Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vitro Radioenhancement Using Ultrasound-Stimulated Microbubbles: A Comparison of Suspension and Adherent Cell States

Background: Ultrasound-stimulated microbubbles (USMB) have shown potential for enhancing radiation treatment via cavitation and sonoporation mechanisms. However, in vitro studies have produced inconsistent results, with adherent cells demonstrating no radioenhancement. This study aims to investigate the effect of cell adherence on in vitro radioenhancement using USMB and radiation. Method: Lung metastases of follicular thyroid carcinoma cells (FTC-238) and non-small cell lung carcinoma cells (NCI-H727) were treated, both when adhered and in suspension, using 1.6% (v/v) Definity™ microbubbles, ~90 s of 2 MHz ultrasound with mechanical index 0.9, and either 3 Gy or 6 Gy of megavoltage (MV) X-rays. The cell viability was measured using an MTS assay 72 h post-treatment, and statistical analysis was conducted using a three-way analysis of variance. Results: Statistically significant differences were observed for cells treated when adherent compared to suspended. An additive effect was detected in NCI-H727 cells treated in suspension, but not while adherent, while no enhancement was observed for FTC-238 cells in either culture state. Conclusions: To the best of our knowledge, this is the first study to directly compare the effect of cell adherence on the radioenhancement potential of USMB in vitro, and the first to do so using a metastatic cell line.
Genetic linkage.

Not all doctors would understand the statement "linkage is now established between a DNA marker and the locus for adult polycystic kidney disease at a recombination fraction of 5%". Yet over the next few years increasing numbers of DNA markers linked to single-gene disorders will become available and clinicians will need to identify patients who could benefit from this knowledge.
Visualizing Wallerian degeneration in the corticospinal tract after sensorimotor cortex ischemia in mice

[INLINE:1] Stroke can cause Wallerian degeneration in regions outside of the brain, particularly in the corticospinal tract. To investigate the fate of major glial cells and axons within affected areas of the corticospinal tract following stroke, we induced photochemical infarction of the sensorimotor cortex leading to Wallerian degeneration along the full extent of the corticospinal tract. We first used a routine, sensitive marker of axonal injury, amyloid precursor protein, to examine Wallerian degeneration of the corticospinal tract. An antibody to amyloid precursor protein mapped exclusively to proximal axonal segments within the ischemic cortex, with no positive signal in distal parts of the corticospinal tract, at all time points. To improve visualization of Wallerian degeneration, we next utilized an orthograde virus that expresses green fluorescent protein to label the corticospinal tract and then quantitatively evaluated green fluorescent protein-expressing axons. Using this approach, we found that axonal degeneration began on day 3 post-stroke and was almost complete by 7 days after stroke. In addition, microglia mobilized and activated early, from day 7 after stroke, but did not maintain a phagocytic state over time. Meanwhile, astrocytes showed relatively delayed mobilization and a moderate response to Wallerian degeneration. Moreover, no anterograde degeneration of spinal anterior horn cells was observed in response to Wallerian degeneration of the corticospinal tract. In conclusion, our data provide evidence for dynamic, pathogenic spatiotemporal changes in major cellular components of the corticospinal tract during Wallerian degeneration.
Global trends in research of endoscopic thyroidectomy from 2013 to 2022: a scientometric and visualization analysis

Introduction Recently, endoscopic thyroidectomy has been developed and applied to thyroid surgery to achieve minimized neck scar formation and enhanced aesthetic outcomes. Our scientometric research in this paper offers a thorough overview of endoscopic thyroidectomy from 2013 to 2022. Methods All pertinent articles on endoscopic thyroidectomy were obtained from the Web of Science Core Collection Database. The data on the number of citations and publications, most prolific countries and institutions, significant authors and journals, top themes, and keywords were analyzed by Biblioshiny, CiteSpace, and VOSviewer. Results There were 758 publications, all of that were found from 2013 to 2022. The output of the annual publication showed an upward trend. A series of cases report by Anuwong et al. published in 2016 received the most citations. The country with the most articles published articles was South Korea, and the two countries with the most collaboration were South Korea and the United States. The most productive journal was Surgical Endoscopy and Other Interventional Techniques. Dionigi G, Kim HY, and Anuwong A were the writers with the most articles published, the highest h- and g-indices, and the strongest link strength, respectively. The keywords “endoscopic thyroidectomy”, “surgical”, “thyroidectomy”, “robotic thyroidectomy”, “experience”, and others were most used. Conclusion The innovative surgical technique, transoral endoscopic thyroidectomy vestibular approach (TOETVA), leaves no scars and produces optimal cosmetic results. However, the long-term oncologic results for thyroid cancer performed with this approach are still missing. This scientometric analysis can offer valuable insights into the present research standing and key focal points in this domain, enabling researchers to gain a precise understanding of the state-of-the-art research in this area.
Modeling, identification and control of an antagonistically actuated joint for telerobotic systems

Within this paper a modeling, identification and control technique for an antagonistically actuated joint consisting of two pneumatically actuated muscles is presented. The antagonistically actuated joint acts as a test bench for control architectures which are going to be used to control an exoskeleton within a telerobotic system. A static and dynamic model of the muscle and the joint is derived and the parameters of the models are identified using a least-squares algorithm. The control architecture, consisting of a inner pressure and an outer position controller is presented. The pressure controller is evaluated using switching valves compared against proportional valves.
Mining negative sequential patterns from frequent and infrequent sequences based on multiple level minimum supports

Negative sequential patterns (NSP) are critical and sometimes much more informative than positive sequential patterns (PSP) in many intelligent systems and applications. However, the existing NSP algorithms do not allow negative items being contained in an element except the NegI-NSP algorithm, which can obtain many meaningful sequences with negative items in an element. NegI-NSP, however, hasn’t considered the following problems: (1) it uses a single minimum support to all size sequences, which is unfair to a long size sequence; (2) it only mines NSP from PSP, not from infrequent positive sequences (IPS), which also contain many useful NSP. So we propose an efficient algorithm, named MLMS-NSP, to mine NSP based on multiple level minimum supports (MLMS) from PSP and IPS. Firstly, MLMS scheme is proposed by assigning different minimum supports to sequences with different sizes. Secondly, IPS are constrained by combining MLMS, and then the NSP is obtained from these IPS. Finally, experimental results show that the MLMS-NSP algorithm can effectively mine NSP from IPS, and the time efficiency is higher than using single minimum support.
Mapping Indonesian potential fishing zone using hierarchical and non-hierarchical clustering

: Indonesia, a maritime nation whose ocean area exceeds its land area, has an abundance of ocean-based natural resources, such as fish, seaweed, coral reefs, and other marine organisms. The fisheries industry is one of the potential sources of extraordinary marine resources for the Indonesian economy. The annual increase or decrease in fish production in Indonesia can be attributed to several factors, including natural influences such as climate and ocean waves, inadequate management of marine resources, unequal distribution of facilities to support increased fish production in Indonesia, and the characteristics of areas that have a significant impact on the resulting fish production. Consequently, the objective of this research is to classify provinces in Indonesia using clustering analysis so that government policy programs can be more focused and directed according to the characteristics of the clusters formed. The application of cluster analysis was based on the development of fish production data for each province in Indonesia from 2017 to 2019 obtained from the website of the Central Statistics Agency (BPS). Clustering analysis using hierarchical and non-hierarchical methods produces a dendrogram using the average linkage DTW hierarchical method, indicating the formation of two optimal clusters. Non-hierarchical clustering with two clusters produces the
Practical volumetric speckle reduction in OCT using deep learning

Speckle reduction has been an active topic of interest in the Optical Coherence Tomography (OCT) community and several techniques have been developed ranging from hardware-based methods, conventional image-processing and deep-learning based methods. The main goal of speckle reduction is to improve the diagnostic utility of OCT images by enhancing the image quality, thereby enhancing the visual interpretation of anatomical structures. We have previously introduced a probabilistic despeckling method based on non-local means for OCT—Tomographic Non-local-means despeckling (TNode). We demonstrated that this method efficiently suppresses speckle contrast while preserving tissue structures with dimensions approaching the system resolution. Despite the merits of this method, it is computationally very expensive: processing a typical retinal OCT volume takes a few hours. A much faster version of TNode with close to real-time performance, while keeping with the open source nature of TNode, could find much greater use in the OCT community. Deep learning despeckling methods have been proposed in OCT, including variants of conditional Generative Adversarial Networks (cGAN) and convolutional neural networks CNN. However, most of these methods have used B-scan compounding as a ground truth, which presents significant limitations in terms of speckle reduced tomograms with preservation of resolution. In addition, all these methods have focused on speckle suppression of individual B-scans, and their performance on volumetric tomograms is unclear: the expectation is that three-dimensional manipulations of these processed tomograms (i.e., en face projections) will contain artifacts due to the B-scan-wise processing, disrupting the continuity of tissue structures along the slow-scan axis. In addition, speckle suppression based on individual B-scans cannot provide the neural network with information on volumetric structures in the training data, and thus is expected to perform poorly on small structures. Indeed, most deep-learning despeckling works have focused on image quality metrics based on demonstrating strong speckle suppression, rather than focusing on preservation of contrast and small tissue structures. To overcome these problems, we propose an entire workflow to enable the wide-spread use of deep-learning speckle suppression in OCT: the ground-truth is generated using volumetric TNode despeckling, and the neural network uses a new cGAN that receives OCT partial volumes as inputs to utilize the three-dimensional structural information for speckle reduction. Because of its reliance on TNode for generating ground-truth data, this hybrid deep-learning–TNode (DL-TNode) framework will be made available to the OCT community to enable easy training and implementation in a multitude of OCT systems without relying on specialty-acquired training data.
QUALITY OF LIFE INDICATORS FOR THE PATIENTS WHO SUFFERED FROM COMMUNITY-ACQUIRED PNEUMONIA CAUSED BY SARS-CoV-2

Introduction. Respiratory pathology, both acute and chronic, remains one of the key problems of modern medicine in Ukraine and in the world in general, which is explained by its significant prevalence, negative impact on patients' work capacity, their quality of life, as well as the economic burden on society. The aim of the study – to analyze the subjective state of health using the SF-36 questionnaire for patients who undergone community-acquired pneumonia caused by SARS-CoV-2 in 1 year after inpatient treatment. Research Methods. A retrospective study of the medical records of 208 patients who had been hospitalized for non-hospital pneumonia with a negative smear test for the SARS-CoV-2 virus was conducted. The severity of pneumonia was calculated according to the PORT scale. The comparison group consisted of patients with SARS-CoV-2 nucleic acid identification and the absence. A telephone survey to assess the quality of life of the participants of the first stage of our study 1 year after discharge from the hospital were performed. The general investigator SF-36 was used to assess quality of life. Statistical analysis of data was carried out using the "STATISTICA 7.0" software. Results and Discussion. Physical functioning according to SF-36 was the lowest in the observation group IV and probably differed in relation to the data of the observation group II and the comparison group. Analyzing the general state of health, the values were found to be probably lower for patients who suffered from community-acquired pneumonia of 3rd and 4th categories of severity compared to the data of the observation group II and the comparison group. Conclusion. Thus, it was established low quality of life in patients after pneumonia caused by COVID-19. Practical value of the work consists in improving diagnostics and therapy, as well as improving the rehabilitation of patients who suffered from community-acquired pneumonia caused by SARS-CoV-2.
Risk Factors for Elevated D-Dimer Levels in Patients with Gastrointestinal Tumors Treated with Endoscopic Submucosal Dissection

Endoscopic submucosal dissection (ESD) is almost always performed with a sedative because of the longer procedure times involved. The risk of post-ESD deep vein thrombosis (DVT) has been reported as relatively high, and D-dimer levels are sometimes elevated after ESD. This retrospective study evaluated factors affecting changes in D-dimer levels from before to after ESD to identify causes of elevated D-dimer levels after ESD. This retrospective analysis included 117 patients with gastrointestinal tumors resected using ESD. After excluding eight patients with pre-ESD levels of D-dimer >1.5 μg/mL, factors correlating with changes in D-dimer from before to after ESD were analyzed using logistic regression analysis in 109 patients. Sedation was accomplished primarily using midazolam, but, because the sedative effect of midazolam shows marked inter-individual variability, a “corrected midazolam dose” was determined by dividing the total midazolam dose by the initial dose to correct for inter-individual differences in the sedative effect of midazolam. This value was used as one potential explanatory variable in the subgroup analysis of the 103 patients who received midazolam. In the subgroup analysis using the corrected midazolam dose as an explanatory variable, only the corrected midazolam dose correlated with a change in D-dimer ≥1.0 μg/mL in multivariate analysis (odds ratio (OR) = 1.5, 95% confidence interval (CI) 0.43–0.95; p = 0.030). The corrected midazolam dose correlated with increases in post-ESD D-dimer levels. This potential relationship indicates that patients undergoing ESD and requiring extended sedation may be at increased risk of DVT.
Temporal manipulation of spatiotemporal optical vortices with an Airy pulse [Invited]

We demonstrate the temporal manipulation of spatiotemporal optical vortices (STOVs) by utilizing Airy pulses. By combining a STOV with an Airy temporal profile, the STOV exhibits nondispersive, self-accelerating, and self-healing features inherited from the Airy pulse propagation. Such features will enhance the control of STOVs in time.
A/B Testing in Network Data with Covariate-Adaptive Randomization

Users linked together through a network often tend to have similar behaviors. This phenomenon is usually known as network interaction. Users’ characteristics, the covariates, are often correlated with their outcomes. Therefore, one should incorporate both the covariates and the network information in a carefully designed randomization to improve the estimation of the average treat-ment effect (ATE) in network A/B testing. In this paper, we propose a new adaptive procedure to balance both the network and the covariates. We show that the imbalance measures with respect to the covariates and the network are O p (1) . We also demonstrate the relationships between the improved balances and the increased efficiency in terms of the mean square error (MSE). Numerical studies demonstrate the advanced performance of the proposed procedure regarding the greater comparability of the treatment groups and the reduction of MSE for estimating the ATE.
Petrosamine Revisited. Experimental and Computational Investigation of Solvatochromism, Tautomerism and Free Energy Landscapes of a Pyridoacridinium Quaternary Salt

Petrosamine (1)—a colored pyridoacridine alkaloid from the Belizean sponge, Petrosia sp. , that is also a potent inhibitor of acetylcholine esterase (AChE)—was investigated by spectroscopic and computational methods. Analysis of the petrosamine-free energy landscapes, pKa and tautomerism, revealed an accurate electronic depiction of the molecular structure of 1 as the di-keto form, with a net charge of q = +1, rather than a dication (q = +2) under ambient conditions of isolation-purification. The pronounced solvatochromism (UV-vis) reported for 1, and related analogs were investigated in detail and is best explained by charge delocalization and stabilization of the ground state (HOMO) of 1 rather than an equilibrium of competing tautomers. Refinement of the molecular structure 1 by QM methods complements published computational docking studies to define the contact points in the enzyme active site that may improve the design of new AChE inhibitors based on the pyridoacridine alkaloid molecular skeleton.
Assessing the genetic diversity of squash (Cucurbita pepo L.) genotypes based on agro-morphological traits and genetic analysis

An experiment was conducted to estimate the genetic variability of 15 indigenous and exotic squash genotypes assessing 18 quantitative and 8 qualitative traits. Results showed that the accessions have high variability in qualitative traits like fruit size, fruit shape, fruit skin colour,lustre and fruit productivity, which allowed selection for considerable gains in these characteristics. The quantitative traits such as fruits yield per plant, fruit weight, length, diameter and total yield per hectare showed the greater phenotypic coefficient of variation (PCV) along with higher heritability which can helps to identify desirable genotypes. The obtained significant and positive correlation between fruit yield with number of leaves, nodes, fruit length, weight and number could assist in selection to improve this crop. Cluster analysis resulted in the formation of 4 groups, confirming the genetic variability among the studied genotypes. Eventually, the attained PCA analysis result revealed that the number of fruits per plant, fruit yield per plant, fruit length and days to first female flowering are the most discriminating traits which are accelerating the variability in squash genotypes. On the basis of the yield and its attributing traits, First Runner is the best genotype suited in this environment.
A Comparison of Three Data-Poor Stock Assessment Methods for the Pink Spiny Lobster Fishery in Mauritania

Several data-poor stock assessment methods have recently been proposed and applied to data-poor fisheries around the world. The Mauritanian pink spiny lobster fishery has a long history of boom and bust dynamics, with large landings, stock collapse, and years-long fishery closures, all happening several times. In this study, we have used catch, fishing efforts, and length-frequency data (LFD) obtained from the fishery in its most recent period of activity, 2015–2019, and historical annual catch records starting in 2006 to fit three data-poor stock assessment methods. These were the length-based Bayesian (LBB) method, which uses LFD exclusively, the Catch-only MSY (CMSY) method, using annual catch data and assumptions about stock resilience, and generalised depletion models in the R package CatDyn combined with Pella-Tomlinson biomass dynamics in a hierarchical inference framework. All three methods presented the stock as overfished. The LBB method produced results that were very pessimistic about stock status but whose reliability was affected by non-constant recruitment. The CMSY method and the hierarchical combination of depletion and Pella-Tomlinson biomass dynamics produced more comparable results, such as similar sustainable harvest rates, but both were affected by large statistical uncertainty. Pella-Tomlinson dynamics in particular demonstrated stock experiencing wide fluctuations in abundance. In spite of uncertain estimates, a clear understanding of the status of the stock as overfished and in need of a biomass rebuilding program emerged as management-useful guidance to steer exploitation of this economically significant resource into sustainability.
Mathematical models can help to better manage fishing

Auteurs : AUGER Pierre, Directeur de Recherche à l’Institut de Recherche pour le Développement (IRD). Membre de l’Unité Mixte Internationale 209 IRD UMMISCO, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes. Membre de l’Académie des Sciences. LETT Christophe, Directeur de Recherche à l’Institut de Recherche pour le Développement (IRD). Membre de l’Unité Mixte Internationale 209 IRD UMMISCO, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes.
Organizational Behavior and Human Decision Processes

Past research paints a mixed picture of rationales in negotiations: Some findings suggest rationales might help, whereas others suggest they may have little effect or backfire. Here, we distinguish between two kinds of rationales buyers commonly employ – constraint rationales (referring to one’s own limited resources) and disparagement rationales (involving critiques of the negotiated object) – and demonstrate their divergent effects. Across four studies, we examined spontaneous rationales and manipulated rationale content, finding that constraint rationales have more positive effects on instrumental (e.g., counteroffers) and relational (e.g., trust) outcomes than disparagement rationales. Mediation analyses suggest constraint, but not disparagement, rationales are taken by sellers as signaling a buyer’s limit. We also demonstrate a role for information, showing that the divergence between these rationales’ effects is attenuated when the seller has little information about their object’s value. Overall, our results show how and why rationales can help or hurt negotiators. 2017 Elsevier Inc. All rights reserved.