ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
[Development and Application of the First Carbon Ion Therapy System in China]

At present, heavy ion is an ideal radiation for cancer treatment, and carbon ion is used in the treatment of many kinds of cancer due to its higher relative biological effect value. In 2019, Wuwei heavy ion center built the first medical heavy ion accelerator-carbon ion radiotherapy system in China, and obtained the registration license from the National Medical Products Administration, and officially received cancer patients in March 2020. This study introduced the development and application of the first carbon ion radiotherapy system in China.
Myotonia congenita: novel mutations in CLCN1 gene

Myotonia congenita belongs to the group of non-dystrophic myotonia caused by mutations of CLCN1gene, which encodes human skeletal muscle chloride channel 1. It can be inherited either in autosomal dominant (Thomsen disease) or recessive (Becker disease) forms. Here we have sequenced all 23 exons and exon-intron boundaries of the CLCN1 gene, in a panel of 5 unrelated Chinese patients with myotonia congenita (2 with dominant and 3 with recessive form). In addition, detailed clinical analysis was performed in these patients to summarize their clinical characteristics in relation to their genotypes. Mutational analyses revealed 7 different point mutations. Of these, we have found 3 novel mutations including 2 missense (R47W, V229M), one splicing (IVS19+2T>C), and 4 known mutations (Y261C,G523D, M560T, G859D). Our data expand the spectrum of CLCN1 mutations and provide insights for genotype-phenotype correlations of myotonia congenita in the Chinese population.
CR-FPN: channel relation feature pyramid network for object detection

Object detection is an important computer vision task, which aims to locate each object and classify them correctly. Since Convolution Neural Network (CNN) high-level feature map contains more semantic information and low-level feature map contains detail information which helps to locate precisely, fusing high-level feature maps with low-level feature maps is proven to be essential for multi-scale object detection. Recent work usually direct combine feature maps from different levels of CNN. However, as each feature map has different extents of discrimination, combining them directly may turn some valuable information into noise and further degrade performance. To address this issue, we focus on the channel relationship from different level of feature maps and propose a novel network, named Channel Relation Feature Pyramid Network (CR-FPN), that captures long-range relation of channel from different level of feature maps by similarity measure function and further to magnify the most relevant channels and suppress the irrelevant channels. Extensive experiments conducted on MS COCO and Pascal VOC datasets demonstrate the effectiveness of our CR-FPN also with competitive performance. Noting that our CR-FPN can apply to several typical state-of-the-art detectors. Using CR-FPN in a basic Cascade R-CNN detector, our method achieves state-of-the-art single model results on the COCO dataset.
Block Copolymer Self-Assembly Directed Synthesis of Porous Materials with Ordered Bicontinuous Structures and Their Potential Applications

Porous materials with their ordered bicontinuous structures have attracted great interest owing to ordered periodic structures as well as 3D interconnected network and pore channels. Bicontinuous structures may favor efficient mass diffusion to the interior of materials, thus increasing the utilization ratio of active sites. In addition, ordered bicontinuous structures confer materials with exceptional optical and magnetic properties, including tunable photonic bandgap, negative refraction, and multiple equivalent magnetization configurations. The attractive structural advantages and physical properties have inspired people to develop strategies for preparing bicontinuous-structured porous materials. Among a few synthetic approaches, the self-assembly of block copolymers represents a versatile strategy to prepare various bicontinuous-structured functional materials with pore sizes and lattice parameters ranging from 1 to 500 nm. This article overviews progress in this appealing area, with an emphasis on the synthetic strategies, the structural control (including topologies, pore sizes, and unit cell parameters), and their potential applications in energy storage and conversion, metamaterials, photonic crystals, cargo delivery and release, nanoreactors, and biomolecule selection.
Inhibitor of Glucosinolate Sulfatases as a Potential Friendly Insecticide to Control Plutella xylostella

The glucosinolate-myrosinase system is a two-component defense system characteristic of cruciferous plants. To evade the glucosinolate-myrosinase system, the crucifer specialist insect, Plutella xylostella, promptly desulfates the glucosinolates into harmless compounds by glucosinolate sulfatases (GSSs) in the gut. In this study, we identified an effective inhibitor of GSSs by virtual screening, molecular docking analysis, and in vitro enzyme inhibition assay. The combined effect of the GSS inhibitor with the plant glucosinolate-myrosinase system was assessed by the bioassay of P. xylostella. We show that irosustat is a GSS inhibitor and the inhibition of GSSs impairs the ability of P. xylostella to detoxify the glucosinolate-myrosinase system, leading to the systematic accumulation of toxic isothiocyanates in larvae, thereby severely affecting feeding, growth, survival, and reproduction of P. xylostella. While fed on the Arabidopsis mutants deficient in myrosinase or glucosinolates, irosustat had no significant negative effect on P. xylostella. These findings reveal that the GSS inhibitor is a novel friendly insecticide to control P. xylostella utilizing the plant glucosinolate-myrosinase system and promote the development of insecticide-plant chemical defense combination strategies.
Ultrasound Matrix ImaginguPart I: The Focused Reflection Matrix, the F-Factor and the Role of Multiple Scattering

This is the first article in a series of two dealing with a matrix approach for aberration quantification and correction in ultrasound imaging. Advanced synthetic beamforming relies on a double focusing operation at transmission and reception on each point of the medium. Ultrasound matrix imaging (UMI) consists in decoupling the location of these transmitted and received focal spots. The response between those virtual transducers form the so-called focused reflection matrix that actually contains much more information than a confocal ultrasound image. In this paper, a time-frequency analysis of this matrix is performed, which highlights the single and multiple scattering contributions as well as the impact of aberrations in the monochromatic and broadband regimes. Interestingly, this analysis enables the measurement of the incoherent input-output point spread function at any pixel of this image. A fitting process enables the quantification of the single scattering, multiple scattering and noise components in the image. From the single scattering contribution, a focusing criterion is defined, and its evolution used to quantify the amount of aberration throughout the ultrasound image. In contrast to the state-of-the-art coherence factor, this new indicator is robust to multiple scattering and electronic noise, thereby providing a contrasted map of the focusing quality at a much better transverse resolution. After a validation of the proof-of-concept based on time-domain simulations, UMI is applied to the in-vivo study of a human calf. Beyond this specific example, UMI opens a new route for speed-of-sound and scattering quantification in ultrasound imaging.
Ferulic acid relieved ulcerative colitis by inhibiting the TXNIP/NLRP3 pathway in rats

Ulcerative colitis (UC) is a disorder of the bowel that is characterized by a chronic inflammatory response. The traditional Chinese herbal medicine ferulic acid (FA) is known for its antioxidant, antiapoptotic, and antiinflammatory properties. However, its role in UC is still unclear. Thus, the current study was conducted to investigate the role of FA in UC. Rats were treated with 2,4,6-triabrobenzene sulfonic acid to induce UC and subjected to FA. Human intestinal microvascular endothelial cells (HIMECs) were treated with tumor necrosis factor-α (TNF-α) and pretreated with FA. Pathological changes in colonic tissues were visualized via hematoxylin-eosin staining. Enzyme linked immunosorbent assay was conducted to detect interleukin (IL)-6, IL-12, and IL-1β levels. Cell morphology was visualized by using a microscope, and viability was detected by using MTT. The percentage of apoptosis was detected via flow cytometry. Western blot analysis was performed to detect the expression of the apoptosis-related proteins thioredoxin-interacting protein (TXNIP) and NOD-like receptor pyrin domain-containing 3 (NLRP3). In vivo FA administration alleviated intestinal injury in UC rats and inhibited inflammatory factor levels (IL-6, IL-12, and IL-1β), apoptosis-related protein expression (caspase-1 and caspase-3) and the TXNIP/NLRP3 signaling pathway. In vitro, TNF-α treatment reduced HIMEC viability, increased cell apoptosis and inflammatory factor levels and activated the TXNIP/NLRP3 signaling pathway. However, FA treatment restored the viability of HIMECs, reduced TNF-α-induced cell apoptosis and inflammation and inhibited the TXNIP/NLRP3 signaling pathway. Furthermore, with increasing FA concentration, the effects were stronger. In summary, FA inhibits the inflammatory injury of endothelial cells in ulcerative colitis or alleviates TNF-α-induced HIMEC injury by inhibiting the TXNIP/NLRP3 signaling pathway.
Evaluating High Performance Pattern Matching on the Automata Processor

In this paper, we study the acceleration of applications that identify all the occurrences of thousands of string-patterns in an input data-stream using the Automata Processor (AP). For this evaluation, we use two applications from two fields, namely, cybersecurity and bioinformatics. The first application, called Fast-SNAP, scans network data for 4312 signatures of intrusion derived from the popular open-source Snort database. Using the resources of a single AP-board, Fast-SNAP can scan for all these signatures at 1 Gbps. The second application, called PROTOMATA, looks for all the occurrences of 1,309 motifs from the PROSITE database in protein sequences. PROTOMATA is up to 68 times faster than the state-of-the-art CPU implementation. As a comparison, we emulate the execution of the same NFAs by programming FPGAs using state-of-the-art techniques. We find that the performance derived by using the resources of a single AP-board, which houses 32 AP-chips, is comparable to that of the resources of five to six large FPGAs. The design techniques used in this paper are generic and may be applicable to the development of similar applications on the AP.
Adopting a "Both/And" Mindset to Address Relationship Violence and Sexual Misconduct (RVSM) in Institutions of Higher Education

Michigan State University (MSU) created a long-term, values-based strategic plan to increase help-seeking and reduce the incidence of relationship violence and sexual misconduct. Creating systemic change in institutions of higher education is challenging, particularly so in the wake of massive institutional crises and betrayal, as we had at MSU. In this paper, we address the challenges and critiques of our strategic planning efforts offered by esteemed scholar-activists: Jacobson López (2023), Hirsch and Khan (2023), McMahon (2023), and Boots et al. (2023).
Hydrogel-Based Smart Contact Lens for Highly Sensitive Wireless Intraocular Pressure Monitoring

Real-time intraocular pressure (IOP) monitoring plays a crucial role in glaucoma diagnosis and treatment. The wireless smart contact lens based on a flexible inductor-capacitor-resistor (LCR) sensor is chip-free and battery-free, demonstrating excellent application potential for physiological signal monitoring. To promote the use of LCR contact lenses for clinical IOP monitoring, reliable, comfortable contact lens materials should be used and excellent sensitivity needs to be realized. Here, we propose a method for producing hydrogel-based smart contact lenses for wireless IOP monitoring that uses the conformal stacking technique, solving the problems of swelling of the hydrogel and spherical integration of the pyramid-microstructured dielectric elastomer. The IOP of the in vitro porcine eye is successfully monitored owing to the high sensitivity of the spherical pyramid-microstructured capacitive pressure sensor and the hydrogel substrate. In addition, a glasses-integrated impedance-matching tunable reader for remote signal measurement is realized by enhancing the signal amplitude and increasing the reading distance, improving the portability of the signal measurement equipment. With the above improved designs, the wireless contact lens system has application potential for clinical IOP monitoring and shows substantial promise for next-generation daily ocular health management.
Novel approaches to human activity recognition based on accelerometer data

An increasing number of works have investigated the use of convolutional neural network (ConvNets) approaches to perform human activity recognition (HAR) based on wearable sensor data. These approaches present state-of-the-art results in HAR, outperforming traditional approaches, such as handcrafted methods and 1D convolutions. Motivated by this, in this work we propose a set of methods to enhance ConvNets for HAR. First, we propose a data augmentation which enables the ConvNets to learn more adequately the patterns of the signal. Second, we exploit the attitude estimation of the accelerometer data to devise a set of novel feature descriptors which allow the ConvNets to better discriminate the activities. Finally, we propose a novel ConvNet architecture to explore the patterns among the accelerometer axes throughout the layers that compose the network. We demonstrate that this is a simpler way of improving the activity recognition instead of proposing more complex architectures, serving as direction to future works with the purpose of building ConvNets architectures. The experimental results show that our proposed methods achieve notable improvements and outperform existing state-of-the-art methods.
Residential Electrical Load Monitoring and Modeling - State of the Art and Future Trends for Smart Homes and Grids

Building energy consumption accounts for a large fraction of the total global energy usage, and considerable energy savings are expected to be achieved in this respect through residential electrical load monitoring. Due to the limitations on the practical implementation of in-depth and expensive monitoring systems, non-intrusive load monitoring (NILM) is becoming a hot topic. In this paper, an overview of the state of the art residential electrical load monitoring is presented. Different from previous reviews, the applications of load monitoring are particularly addressed, based on which, technical challenges of load monitoring techniques, including NILM, are identified and thoroughly discussed, together with possible developments and trends predicted from the authors' perspective.
Parallel Computing of Graph-based Functions in ReRAM

Resistive Random Access Memory (ReRAM) is an emerging non-volatile memory technology. Besides its low power consumption and its high scalability, its inherent computation capabilities make ReRAM especially interesting for future computer architectures. Merging computations into the memory is a promising solution for overcoming the memory bottleneck. To perform computations in ReRAM, efficient synthesis strategies for Boolean functions have to be developed. In this article, we give a thorough presentation of how to employ parallel computing capabilities of ReRAM for the synthesis of functions given state-of-the-art graph-based representations AIGs or BDDs. Additionally, we introduce a new graph-based representation called m-And-Inverter Graph (m-AlGs), which allows us to fully exploit the computing capabilities of ReRAM. In the simulations, we show that our proposed approaches outperform state-of-the art synthesis strategies, and we show the superiority of m-AIGs over the standard AIG representation for ReRAM-based synthesis.
A Distributed Amplifier System for Bilayer Lipid Membrane (BLM) Arrays With Noise and Individual Offset Cancellation

Lipid bilayer membrane (BLM) arrays are required for high throughput analysis, for example drug screening or advanced DNA sequencing. Complex microfluidic devices are being developed but these are restricted in terms of array size and structure or have integrated electronic sensing with limited noise performance. We present a compact and scalable multichannel electrophysiology platform based on a hybrid approach that combines integrated state-of-the-art microelectronics with low-cost disposable fluidics providing a platform for high-quality parallel single ion channel recording. Specifically, we have developed a new integrated circuit amplifier based on a novel noise cancellation scheme that eliminates flicker noise derived from devices under test and amplifiers. The system is demonstrated through the simultaneous recording of ion channel activity from eight bilayer membranes. The platform is scalable and could be extended to much larger array sizes, limited only by electronic data decimation and communication capabilities.
Intra-tumoral infiltration of adipocyte facilitates the activation of antitumor immune response in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal malignancy that is characterized by an immunosuppressive microenvironment. The immune suppression in PDAC is largely driven by heterogeneous stromal and tumor cells. However, how adipocyte in the tumor microenvironment (TME) is related to the immune cell infiltration in PDAC has rarely been published. We identified adipocytes by performing bioinformatics analyses, and explored the clinical outcomes and TME characters in PDAC with different levels of adipocyte infiltration. Interestingly, in contrast to adiposity, high adipocyte infiltration in the TME was related to significantly increased median overall survival and a lower total tumor mutational burden. Functionally, high adipocyte infiltration was associated with the immune response, particularly with the abundant cytokine infiltration in PDAC samples. Moreover, adipocyte infiltration in the TME was positively associated with anticancer signatures in the immune microenvironment. Immunohistochemistry and RT-PCR were performed with PDAC tissue samples from our center to study the expression of adipocytes in PDAC. The mature adipocytes were strongly associated with the immune composition and prognosis of patients with PDAC. Primary adipocytes were isolated from mice to construct a PDAC transplantation tumor model. In vivo experiments showed that adipocytes elicited increased CD8+ T cell infiltration and potent antitumor activity in tumor-bearing mice. Overall, we innovatively found that adipocytes facilitated the antitumor immune response in the TME by performing mouse experiments and analyzing PDAC samples. This study provides a new perspective on the activation of the immune microenvironment in PDAC.
Phylogeography, hybridization, and species discovery in the Etheostoma nigrum complex (Percidae: Etheostoma: Boleosoma)

The history of riverine fish diversification is largely a product of geographic isolation. Physical barriers that reduce or eliminate gene flow between populations facilitate divergence via genetic drift and natural selection, eventually leading to speciation. For freshwater organisms, diversification is often the product of drainage basin rearrangements. In young clades where the history of isolation is the most recent, evolutionary relationships can resemble a tangled web. One especially recalcitrant group of freshwater fishes is the Johnny Darter (Etheostoma nigrum) species complex, where traditional taxonomy and molecular phylogenetics indicate a history of gene flow and conflicting inferences of species diversity. Here we assemble a genomic dataset using double digest restriction site associated DNA (ddRAD) sequencing and use phylogenomic and population genetic approaches to investigate the evolutionary history of the complex of species that includes E. nigrum, E. olmstedi, E. perlongum, and E. susanae. We reveal and validate several evolutionary lineages that we delimit as species, highlighting the need for additional work to formally describe the diversity of the Etheostoma nigrum complex. Our analyses also identify gene flow among recently diverged lineages, including one instance involving E. susanae, a localized and endangered species. Phylogeographic structure within the Etheostoma nigrum species complex coincides with major geologic events, such as parallel divergence in river basins during Pliocene inundation of the Atlantic coastal plain and multiple northward post-glacial colonization routes tracking river basin rearrangements. Our study serves as a nuanced example of how low dispersal rates coupled with geographic isolation among disconnected river systems in eastern North America has produced one of the world's freshwater biodiversity hotspots.
Real-time image marbleization

We present a new real-time image marbleization method that converts an image into a marble-like appearance automatically. The approach models the marbleization process as a two-dimensional fluid dynamics problem, whereby color advection of an input image results in a marbleized image. During the fluid dynamics simulation, we add a pixel-level external force field which is tangent to salient features in the image. The forces are computed from the image characteristics without user intervention. A stylized image with marble-like appearance is easily created that maintains the basic shape of objects in the input image. The entire modeling framework is implemented on a graphics processing unit, thus enabling real-time visual feedback. This approach provides a new tool to design figurative marbling textures without mixing of colors, which are almost impossible with previous computer-generated marbling methods.
GA-CNN: Convolutional Neural Network Based on Geometric Algebra for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in hyperspectral images (HSIs) classification, which is widely used for the analysis of remotely sensed images. HSI includes spectral and spatial information from several hundreds of spectral data channels. Recent CNN models deal with various bands of HSIs as independent channels, which may lead to the loss of dependencies between different channels or the loss of associated information between each channel and the global. This article proposes a novel CNN model based on geometric algebra (GA), dubbed GA-CNN, to process the HSIs in a holistic way without losing the interrelationship among channels. Specifically, taking advantage of GA, different band images are represented as GA multivectors to capture the inherent structures and preserve the correlation of those channels. In particular, all the basic modules of our model, such as convolutional layers and the backpropagation algorithm, are extended to the GA domain. We evaluate the performance of the proposed GA-CNN model in classification tasks on four well-known HSI datasets. The experimental results indicate that our GA-CNN model outperforms traditional and state-of-the-art real-valued CNNs with higher classification accuracy and fewer model parameters.