ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Efficacy of a recombinant M-like protein, SimA as a subunit vaccine candidate against Streptococcus parauberis infection in olive flounder, Paralichthys olivaceus

Streptococcus parauberis, a gram-positive cocci, causes bacterial disease in farmed fish. The recent increase in S. parauberis infection in aquatic farms in South Korea has justified the importance of vaccine development for the prevention of this disease. In this study, we evaluated the effect of subunit vaccines prepared from recombinant M-like protein (SimA) and fibrinogen-binding protein (FBP) candidates with an aluminum hydroxide adjuvant against S. parauberis infection in olive flounder Paralichthys olivaceus. For the in vivo experiment, fish (average length, 7.18 cm; average weight, 3.5 g) were injected intraperitoneally with: phosphate buffer saline (PBS, group 1), PBS/aluminum hydroxide (group 2), FBP/aluminum hydroxide (group 3), SimA/aluminum hydroxide (group 4), and SimA/FBP/aluminum hydroxide (group 5). After 3 weeks, the fish in each group were boosted using PBS (group 1 and 2), FBP (group 3), SimA (group 4), and SimA/FBP (group 5) without adjuvant. We found that the relative percent survival of fish after S. parauberis exposure in group 2, 3, 4, and 5 was 6.25%, 18.75%, 50%, and 12.5%, respectively, whereas the mortality in groups 1 was 80%, respectively. We performed Western blot, ELISA, and quantitative real time RT-PCR (qRT-PCR) after vaccination to investigate the further efficacy of the vaccine. Western blot and ELISA of vaccinated fish serum confirmed the production of specific antibodies against SimA and FBP. Furthermore, results of qRT-PCR showed that recombinant protein SimA induced a remarkably specific-antibody response compared with that in FBP or control and increased the expression of various immune response-related genes including interleukin-8 (IL-8), toll-like receptor 2 (TLR2), tumor necrosis factor-α (TNF-α), CD4-1, and MHC II. Thus, these results indicate that SimA is a potent vaccine candidate for protection against S. parauberis infection.
Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5x on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.
Semantic Graph Attention With Explicit Anatomical Association Modeling for Tooth Segmentation From CBCT Images

Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches.
Reverse logistics: the state of the art and future perspectives

This paper characterizes the state of the art in the area of reverse logistics by analyzing publications in the ScienceDirect and Web of Science (WOS) databases through bibliometric and systematic analysis. From a sample of 462 papers, leading journals, authors, research institutions and countries, as well as research partnerships were identified. It was noted that publications on the subject have steadily increased over the years, with the United States having the largest number of publications and partnerships. Brazilian researchers should seek partnerships with foreign institutions, since they have demonstrated the potential to enrich discussions and foster quality publications. This paper identified the main studies in this area, as well as the gaps that could guide future researches.
A State of the Art of Power Transmission Line Maintenance Robots

This paper dealt with a state-of-the-art maintenance robots for power transmission lines (PTL). The paper summarized the PTL maintenance robots that have been developed by eight major research institutions. The main features of the robot navigation were derived and classified through analysis of researches conducted by those institutions. The derived six main features were analyzed quantitatively and qualitatively through readiness level assessment. For this, system readiness level (SRL) was used to evaluate the readiness level of overall system. The SRL was obtained using. technology readiness level (TRL) and integration readiness level (IRL). In addition, the readiness level for each feature was reviewed through statistical analysis using box and whisker plot. In conclusion, we confirmed the status and direction of research and development on PTL maintenance robots by analyzing conventional robots with respect to six main features.
Antiskin Aging Effects of Indole Alkaloid N-Glycoside from Ginkgo Fruit ( Ginkgo biloba fruit) on TNF-α-Exposed Human Dermal Fibroblasts

Human skin aging has internal and external factors, both of which are characterized by TNF-α overproduction. Therefore, we aimed to identify a natural product that suppresses the damage that occurs in cutaneous dermal fibroblasts exposed to TNF-α. The protective effects of the indole alkaloid N-glycoside, ginkgoside B dimethyl ester (GBDE), isolated from ginkgo fruit (Ginkgo biloba fruit) were evaluated in TNF-α stimulated human dermal fibroblasts (HDFs). GBDE inhibited TNF-α-induced MMP-1 expression to 2.2 ± 0.1-fold (p < 0.01) and reversed the decrease in collagen levels to 0.4 ± 0.00-fold (p < 0.01) at 50 μM. The effect of GBDE was due to the suppression of the phospolylaton of MAPKs (ERK, 0.47 ± 0.05; JNK, 1.21 ± 0.07; p38, 0.77 ± 0.07-folds, p < 0.001) and Akt (0.14 ± 0.03-fold, p < 0.001) compared to the TNF-α group. GBDE also reduced the expression of COX-2 to 2.06 ± 0.12-fold (p < 0.001) and increased the expression of HO-1 to 10.64 ± 0.2-fold (p < 0.001). In addition, GBDE inhibited the expression of the pro-inflammatory cytokines (IL-8, 2.2 ± 0.0; IL-1β, 1.6 ± 0.0; IL-6, 2.0 ± 0.10-folds, p < 0.05). These results provide experimental evidence that GBDE can protect against skin damage, including aging.
BiTransformer: augmenting semantic context in video captioning via bidirectional decoder

Video captioning is an important problem involved in many applications. It aims to generate some descriptions of the content of a video. Most of existing methods for video captioning are based on the deep encoder-decoder models, particularly, the attention-based models (say Transformer). However, the existing transformer-based models may not fully exploit the semantic context, that is, only using the left-to-right style of context but ignoring the right-to-left counterpart. In this paper, we introduce a bidirectional (forward-backward) decoder to exploit both the left-to-right and right-to-left styles of context for the Transformer-based video captioning model. Thus, our model is called bidirectional Transformer (dubbed BiTransformer). Specifically, in the bridge of the encoder and forward decoder (aiming to capture the left-to-right context) used in the existing Transformer-based models, we plug in a backward decoder to capture the right-to-left context. Equipped with such bidirectional decoder, the semantic context of videos will be more fully exploited, resulting in better video captions. The effectiveness of our model is demonstrated over two benchmark datasets, i.e., MSVD and MSR-VTT,via comparing to the state-of-the-art methods. Particularly, in terms of the important evaluation metric CIDEr, the proposed model outperforms the state-of-the-art models with improvements of 1.2% in both datasets.
A Survey on Mobile Learning for Adult Learners: State-of-the-Art, Taxonomy, and Challenges

Adult learner performance has become a significant challenge in education. Adult learners are identified as being of productive age in engaging with multiple commitments, including studying. The multiple engagements make managing their time and focus difficult, contributing to a high institutional dropout rate. This situation has reduced the number of highly skilled and professional workers in the country because of the failure to meet academic requirements. In the current digital world, every aspect of education includes some technology elements. However, most adult learners are reluctant to utilize mobile technology for learning. Therefore, this review paper aims to provide state of the art on mobile learning (m-Learning) for adult learners. Considering a set of inclusion and exclusion criteria, 135 related articles from IEEE, Google Scholar, Springer Link, Science Direct, Taylor and Francis, and Wiley digital databases were extracted and reviewed. State of the art were discussed in terms of functions of m-Learning, effectiveness, and m-Learning as a tool. Keywords from articles were extracted and the taxonomy of m-Learning was discussed under five classifications (behavioural intention, technological support, educational content, learner coordination, and instructional design), representing the same meaning and features discussed in the articles. The findings can raise awareness among mobile learning practitioners to provide more effective services for adult learners. Meanwhile, higher education institutions can be better redesigned in terms of pedagogy to fit the needs of adult learners with mobile learning.
Patient-Derived Organoids for In Vivo Validation of In Vitro Data

Patient-derived organoids are promising tumor models for functional validation of next-generation sequencing-based therapy recommendations. In times of rapidly advancing precision oncology approaches in everyday clinical processes, reliable and valid tumor models are required. Tumor organoids consist of tumor "stem" cells, differentiated (epithelial) tumor, and stroma cells. The cellular architecture and interactions closely mimic the original patient tumor. These organoids can be implanted into immunodeficient mice, generating patient-derived organoid-derived xenografts, thus enabling in vitro to in vivo transfer. Most importantly, the high clinical relevance of PDO models is maintained in this conversion. This protocol describes in detail the methods and techniques as well as the materials necessary to generate in vitro PDO and in vivo PDO-derived xenograft models. The elaborate process description starts with the processing of freshly obtained tumor tissue. The proceedings include tissue processing, organoid culturing, PDO implantation into immunodeficient mice, tumor explantation, and finally tumor preservation. All these proceedings are described in this timely chronological order. This protocol will enable researchers to generate PDO models from freshly received tumor tissue and generate PDO-derived xenografts. Models generated according to these methods are suitable for a very broad research spectrum.
A survey of recent grinding wheel topography models

This paper provides a survey of grinding wheel topography models. Recent 1D, 2D, and 3D models are reviewed, and the important model components for a state-of-the-art 3D topography model are identified. Future directions for topography modeling are recommended and, based on this survey, a general modelling approach using grain size, shape, arrangement, and wheel dressing is proposed. (c) 2005 Elsevier Ltd. All rights reserved.
User-Driven Fine-Tuning for Beat Tracking

The extraction of the beat from musical audio signals represents a foundational task in the field of music information retrieval. While great advances in performance have been achieved due the use of deep neural networks, significant shortcomings still remain. In particular, performance is generally much lower on musical content that differs from that which is contained in existing annotated datasets used for neural network training, as well as in the presence of challenging musical conditions such as rubato. In this paper, we positioned our approach to beat tracking from a real-world perspective where an end-user targets very high accuracy on specific music pieces and for which the current state of the art is not effective. To this end, we explored the use of targeted fine-tuning of a state-of-the-art deep neural network based on a very limited temporal region of annotated beat locations. We demonstrated the success of our approach via improved performance across existing annotated datasets and a new annotation-correction approach for evaluation. Furthermore, we highlighted the ability of content-specific fine-tuning to learn both what is and what is not the beat in challenging musical conditions.
Locative Meaning-making: An Arts-based Approach to Learning for Sustainable Development

The term sustainable development is often criticized for having lost credibility due to a lack of clear-cut delineation. The same holds true for education designed to foster sustainable development often referred to as education for sustainable development (ESD). This contribution agrees that the term suffers from a want of meaning, but argues that the persistent hunt for a definition-i.e., a fixed generic description-produces rather than resolves this deficit. What sustainable development means is context and time dependent and is therefore necessarily ambiguous, open-ended and dynamic. Hence, the success of ESD depends on the paradoxical imperative of reducing vagueness while at the same time maintaining ambiguity. This paper explores how this can be established and proposes a process informed by the arts. Drawing from dialogic practices, site-specific theatre and a project conducted in a British village, this writing discusses elements that constitute a process of. context-based meaning finding.. It concludes that ESD essentially starts with and revolves around re-embedding SD in life and the act of living, engaging people in place through processes in which communities yield their own, context and time specific interpretations of sustainable development.
Detecting Intimate Partner Violence During Pregnancy Using Municipal Pregnancy Registration Records: An Administrative Data Analysis

Intimate partner violence (IPV) during pregnancy needs to be prevented because it leads to negative health outcomes for both the mother and offspring. However, it is not easy to detect women who suffer from IPV by health practitioners or public health staff due to stigma attached to it or hesitation of the women to disclose it. The aim of this study is to develop a scale using pregnancy registration records to detect IPV during pregnancy. We used administrative data of pregnancy registration records of Adachi City, Tokyo, in the 2016 fiscal year (N = 5,990). IPV was assessed at the first interview or another opportunity for further assessment by a public health nurse. The data include registration information, demographics, health and perinatal status, and social environment. Multiple logistic regression model was used to predict IPV. IPV cases were found for 24 (0.4%) cases. Subsequent child (odds ratio [OR]: 3.45, 95% confidence interval [CI] [1.02-11.6]), single marital status (OR: 7.96, 95% CI [2.88-22.2]), thinness (OR: 3.17, 95% CI [1.13-8.90]), past pregnancy of four or more times (OR: 5.25, 95% CI [1.35-20.4]), having trouble with family member (OR: 5.45, 95% CI [1.95, 15.2]), and poverty (OR: 6.27, 95% CI [2.25-17.5]) showed significant association with IPV. These variables detected IPV with good predictive power (area under receiver operating characteristic curve = 0.89, 95% CI [0.81-0.98]). We showed strong detectability of IPV during pregnancy using a scale based on pregnancy registration records in which IPV was not asked directly. The current study is useful to detect IPV during pregnancy and prevent further adverse health outcomes due to IPV during pregnancy.
Prolonged Therapy Is Not Associated with Delayed Identification of Recurrent Intra-Abdominal Infection

Background: The Study to Optimize Peritoneal Infection Therapy (STOP-IT) Trial identified an association between prolonged antibiotic therapy and delayed identification of recurrent intra-abdominal infection (IAI). However, this association has not been observed in other studies. The purpose of this study was to evaluate the association between recurrent IAIs and the duration of antibiotic agents. Patients and Methods: Adult patients from 2016 to 2020 who underwent a source control procedure for a colon-related complicated IAI were identified. Patients not meeting the inclusion criteria were excluded. Demographics, comorbidities, post-operative antibiotic duration, and presence of secondary intra-abdominal infection were recorded. The primary outcome was the time to identification of secondary IAI. Delayed identification of recurrent infection was identified as 10 or more days following source control procedure. Statistical analysis using χ2, Fisher exact, and Wilcoxon rank sum were used where appropriate. Results: Seventy-six of the patients identified met inclusion criteria, and 17 (22.4%) of those patients had a recurrent IAI. Patients with recurrent infections were slightly younger (64 vs. 60 years; p = 0.01) and had lower rates of pre-operative anticoagulation (50.8% vs. 17.6%). There were no differences in the initial length of antibiotic therapy after source control between the recurrent infection and non-recurrent groups (p = 0.6). There was a difference in total days of antibiotic use between the two groups, with the recurrent infection group averaging 10 more days of antibiotic use than the non-recurrence group (p < 0.0001). In those patients with a recurrence, there were no differences in median days to identification (9 vs. 11.5 days; p = 0.29) or the rate of those with delayed identification of recurrent infection (44.4% vs. 75%; p = 0.33). Conclusions: Similar to the STOP-IT Trial we failed to identify an association between the duration of post-operative antibiotic agents and recurrent infection. However, we further failed to identify an association between the prolonged post-operative courses and the timing of identification of the recurrent infection. Further evaluation is needed to determine if prolonged therapy delays the identification of recurrent infection.
Web mining in soft computing framework: Relevance, state of the art and future directions

This paper summarizes the different characteristics of web data, the basic components of web mining and its different types, and their current states of the art. The reason for considering web mining, a separate field from data mining, is explained. The limitations of some of the existing web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) highlighted. A survey of the existing literature on "soft web mining" is provided along with the commercially available systems. The prospective areas of web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft web mining" systems is explained. An extensive bibliography is also provided.
Innovative application research on the combination of art design and engineering practice education under the background of new media

Under the background of the continuous impact of new media, with the deepening of art education reform, more creative teaching thinking and teaching methods are integrated into the art design education classroom in universities. In the continuous progress of art design major, how to change the traditional art education classroom and make the teaching more innovative has become one of the research contents that the teaching workers focus on. Therefore, in order to export interdisciplinary talents to the society, it is an inevitable trend for universities to act interdisciplinary teaching and learning mode in combination with the background of new engineering and the widely used STEAM concept. This paper analyzes the teaching mode of art design major in higher education, and then analyzes the current situation of engineering practice courses in universities. According to the characteristics of art students and engineering students, it explores the interdisciplinary teaching mode of introducing art design elements into engineering practice courses, which can provide the orderly development of art education with reference in universities.
Sensitivity analysis of coupled interconnects for RFIC applications

This paper investigates the sensitivity of on-wafer coupled interconnects to the Si CMOS process parameters. Experiments are conducted to emulate state-of-the-art and future technologies. Some important parameters characterizing the coupled interconnects have been examined. The influence of the process parameters on transmission, reflection, near-end, and far-end crosstalk capacities of the coupled interconnects are discussed.