ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Interval between prior SARS-CoV-2 infection and booster vaccination impacts magnitude and quality of antibody and B cell responses

SARS-CoV-2 mRNA booster vaccines provide protection from severe disease, eliciting strong immunity that is further boosted by previous infection. However, it is unclear whether these immune responses are affected by the interval between infection and vaccination. Over a 2-month period, we evaluated antibody and B cell responses to a third-dose mRNA vaccine in 66 individuals with different infection histories. Uninfected and post-boost but not previously infected individuals mounted robust ancestral and variant spike-binding and neutralizing antibodies and memory B cells. Spike-specific B cell responses from recent infection (<180 days) were elevated at pre-boost but comparatively less so at 60 days post-boost compared with uninfected individuals, and these differences were linked to baseline frequencies of CD27lo B cells. Day 60 to baseline ratio of BCR signaling measured by phosphorylation of Syk was inversely correlated to days between infection and vaccination. Thus, B cell responses to booster vaccines are impeded by recent infection.
Robust Adaptive Extended Kalman Filtering for Real Time MR-Thermometry Guided HIFU Interventions

Real time magnetic resonance (MR) thermometry is gaining clinical importance formonitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.
FLASH X-ray spares intestinal crypts from pyroptosis initiated by cGAS-STING activation upon radioimmunotherapy

DNA-damaging treatments such as radiotherapy (RT) have become promising to improve the efficacy of immune checkpoint inhibitors by enhancing tumor immunogenicity. However, accompanying treatment-related detrimental events in normal tissues have posed a major obstacle to radioimmunotherapy and present new challenges to the dose delivery mode of clinical RT. In the present study, ultrahigh dose rate FLASH X-ray irradiation was applied to counteract the intestinal toxicity in the radioimmunotherapy. In the context of programmed cell death ligand-1 (PD-L1) blockade, FLASH X-ray minimized mouse enteritis by alleviating CD8+ T cell-mediated deleterious immune response compared with conventional dose rate (CONV) irradiation. Mechanistically, FLASH irradiation was less efficient than CONV X-ray in eliciting cytoplasmic double-stranded DNA (dsDNA) and in activating cyclic GMP-AMP synthase (cGAS) in the intestinal crypts, resulting in the suppression of the cascade feedback consisting of CD8+ T cell chemotaxis and gasdermin E-mediated intestinal pyroptosis in the case of PD-L1 blocking. Meanwhile, FLASH X-ray was as competent as CONV RT in boosting the antitumor immune response initiated by cGAS activation and achieved equal tumor control in metastasis burdens when combined with anti-PD-L1 administration. Together, the present study revealed an encouraging protective effect of FLASH X-ray upon the normal tissue without compromising the systemic antitumor response when combined with immunological checkpoint inhibitors, providing the rationale for testing this combination as a clinical application in radioimmunotherapy.
A Logarithmic Opinion Pool Based STAPLE Algorithm for the Fusion of Segmentations With Associated Reliability Weights

Pelvic floor dysfunction is common in women after childbirth and precise segmentation of magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment of patients. However, because of the complexity of its structures, manual segmentation of the pelvic floor is challenging and suffers fromhigh inter and intra-rater variability of expert raters. Multiple template fusion algorithms are promising segmentation techniques for these types of applications, but they have been limited by imperfections in the alignment of templates to the target, and by template segmentationerrors. A number of algorithms sought to improve segmentation performance by combining image intensities and template labels as two independent sources of information, carrying out fusion through local intensity weighted voting schemes. This class of approach is a form of linear opinion pooling, and achieves unsatisfactory performance for this application. We hypothesized that better decision fusion could be achieved by assessing the contribution of each template in comparison to a reference standard segmentation of the target image and developed a novel segmentation algorithm to enable automatic segmentation of MRI of the female pelvic floor. The algorithm achieves high performance by estimating and compensating for both imperfect registration of the templates to the target image and template segmentation inaccuracies. A local image similarity measure is used to infer a local reliability weight, which contributes to the fusion through a novel logarithmic opinion pooling. We evaluated our new algorithm in comparison to nine state-of-the-art segmentation methods and demonstrated our algorithm achieves the highest performance.
An integrated approach for process monitoring using wavelet analysis and competitive neural network

A novel framework involving both a detection module and a classification module is proposed for the recognition of the six main types of process signals. In particular, a multi-scale wavelet filter is used for denoising and its performance is compared with that of single-scale linear filters. Moreover, two kinds of competitive neural networks, based on learning vector quantization (LVQ) and adaptive resonance theory ( ART), are adopted for the task of pattern classification and benchmarking. Our results show that denoising through a wavelet filter is best for pattern classification, and the classification accuracy with respect to six predefined categories using a LVQ-X network is a little better than using an ART network. However, when an unexpected novel pattern occurs within the process, LVQ will force the novel pattern to be classified into one of those predefined categories that is most similar to the novel pattern. On the contrary, ART will automatically construct a new class when the similarity measured between the novel pattern and the most similar category is too small to be incorporated. Therefore, under the consideration of the stability - plasticity dilemma, our simplified ART network based on multi-scale wavelet denoising provides a more promising way to adapt unexpected novel patterns.
Creative, embodied practices, and the potentialities for sustainability transformations

This paper argues for an integrative approach to sustainability transformations, one that reconnects body and mind, that fuses art and science and that integrates diverse forms of knowledge in an open, collaborative and creative way. It responds to scholarship emphasizing the importance of connecting disparate ways of knowing, including scientific, artistic, embodied and local knowledges to better understand environmental change and to foster community resilience and engagement. This paper draws on the experience of an arts-based project in Lisbon, Portugal, and explores embodied and performative practices and their potential for climate change transformations. It puts forward and enlivens an example, where such forms of engaging communities can provide new insight into how equitable, just and sustainable transformations can come about. The process involved a series of interactive workshops with diverse arts-based methods and embodied practices to create performative material. From this process, a space emerged for the creation of meaning about climate change. Three key elements stood out in this process as being potentially important for the emergence of meaning-making and for understanding the impact of the project: the use of metaphors, embedding the project locally, and the use of creative, embodied practices. This furthers research, suggesting that the arts can play a critical role in engaging people with new perspectives on climate change and sustainability issues by offering opportunities for critical reflection and providing spaces for creative imagination and experimentation. Such processes may be important for contributing to the changes needed to realize transformations to sustainability.
Optimisation of low voltage power MOSFET components for high current applications

Achievable current density of low voltage power MOSFET components has increased significantly over the past years. Those devices are mainly used in converters for automotive auxiliary drives or renewable energy. The paper outlines state of the art technology and gives an outlook on further development, taking into account the particular requirements of the aforementioned demanding applications.
Fragile Bits in Palmprint Recognition

Recent years have witnessed a growing interest in developing automatic palmprint recognition methods. Among them, coding-based ones, representing the texture of a palmprint using a binary code, are most prevalent and successful. We find that not all bits in a code map generated by a specific coding scheme are equally consistent. A bit is deemed fragile if its value changes across code maps created from different images of the same palmprint. In this paper, we first analyze the fragile bits phenomenon in a state-of-the-art palmprint coding scheme, namely, binary orientation co-occurrence vector (BOCV). Then, based on our analysis, we extend BOCV to E-BOCV by incorporating fragile bits information in appropriate ways. Experiments conducted on the benchmark dataset demonstrate that E-BOCV can achieve the highest verification accuracy among all the state-of-the-art palmprint verification methods evaluated. To our knowledge, this is the first work investigating the fragile bits of coding-based palmprint recognition approaches.
Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging

Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov's smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.
Identification, taste characterization, and molecular docking study of a novel microbiota-derived umami peptide

Umami peptides are an important taste substance in fermented foods. However, in the absence of known microbiota-derived umami peptides, the understanding of the umami mechanism remains unclear. Tetragenococcus halophilus, a dominant fermentation bacteria, may be an important source of umami peptides. Accordingly, T. halophilus fermentation broth was fractioned by ethanol precipitation, gel chromatography, and reverse phase-high performance liquid chromatography. The isolated peptide fraction with the most intense umami taste was screened by amino acid composition and sensory analyses. Finally, three novel microbiota-derived peptides (DFE, LAGE, and QLQ) were identified, synthesized, and characterized for taste. Among them, only DFE had umami and umami-enhancing abilities improving multiple tastes. Molecular docking studies indicated that DEF binds to T1R1/T1R3 receptors through hydrogen bonding and electrostatic interactions involving receptor residues Ser332, Ser256, ASN41, His125, etc. This study highlights the critical role of microbiota-derived peptides in the umami taste of fermented foods.
Digital Airborne Photogrammetry-A New Tool for Quantitative Remote Sensing?-A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images. The analysis is based on a literature research and a questionnaire submitted to various interest groups related to the photogrammetric process. An important contribution to this paper is a characterization of the photogrammetric image acquisition and image product generation systems. The questionnaire revealed many weaknesses in current processes, but the future prospects of radiometrically quantitative photogrammetry are promising.
Safety evaluation of the food enzyme β-galactosidase from the genetically modified Kluyveromyces lactis strain KLA

The food enzyme β-galactosidase (β-d-galactoside galactohydrolase; EC 3.2.1.23) is produced with the genetically modified Kluyveromyces lactis strain KLA by DSM Food Specialties B.V. The genetic modifications did not give rise to safety concerns. The food enzyme was considered free from viable cells of the production organism and its DNA. The food enzyme is intended to be used for the lactose hydrolysis in milk processing, production of fermented milk products and whey processing. It is also intended for lactose hydrolysis in milk products at home. Dietary exposure to the food enzyme-total organic solids (TOS) was estimated to be up to 11.876 mg TOS/kg body weight per day in European populations. The production strain of the food enzyme fulfils the requirements for the Qualified Presumption of Safety (QPS) approach to safety assessment. As no concerns arising from its genetic modification or from the manufacturing process have been identified, the Panel considered that toxicological tests are not needed for the assessment of this food enzyme. A search for similarity of the amino acid sequence of the food enzyme to known allergens was made and no match was found. The Panel considered that, under the intended conditions of use, the risk of allergic reactions by dietary exposure cannot be excluded, but the likelihood for this to occur is low. The Panel concluded that this food enzyme does not give rise to safety concerns under the intended conditions of use.
Pemphigus vegetans misdiagnosed as condylomata acuminata: A case report

Pemphigus vegetans is a rare variant of pemphigus vulgaris, characterized by vegetating lesions primarily in the flexures. A 41-year-old male patient presented with pemphigus vegetans highly mimicking condylomata acuminata, which led to mistreatment. Careful analysis of clinical and laboratory findings enabled us to reach a correct diagnosis and successful treatment.
Evaluation of the antimicrobial attribute of bioactive peptides derived from colostrum whey fermented by Lactobacillus against diarrheagenic E. coli strains

Colostrum known as "liquid gold" contains approximately 60-80% of whey proteins that can be a great source of bioactive peptide production. Therefore, this study aimed to perform a comparative antimicrobial evaluation of the bioactive peptide generated from L. rhamnosus C25, L. rhamnosus C6, and L. casei NCDC17 fermented colostrum whey. Peptide fractions 10 kDa, 5 kDa, and 3 kDa were isolated using their respective molecular weight cut-off membranes and antimicrobial activity was evaluated against diarrheagenic E. coli strains. The higher inhibition was shown by < 10 kDa peptide fractions from L. rhamnosus C25 fermented colostrum whey and the zone of inhibition was 15 ± 0.06 (E. coli MTCC 723), 17 ± 0.04 (E. coli MTCC 724), 18 ± 0.05 (E. coli MTCC 725), and 17 ± 0.02 (E. coli ATCC 25922). In addition, ST-1 and LT-1 genes of E. coli strains were also confirmed using PCR which is responsible for the diarrheagenic property. Further, the interaction of potent peptides against E. coli strains was also observed by scanning electron microscope. Hence, the significance of the present study emphasized that these bioactive peptides generated from fermented colostrum whey can be used as ingredients in functional food against diarrhoea.
Orientia tsutsugamushi in Chiggers and Small Mammals in Laos

Background: Scrub typhus is a leading cause of febrile illness in Laos and accounts for a high burden of disease. There have been no previous studies on the causative agent, Orientia tsutsugamushi, in vector mites ("chiggers") or their small mammal hosts in Laos. Materials and Methods: Small mammals and free-living chiggers were trapped in districts of Vientiane Province and Capital. Tissues were tested for O. tsutsugamushi by PCR and serum for IgG to O. tsutsugamushi by immunofluorescence assays (IFAs). Chiggers removed from small mammals and collected in their free-living stage using black plates were identified and tested for O. tsutsugamushi by PCR. Results: Over an 18-month period, 131 small mammals of 14 species were collected in 5 districts. Seventy-eight of 131 small mammals were infested with chiggers, but all tissues were O. tsutsugamushi PCR negative. Eighteen species of chigger were identified and 1,609 were tested by PCR. A single pool of chiggers tested O. tsutsugamushi positive. Sera from 52 small mammals were tested by IFA, with 16 testing positive. Conclusions: These are the first molecular and serological data on O. tsutsugamushi in chiggers and small mammals in Laos. Further studies are needed to better understand the key vector species and ecology of scrub typhus in areas with high disease incidence in Laos.
Multimodal Emotion Recognition With Transformer-Based Self Supervised Feature Fusion

Emotion Recognition is a challenging research area given its complex nature, and humans express emotional cues across various modalities such as language, facial expressions, and speech. Representation and fusion of features are the most crucial tasks in multimodal emotion recognition research. Self Supervised Learning (SSL) has become a prominent and influential research direction in representation learning, where researchers have access to pre-trained SSL models that represent different data modalities. For the first time in the literature, we represent three input modalities of text, audio (speech), and vision with features extracted from independently pre-trained SSL models in this paper. Given the high dimensional nature of SSL features, we introduce a novel Transformers and Attention-based fusion mechanism that can combine multimodal SSL features and achieve state-of-the-art results for the task of multimodal emotion recognition. We benchmark and evaluate our work to show that our model is robust and outperforms the state-of-the-art models on four datasets.
A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models

In this paper, we propose a simple signal estimation algorithm based on multiple wavelet representations and Gaussian observation models. The proposed algorithm has two major steps: a joint-optimum estimation of the wavelet coefficients and an averaging of the denoised images. Experimental results show that the denoising performance of proposed algorithm is comparable to that of the state of the art. (c) 2006 Elsevier B.V. All rights reserved.