Improving Scientific Literature Classification: A Parameter-Efficient Transformer-Based Approach

Transformer-based models have been utilized in natural language processing (NLP) for a wide variety of tasks like summarization, translation, and conversational agents. These models can capture long-term dependencies within the input, so they have significantly more representational capabilities than Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Nevertheless, these models require significant computational resources in terms of high memory usage, and extensive training time. In this paper, we propose a novel document categorization model, with improved parameter efficiency that encodes text using a single, lightweight, multiheaded attention encoder block. The model also uses a hybrid word and position embedding to represent input tokens. The proposed model is evaluated for the Scientific Literature Classification task (SLC) and is compared with state-of-the-art models that have previously been applied to the task. Ten datasets of varying sizes and class distributions have been employed in the experiments. The proposed model shows significant performance improvements, with a high level of efficiency in terms of parameter and computation resource requirements as compared to other transformer-based models, and outperforms previously used methods

Lokalna primjena hemina unapređuje liječenje rana u štakora s dijabetesom izazvanim streptozotocinom

Hemin may be of potential therapeutic value in wound healing management in diabetics. It is an inducer of heme oxygenase-1, an enzyme which degrades heme and participates in cellular protection against oxidative stress, inflammation and apoptosis. Thus, in the present study, hemin (0.5%) was applied topically over excision wounds, and its therapeutic effect in wound healing evaluated in diabetic rats. Topical hemin application significantly increased the percentage of wound contraction on day 2 in diabetic rats, however, povidone-iodine did the same on day 7 compared to the diabetic control. A significant increase in hydroxyproline and glucosamine content was found on day 14 in the hemin treated wounds of diabetic rats vs. the diabetic control. The histology of the hemin treated rats was in agreement with the cellular proliferation and collagen synthesis in granulation tissue. Hemin significantly increases cytokine IL-10 and decreases TNF-α in the granulation tissue of the healed wounds of diabetic rats. The finding showing the pro-healing effects of hemin was endorsed by inhibition of mRNA expression of pro-inflammatory cytokine TNF-α and adhesion molecule ICAM-1, and up-regulation of anti-inflammatory cytokine IL-10 mRNA. Hence, topical hemin application (i) helps in early and fast wound contraction (ii) enhances the hydroxyproline and glucosamine content of wounds and (iii) modulates pro-healing mRNA expression of cytokines.Hemin ima potencijalnu terapijsku vrijednost u liječenju rana u dijabetičara. On potiče hem-oksigenazu-1, enzim koji razgrađuje hem i sudjeluje u staničnoj zaštiti od oksidacijskog stresa, upale i apoptoze. U ovom je istraživanju hemin (0,5 %) primijenjen lokalno na ekscizijske rane te je procijenjen njegov terapijski učinak na cijeljenje rana u dijabetičnih štakora. Lokalna aplikacija hemina znakovito je povećala postotak zatvaranja rana 2. dan u dijabetičnih štakora, što je učinio i povidon-jod 7. dan u kontrolnoj skupini. Znakovit porast sadržaja hidroksiprolina i glukozamina pronađen je 14. dan u dijabetičnih štakora čije su rane tretirane heminom, za razliku od kontrolne skupine. Histologija je u štakora tretiranih heminom bila u skladu sa staničnom proliferacijom i sintezom kolagena u granulacijskom tkivu. Hemin je znakovito povisio citokin IL-10 i smanjio TNF-α u granulacijskom tkivu dijabetičnih štakora sa zacijeljenim ranama. Taj nalaz odgovara ljekovitom učinku hemina što je podržano inhibicijom ekspresije mRNA proupalnog citokina TNF-α i adhezijom molekule ICAM-1 te regulacijom protuupalnog cittokina IL-10 mRNA. Dakle, lokalna primjena hemina pomaže (i) u ranoj i brzoj kontrakciji rana (ii), poboljšava sadržaj hidroksiprolina i glukozamina u ranama i (iii) prilagođuje mRNA ekspresiju citokina u smjeru cijeljenja rane

A CRISPR way for accelerating cereal crop improvement: Progress and challenges

Humans rely heavily on cereal grains as a key source of nutrients, hence regular improvement of cereal crops is essential for ensuring food security. The current food crisis at the global level is due to the rising population and harsh climatic conditions which prompts scientists to develop smart resilient cereal crops to attain food security. Cereal crop improvement in the past generally depended on imprecise methods like random mutagenesis and conventional genetic recombination which results in high off targeting risks. In this context, we have witnessed the application of targeted mutagenesis using versatile CRISPR-Cas systems for cereal crop improvement in sustainable agriculture. Accelerated crop improvement using molecular breeding methods based on CRISPR-Cas genome editing (GE) is an unprecedented tool for plant biotechnology and agriculture. The last decade has shown the fidelity, accuracy, low levels of off-target effects, and the high efficacy of CRISPR technology to induce targeted mutagenesis for the improvement of cereal crops such as wheat, rice, maize, barley, and millets. Since the genomic databases of these cereal crops are available, several modifications using GE technologies have been performed to attain desirable results. This review provides a brief overview of GE technologies and includes an elaborate account of the mechanisms and applications of CRISPR-Cas editing systems to induce targeted mutagenesis in cereal crops for improving the desired traits. Further, we describe recent developments in CRISPR-Cas–based targeted mutagenesis through base editing and prime editing to develop resilient cereal crop plants, possibly providing new dimensions in the field of cereal crop genome editing

Analysis of Mitochondrial DNA Sequences in Childhood Encephalomyopathies Reveals New Disease-Associated Variants

BACKGROUND: Mitochondrial encephalomyopathies are a heterogeneous group of clinical disorders generally caused due to mutations in either mitochondrial DNA (mtDNA) or nuclear genes encoding oxidative phosphorylation (OXPHOS). We analyzed the mtDNA sequences from a group of 23 pediatric patients with clinical and morphological features of mitochondrial encephalopathies and tried to establish a relationship of identified variants with the disease. METHODOLOGY/PRINCIPLE FINDINGS: Complete mitochondrial genomes were amplified by PCR and sequenced by automated DNA sequencing. Sequencing data was analyzed by SeqScape software and also confirmed by BLASTn program. Nucleotide sequences were compared with the revised Cambridge reference sequence (CRS) and sequences present in mitochondrial databases. The data obtained shows that a number of known and novel mtDNA variants were associated with the disease. Most of the non-synonymous variants were heteroplasmic (A4136G, A9194G and T11916A) suggesting their possibility of being pathogenic in nature. Some of the missense variants although homoplasmic were showing changes in highly conserved amino acids (T3394C, T3866C, and G9804A) and were previously identified with diseased conditions. Similarly, two other variants found in tRNA genes (G5783A and C8309T) could alter the secondary structure of Cys-tRNA and Lys-tRNA. Most of the variants occurred in single cases; however, a few occurred in more than one case (e.g. G5783A and A10149T). CONCLUSIONS AND SIGNIFICANCE: The mtDNA variants identified in this study could be the possible cause of mitochondrial encephalomyopathies with childhood onset in the patient group. Our study further strengthens the pathogenic score of known variants previously reported as provisionally pathogenic in mitochondrial diseases. The novel variants found in the present study can be potential candidates for further investigations to establish the relationship between their incidence and role in expressing the disease phenotype. This study will be useful in genetic diagnosis and counseling of mitochondrial diseases in India as well as worldwide

Springer SLC Dataset

A dataset containing scientific literature abstracts labeled according to their domains and areas

Springer SLC Dataset

A dataset containing scientific literature abstracts labeled according to their domains and areas

Nature SLC Dataset

A dataset containing scientific literature abstracts labelled according to their domains and areas

An Annotation Engine for Supporting Video Database Population

Abstract–Segmentation, video data modeling, and annotation are indispensable operations necessary for creating and populating a video database. To support such video databases, annotation data can be collected as metadata for the database and subsequently used for in-dexing and query evaluation. In this paper we describe the design and development of a video annotation engine, called Vane, intended to solve this problem as a domain-independent video annotation application. Using the Vane tool, the annotation of raw video data is achieved through metadata collection. This process, which is performed semi-automatically, produces tailored SGML documents whose purpose is to describe information about the video content. These docu-ments constitute the metadatabase component of the video database. The video data model which has been developed for the metadata, is as open as possible for multiple domain-specific applications. The tool is currently in use to annotate a video archive comprised of educational and news video content

SVM based Generative Adverserial networks for Federated learning and Edge Computing Attack Model and Outpoising

Machine learning algorithms are prone to attacks: An attackers can use the malicious nodes to attack the training dataset to manipulate the process of learning and reduce the efficiency of the algorithm working performance. Optimal poisoning attacks have already been proposed to evaluate worst case scenarios, modelling attacks as a bilevel optimization problem. Solving these problems is computationally demanding and has limited applicability for some models such as deep networks. In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i.e. samples that look like genuine data points but that reduce the accuracy of the classifier in the process of training process. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks