication

A low-profile monolayer dual-port orthomode antenna is presented for enhanced in-band full-duplex (IBFD) communication of wearable biomedical devices. The proposed antenna consists of a center patch surrounded by a complementary slot within a larger peripheral patch. The two ports, which are at opposite lateral sides in a coaxial configuration, are responsible for the Tx/Rx channels respectively. By implementing an asymmetric displacement from the geometric axis, an odd surface current mode is excited via Port 2 (Rx mode), while the even mode is achieved through Port 1 (Tx mode). This mode orthogonality implementation allows a high isolation of > 35 dB between Tx/Rx channels across the central frequency of 6 GHz without circuit elements. In addition, a −10 dB bandwidth of 280 MHz (4.7%) and 480 MHz (8%) is achieved for Port 1 and 2 respectively which supports stable communication channels. The antenna is verified to simultaneously support a unidirectional radiation pattern towards the broadside (Port 1) and a multidirectional hybrid radiation pattern (Port 2) composed of bidirectional $y$ -pol radiation (120° and 60°) as well as broadside $x$ -pol radiation. Both Tx/Rx channels achieve a large front-to-back ratio of > 15 dB. The free space characterization results agree well with the full-wave simulations. Furthermore, the odd/even modes are determined to be stable under local deformations and human loading effects. In conclusion, this monolayer orthomode full-duplex antenna is suitable for various wearable applications due to its low-profile, highly manufacturable design and robustness to environmental imperfections.
Expanding the Armamentarium of Donor Sites in Microvascular Head and Neck Reconstruction

The field of microsurgical head and neck reconstruction has witnessed tremendous advancements in recent years. While the historic goals of reconstruction were simply to maximize flap survival, optimizing both aesthetic and functional outcomes has now become the priority. With an increased understanding of perforator anatomy, improved technology in instruments and microscopes, and high flap success rates, the reconstructive microsurgeon can push the envelope in harvesting and designing the ideal flap to aid patients following tumor extirpation. Furthermore, with improvements in cancer treatment leading to improved patient survival and prognosis, it becomes increasingly important to have a broader repertoire of donor sites. The present review aims to provide a review of newly emerging soft tissue flap options in head and neck reconstruction. While certainly a number of bony flap options also exist, the present review will focus on soft tissue flaps that can be harvested reliably from a variety of alternate donor sites. From the upper extremity, the ulnar forearm as well as the lateral arm, and from the lower extremity, the profunda artery perforator, medial sural artery perforator, and superficial circumflex iliac perforator flaps will be discussed, and we will provide details to aid reconstructive microsurgeons in incorporating these alternative flaps into their armamentarium.
Recent Trends in the Synthesis and Bioactivity of Coumarin, Coumarin–Chalcone, and Coumarin–Triazole Molecular Hybrids

Molecular hybridization represents a new approach in drug discovery in which specific chromophores are strategically combined to create novel drugs with enhanced therapeutic effects. This innovative strategy leverages the strengths of individual chromophores to address complex biological challenges, synergize beneficial properties, optimize pharmacokinetics, and overcome limitations associated with single-agent therapies. Coumarins are documented to possess several bioactivities and have therefore been targeted for combination with other active moieties to create molecular hybrids. This review summarizes recent (2013–2023) trends in the synthesis of coumarins, as well as coumarin–chalcone and coumarin–triazole molecular hybrids. To cover the wide aspects of this area, we have included differently substituted coumarins, chalcones, 1,2,3– and 1,2,4–triazoles in this review and considered the point of fusion/attachment with coumarin to show the diversity of these hybrids. The reported syntheses mainly relied on well-established chemistry without the need for strict reaction conditions and usually produced high yields. Additionally, we discussed the bioactivities of the reported compounds, including antioxidative, antimicrobial, anticancer, antidiabetic, and anti-cholinesterase activities and commented on their IC50 where possible. Promising bioactivity results have been obtained so far. It is noted that mechanistic studies are infrequently found in the published work, which was also mentioned in this review to give the reader a better understanding. This review aims to provide valuable information to enable further developments in this field.
Effect of Dung Quantity and Quality on Greenhouse Gas Fluxes From Tropical Pastures in Kenya

To improve estimates of agricultural greenhouse gas emissions in sub‐Saharan Africa, we measured over six individual periods of 25–29 days fluxes of methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) with subdaily time resolution from dung patches of different quality (C/N ratio: 23–41) and quantity (0.5 and 1.0 kg) on a Kenyan rangeland during dry and wet seasons. Methane emissions peaked following dung application, whereas N2O and CO2 fluxes from dung patches were similar to fluxes from rangeland soils receiving no N additions. Greenhouse gas emissions scaled linearly with dung quantity during both seasons. Dung with a low (23) C/N ratio produced up to 10 times more CH4 than dung with a high (41) C/N ratio. Overall, CH4 emission factors (EFs) ranged from 0.001 to 0.042%, lower than those derived in temperate regions. Cumulative CO2 and N2O emissions were similar for all treatments across the different seasons. The N2O EF ranged from 0 to 0.01%, less than 1% of the Intergovernmental Panel on Climate Change Tier 1 default EF (2%) for N2O emissions from dung and urine patches, likely because of the low dung N content (9.7–16.5 g N kg−1 dry matter). However, these results were consistent with the updated cattle dung EF (0.2%) developed for Kenya in 2016/2017 (EF database ID# 422665). In view of the wide range of climates, soils, and management practices across sub‐Saharan Africa, development of robust GHG EFs from dung patches for SSA requires additional studies.
Screening TLR4 Binding Peptide from Naja atra Venom Glands Based on Phage Display

Toll-like receptor 4 (TLR4) is a crucial inflammatory signaling pathway that can serve as a potential treatment target for various disorders. A number of inhibitors have been developed for the TLR4 pathway, and although no inhibitors have been approved for clinical use, most have been screened against the TLR4-MD2 conformation. The venom gland is the organ of venomous snakes that secretes substances that are toxic to other animals. The level of gene transcription in venom glands is different from that in other tissues, includes a large number of biologically active ingredients, and is an important natural resource for the development of new drugs. We constructed a T7 phage display library using the cobra (Naja atra) venom gland from the Guangdong Snake Breeding Plant and performed three rounds of screening with TLR4 as the target, randomly selecting monoclonal phage spots for PCR followed by Sanger sequencing. The obtained sequences were subjected to length analysis, molecular docking, solubility prediction, and stability prediction, and a peptide containing 39 amino acids (NA39) was finally screened out. The BLAST results indicated that NA39 was a sequence in RPL19 (Ribosomal Protein L19). After peptide synthesis, the binding ability of NA39 to TLR4 was verified by the surface plasmon resonance (SPR) technique. In this study, a new peptide that can specifically bind TLR4 was successfully screened from the cobra venom gland cDNA library, further demonstrating the effectiveness of phage display technology in the field of drug discovery.
Evaluating the Non-Monetary Impacts of Major Events, Infrastructure, and Institutions

Dans ma these, j'utilise des methodes recentes de microeconometrie appliquee pour evaluer les impacts des evenements majeurs (catastrophe de Fukushima Daiichi, Jeux Olympiques), de l'infrastructure (utilisation des terrains urbains, eoliennes) et des institutions dans les systemes educatifs) sur le bien-etre individuel, la sante et le comportement. Tout au long de mes articles, j'utilise des donnees longitudinales sur les menages, en partie fusionnees avec des donnees spatiales tres detaillees, tout en accordant une attention particuliere a l'identification des effets causaux.
Comparison of En Masse Repair versus Separate Double-Layer Repair for Delaminated Rotator Cuff Tears: A Systematic Review and Meta-Analysis

Background: Delamination of cuff tendons has a negative impact on outcomes following arthroscopic rotator cuff repair (RCR). The purpose of this study is to compare en masse repair (EMR) and separate double-layer repair (SDLR) for delaminated rotator cuff tears. Methods: A systematic literature search was conducted on major databases (MEDLINE/PubMed, EMBASE, Cochrane Library, and Scopus) until 1 June 2023. Comparative studies with a minimum 24-month follow-up of patients undergoing arthroscopic RCR for delaminated tears were included. The outcomes assessed retear rates and functional outcomes. Results: Five eligible studies involving 325 cases were analyzed. The meta-analysis showed no significant difference in retear rates between SDLR and EMR for delaminated tears (OR = 0.73, 95% CI: 0.35–1.49). However, the meta-analysis demonstrated a significant intergroup difference in favor of the SDLR for the total Constant score (SMD = 0.68, 95% CI: 0.35 to 1.02), SST score (SMD = 0.37, 95% CI: 0.02 to 0.71), and postoperative range of abduction (SMD = 0.34, 95% CI: 0.03 to 0.64). Conclusion: The evidence suggests that the SDLR in arthroscopic RCR for delaminated rotator cuff tears leads to improved short-term functional outcomes and range of motion compared to EMR. However, there is no significant difference in retear risk between the two approaches.
Coupling of Precipitation and Cloud Structures in Oceanic Extratropical Cyclones to Large-Scale Moisture Flux Convergence

Precipitation (from TMPA) and cloud structures (from MODIS) in extratropical cyclones (ETCs) are modulated by phases of large-scale moisture flux convergence (from MERRA-2) in the sectors of ETCs, which are studied in a new coordinate system with directions of both surface warm fronts (WFs) and surface cold fronts (CFs) fixed. The phase of moisture flux convergence is described by moisture dynamical convergence Qcnvg and moisture advection Qadvt. Precipitation and occurrence frequencies of deep convective clouds are sensitive to changes in Qcnvg, while moisture tendency is sensitive to changes in Qadvt. Increasing Qcnvg and Qadvt during the advance of the WF is associated with increasing occurrences of both deep convective and high-level stratiform clouds. A rapid decrease in Qadvt with a relatively steady Qcnvg during the advance of the CF is associated with high-level cloud distribution weighting toward deep convective clouds. Behind the CF (cold sector or area with polar air intrusion), the moisture flux is divergent with abundant low- and midlevel clouds. From deepening to decaying stages, the pre-WF and WF sectors experience high-level clouds shifting to more convective and less stratiform because of decreasing Qadvt with relatively steady Qcnvg, and the CF experiences shifting from high-level to midlevel clouds. Sectors of moisture flux divergence are less influenced by cyclone evolution. Surface evaporation is the largest in the cold sector and the CF during the deepening stage. Deepening cyclones are more efficient in poleward transport of water vapor.
Nucleic-Acid-Based Molecular Fungal Diagnostics: A Way to a Better Future

The world has seen a tremendous increase in the number of fungal infections during the past two decades. Recently, the World Health Organisation released the pathogen priority list for fungal infections, signifying the importance of these infections in the fields of research and public health. Microbiology laboratories demand an upgrade in the diagnostic system to keep up with the increased burden of these infections. Diagnosis of fungal infections using conventional techniques has always faced limitations in terms of specificity, sensitivity, and turnaround time. Although these methods are the core pillars of the diagnosis, there is an increased need for molecular approaches. Molecular techniques have revolutionised the field of fungal diagnostics. The diverse array of molecular techniques, including techniques like Polymerase Chain Reaction (PCR), have emerged as a cornerstone in fungal diagnostics. Molecular techniques have transformed fungal diagnostics, providing powerful tools for the rapid and accurate identification of pathogens. As these technologies continue to evolve, their integration into routine clinical practice holds the promise of improving patient outcomes through timely and targeted antifungal interventions. This review will cover the molecular approaches involved in fungal diagnostics, moving from the basic techniques to the advanced-level nucleic-acid-based molecular approaches providing a high throughput and decreased turnaround time for the diagnosis of serious fungal infections.
Microstructure Evolution and Online Thermal Resistance of Ag Sintered SiC/DBA Power Module During Power Cycling

With the increasing power and miniaturization of power electronics, the power cycling reliability and thermal management of power modules have become increasingly crucial. To address this issue, our Lab. has developed a novel instrument that allows simultaneous power cycling and real-time thermal resistance measurement. Utilizing this specific instrument, we investigated the microstructural evolution of the die attachment layer during power cycling and monitored the real-time thermal resistance of the SiC-TEG (Silicon Carbide-Thermal Engineering Group) heater chip/sintered Ag/DBA (Directed Bonded Aluminum with ceramic core of Si3N4) structure. Additionally, strategies to improve the degradation of the microstructure were proposed. The results indicate that after 10000 power cycles, numerous cracks were observed in the sintered Ag layer. However, the addition of 5 wt. % A1 suppressed the generation of cracks significantly. The addition of A1 particles has little effect on the transient thermal resistance during power cycling.
Spatio-temporal terrain classification for mapping and robot localization

Detection and classification of the surrounding terrain is a fundamental ability of a mobile robot in outdoor navigation to enable safe and efficient path planning. Our robot is equipped with a 3D LiDAR and a color camera, since these sensors complement each other very well. The terrain in front of the robot is divided into a grid, and each grid cell is classified individually using the sensor measurements. A new method for 3D LiDAR-based terrain classification with easy-to-compute and yet discriminative 3D features based on intensity and roughness histograms is presented. The feature extraction for one grid only takes 1.5 ms on average. For classification, we use Random forests since they outperform all other tested classifiers. When only considering the two classes of asphalt and grass the classification results are consistently above 99.9% for different tested light conditions. Camera-based texture classification with Local ternary patterns is used in addition, and for grid cells where data of both sensors are present, the results are fused accordingly. To exploit the fact that terrain appears in contiguous areas, spatial dependencies between the individual cells of the terrain grid are taken into account by modeling the grid as a Conditional random field. An approximately optimal configuration of terrain labels is found by optimizing featureand neighborhood-dependent energy terms using Gibbs sampling in a simulated-annealing scheme, and an efficient way for describing the neighborhood-dependent energy is presented. In our experiments considering the four terrain classes asphalt, cobblestones, grass, and gravel, using spatial dependencies improves results significantly, and we get a classification rate of 96.8%, in contrast to 81.5% when classifying grid cells individually. As the robot moves, we constantly update a terrain map with the current classification result. In this way, we are not only able to exploit temporal dependencies, but we are building whole terrain maps of the environment. We show how to efficiently incorporate the classification result of the current terrain grid into the map, how to combine it with results from previous time steps, and how to integrate the label configuration obtained from simulated annealing. In experiments using a new data set, spatial classification yields a classification rate of 92.2%, only using temporal classification yields 96.8%, and finally spatio-temporal classification yields the best result with 98.4%. By additionally integrating information about obstacles, we can generate meaningful terrain and elevation maps of the robot’s environment. Spatio-temporal classification and elevation mapping take 23.9 ms on average, which corresponds to about 41.8 Hz. This shows that our classification method is real-time capable. Finally, we show how to use these maps for a semantic localization of the robot.
Role of non-invasive coronary imaging in stable angina

Chest pain represents a symptom of significant clinical concern due to the potential for lethal etiologies. Accordingly, it is critical to ascertain the presence of stable angina through various diagnostic tests to inform subsequent therapeutic strategies. Stable angina, while potentially progressing to more severe conditions if left untreated, suffers from a paucity of research regarding its management compared to other more fatal causes of chest pain. Recent advancements in radiological imaging necessitate a re-evaluation of the array and functionality of diagnostic tests, with particular emphasis on prioritizing non-invasive methods such as electrocardiography and echocardiography. This study undertakes a comprehensive review of the literature pertaining to various diagnostic tests for stable angina. We conclude that the management of a patient presenting with chest pain encompasses a continuum of care, beginning with a detailed patient history to estimate pre-test probability and culminating in computed tomography coronary angiography. This continuum is highly individualized, taking into account patient-specific variables, disease burden, and test indications. In an era of rapid research advancement, our findings delineate the optimal sequence of initial diagnostic tests, emphasizing the role of current non- invasive imaging modalities as outlined in standard clinical guidelines.
Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due to a global shortage of psychiatrists, resulting in delayed diagnoses with lasting consequences on individual’s well-being and the societal impact. Recently, machine learning methodologies have been incorporated into healthcare systems to facilitate the diagnosis and enhance the potential prediction of treatment outcomes for mental health conditions. In ADHD detection, the previous research focused on utilizing functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals, which require costly equipment and trained personnel for data collection. In recent years, speech and text modalities have garnered increasing attention due to their cost-effectiveness and non-wearable sensing in data collection. In this research, conducted in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, we gathered audio data from both ADHD patients and normal controls based on the clinically popular Diagnostic Interview for ADHD in adults (DIVA). Subsequently, we transformed the speech data into text modalities through the utilization of the Google Cloud Speech API. We extracted both acoustic and text features from the data, encompassing traditional acoustic features (e.g., MFCC), specialized feature sets (e.g., eGeMAPS), as well as deep-learned linguistic and semantic features derived from pre-trained deep learning models. These features are employed in conjunction with a support vector machine for ADHD classification, yielding promising outcomes in the utilization of audio and text data for effective adult ADHD screening. Clinical impact: This research introduces a transformative approach in ADHD diagnosis, employing speech and text analysis to facilitate early and more accessible detection, particularly beneficial in areas with limited psychiatric resources. Clinical and Translational Impact Statement: The successful application of machine learning techniques in analyzing audio and text data for ADHD screening represents a significant advancement in mental health diagnostics, paving the way for its integration into clinical settings and potentially improving patient outcomes on a broader scale.