Polar Cremona Transformations and Monodromy of Polynomials

Consider the gradient map associated to any non-constant homogeneous polynomial f\in \C[x_0,...,x_n] of degree $d$, defined by \phi_f=grad(f): D(f)\to \CP^n, (x_0:...:x_n)\to (f_0(x):...:f_n(x)) where D(f)=\{x\in \CP^n; f(x)\neq 0\} is the principal open set associated to $f$ and $f_i=\frac{\partial f}{\partial x_i}$. This map corresponds to polar Cremona transformations. In Proposition \ref{p1} we give a new lower bound for the degree $d(f)$ of $\phi_f$ under the assumption that the projective hypersurface $V:f=0$ has only isolated singularities. When $d(f)=1$, Theorem \ref{t4} yields very strong conditions on the singularities of $V$.Comment: 8 page

Overview of Cell Signaling Pathways in Cancer

The final authenticated version is available online at https://doi.org/10.1007/978-3-319-95228-4_12

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

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

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

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

Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures.

International audienceIn the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks

Trazodone regulates neurotrophic/growth factors, mitogen-activated protein kinases and lactate release in human primary astrocytes

Background: In the central nervous system, glial cells provide metabolic and trophic support to neurons and respond to protracted stress and insults by up-regulating inflammatory processes. Reactive astrocytes and microglia are associated with the pathophysiology of neuronal injury, neurodegenerative diseases and major depression, in both animal models and human brains. Several studies have reported clear anti-inflammatory effects of anti-depressant treatment on astrocytes, especially in models of neurological disorders. Trazodone (TDZ) is a triazolopyridine derivative that is structurally unrelated to other major classes of antidepressants. Although the molecular mechanisms of TDZ in neurons have been investigated, it is unclear whether astrocytes are also a TDZ target. Methods: The effects of TDZ on human astrocytes were investigated in physiological conditions and following inflammatory insult with lipopolysaccharide (LPS) and tumour necrosis factor-aα (TNF-aα). Astrocytes were assessed for their responses to pro-inflammatory mediators and cytokines, and the receptors and signalling pathways involved in TDZ-mediated effects were evaluated. Results: TDZ had no effect on cell proliferation, but it decreased pro-inflammatory mediator release and modulated trophic and transcription factor mRNA expression. Following TDZ treatment, the AKT pathway was activated, whereas extracellular signal-regulated kinase and c-Jun NH2-terminal kinase were inhibited. Most importantly, a 72-h TDZ pre-treatment before inflammatory insult completely reversed the anti-proliferative effects induced by LPS-TNF-aα. The expression or the activity of inflammatory mediators, including interleukin-6, c-Jun NH2-terminal kinase and nuclear factor ΚB, were also reduced. Furthermore, TDZ affected astrocyte metabolic support to neurons by counteracting the inflammation-mediated lactate decrease. Finally, TDZ protected neuronal-like cells against neurotoxicity mediated by activated astrocytes. These effects mainly involved an activation of 5-HT1A and an antagonism at 5-HT2A/C serotonin receptors. Fluoxetine, used in parallel, showed similar final effects nevertheless it activates different receptors/intracellular pathways. Conclusions: Altogether, our results demonstrated that TDZ directly acts on astrocytes by regulating intracellular signalling pathways and increasing specific astrocyte-derived neurotrophic factor expression and lactate release. TDZ may contribute to neuronal support by normalizing trophic and metabolic support during neuroinflammation, which is associated with neurological diseases, including major depression