Neutrinoless Double Beta Decay: 2015 Review

The discovery of neutrino masses through the observation of oscillations boosted the importance of neutrinoless double beta decay (0 nu beta beta). In this paper, we review the main features of this process, underlining its key role from both the experimental and theoretical point of view. In particular, we contextualize the 0 nu beta beta in the panorama of lepton number violating processes, also assessing some possible particle physics mechanisms mediating the process. Since the 0 nu beta beta existence is correlated with neutrino masses, we also review the state of the art of the theoretical understanding of neutrinomasses. In the final part, the status of current 0 nu beta beta experiments is presented and the prospects for the future hunt for 0 nu beta beta are discussed. Also, experimental data coming from cosmological surveys are considered and their impact on 0 nu beta beta expectations is examined

Toward the discovery of matter creation with neutrinoless β β decay

The discovery of neutrinoless β β decay could soon be within reach. This hypothetical ultrarare nuclear decay offers a privileged portal to physics beyond the standard model of particle physics. Its observation would constitute the discovery of a matter-creating process, corroborating leading theories of why the Universe contains more matter than antimatter, and how forces unify at high energy scales. It would also prove that neutrinos and antineutrinos are not two distinct particles but can transform into each other, with their mass described by a unique mechanism conceived by Majorana. The recognition that neutrinos are not massless necessitates an explanation and has boosted interest in neutrinoless β β decay. The field stands now at a turning point. A new round of experiments is currently being prepared for the next decade to cover an important region of parameter space. In parallel, advances in nuclear theory are laying the groundwork to connect the nuclear decay with the underlying new physics. Meanwhile, the particle theory landscape continues to find new motivations for neutrinos to be their own antiparticle. This review brings together the experimental, nuclear theory, and particle theory aspects connected to neutrinoless β β decay to explore the path toward, and beyond, its discovery

Impulsivity Markers in Parkinsonian Subthalamic Single-Unit Activity

Impulsive-compulsive behaviors are common in Parkinson's disease (PD) patients. However, the basal ganglia dysfunctions associated with high impulsivity have not been fully characterized. The objective of this study was to identify the features associated with impulsive-compulsive behaviors in single neurons of the subthalamic nucleus (STN)

Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics

Multisensory features of peripersonal space representation: an analysis via neural network modelling.

The peripersonal space (PPS) is the space immediately surrounding the body. It is coded in the brain in a multisensory, body part-centered (e.g. hand-centered, trunk-centered), modular fashion. This is supported by the existence of multisensory neurons (in fronto-parietal areas) with tactile receptive field on a specific body part (hand, arm, trunk, etc.) and visual/auditory receptive field surrounding the same body part. Recent behavioural results (Serino et al. Sci Rep 2015), obtained by using an audio-tactile paradigm, have further supported the existence of distinct PPS representations, each specific of a single body part (hand, trunk, face) and characterized by specific properties. That study has also evidenced that the PPS representations– although distinct – are not independent. In particular, the hand-PPS loses its properties and assumes those of the trunk-PPS when the hand is close to the trunk, as the hand-PPS was encapsulated within the trunk-PPS. Similarly, the face-PPS appears to be englobed into the trunk-PPS. It remains unclear how this interaction, which manifests behaviourally, can be implemented at a neural level by the modular organization of PPS representations. The aim of this Thesis is to propose a neural network model to help the comprehension of the underlying neurocomputational mechanisms. The model includes three subnetworks devoted to the single PPS representations around the hand, face and the trunk. Furthermore, interaction mechanisms– controlled by proprioceptive neurons – have been postulated among the subnetworks. The network is able to reproduce the behavioural data, explaining them in terms of neural properties and response. Moreover, the network provides some novel predictions, that can be tested in vivo. One of this prediction has been tested in this work, by performing an ad-hoc behavioural experiment at the Laboratory of Cognitive Neuroscience (Campus Biotech, Geneva) under the supervision of the neuropsychologist Dr Serino

A Neural Network Model of Peripersonal Space Representation Around Different Body Parts

The Peripersonal Space (PPS), the space immediately surrounding the body, is coded in a multisensory, body part-centered (e.g hand-centered, trunk-centered), modular fashion. This coding is ascribed to multisensory neurons that integrate tactile stimuli on a specific body part (e.g. hand, trunk) with visual/auditory information occurring near the same body part. A recent behavioral study, using an audiotactile psychophysical paradigm, evidenced that different body parts (hand and trunk) have distinct but not independent PPS representations. The hand-PPS exhibited properties different from the trunk-PPS when the hand was placed far from the trunk, while it assumed the same properties as the trunk-PPS when the hand was placed near the trunk. Here, we propose a neural network model to help unrevealing the underlying neurocomputational mechanisms. The model includes two subnetworks, devoted to PPS representations around the hand and around the trunk. Each subnetwork contains two areas of unisensory (tactile and auditory) neurons communicating, via feedforward and feedback synapses, with a pool of audiotactile multisensory neurons. The two subnetworks are characterized by different properties of the multisensory neurons. An interaction mechanism is postulated between the two subnetworks, controlled by proprioceptive neurons coding the hand position. Results show that the network is able to reproduce the behavioral data. Network mechanisms are commented and novel predictions provided

35. Healthcare (Data Science in)

This Encyclopedia brings together jurists, computer scientists, and data analysts to map the emerging field of data science and law for the first time, uncovering the challenges, opportunities, and fault lines that arise as these groups are increasingly thrown together by expanding attempts to regulate and adapt to a data-driven world. It explains the concepts and tools at the crossroads of the many disciplines involved in data science and law, bridging scientific and applied domains. Entries span algorithmic fairness, consent, data protection, ethics, healthcare, machine learning, patents, surveillance, transparency and vulnerability.Peer ReviewedPreprin