ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
New Generation Hole Transporting Materials for Perovskite Solar Cells: Amide-Based Small-Molecules with Nonconjugated Backbones

State-of-the-art perovskite-based solar cells employ expensive, organic hole transporting materials (HTMs) such as Spiro-OMeTAD that, in turn, limits the commercialization of this promising technology. Herein an HTM (EDOT-Amide-TPA) is reported in which a functional amide-based backbone is introduced, which allows this material to be synthesized in a simple condensation reaction with an estimated cost of <$5 g(-1). When employed in perovskite solar cells, EDOT-Amide-TPA demonstrates stabilized power conversion efficiencies up to 20.0% and reproducibly outperforms Spiro-OMeTAD in direct comparisons. Time resolved microwave conductivity measurements indicate that the observed improvement originates from a faster hole injection rate from the perovskite to EDOT-Amide-TPA. Additionally, the devices exhibit an improved lifetime, which is assigned to the coordination of the amide bond to the Li-additive, offering a novel strategy to hamper the migration of additives. It is shown that, despite the lack of a conjugated backbone, the amide-based HTM can outperform state-of-the-art HTMs at a fraction of the cost, thereby providing a novel set of design strategies to develop new, low-cost HTMs.
Transcription factor DegU-mediated multi-pathway regulation on lichenysin biosynthesis in Bacillus licheniformis

Lichenysin, producted by Bacillus licheniformis, is an important cyclic lipopeptide biosurfactant, which has potential applications in oil exploitation, drug development, biological control of agriculture and bioremediation. While studies are lacking on metabolism regulation of lichenysin biosynthesis, which limits metabolic engineering and large-scale production of lichenysin. In this study, the yield of lichenysin was improved obviously by 13.6 folds to 2.18 ± 0.03 g/L in degU deletion strain (WX02△degU) compared with the wild-type strain (WX02) and completely inhibited in degU overexpressed strain (WX02/pHY-degU). We further proved that DegU, a transcription factor plays a significant role in multicellular behavior, is a key negative regulator of lichenysin synthesis lchA operon. But interestingly, lichenysin yield was still inhibited by overexpressing DegU in the promoter-substituted strain (WX02-PP43lch), in which promoter of lchA operon cannot be controlled by DegU. Thus, through 13C-metabolic flux analysis, we found that deletion of degU also enhanced glucose uptake, branched chain amino acid synthesis, and fatty acid synthesis, while decrease acetoin synthesis, which is beneficial for the supply of lichenysin precursors. Further experiments demonstrate that DegU regulates these pathways by binding to the promoter regions of related genes. Overall, we systematically investigated the multi-pathway regulation network mediated by DegU on lichenysin biosynthesis, which not only contributes to the further metabolic engineering for lichenysin high-production, but sheds light on studies of transcription factor regulation.
A novel multiple description coding scheme compatible with the JPEG2000 decoder

In this letter we propose a novel technique to generate rate-distortion optimized multiple descriptions of images, exploiting the rate-allocation strategy embedded in the JPEG2000 encoder. The proposed scheme can be applied to any encoding algorithm, given that the rate allocation is based on code-block truncation. The method yields excellent performance in terms of both central and side distortion, outperforming state-of-the art techniques. Moreover, the single description decoding is fully compatible with the JPEG2000 Part 1 decoder.
X-Net: A Binocular Summation Network for Foreground Segmentation

In foreground segmentation, it is challenging to construct an effective background model to learn the spatial-temporal representation of the background. Recently, deep learning-based background models (DBMs) with the capability of extracting high-level features have shown remarkable performance. However, the existing state-of-the-art DBMs deal with video segmentation as single-image segmentation and ignore temporal cues in video sequences. To exploit temporal data sufficiently, this paper proposes a multi-input multi-output (MIMO) DBM framework for the first time, which is partially inspired by the binocular summation effect in human eyes. Our framework is an X-shaped network which allows the DBM to track temporal changes in a video sequence. Moreover, each output branch of our model could receive visual signals from two similar input frames simultaneously like the binocular summation mechanism. In addition, our model can be trained end-to-end using only a few training examples without any post-processing. We evaluate our method on the largest dataset for change detection (CDnet 2014) and achieve the state-of-the-art performance by an average overall F-Measure of 0.9920.
Partially Linear Estimation With Application to Sparse Signal Recovery From Measurement Pairs

We address the problem of estimating a random vector X from two sets of measurements Y and Z, such that the estimator is linear in Y. We show that the partially linear minimum mean-square error (PLMMSE) estimator does not require knowing the joint distribution of X and Y in full, but rather only its second-order moments. This renders it of potential interest in various applications. We further show that the PLMMSE method is minimax-optimal among all estimators that solely depend on the second-order statistics X of Y and. We demonstrate our approach in the context of recovering a signal, which is sparse in a unitary dictionary, from noisy observations of it and of a filtered version. We show that in this setting PLMMSE estimation has a clear computational advantage, while its performance is comparable to state-of-the-art algorithms. We apply our approach both in static and in dynamic estimation applications. In the former category, we treat the problem of image enhancement from blurred/noisy image pairs. We show that PLMMSE estimation performs only slightly worse than state-of-the art algorithms, while running an order of magnitude faster. In the dynamic setting, we provide a recursive implementation of the estimator and demonstrate its utility in tracking maneuvering targets from position and acceleration measurements.
Culture media composition influences patient-derived organoid ability to predict therapeutic responses in gastrointestinal cancers

BACKGROUNDA patient-derived organoid (PDO) platform may serve as a promising tool for translational cancer research. In this study, we evaluated PDO's ability to predict clinical response to gastrointestinal (GI) cancers.METHODSWe generated PDOs from primary and metastatic lesions of patients with GI cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma. We compared PDO response with the observed clinical response for donor patients to the same treatments.RESULTSWe report an approximately 80% concordance rate between PDO and donor tumor response. Importantly, we found a profound influence of culture media on PDO phenotype, where we showed a significant difference in response to standard-of-care chemotherapies, distinct morphologies, and transcriptomes between media within the same PDO cultures.CONCLUSIONWhile we demonstrate a high concordance rate between donor tumor and PDO, these studies also showed the important role of culture media when using PDOs to inform treatment selection and predict response across a spectrum of GI cancers.TRIAL REGISTRATIONNot applicable.FUNDINGThe Joan F. & Richard A. Abdoo Family Fund in Colorectal Cancer Research, GI Cancer program of the Mayo Clinic Cancer Center, Mayo Clinic SPORE in Pancreatic Cancer, Center of Individualized Medicine (Mayo Clinic), Department of Laboratory Medicine and Pathology (Mayo Clinic), Incyte Pharmaceuticals and Mayo Clinic Hepatobiliary SPORE, University of Minnesota-Mayo Clinic Partnership, and the Early Therapeutic program (Department of Oncology, Mayo Clinic).
Loss of Rai1 enhances hippocampal excitability and epileptogenesis in mouse models of Smith-Magenis syndrome

Hyperexcitability of brain circuits is a common feature of autism spectrum disorders (ASDs). Genetic deletion of a chromatin-binding protein, retinoic acid induced 1 (RAI1), causes Smith-Magenis syndrome (SMS). SMS is a syndromic ASD associated with intellectual disability, autistic features, maladaptive behaviors, overt seizures, and abnormal electroencephalogram (EEG) patterns. The molecular and neural mechanisms underlying abnormal brain activity in SMS remain unclear. Here we show that panneural Rai1 deletions in mice result in increased seizure susceptibility and prolonged hippocampal seizure duration in vivo and increased dentate gyrus population spikes ex vivo. Brain-wide mapping of neuronal activity pinpointed selective cell types within the limbic system, including the hippocampal dentate gyrus granule cells (dGCs) that are hyperactivated by chemoconvulsant administration or sensory experience in Rai1-deficient brains. Deletion of Rai1 from glutamatergic neurons, but not from gamma-aminobutyric acidergic (GABAergic) neurons, was responsible for increased seizure susceptibility. Deleting Rai1 from the Emx1Cre-lineage glutamatergic neurons resulted in abnormal dGC properties, including increased excitatory synaptic transmission and increased intrinsic excitability. Our work uncovers the mechanism of neuronal hyperexcitability in SMS by identifying Rai1 as a negative regulator of dGC intrinsic and synaptic excitability.
Communal visual histories to detect environmental change in northern areas: Examples of emerging North American and Eurasian practices

This article explores the pioneering potential of communal visual-optic histories which are recorded, painted, documented, or otherwise expressed. These materials provide collective meanings of an image or visual material within a specific cultural group. They potentially provide a new method for monitoring and documenting changes to ecosystem health and species distribution, which can effectively inform society and decision makers of Arctic change. These visual histories can be positioned in a continuum that extends from rock art to digital photography. They find their expressions in forms ranging from images to the oral recording of knowledge and operate on a given cultural context. For monitoring efforts in the changing boreal zone and Arctic, a respectful engagement with visual histories can reveal emerging aspects of change. The examples from North America and case studies from Eurasia in this article include Inuit sea ice observations, Yu'pik visual traditions of masks, fish die-offs in a sub-boreal catchment area, permafrost melt in the Siberian tundra and early, first detection of a scarabaeid beetle outbreak, a Southern species in the Skolt Sami area. The pros and cons of using these histories and their reliability are reviewed.
A Scalable Privacy-Preserving Multi-Agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading

Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers' distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers' energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer's addition to rebound peaks. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community's cost and peak demand.
Recent Progress of Carbon Dots for Air Pollutants Detection and Photocatalytic Removal: Synthesis, Modifications, and Applications

Rapid industrialization has inevitably led to serious air pollution problems, thus it is urgent to develop detection and treatment technologies for qualitative and quantitative analysis and efficient removal of harmful pollutants. Notably, the employment of functional nanomaterials, in sensing and photocatalytic technologies, is promising to achieve efficient in situ detection and removal of gaseous pollutants. Among them, carbon dots (CDs) have shown significant potential due to their superior properties, such as controllable structures, easy surface modification, adjustable energy band, and excellent electron-transfer capacities. Moreover, their environmentally friendly preparation and efficient capture of solar energy provide a green option for sustainably addressing environmental problems. Here, recent advances in the rational design of CDs-based sensors and photocatalysts are highlighted. An overview of their applications in air pollutants detection and photocatalytic removal is presented, especially the diverse sensing and photocatalytic mechanisms of CDs are discussed. Finally, the challenges and perspectives are also provided, emphasizing the importance of synthetic mechanism investigation and rational design of structures.
Building Relationships between Museums and Schools: Reggio Emilia as a Bridge to Educate Children about Heritage

Schools and museums represent essential spaces for the development of learning and understanding of the world surrounding us through the arts and heritage. One of the things learned in the COVID crisis is that it is key to build bridges between schools and museums to support their educational activities, regardless of the possibility to access these spaces in person. School teachers and museum educators have the opportunity to develop a critical and creative citizenry by collaborating in the design of learning activities that can bring the museums to schools and schools to the museum by adopting the Reggio Emilia approach. The results of the study arise from a triangulation of data, as we contrasted the literature about the Reggio Emilia approach with the practices of museums that use such a philosophy and with the analysis of a series of interviews with experts in early childhood education and Reggio Emilia in order to identify a series of good practices, which we used to delineate recommendations to foster the adoption of this model and establish relationships between schools and museums, enhancing the opportunities to develop critical and creative thinking throughout activities and to understand the heritage and the arts, thus fostering citizenship from an early childhood.
Hybrid polymeric therapeutic microcarriers for thermoplasmonic-triggered release of resveratrol

Diabetic retinopathy (DR) is a severe ocular complication that causes retinal damage, being one of the leading causes of blindness globally, thus the development of new strategies to prevent and treat DR as well as other degenerative diseases is highly desired. This work is focused on the design and fabrication of an ingenious model of polymeric microcapsules (MC) for controlled drug delivery in human retina cells able to carry therapeutic resveratrol (RSV) molecules in tandem with active anisotropic gold bipyramidal nanoparticles (AuBPs) as efficient photothermal agents. Specifically, MC were developed via a Layer-by-Layer deposition technique, by successively adding oppositely charged polyelectrolytes on a RSV-conjugated calcium carbonate (CaCO3) core. For the monitorization and localization of the as-formed spherical fluorescent MC inside human retina pigmented epithelial (RPE) D407 cells, fluorescein isothiocyanate, a Food and Drug Administration approved fluorophore, was attached between the polyelectrolytes layers. High-performance liquid chromatography analysis revealed a loading efficiency of over 90% of RSV on the CaCO3 core and demonstrates its release upon NIR irradiation as a consequence of the thermoplasmonic effect of MC. The cytotoxicity of the RSV-carrying MC inside human retina cells was assessed by WST-1 assay. Finally, cellular internalization and localization of the MC inside living RPE cells were monitored via Conventional Fluorescence and Re-Scanning Confocal Fluorescence Microscopy. This research seeks to take use of the novel MC and implement them as potential intraocular RSV delivery vehicles for the therapy of DR.
Towards Pointsets Representation Learning via Self-Supervised Learning and Set Augmentation

Deep metric learning is a supervised learning paradigm to construct a meaningful vector space to represent complex objects. A successful application of deep metric learning to pointsets means that we can avoid expensive retrieval operations on objects such as documents and can significantly facilitate many machine learning and data mining tasks involving pointsets. We propose a self-supervised deep metric learning solution for pointsets. The novelty of our proposed solution lies in a self-supervision mechanism that makes use of a distribution distance for set ranking called the Earth's Mover Distance (EMD) to generate pseudo labels and a pointset augmentation method for supporting the learning solution. Our experimental studies on documents, graphs, and point clouds datasets show that our proposed solutions outperform baselines and state-of-the-art approaches under the unsupervised settings. The learned self-supervised representation can also be used as a pre-trained model, which can boost downstream tasks with a fine-tuning step and outperform state-of-the-art language models.
Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals

Sketch-based Image Retrieval (SBIR) is one important branch of Content-based Image Retrieval (CBIR). SBIR means dealing with retrieval using simple edge or contour images. However, SBIR is more difficult than CBIR due to the lack of visual information, this makes the Bag-of-Words (BoW) or codebook in SBIR hard to construct. In this paper, we propose a novel SBIR framework based on Product Quantization (PQ) with sparse coding (SC) to construct an optimized codebook. By using state-of-the-art local descriptors, we transform sketch images into features and then build the optimized codebook using PQ-based SC. In the retrieval stage, we can obtain a better representation of the query sketch and testing images by the optimized codebook with coding quantization residuals, by which the information loss during feature encoding process can be reduced; similarity computing is implemented by comparing the feature histograms between a query sketch and the testing data for the final results. We demonstrate the superiority and effectiveness of the proposed SBIR by comparing it with several state-of-the-art methods on three public sketch datasets.
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods.
Wavelet kernel learning

This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, hence the name "wavelet kernel learning". This problem is posed as a multiple kernel learning problem, where the number of kernels can be very large. For solving such a problem, we introduce a novel multiple kernel learning algorithm based on active constraints methods. We furthermore propose some variants of this algorithm that can produce approximate solutions more efficiently. Empirical analysis show that our active constraint MKL algorithm achieves state-of-the art efficiency. When used for wavelet kernel learning, our experimental results show that the approaches we propose are competitive with respect to the state-of-the-art on brain-computer interface and Brodatz texture datasets. (C) 2011 Elsevier Ltd. All rights reserved.
Static Grid Equivalent Models Based on Artificial Neural Networks

Power systems are rapidly and significantly changing due to the increasing penetration of distributed energy resources (DERs) and the rapid growth of widespread grid interconnections. An increasing number of grid operators is thus interested in the reduced equivalent representation of a large, interconnected power system to reduce the amount of required computational resources and data exchange, e.g., between grid operators. However, state-of-the-art grid equivalents become more and more inapplicable since they are analytically calculated for one specific grid state. They cannot properly be adapted to grid state changes and the behavior of the increasingly used controllers, such as reactive power controllers of DERs. Therefore, an innovative grid equivalent based on artificial neural networks (ANN) is proposed which overcomes the drawbacks of the state-of-the-art grid equivalents as follows: 1) Using supervised ANNs with feedforward and recurrent architectures, power systems can be equivalently represented adaptively and thus more accurately. 2) A feature selection method identifies the elements in the grid with high sensitivity on the boundary enabling a reduction of grid data required for the ANN-based equivalent. 3) To guarantee data confidentiality and cybersecurity, an additional unsupervised ANN, an Autoencoder, is used for obfuscation of the data which is required to be exchanged among grid operators, while the relevant information of the original data is preserved, maintaining the estimation accuracy. The ANN-based approach is analyzed and evaluated with two German benchmark grids and representative scenarios. The results demonstrate that the proposed approach outperforms the state-of-the-art radial equivalent independent method.