Role of Artificial Intelligence in the Internet of Things (IoT) Cybersecurity

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these three topics with the purpose of comprehensively presenting and summarizing relevant literature in these fields

Altered Cortico-Striatal–Thalamic Connectivity in Relation to Spatial Working Memory Capacity in Children with ADHD

Introduction: Attention deficit hyperactivity disorder (ADHD) captures a heterogeneous group of children, who are characterized by a range of cognitive and behavioral symptoms. Previous resting-state functional connectivity MRI (rs-fcMRI) studies have sought to understand the neural correlates of ADHD by comparing connectivity measurements between those with and without the disorder, focusing primarily on cortical–striatal circuits mediated by the thalamus. To integrate the multiple phenotypic features associated with ADHD and help resolve its heterogeneity, it is helpful to determine how specific circuits relate to unique cognitive domains of the ADHD syndrome. Spatial working memory has been proposed as a key mechanism in the pathophysiology of ADHD. Methods: We correlated the rs-fcMRI of five thalamic regions of interest (ROIs) with spatial span working memory scores in a sample of 67 children aged 7–11 years [ADHD and typically developing children (TDC)]. In an independent dataset, we then examined group differences in thalamo-striatal functional connectivity between 70 ADHD and 89 TDC (7–11 years) from the ADHD-200 dataset. Thalamic ROIs were created based on previous methods that utilize known thalamo-cortical loops and rs-fcMRI to identify functional boundaries in the thalamus. Results/Conclusion: Using these thalamic regions, we found atypical rs-fcMRI between specific thalamic groupings with the basal ganglia. To identify the thalamic connections that relate to spatial working memory in ADHD, only connections identified in both the correlational and comparative analyses were considered. Multiple connections between the thalamus and basal ganglia, particularly between medial and anterior dorsal thalamus and the putamen, were related to spatial working memory and also altered in ADHD. These thalamo-striatal disruptions may be one of multiple atypical neural and cognitive mechanisms that relate to the ADHD clinical phenotype

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

Transcriptome profiling of sheep granulosa cells and oocytes during early follicular development obtained by Laser Capture Microdissection

<p>Abstract</p> <p>Background</p> <p>Successful achievement of early folliculogenesis is crucial for female reproductive function. The process is finely regulated by cell-cell interactions and by the coordinated expression of genes in both the oocyte and in granulosa cells. Despite many studies, little is known about the cell-specific gene expression driving early folliculogenesis. The very small size of these follicles and the mixture of types of follicles within the developing ovary make the experimental study of isolated follicular components very difficult.</p> <p>The recently developed laser capture microdissection (LCM) technique coupled with microarray experiments is a promising way to address the molecular profile of pure cell populations. However, one main challenge was to preserve the RNA quality during the isolation of single cells or groups of cells and also to obtain sufficient amounts of RNA.</p> <p>Using a new LCM method, we describe here the separate expression profiles of oocytes and follicular cells during the first stages of sheep folliculogenesis.</p> <p>Results</p> <p>We developed a new tissue fixation protocol ensuring efficient single cell capture and RNA integrity during the microdissection procedure. Enrichment in specific cell types was controlled by qRT-PCR analysis of known genes: six oocyte-specific genes (<it>SOHLH2</it>, <it>MAEL</it>, <it>MATER</it>, <it>VASA</it>, <it>GDF9</it>, <it>BMP15</it>) and three granulosa cell-specific genes (<it>KL</it>, <it>GATA4</it>, <it>AMH</it>).</p> <p>A global gene expression profile for each follicular compartment during early developmental stages was identified here for the first time, using a bovine Affymetrix chip. Most notably, the granulosa cell dataset is unique to date. The comparison of oocyte vs. follicular cell transcriptomes revealed 1050 transcripts specific to the granulosa cell and 759 specific to the oocyte.</p> <p>Functional analyses allowed the characterization of the three main cellular events involved in early folliculogenesis and confirmed the relevance and potential of LCM-derived RNA.</p> <p>Conclusions</p> <p>The ovary is a complex mixture of different cell types. Distinct cell populations need therefore to be analyzed for a better understanding of their potential interactions. LCM and microarray analysis allowed us to identify novel gene expression patterns in follicular cells at different stages and in oocyte populations.</p

Altered cortico-striatal-thalamic connectivity in relation to spatial working memory capacity in children with ADHD

Introduction: Attention deficit hyperactivity disorder (ADHD) captures a heterogeneous group of children, who are characterized by a range of cognitive and behavioral symptoms. Previous resting state functional connectivity (rs-fcMRI) studies have sought to understand the neural correlates of ADHD by comparing connectivity measurements between those with and without the disorder, focusing primarily on cortical-striatal circuits mediated by the thalamus. To integrate the multiple phenotypic features associated with ADHD and help resolve its heterogeneity, it is helpful to determine how specific circuits relate to unique cognitive domains of the ADHD syndrome. Spatial working memory has been proposed as a key mechanism in the pathophysiology of ADHD.Methods: We correlated the rs-fcMRI of five thalamic regions of interest with spatial span working memory scores in a sample of 67 children aged 7-11 years (ADHD and typically developing children; TDC). In an independent dataset, we then examined group differences in thalamo-striatal functional connectivity between 70 ADHD and 89 TDC (7-11 years) from the ADHD-200 dataset. Thalamic regions of interest were created based on previous methods that utilize known thalamo-cortical loops and rs-fcMRI to identify functional boundaries in the thalamus.Results/Conclusions: Using these thalamic regions, we found atypical rs-fcMRI between specific thalamic groupings with the basal ganglia. To identify the thalamic connections that relate to spatial working memory in ADHD, only connections identified in both the correlational and comparative analyses were considered. Multiple connections between the thalamus and basal ganglia, particularly between medial and anterior dorsal thalamus and the putamen, were related to spatial working memory and also altered in ADHD. These thalamo-striatal disruptions may be one of multiple atypical neural and cognitive mechanisms that relate to the ADHD clinical phenotype

Structural and Functional Rich Club Organization of the Brain in Children and Adults

<div><p>Recent studies using Magnetic Resonance Imaging (MRI) have proposed that the brain’s white matter is organized as a rich club, whereby the most highly connected regions of the brain are also highly connected to each other. Here we use both functional and diffusion-weighted MRI in the human brain to investigate whether the rich club phenomena is present with functional connectivity, and how this organization relates to the structural phenomena. We also examine whether rich club regions serve to integrate information between distinct brain systems, and conclude with a brief investigation of the developmental trajectory of rich-club phenomena. In agreement with prior work, both adults and children showed robust structural rich club organization, comprising regions of the superior medial frontal/dACC, medial parietal/PCC, insula, and inferior temporal cortex. We also show that these regions were highly integrated across the brain’s major networks. Functional brain networks were found to have rich club phenomena in a similar spatial layout, but a high level of segregation between systems. While no significant differences between adults and children were found structurally, adults showed significantly greater functional rich club organization. This difference appeared to be driven by a specific set of connections between superior parietal, insula, and supramarginal cortex. In sum, this work highlights the existence of both a structural and functional rich club in adult and child populations with some functional changes over development. It also offers a potential target in examining atypical network organization in common developmental brain disorders, such as ADHD and Autism.</p></div

Regions that overlap between functional and structural rich clubs in adults.

<p>Overlapping regions, colored in yellow, defined as having degree k> = 14 in both the structural and functional group networks (the rich clubs). Structural-only and functional-only rich nodes are colred in green and red, respectively.</p

Community detection in functional and structural group networks from healthy adults.

<p>Brain regions are colored according to which community they belong to. In the functional network (left), six predominant communities were identified, comprising the Default Mode (red), Cingulo-opercular (pink), Fronto-parietal (yellow), Visual (blue), Orbitofrontal/Limbic (dark red), and Somatosensory (light blue) systems. Communities resembling analogues of the functional systems were identified in the structural network as well (right).</p

Comparison of functional rich club phenomena and network organization in adults versus in children.

<p>(<b>a</b>) Normalized rich club coefficients for functional data are shown for weighted (adults = red, solid; children = pink, solid), and unweighted (adults = brown, dashed; children = tan, dashed) networks. Significant differences are indicated with an asterisk at the top of the graph for weighted networks, and at the bottom for unweighted. Significant differences are observed (adults>children) across a wide range of k. (<b>b</b>) Rich club regions are displayed for the point of maximal difference in rich club coefficients that was significant (k> = 19). While many regions overlap (red arrows, for example), there are bilateral regions that appear only in adults (blue arrows, for example). (<b>c</b>) Rich club connections (k> = 19) are depicted for adults and for children. (<b>d</b>) Comparisons of the full, unfiltered matrices for adult vs. children subjects (non-group-averaged) using the Network-Based Statistic shows a single bilateral cluster of connections between regions of the insula, supramarginal, and superior parietal cortex. Cluster size was significantly greater in number than what we would expect at random (p<.01, one-tailed, T>4.5; significant clusters centered around the same nodes were also observed at T>4 and T>5 thresholds (p<.05, data not shown)). These connections primarily linked regions of the adult functional rich club as seen in (b) (k > = 19; lightly colored).</p

Comparison of structural rich club organization in adults versus in children.

<p>(Left) Normalized rich club coefficients for structural data are shown for weighted (adults = red, solid; children = pink, solid), and unweighted (adults = brown, dashed; children = tan, dashed) networks. No significant differences were observed for weighted or unweighted coefficients. (Right) Regions with degree k> = 14 are shown, to facilitate direct comparison with adults. Results indicate substantial overlap in spatial layout with adults (Fig. 1a).</p