Massive vectors and loop observables: the g−2g-2 case

We discuss the use of massive vectors for the interpretation of some recent experimental anomalies, with special attention to the muon $g-2$. We restrict our discussion to the case where the massive vector is embedded into a spontaneously broken gauge symmetry, so that the predictions are not affected by the choice of an arbitrary energy cut-off. Extended gauge symmetries, however, typically impose strong constraints on the mass of the new vector boson and for the muon $g-2$ they basically rule out, barring the case of abelian gauge extensions, the explanation of the discrepancy in terms of a single vector extension of the standard model. We finally comment on the use of massive vectors for $B$-meson decay and di-photon anomalies.Comment: 25 pages, 1 figure. References added, to appear in JHE

Isolation rearing reduces neuronal excitability in dentate gyrus granule cells of adolescent C57BL/6J mice: role of gabaergic tonic currents and neurosteroids

Early-life exposure to stress, by impacting on a brain still under development, is considered a critical factor for the increased vulnerability to psychiatric disorders and abuse of psychotropic substances during adulthood. As previously reported, rearing C57BL/6J weanling mice in social isolation (SI) from their peers for several weeks, a model of prolonged stress, is associated with a decreased plasma and brain levels of neuroactive steroids such as 3α,5α-THP, with a parallel up-regulation of extrasynaptic GABAA receptors (GABAAR) in dentate gyrus (DG) granule cells compared to group-housed (GH) mice. In the present study, together with the SI-induced decrease in plasma concentration of both progesterone and 3a,5a-THP, and an increase in THIP-stimulated GABAergic tonic currents, patch-clamp analysis of DG granule cells revealed a significant decrease in membrane input resistance and action potential (AP) firing rate, in SI compared to GH mice, suggesting that SI exerts an inhibitory action on neuronal excitability of these neurons. Voltage-clamp recordings of glutamatergic spontaneous excitatory postsynaptic currents (sEPSCs) revealed a SI-associated decrease in frequency as well as a shift from paired-pulse (PP) depression to PP facilitation (PPF) of evoked EPSCs, indicative of a reduced probability of glutamate release. Daily administration of progesterone during isolation reverted the changes in plasma 3α,5α-THP as well as in GABAergic tonic currents and neuronal excitability caused by SI, but it had only a limited effect on the changes in the probability of presynaptic glutamate release. Overall, the results obtained in this work, together with those previously published, indicate that exposure of mice to SI during adolescence reduces neuronal excitability of DG granule cells, an effect that may be linked to the increased GABAergic tonic currents as a consequence of the sustained decrease in plasma and hippocampal levels of neurosteroids. All these changes may be consistent with cognitive deficits observed in animals exposed to such type of prolonged stres

Monitoraggio e salvaguardia delle razze asinine autoctone della Sardegna attraverso l'impiego di marcatori molecolari

Sardo and Asinara donkey are classified as endangered and critical rispectively (Risk status classification–FAO). Preservation of these genetic resources is of fundamental importance to protect animal biodiversity. To evaluate the genetic variability of Sardinian donkeys and to estimate their relationships with other breeds, we genotyped 130 individuals from 7 italian breeds using 3 FISSR(Fluorescent Inter Simple Sequence Repeat) markers. We also sequenced 120 individuals for 383bp of the mitochondrial HVSI region. Although FISSR have been never used in donkey, they appear to be effective tools for genotyping analysis.In this study, Bayesian cluster analysis underline that the two sardinian breeds are well-separate from other breeds, and high genetic similarity between them.Very different patterns were found between Asinara and Barockesel donkey, despite their phenotypic similarity. In mitochondrial (HVSI region) analysis, AMOVA and Haplotype analysis reveal that Sardo and Asinara donkey are poorly differentiated within Asinara Island, whereas haplotype differences are evident between Sardinia vs Asinara populations. The median joining network shows both typical donkey CLADE1 (Somalicus) and CLADE2 (Nubian) in Sardinian population, with high prevalence of CLADE1, whereas Asinara population has haplotypes assigned exclusively to CLADE2 in Asinara breed. These results suggest the potential use these genetic for conservation plans

Bi-directional Modulation of Hyperpolarization-Activated Cation Currents (Ih) by Ethanol in Rat Hippocampal CA3 Pyramidal Neurons

It is widely acknowledged that ethanol (EtOH) can alter many neuronal functions, including synaptic signaling, firing discharge, and membrane excitability, through its interaction with multiple membrane proteins and intracellular pathways. Previous work has demonstrated that EtOH enhances the firing rate of hippocampal GABAergic interneurons and thus the presynaptic GABA release at CA1 and CA3 inhibitory synapses through a positive modulation of the hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channels. Activation of HCN channels produce an inward current, commonly called Ih, which plays an essential role in generating/regulating specific neuronal activities in GABAergic interneurons and principal glutamatergic pyramidal neurons such as those in the CA3 subregion. Since the direct effect of EtOH on HCN channels expressed in CA3 pyramidal neurons was not thoroughly elucidated, we investigated the possible interaction between EtOH and HCN channels and the impact on excitability and postsynaptic integration of these neurons. Patch-clamp recordings were performed in single CA3 pyramidal neurons from acute male rat coronal hippocampal slices. Our results show that EtOH modulates HCN-mediated Ih in a concentration-dependent and bi-directional manner, with a positive modulation at lower (20 mM) and an inhibitory action at higher (60-80 mM) concentrations. The modulation of Ih by EtOH was mimicked by forskolin, antagonized by different drugs that selectively interfere with the AC/cAMP/PKA intracellular pathway, as well as by the selective HCN inhibitor ZD7288. Altogether, these data further support the evidence that HCN channels may represent an important molecular target through which EtOH may regulate neuronal activity

Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes

Explaining vulnerabilities of deep learning to adversarial malware binaries

Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes

Sex-dependent changes of hippocampal synaptic plasticity and cognitive performance in C57BL/6J mice exposed to neonatal repeated maternal separation

The repeated maternal separation (RMS) is a useful experimental model in rodents to study the long-term influence of early-life stress on brain neurophysiology. We here investigated the influence of RMS exposure on hippocampal inhibitory and excitatory synaptic transmission, long-term synaptic plasticity and the related potential alterations in learning and memory performance in adult male and female C57Bl/6J mice. Mice were separated daily from their dam for 360 min, from postnatal day 2 (PND2) to PND17, and experiments were performed at PND60. Patch-clamp recordings in hippocampal CA1 pyramidal neurons revealed a significant enhancement of GABAergic miniature IPSC (mIPSC) frequency and a decrease in the amplitude of glutamatergic mEPSCs in male mice exposed to RMS. Only a slight but significant reduction in the amplitude of GABAergic mIPSCs was observed in females exposed to RMS compared to the relative controls. A marked increase in long-term depression (LTD) at CA3-CA1 glutamatergic synapses and in the response to the CB1r agonist win55,212 were detected in RMS male but not female mice. An impaired spatial memory and a reduced preference for novelty were observed in males exposed to RMS but not in females. A single injection of -ethynyl estradiol at PND2 prevented the changes observed in RMS male mice, suggesting that estrogens may play a protective role early in life against the exposure to stressful conditions. Our findings strengthen the idea of a sex-dependent influence of RMS on long-lasting modifications in synaptic transmission, effects that may be relevant to cognitive performance

Neurosteroidi: i modulatori endogeni delle emozioni

The discovery that facilitation or inhibition of γ aminobutyric acid (GABA)-mediated neurotransmission results in anxiolytic versus anxiogenic, hypnotic versus somnolitic, and anticonvulsant versus convulsant effects, respectively, provided important early insight into the physiology and pharmacology of central GABAergic transmission. This realization, together with subsequent evidence that high-affinity recognition sites for positive and negative allosteric modulators of GABAA receptors are located on these GABA-gated Cl– channels, led to the concept that GABAA receptors contribute directly not only to the pharmacology but also to the neurobiology and physiopathology of a variety of neurological and psychiatric diseases characterized by changes in emotional state, sleep pattern, or neuronal excitability. These findings have suggested the hypothesis that the brain and peripheral organs in mammals might produce endogenous compounds that selectively modulate central GABAA receptor function. Evidence directly supporting this hypothesis has been provided over the last decade by the discovery that steroid hormones synthesized in the brain or in peripheral organs are among the most selective, potent, and efficacious allosteric modulators of GABAA receptors. Neurosteroids are steroid derivatives that are synthesized de novo from cholesterol in the central nervous system (CNS), some of which modulate GABAA receptor function with potencies and efficacies similar to or greater than those of benzodiazepines and barbiturates. These molecules have thus been suggested to be the endogenous modulators of GABAA receptor–mediated neurotransmission. In fact some of these molecules have the capability to modulate synaptic activity by binding to membrane sites associated with ligand-gated ionotropic receptors including GABAA receptors. Here we summarize some of the most recent evidences obtained by our and other laboratories pertaining the role of two neuroactive steroids allopregnanolone (AP) and tetrahydrodeoxycorticosterone (THDOC) actives in modulating the function and plasticity of GABAA receptors in nature

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

Evaluating robustness of machine-learning models to adversarial examples is a challenging problem. Many defenses have been shown to provide a false sense of security by causing gradient-based attacks to fail, and they have been broken under more rigorous evaluations. Although guidelines and best practices have been suggested to improve current adversarial robustness evaluations, the lack of automatic testing and debugging tools makes it difficult to apply these recommendations in a systematic manner. In this work, we overcome these limitations by (i) defining a set of quantitative indicators which unveil common failures in the optimization of gradient-based attacks, and (ii) proposing specific mitigation strategies within a systematic evaluation protocol. Our extensive experimental analysis shows that the proposed indicators of failure can be used to visualize, debug and improve current adversarial robustness evaluations, providing a first concrete step towards automatizing and systematizing current adversarial robustness evaluations. Our open-source code is available at: https://github.com/pralab/IndicatorsOfAttackFailure