A Brief Review on phytoconstituents and Ethnopharmacology of Scoparia dulcis Linn. (Scrophulariaceae)

Scoparia dulcis Linn.(S. dulcis)or sweet broom weed commonly known as Mithipatti and Bana Dhania in Western Orissa, it is also known as ‘GhodaTulsi’in Hindi. The present review attempts to narrate the chemical constituents of S.dulcis and their uses. S.dulcis is rich in flavones, terpenes and steroids. Main chemical constituents such as scoparic acid A-C, scopadulcic acid A and B, scopadulciol, scopadulin and ammelin have been shown to contribute to the observed medicinal effect of the plant. In this review we have composed the structure and functions of those active ingredients with their melting point and other physical properties individually.Some aspects of the several speculated pharmacological properties of S. dulcis have been validated by scientific research, which includes the presence of hypoglycaemic and anti-tumour promoting compound. It also has antimicrobial and antifungal effects as well as antihyperlipidemic action

Back to Massey: Impressively fast, scalable and tight security evaluation tools

None of the existing rank estimation algorithms can scale to large cryptographic keys, such as 4096-bit (512 bytes) RSA keys. In this paper, we present the first solution to estimate the guessing entropy of arbitrarily large keys, based on mathematical bounds, resulting in the fastest and most scalable security evaluation tool to date. Our bounds can be computed within a fraction of a second, with no memory overhead, and provide a margin of only a few bits for a full 128-bit AES key

Computing the everyday: social media as data platforms

We conceive social media platforms as sociotechnical entities that variously shape user platform involvement and participation. Such shaping develops along three fundamental data operations that we subsume under the terms of encoding, aggregation, and computation. Encoding entails the engineering of user platform participation along narrow and standardized activity types (e.g., tagging, liking, sharing, following). This heavily scripted platform participation serves as the basis for the procurement of discrete and calculable data tokens that are possible to aggregate and, subsequently, compute in a variety of ways. We expose these operations by investigating a social media platform for shopping. We contribute to the current debate on social media and digital platforms by describing social media as posttransactional spaces that are predominantly concerned with charting and profiling the online predispositions, habits, and opinions of their user base. Such an orientation sets social media platforms apart from other forms of mediating online interaction. In social media, we claim, platform participation is driven toward an endless online conversation that delivers the data footprint through which a computed sociality is made the source of value creation and monetization

Signaling pathways responsible for the rapid antidepressant-like effects of a GluN2A-preferring NMDA receptor antagonist

In a previous study we found that the preferring GluN2A receptor antagonist, NVP-AAM077, elicited rapid antidepressant-like effects in the forced swim test that was related to the release of glutamate and serotonin in the medial prefrontal cortex. In the present work we sought to examine the duration of this behavioral effect as well as the molecular readouts involved. Our results showed that NVP-AAM077 reduced the immobility in the forced swim test 30?min and 24?h after its administration. However, this effect waned 7 days later. The rapid antidepressant-like response seems to be associated with increases in the GluA1 subunit of ?-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, mammalian target of rapamycin (mTOR) signaling, glia markers such as glial fibrillary acidic protein (GFAP) and excitatory amino acid transporter 1 (EAAT1), and a rapid mobilization of intracellular stores of brain-derived neurotrophic factor (BDNF) in the medial prefrontal cortex.Acknowledgements: M.G.-S. was recipient of a contract from the Sistema Nacional de Garantía Juvenil co-funded by the European Social Fund. We also thank Novartis for the generous gift of NVP-AAM077. This work was supported by the Instituto de Salud Carlos III, Subdirección General del Evaluación y Fomento de la Investigación (FIS Grants PI13/00038 and PI16/00217) that were co-funded by the European Regional Development Fund (‘A way to build Europe’). Funding from the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III is also acknowledged

Computational Fluid Dynamics of Catalytic Reactors

Today, the challenge in chemical and material synthesis is not only the development of new catalysts and supports to synthesize a desired product, but also the understanding of the interaction of the catalyst with the surrounding flow field. Computational Fluid Dynamics or CFD is the analysis of fluid flow, heat and mass transfer and chemical reactions by means of computer-based numerical simulations. CFD has matured into a powerful tool with a wide range of applications in industry and academia. From a reaction engineering perspective, main advantages are reduction of time and costs for reactor design and optimization, and the ability to study systems where experiments can hardly be performed, e.g., hazardous conditions or beyond normal operation limits. However, the simulation results will always remain a reflection of the uncertainty in the underlying models and physicochemical parameters so that in general a careful experimental validation is required. This chapter introduces the application of CFD simulations in heterogeneous catalysis. Catalytic reactors can be classified by the geometrical design of the catalyst material (e.g. monoliths, particles, pellets, washcoats). Approaches for modeling and numerical simulation of the various catalyst types are presented. Focus is put on the principal concepts for coupling the physical and chemical processes on different levels of details, and on illustrative applications. Models for surface reaction kinetics and turbulence are described and an overview on available numerical methods and computational tools is provided

One trace is all it takes: Machine Learning-based Side-channel Attack on EdDSA

Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. The results with convolutional neural networks (CNNs) are especially impressive as we are able to break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations

Reduced conditioned fear response in mice that lack Dlx1 and show subtype-specific loss of interneurons

The inhibitory GABAergic system has been implicated in multiple neuropsychiatric diseases such as schizophrenia and autism. The Dlx homeobox transcription factor family is essential for development and function of GABAergic interneurons. Mice lacking the Dlx1 gene have postnatal subtype-specific loss of interneurons and reduced IPSCs in their cortex and hippocampus. To ascertain consequences of these changes in the GABAergic system, we performed a battery of behavioral assays on the Dlx1 mutant mice, including zero maze, open field, locomotor activity, food intake, rotarod, tail suspension, fear conditioning assays (context and trace), prepulse inhibition, and working memory related tasks (spontaneous alteration task and spatial working memory task). Dlx1 mutant mice displayed elevated activity levels in open field, locomotor activity, and tail suspension tests. These mice also showed deficits in contextual and trace fear conditioning, and possibly in prepulse inhibition. Their learning deficits were not global, as the mutant mice did not differ from the wild-type controls in tests of working memory. Our findings demonstrate a critical role for the Dlx1 gene, and likely the subclasses of interneurons that are affected by the lack of this gene, in behavioral inhibition and associative fear learning. These observations support the involvement of particular components of the GABAergic system in specific behavioral phenotypes related to complex neuropsychiatric diseases

Gene Expression Changes in the Prefrontal Cortex, Anterior Cingulate Cortex and Nucleus Accumbens of Mood Disorders Subjects That Committed Suicide

Suicidal behaviors are frequent in mood disorders patients but only a subset of them ever complete suicide. Understanding predisposing factors for suicidal behaviors in high risk populations is of major importance for the prevention and treatment of suicidal behaviors. The objective of this project was to investigate gene expression changes associated with suicide in brains of mood disorder patients by microarrays (Affymetrix HG-U133 Plus2.0) in the dorsolateral prefrontal cortex (DLPFC: 6 Non-suicides, 15 suicides), the anterior cingulate cortex (ACC: 6NS, 9S) and the nucleus accumbens (NAcc: 8NS, 13S). ANCOVA was used to control for age, gender, pH and RNA degradation, with P≤0.01 and fold change±1.25 as criteria for significance. Pathway analysis revealed serotonergic signaling alterations in the DLPFC and glucocorticoid signaling alterations in the ACC and NAcc. The gene with the lowest p-value in the DLPFC was the 5-HT2A gene, previously associated both with suicide and mood disorders. In the ACC 6 metallothionein genes were down-regulated in suicide (MT1E, MT1F, MT1G, MT1H, MT1X, MT2A) and three were down-regulated in the NAcc (MT1F, MT1G, MT1H). Differential expression of selected genes was confirmed by qPCR, we confirmed the 5-HT2A alterations and the global down-regulation of members of the metallothionein subfamilies MT 1 and 2 in suicide completers. MTs 1 and 2 are neuro-protective following stress and glucocorticoid stimulations, suggesting that in suicide victims neuroprotective response to stress and cortisol may be diminished. Our results thus suggest that suicide-specific expression changes in mood disorders involve both glucocorticoids regulated metallothioneins and serotonergic signaling in different regions of the brain

Resilient emotionality and molecular compensation in mice lacking the oligodendrocyte-specific gene Cnp1

Altered oligodendrocyte structure and function is implicated in major psychiatric illnesses, including low cell number and reduced oligodendrocyte-specific gene expression in major depressive disorder (MDD). These features are also observed in the unpredictable chronic mild stress (UCMS) rodent model of the illness, suggesting that they are consequential to environmental precipitants; however, whether oligodendrocyte changes contribute causally to low emotionality is unknown. Focusing on 2′-3′-cyclic nucleotide 3′-phosphodiesterase (Cnp1), a crucial component of axoglial communication dysregulated in the amygdala of MDD subjects and UCMS-exposed mice, we show that altered oligodendrocyte integrity can have an unexpected functional role in affect regulation. Mice lacking Cnp1 (knockout, KO) displayed decreased anxiety- and depressive-like symptoms (i.e., low emotionality) compared with wild-type animals, a phenotypic difference that increased with age (3–9 months). This phenotype was accompanied by increased motor activity, but was evident before neurodegenerative-associated motor coordination deficits (⩽9–12 months). Notably, Cnp1KO mice were less vulnerable to developing a depressive-like syndrome after either UCMS or chronic corticosterone exposure. Cnp1KO mice also displayed reduced fear expression during extinction, despite normal amygdala c-Fos induction after acute stress, together implicating dysfunction of an amygdala-related neural network, and consistent with proposed mechanisms for stress resiliency. However, the Cnp1KO behavioral phenotype was also accompanied by massive upregulation of oligodendrocyte- and immune-related genes in the basolateral amygdala, suggesting an attempt at functional compensation. Together, we demonstrate that the lack of oligodendrocyte-specific Cnp1 leads to resilient emotionality. However, combined with substantial molecular changes and late-onset neurodegeneration, these results suggest the low Cnp1 seen in MDD may cause unsustainable and maladaptive molecular compensations contributing to the disease pathophysiology