Explore the vast range of titles available.

Breast cancer is a deadly disease with a high mortality rate among PAN cancers. The advancements in biomedical information retrieval techniques have been beneficial in developing early prognosis and diagnosis systems for cancer patients. These systems provide the oncologist with plenty of information from several modalities to make the correct and feasible treatment plan for breast cancer patients and protect them from unnecessary therapies and their toxic side effects. The cancer patient’s related information can be collected using various modalities like clinical, copy number variation, DNA-methylation, microRNA sequencing, gene expression, and histopathological whole slide images. High dimensionality and heterogeneity in these modalities demand the development of some intelligent systems to understand related features to the prognosis and diagnosis of diseases and make correct predictions. In this work, we have studied some end-to-end systems having two main components : (a) dimensionality reduction techniques applied to original features from different modalities and (b) classification techniques applied to the fusion of reduced feature vectors from different modalities for automatic predictions of breast cancer patients into two categories: short-time and long-time survivors. Principal component analysis (PCA) and variational auto-encoders (VAEs) are used as the dimensionality reduction techniques, followed by support vector machines (SVM) or random forest as the machine learning classifiers. The study utilizes raw, PCA, and VAE extracted features of the TCGA-BRCA dataset from six different modalities as input to the machine learning classifiers. We conclude this study by suggesting that adding more modalities to the classifiers provides complementary information to the classifier and increases the stability and robustness of the classifiers. In this study, the multimodal classifiers have not been validated on primary data prospectively.

We explore the efficacy of machine learning (ML) in characterizing exoplanets into different classes. The source of the data used in this work is University of Puerto Rico’s Planetary Habitability Laboratory’s Exoplanets Catalog (PHL-EC). We perform a detailed analysis of the structure of the data and propose methods that can be used to effectively categorize new exoplanet samples. Our contributions are twofold. We elaborate on the results obtained by using ML algorithms by stating the accuracy of each method used and propose a paradigm to automate the task of exoplanet classification for relevant outcomes. In particular, we focus on the results obtained by novel neural network architectures for the classification task, as they have performed very well despite complexities that are inherent to this problem. The exploration led to the development of new methods fundamental and relevant to the context of the problem and beyond. The data exploration and experimentation also result in the development of a general data methodology and a set of best practices which can be used for exploratory data analysis experiments.

What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone running into billions, possibly a number greater than the number of stars, it is high time to start characterizing them, sorting them into classes/types just like stars, to better understand their formation paths, their properties and, ultimately, their ability to beget or sustain life. After all, we do have life thriving on one of these billions of planets, why not on others? Which planets are better suited for life and which ones are definitely not worth spending expensive telescope time on? We need to find sort of quick assessment score, a metric, using which we can make a list of promising planets and dedicate our efforts to them. Exoplanetary habitability is a transdisciplinary subject integrating astrophysics, astrobiology, planetary science, and even terrestrial environmental sciences. It became a challenging problem in astroinformatics, an emerging area in computational astronomy. Here, we review the existing metrics of habitability and the new classification schemes (machine learning (ML), neural networks, activation functions) of extrasolar planets, and provide an exposition of the use of computational intelligence techniques to evaluate habitability scores and to automate the process of classification of exoplanets. We examine how solving convex optimization techniques, as in computing new metrics such as Cobb–Douglas habitability score (CDHS) and constant elasticity earth similarity approach (CEESA), cross-validates ML-based classification of exoplanets. Despite the recent criticism of exoplanetary habitability ranking, we are sure that this field has to continue and evolve to use all available machinery of astroinformatics, artificial intelligence (AI) and machine learning. It might actually develop into a sort of same scale as stellar types in astronomy, to be used as a quick tool of screening exoplanets in important characteristics in search for potentially habitable planets (PHPs), or Earth-like planets, for detailed follow-up targets.

Quantification of habitability is a complex task. Previous attempts at measuring habitability are well documented. Classification of exoplanets, on the other hand, is a different approach and depends on quality of training data available in habitable exoplanet catalogs. Classification is the task of predicting labels of newly discovered planets based on available class labels in the catalog. We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets. Neural networks, although a powerful engine in supervised methods, often require expensive tuning efforts for optimized performance. Habitability classes are hard to discriminate, especially when attributes used as hard markers of separation are removed from the data set. The solution is approached from the point of investigating analytical properties of the proposed activation functions. The theory of ordinary differential equations and fixed point are exploited to justify the “lack of tuning efforts” to achieve optimal performance compared to traditional activation functions. Additionally, the relationship between the proposed activation functions and the more popular ones is established through extensive analytical and empirical evidence. Finally, the activation functions have been implemented in plain vanilla feed-forward neural network to classify exoplanets. The mathematical exercise supplements the grand idea of classifying exoplanets, computing habitability scores/indices and automatic grouping of the exoplanets converging at some level.

Catharanthus roseus (The Madagaskar Periwinkle) plant is commercially valued for harbouring more than 130 bioactive terpenoid indole alkaloids (TIAs). Amongst these, two of the leaf-derived bisindole alkaloids—vinblastine and vincristine—are widely used in several anticancer chemotherapies. The great pharmacological values, low in planta occurrence, unavailability of synthetic substitutes and exorbitant market cost of these alkaloids have prompted scientists to understand the basic architecture and regulation of biosynthesis of these TIAs in C. roseus plant and its cultured tissues. The knowledge gathered over a period of 30 years suggests that the TIA biosynthesis is highly regulated by developmental and environmental factors and operates through a complex multi-step enzymatic network. Extensive spatial and temporal cross talking also occurs at inter- and intracellular levels in different plant organs during TIA biogenesis. A close association of indole, methylerythritol phosphate and secoiridoid monoterpenoid pathways and involvement of at least four cell types (epidermis, internal phloem-associated parenchyma, laticifers and idioblasts) and five intracellular compartments (chloroplast, vacuole, nucleus, endoplasmic reticulum and cytosol) have been implicated with this biosynthetic mechanism. Accordingly, the research in this area is primarily advancing today to address and resolve six major issues namely: precise localization and expression of pathway enzymes using modern in situ RNA hybridization tools, mechanisms of intra- and intercellular trafficking of pathway intermediates, cloning and functional validation of genes coding for known or hitherto unknown pathway enzymes, mechanism of global regulation of the pathway by transcription factors, control of relative diversion of metabolite flux at crucial branch points and finally, strategising the metabolic engineering approaches to improve the productivity of the desired TIAs in plant or corresponding cultured tissues. The present literature update has been compiled to provide a brief overview of some of the emerging developments in our current understanding of TIA metabolism in C. roseus.

The rate at which interstellar habitable planets are being discovered would naturally warrant consideration and exploration of a number of related issues. While the physical conditions that can support persistent contact demand structural similarity of an extra-solar planet (exoplanet) to Earth, and the necessary bio-chemical conditions needed to sustain life, potential for interstellar trade and extraction remain valid nonetheless. Based on the aspects that are commonly referred to as Earth similarity and habitability, we propose a novel bi-objective optimization framework as a tool to measure Earth similarity score (CDHS). This is followed by conjectures on possible interactions between Earth similarity and habitability, via two variants of penalized multi-objective particle swarm optimization, namely speed constrained multi-objective PSO (SMPSO) and a novel variant of multi-objective quantum PSO (MOQPSO). The optimization framework dispenses of classical gradient descent/ascent approach (GD/GA) by replacing it with SMPSO and MOQPSO. The approach to the input–output relations commonly adopted in production economics can be a natural influence for modeling habitability in exoplanets. An insightful demonstration establishes this claim. The scores reveal potentially habitable planets for interstellar trade. An analytical model of colonization in an exoplanet is also presented where we derive conditions for interstellar resource extraction and the volume of trade as function of time.

Twenty-three pharmaceutically important plants, namely, Elaeocarpus spharicus, Rheum emodi, Indigofera tinctoria, Picrorrhiza kurroa, Bergenia ciliata, Lavandula officinalis, Valeriana wallichii, Coleus forskohlii, Gentiana kurroo, Saussurea lappa, Stevia rebaudiana, Acorus calamus, Pyrethrum cinerariaefolium, Aloe vera, Bacopa monnieri, Salvia sclarea, Glycyrrhiza glabra, Swertia cordata, Psoralea corylifolia, Jurinea mollis, Ocimum sanctum, Paris polyphylla, and Papaver somniferum, which are at the verge of being endangered due to their overexploitation and collection from the wild, were successfully established in vitro. Collections were made from the different biodiversity zones of India including Western Himalaya, Northeast Himalaya, Gangetic plain, Western Ghats, Semiarid Zone, and Central Highlands. Aseptic cultures were raised at the morphogenic level of callus, suspension, axillary shoot, multiple shoot, and rooted plants. Synseeds were also produced from highly proliferating shoot cultures of Bacopa monnieri, Glycyrrhiza glabra, Stevia rebaudiana, Valeriana wallichii, Gentiana kurroo, Lavandula officinalis, and Papaver somniferum. In vitro flowering was observed in Papaver somniferum, Psoralea corylifolia, and Ocimum sanctum shoots cultures. Out of 23 plants, 18 plants were successfully hardened under glasshouse conditions.

Exertion of wireless sensor networks has been increasing in recent years, and it imprints in almost all the technologies such as machine industry, medical, military and civil applications. Due to rapid growth in electronic fabrication technology, low cost, efficient, multifunctional and accurate sensors can be produced and thus engineers tend to incorporate many sensors in the area of deployment. As the number of sensors in the field increases, the probability of failure committed by these sensors also increases. Hence, efficient algorithms to detect and recover the failure of sensors are paramount. The current work concentrates mainly on mechanisms to detect sensor node failures on the basis of the delay incurred in propagation and also the energy associated with sensors in the field of deployment. The simulation shows that the algorithm plays in the best possible way to detect the failure in sensors. Finally, the Boolean sensing model is considered to calculate the network coverage of the wireless sensor network for various numbers of nodes in the network.

Ginseng, an oriental gift to the world of healthcare and preventive medicine, is among the top ten medicinal herbs globally. The constitutive triterpene saponins, ginsenosides, or panaxosides are attributed to ginseng’s miraculous efficacy towards anti-aging, rejuvenating, and immune-potentiating benefits. The major ginsenosides such as Rb1, Rb2, Rc, Rd., Re, and Rg1, formed after extensive glycosylations of the aglycone “dammaranediol,” dominate the chemical profile of this genus in vivo and in vitro. Elicitations have successfully led to appreciable enhancements in the production of these major ginsenosides. However, current research on ginseng biotechnology has been focusing on the enrichment or production of the minor ginsenosides (the less glycosylated precursors of the major ginsenosides) in ginseng preparations, which are either absent or are produced in very low amounts in nature or via cell cultures. The minor ginsenosides under current scientific scrutiny include diol ginsenosides such as Rg3, Rh2, compound K, and triol ginsenosides Rg2 and Rh1, which are being touted as the next “anti-neoplastic pharmacophores,” with better bioavailability and potency as compared to the major ginsenosides. This review aims at describing the strategies for ginsenoside production with special attention towards production of the minor ginsenosides from the major ginsenosides via microbial biotransformation, elicitations, and from heterologous expression systems.

Growth and in vitro asiaticoside accumulation in multiple shoot cultures of Centella asiatica (L.) Urban was studied as a function of nutrient manipulations in the culture media. Shoot cultures raised in liquid Murashige and Skoog medium supplemented with 2.5 mg/l kinetin attained a growth index (GI) of 6.06 along with the highest asiaticoside content of 3.8 mg/g dry weight on the 35th day of the culture cycle. The shoot growth and asiaticoside accumulation were found to be influenced by the relative proportions of NH4 +-N:NO3 −-N or Cu2+ concentration in the medium. Asiaticoside content in shoots increased from 5.3 to 8.9 and 8.7 mg/g dry weight when total nitrogen concentration of 60 mM in the control medium was reduced to 50 and 40 mM with a corresponding change in NH4 +:NO3 − ratio from 20:40 to 20:30 or 20:20, respectively. Total nitrogen level higher than 60 mM drastically reduced the asiaticoside concentration in these in vitro shoot cultures. Medium devoid of Cu2+ significantly favored higher asiaticoside accumulation in the cultured tissue (7.05 mg/g dry weight) along with an improved biomass production (GI = 7.7) when compared with shoots reared on the control medium with 0.10 μM Cu2+ (GI = 5.8; asiaticoside content = 4.4 mg/g dry weight). Carbohydrate enrichment of the medium by increasing the sucrose concentration from 3.0 to 5.0 or 7.0% was also beneficial for biomass and asiaticoside production with GI = 17.1 and 16.9 and asiaticoside content = 7.2 and 5.2 mg/g dry weight, respectively, in comparison to control cultures maintained on medium containing 3.0% sucrose. The procedure described here provides a viable production platform for generating clean and quality material from Centella with high bioactive content.