Following instructions from working memory: Why does action at encoding and recall help?

Two experiments investigated the consequences of action at encoding and recall on the ability to follow sequences of instructions. Children aged 7–9 years recalled sequences of spoken action commands under presentation and recall conditions that either did or did not involve their physical performance. In both experiments, recall was enhanced by carrying out the instructions as they were being initially presented and also by performing them at recall. In contrast, the accuracy of instruction-following did not improve above spoken presentation alone, either when the instructions were silently read or heard by the child (Experiment 1), or when the child repeated the spoken instructions as they were presented (Experiment 2). These findings suggest that the enactment advantage at presentation does not simply reflect a general benefit of a dual exposure to instructions, and that it is not a result of their self-production at presentation. The benefits of action-based recall were reduced following enactment during presentation, suggesting that the positive effects of action at encoding and recall may have a common origin. It is proposed that the benefits of physical movement arise from the existence of a short-term motor store that maintains the temporal, spatial, and motoric features of either planned or already executed actions.

Mutations in CTC1, Encoding Conserved Telomere Maintenance Component 1, Cause Coats Plus

Coats plus is a highly pleiotropic disorder particularly affecting the eye, brain, bone and gastrointestinal tract. Here, we show that Coats plus results from mutations in CTC1, encoding conserved telomere maintenance component 1, a member of the mammalian homolog of the yeast heterotrimeric CST telomeric capping complex. Consistent with the observation of shortened telomeres in an Arabidopsis CTC1 mutant and the phenotypic overlap of Coats plus with the telomeric maintenance disorders comprising dyskeratosis congenita, we observed shortened telomeres in three individuals with Coats plus and an increase in spontaneous γH2AX-positive cells in cell lines derived from two affected individuals. CTC1 is also a subunit of the α-accessory factor (AAF) complex, stimulating the activity of DNA polymerase-α primase, the only enzyme known to initiate DNA replication in eukaryotic cells. Thus, CTC1 may have a function in DNA metabolism that is necessary for but not specific to telomeric integrity.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

Continuum tensor network field states, path integral representations and spatial symmetries

A natural way to generalize tensor network variational classes to quantum field systems is via a continuous tensor contraction. This approach is first illustrated for the class of quantum field states known as continuous matrix-product states (cMPS). As a simple example of the path-integral representation we show that the state of a dynamically evolving quantum field admits a natural representation as a cMPS. A completeness argument is also provided that shows that all states in Fock space admit a cMPS representation when the number of variational parameters tends to infinity. Beyond this, we obtain a well-behaved field limit of projected entangled-pair states (PEPS) in two dimensions that provide an abstract class of quantum field states with natural symmetries. We demonstrate how symmetries of the physical field state are encoded within the dynamics of an auxiliary field system of one dimension less. In particular, the imposition of Euclidean symmetries on the physical system requires that the auxiliary system involved in the class' definition must be Lorentz-invariant. The physical field states automatically inherit entropy area laws from the PEPS class, and are fully described by the dissipative dynamics of a lower dimensional virtual field system. Our results lie at the intersection many-body physics, quantum field theory and quantum information theory, and facilitate future exchanges of ideas and insights between these disciplines.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

An Exploration of Auditory Brainstem Encoding of Stop Consonants in Infants and Implications for Language Outcomes

Current trends in speech-language pathology focus on early intervention as the preferred tool for promoting the best possible outcomes in children with language disorders. Neuroimaging techniques are being studied as promising tools for flagging at-risk infants. In this study, the auditory brainstem response (ABR) to the syllables /ba/ and /ga/ was examined in 41 infants between 3 and 12 months of age as a possible tool to predict language development in toddlerhood. The MacArthur-Bates Communicative Development Inventory (MCDI) was used to assess language development at 18 months of age. The current study compared the periodicity of the responses to the stop consonants and phase differences between /ba/ and /ga/ in both at-risk and low-risk groups. The study also examined whether there are correlations among ABR measures (periodicity and phase differentiation) and language development. The study found that these measures predict language development at 18 months.

Fundamental limitations in the purifications of tensor networks

We show a fundamental limitation in the description of quantum many-body mixed states with tensor networks in purification form. Namely, we show that there exist mixed states which can be represented as a translationally invariant (TI) matrix product density operator valid for all system sizes, but for which there does not exist a TI purification valid for all system sizes. The proof is based on an undecidable problem and on the uniqueness of canonical forms of matrix product states. The result also holds for classical states.

Novel Mutation in the ATP-Binding Cassette Transporter A3 (ABCA3) Encoding Gene Causes Respiratory Distress Syndrome in A Term Newborn in Southwest Iran

Introduction: ABCA3 glycoprotein belongs to the ATP-binding cassette (ABC) superfamily of transporters, which utilize the energy derived from hydrolysis of ATP for the translocation of a wide variety of substrates across the plasma membrane. Mutations in the ABCA3 gene are knowingly causative for fatal surfactant deficiency, particularly respiratory distress syndrome (RDS) in term babies. Case Presentation: In this study, Sanger sequencing of the whole ABCA3 gene (NCBI NM_001089) was performed in a neonatal boy with severe RDS. A homozygous mutation has been identified in the patient. Parents were heterozygous for the same missense mutation GGA > AGA at position 202 in exon 6 of the ABCA3 gene (c.604G > A; p.G202R). Furthermore, 70 normal individuals have been analyzed for the mentioned change with negative results. Conclusions: Regarding Human Genome Mutation Database (HGMD) and other literature recherche, the detected change is a novel mutation and has not been reported before. Bioinformatics mutation predicting tools prefer it as pathogenic.

Characterization of the poxAB operon encoding a class D carbapenemase in Pseudomonas aeruginosa

Pseudomonas aeruginosa is a dreaded opportunistic pathogen that causes severe and often intractable infections in immunocompromised and critically ill patients. This bacterium is also the primary cause of fatal lung infections in patients with cystic fibrosis and a leading nosocomial pathogen responsible for nearly 10% of all hospital-acquired infections. P. aeruginosa is intrinsically recalcitrant to most classes of antibiotics and has the ability to acquire additional resistance during treatment. In particular, resistance to the widely used β-lactam antibiotics is frequently mediated by the expression of AmpC, a chromosomally encoded β-lactamase that is ubiquitously found in P. aeruginosa strains. This dissertation delved into the role of a recently reported chromosomal β-lactamase in P. aeruginosa called PoxB. To date, no detailed studies have addressed the regulation of poxB expression and its contribution to β-lactam resistance in P. aeruginosa. In an effort to better understand the role of this β-lactamase, poxB was deleted from the chromosome and expressed in trans from an IPTG-inducible promoter. The loss of poxB did not affect susceptibility. However, expression in trans in the absence of ampC rendered strains more resistant to the carbapenem β-lactams. The carbapenem-hydrolyzing phenotype was enhanced, reaching intermediate and resistant clinical breakpoints, in the absence of the carbapenem-specific outer membrane porin OprD. As observed for most class D β-lactamases, PoxB was only weakly inhibited by the currently available β-lactamase inhibitors. Moreover, poxB was shown to form an operon with the upstream located poxA, whose expression in trans decreased pox promoter (Ppox) activity suggesting autoregulation. The transcriptional regulator AmpR negatively controlled Ppox activity, however no direct interaction could be demonstrated. A mariner transposon library identified genes involved in the transport of polyamines as potential regulators of pox expression. Unexpectedly, polyamines themselves were able induce resistance to carbapenems. In summary, P. aeruginosa carries a chromosomal-encoded β-lactamase PoxB that can provide resistance against the clinically relevant carbapenems despite its narrow spectrum of hydrolysis and whose activity in vivo may be regulated by polyamines.^

Effects of alcohol intoxication and encoding conditions on eyewitness memory

Several researchers have investigated the effects of alcohol on memory. Few researchers have studied the effects of alcohol on an eyewitness's recall and recognition of crime events. This study proposed to examine the effects of alcohol and viewing conditions on subjects' ability to recall information regarding a videotaped bank robbery. Thirty male and 22 female subjects participated in a 2 (consumption: alcohol v. no alcohol) x 2 (lighting: good v. poor) factorial experiment with Average Accuracy and Total Amount of Information recalled as the primary dependent measures. There was no significant difference between the Intoxicated and Sober subjects regarding the amount of information recalled or their average accuracy. The main effect for lighting conditions and gender differences were also not significant.

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

The present study characterized two fiber pathways important for language, the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) and the frontal aslant tract (FAT), and related these tracts to speech, language, and literacy skill in children five to eight years old. We used Diffusion Tensor Imaging (DTI) to characterize the fiber pathways and administered several language assessments. The FAT was identified for the first time in children. Results showed no age-related change in integrity of the FAT, but did show age-related change in the left (but not right) SLF/AF. Moreover, only the integrity of the right FAT was related to phonology but not audiovisual speech perception, articulation, language, or literacy. Both the left and right SLF/AF related to language measures, specifically receptive and expressive language, and language content. These findings are important for understanding the neurobiology of language in the developing brain, and can be incorporated within contemporary dorsal-ventral-motor models for language.

Convergent bioenergetic defects in Coenzyme Q10 depleted cells by pharmacological inhibition of coq2 enzyme (p-hydroxybenzoate polyprenyl transferase) and by genome editing technology targeting the encoding gene (COQ2)

Primary CoQ10 deficiency diseases encompass a heterogeneous spectrum of clinical phenotypes. Among these, defect or mutation on COQ2 gene, encoding a para-hydroxybenzoate polyprenyl transferase, have been associated with different diseases. Understanding the functional and metabolic impact of COQ2 mutation and the consequent CoQ10 deficiency is still a matter of debate. To date the aetiology of the neurological phenotypes correlated to CoQ10 deficiency does not present a clear genotype-phenotype association. In addition to the metabolic alterations due to Coenzyme Q depletion, the impairment of mitochondrial function, associated with the reduced CoQ level, could play a significant role in the metabolic flexibility of cancer. This study aimed to characterize the effect of varying degrees of CoQ10 deficiency and investigate the multifaceted aspect of CoQ10 depletion and its impact on cell metabolism. To induced CoQ10 depletion, different cell models were used, employing a chemical and genome editing approach. In T67 and MCF-7 CoQ10 depletion was achieved by a competitive inhibitor of the enzyme, 4-nitrobenzoate (4-NB), whereas in SH-SY5Y the COQ2 gene was edited via CRISPR-Cas9 cutting edge technology.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

Uncovering the encoding and transmission of behaviourally relevant information in neural activity

Most cognitive functions require the encoding and routing of information across distributed networks of brain regions. Information propagation is typically attributed to physical connections existing between brain regions, and contributes to the formation of spatially correlated activity patterns, known as functional connectivity. While structural connectivity provides the anatomical foundation for neural interactions, the exact manner in which it shapes functional connectivity is complex and not yet fully understood. Additionally, traditional measures of directed functional connectivity only capture the overall correlation between neural activity, and provide no insight on the content of transmitted information, limiting their ability in understanding neural computations underlying the distributed processing of behaviorally-relevant variables. In this work, we first study the relationship between structural and functional connectivity in simulated recurrent spiking neural networks with spike timing dependent plasticity. We use established measures of time-lagged correlation and overall information propagation to infer the temporal evolution of synaptic weights, showing that measures of dynamic functional connectivity can be used to reliably reconstruct the evolution of structural properties of the network. Then, we extend current methods of directed causal communication between brain areas, by deriving an information-theoretic measure of Feature-specific Information Transfer (FIT) quantifying the amount, content and direction of information flow. We test FIT on simulated data, showing its key properties and advantages over traditional measures of overall propagated information. We show applications of FIT to several neural datasets obtained with different recording methods (magneto and electro-encephalography, spiking activity, local field potentials) during various cognitive functions, ranging from sensory perception to decision making and motor learning. Overall, these analyses demonstrate the ability of FIT to advance the investigation of communication between brain regions, uncovering the previously unaddressed content of directed information flow.