High Probability Of Success Research Articles

Pharmacokinetic/pharmacodynamic evaluation of the antimicrobial therapy of pneumococcal invasive disease in adults in post-PCV13 vaccine period in Madrid, Spain.

The objective of our study was to evaluate by pharmacokinetic/pharmacodynamic (PK/PD) analysis, if the antimicrobials used for the treatment of invasive pneumococcal disease (IPD) in adults, including meningitis, are adequate considering the susceptibility profile of S. pneumoniae in Spain after the implantation of PVC13 vaccine. Pharmacokinetic parameters of benzylpenicillin and cefotaxime were obtained from the literature, and susceptibility data of invasive S. pneumoniae strains recovered in 2017 (post-PCV13 vaccination period) were provided by the Public Health Regional Laboratory of Madrid. We have also studied levofloxacin because it is used to treat pneumococcal pneumonia previously to be diagnosed as bacteremic pneumonia. Monte Carlo simulation was used to estimate the probability of target attainment (PTA) and the cumulative fraction of response (CFR). All doses of benzylpenicillin except 2 mU q6h provide a high probability of treatment success for MIC values ≤ 1 mg/L; 4 mU q4h is even useful for MIC values up to 4 mg/L. This high dose, used for the treatment of meningitis, also provides high probability of treatment success for MIC ≤ 0.5 mg/L. At the susceptibility EUCAST breakpoint (≤ 0.5 mg/L), cefotaxime provides a high rate of PD target achievement, even at the lowest dose (1 g q8h). For meningitis, 2 g q6h ensures probabilities of target attainment ≥90% for MIC up to 1 mg/L. Our study confirms that after the implementation of PCV13 vaccine, the treatment with benzylpenicillin and cefotaxime provides high probability of the therapy success of IPD, including meningitis.

Fundamental Lower Bounds on Number of Random Measurements for Sparse Tensor Signal Reconstruction

This paper deals with a fundamental aspect of the problem of robustly reconstructing sparse tensor signals via convex optimization in random setting. The traditional vector signal model is extended to tensor model and tensor-space based probability analysis methods are applied to establish the critical results. In particular, by means of an innovative tensor width estimation and asymptotic convex geometric analysis, fundamental lower bounds on number of random measurements to guarantee high successful reconstruction probability are established. In comparison with most current works based on vector signal model (1-order tensor), these bounds establish foundations to develop effective algorithms for reconstructing high-order sparse tensor signals which are emerging in various data-intensive intelligent signal processing applications.

A quantum search decoder for natural language processing

Probabilistic language models, e.g. those based on recurrent neural networks such as long short-term memory models (LSTMs), often face the problem of finding a high probability prediction from a sequence of random variables over a set of tokens. This is commonly addressed using a form of greedy decoding such as beam search, where a limited number of highest-likelihood paths (the beam width) of the decoder are kept, and at the end the maximum-likelihood path is chosen. In this work, we construct a quantum algorithm to find the globally optimal parse (i.e. for infinite beam width) with high constant success probability. When the input to the decoder follows a power law with exponent k > 0, our algorithm has runtime Rnf(R, k), where R is the alphabet size, n the input length; here f < 1/2, and frightarrow 0 exponentially fast with increasing k, hence making our algorithm always more than quadratically faster than its classical counterpart. We further modify our procedure to recover a finite beam width variant, which enables an even stronger empirical speedup while still retaining higher accuracy than possible classically. Finally, we apply this quantum beam search decoder to Mozilla’s implementation of Baidu’s DeepSpeech neural net, which we show to exhibit such a power law word rank frequency.

Certainty-Based Marking on Multiple-Choice Items: Psychometrics Meets Decision Theory.

When a response to a multiple-choice item consists of selecting a single-best answer, it is not possible for examiners to differentiate between a response that is a product of knowledge and one that is largely a product of uncertainty. Certainty-based marking (CBM) is one testing format that requires examinees to express their degree of certainty on the response option they have selected, leading to an item score that depends both on the correctness of an answer and the certainty expressed. The expected score is maximized if examinees truthfully report their level of certainty. However, prospect theory states that people do not always make rational choices of the optimal outcome due to varying risk attitudes. By integrating a psychometric model and a decision-making perspective, the present study looks into the response behaviors of 334 first-year students of physiotherapy on six multiple-choice examinations with CBM in a case study. We used item response theory to model the objective probability of students giving a correct response to an item, and cumulative prospect theory to estimate their risk attitudes when students choose to report their certainty. The results showed that with the given CBM scoring matrix, students' choices of a certainty level were affected by their risk attitudes. Students were generally risk averse and loss averse when they had a high success probability on an item, leading to an under-reporting of their certainty. Meanwhile, they were risk seeking in case of small success probabilities on the items, resulting in the over-reporting of certainty.

Clinical factors associated with successful embolization of lower gastrointestinal bleeding.

To develop a model of clinical factors that may predict: (1) technically and clinically successful embolization of a bleeding vessel at digital subtraction angiography (DSA) for lower gastrointestinal bleed (LGIB); (2) a negative DSA in the presence of positive CT-mesenteric angiography (CTMA) for LGIB. A retrospective cohort study of all DSAs conducted with intent for embolization for acute LGIB over a 10-year period was undertaken. Pre-procedural and intra-procedural clinical variables were evaluated using uni- and multi-variate analysis. One hundred and twenty-three DSAs were evaluated. Technical success was 81% and clinical success 78% where DSA was positive. Technical success was associated with super-selective approach, contrast extravasation on CT, haemoglobin drop, anatomical source and time from CT to DSA on univariate analysis. On multivariate analysis, time from CT to DSA was significant with a higher success probability within 120 min with different factors being salient depending on degree of delay. Clinical success was only associated with activated partial thromboplastin time (<27.5s). A negative DSA was associated with anatomical source, haemodynamic stability, platelet count and time from CT to DSA on univariate analysis. The latter three remained so on multivariate analysis. A triaging approach to utilizing emergency DSA may be helpful. If prolonged delay between CT and DSA is anticipated, with haemodynamic stability and a near-normal platelet count, the DSA may not be fruitful. Technical success may be more likely if DSA occurs within 120 min. Clinical success may be more likely if activated partial thromboplastin time is within normal range.

Entanglement concentration for six-particle cluster states

Multi-particle cluster states play a significant role in quantum information processing. However, due to the inevitable interaction with the environment in the transmission process, the fidelity of entanglement decreases. To distill the perfect cluster states, we present two schemes for arbitrary six-particle cluster states. POVM local unitary operators that are obtained by solving the equation according to the properties of cluster states are used in the first protocol. The second protocol is based on cross-Kerr nonlinearity which is exploited to check parity between original qubit with ancillary single qubit and it can achieve a higher probability of success through iteration. Furthermore, we can generalize the first method to concentrate entanglement on even number of particles. Our protocol will be useful in practical applications.

Identifying therapeutic drug targets using bidirectional effect genes

Prioritizing genes for translation to therapeutics for common diseases has been challenging. Here, we propose an approach to identify drug targets with high probability of success by focusing on genes with both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). We find 98 BEST genes for a variety of indications. Drugs targeting those genes are 3.8-fold more likely to be approved than non-BEST genes. We focus on five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants in those genes result in significantly increased risk for idiopathic short stature (ISS) (OR = 2.75, p = 3.99 × 10−8). Finally, using functional experiments, we demonstrate that adding an exogenous CNP analog (encoded by NPPC) rescues the phenotype, thus validating its potential as a therapeutic treatment for ISS. Our results show the value of looking for bidirectional effects to identify and validate drug targets.

Femtosecond Laser Writing of Spin Defects in Hexagonal Boron Nitride

Optically active spin defects in wide-bandgap materials have many potential applications in quantum information and quantum sensing. Spin defects in two-dimensional layered van der Waals materials are just emerging to be investigated. Here we demonstrate that optically-addressable spin ensembles in hexagonal boron nitride (hBN) can be generated by femtosecond laser irradiation. We observe optically detected magnetic resonance (ODMR) of hBN spin defects created by laser irradiation. We show that the creation of spin defects in hBN is strongly affected by the pulse energy of the femtosecond laser. When the laser pulse number is less than a few thousand, the pulse number only affects the density of defects but not the type of defects. With proper laser parameters, spin defects can be generated with a high probability of success. Our work provides a convenient way to create spin defects in hBN by femtosecond laser writing, which shows promising prospects for quantum technologies.

Approval success rates of drug candidates based on target, action, modality, application, and their combinations.

The current success rate of a drug candidate, from the beginning of the clinical trial to receiving marketing approval, is about 10%–20%, and it has not changed during the past few decades. Therefore, pharmaceutical companies are under pressure to select one compound, among many others, with a high probability of success. The differences in drug features affect their probabilities of approval success. In this study, we examined the approval success rates of drug candidates, developed in the United States, the European Union, or Japan, by focusing on four parameters (“drug target,” “drug action,” “drug modality,” and “drug application”) and their combinations, and identified factors that conditioned the outcome of the drug development process. We obtained a total success rate of 12.8%, after evaluating 3999 compounds. Moreover, after analyzing the combinations of these parameters, the approval success rates of drugs that corresponded to the following categories—a stimulant in drug action or an enzyme in drug target and biologics (excluding monoclonal antibody) in drug modality—were high (34.1% and 31.3%, respectively). Univariate and multivariate logistic regression analyses revealed that stimulant in drug action, and “B” (blood and blood forming organs), “G” (genito‐urinary system and sex), and “J” (anti‐infectives for systemic use) in drug application were statistically associated with high approval success rates. We found several parameters and their combinations that affected drug approval success rates. Our results could assist pharmaceutical companies in evaluating the probability of success of their drug candidates and, thus, in efficiently conducting the clinical development process.

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network.

Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals.

A New QUBO Objective Function for Solving the Maximum Common Subgraph Isomorphism Problem Via Quantum Annealing

We present QUBO objective functions for the maximum common subgraph isomorphism problem, proved their correctness and illustrate them with a few toy problems. Very small-scale problems can be solved by the D-Wave 2X machine with high enough success probabilities; however, for larger scale problems, the success probability drops significantly.

Prediction of extubation outcome in mechanically ventilated patients: Development and validation of the Extubation Predictive Score (ExPreS).

Despite the best efforts of intensive care units (ICUs) professionals, the extubation failure rates in mechanically ventilated patients remain in the range of 5%–30%. Extubation failure is associated with increased risk of death and longer ICU stay. This study aimed to identify respiratory and non-respiratory parameters predictive of extubation outcome, and to use these predictors to develop and validate an “Extubation Predictive Score (ExPreS)” that could be used to predict likelihood of extubation success in patients receiving invasive mechanical ventilation (IMV). Derivation cohort was composed by patients aged ≥18 years admitted to the ICU and receiving IMV through an endotracheal tube for >24 hours. The weaning process followed the established ICU protocol. Clinical signs and ventilator parameters of patients were recorded during IMV, in the end phase of weaning in pressure support ventilation (PSV) mode, with inspiratory pressure of 7 cm H2O over the PEEP (positive end expiratory pressure). Patients who tolerated this ventilation were submitted to spontaneous breathing trial (SBT) with T-tube for 30 minutes. Those who passed the SBT and a subsequent cuff-leak test were extubated. The primary outcome of this study was extubation success at 48 hours. Parameters that showed statistically significant association with extubation outcome were further investigated using the receiver operating characteristics (ROC) analysis to assess their predictive value. The area under the curve (AUC) values were used to select parameters for inclusion in the ExPreS. Univariable logistic regression analysis and ROC analysis were performed to evaluate the performance of ExPreS. Patients’ inclusion and statistical analyses for the prospective validation cohort followed the same criteria used for the derivation cohort and the decision to extubate was based on the ExPreS result. In the derivation cohort, a total of 110 patients were extubated: extubation succeeded in 101 (91.8%) patients and failed in 9 (8.2%) patients. Rapid shallow-breathing index (RSBI) in SBT, dynamic lung compliance, duration of IMV, muscle strength, estimated GCS, hematocrit, and serum creatinine were significantly associated with extubation outcome. These parameters, along with another parameter—presence of neurologic comorbidity—were used to create the ExPreS. The AUC value for the ExPreS was 0.875, which was higher than the AUCs of the individual parameters. The total ExPreS can range from 0 to 100. ExPreS ≥59 points indicated high probability of success (OR = 23.07), while ExPreS ≤44 points indicated low probability of success (OR = 0.82). In the prospective validation cohort, 83 patients were extubated: extubation succeeded in 81 (97.6%) patients and failed in 2 (2.4%) patients. The AUC value for the ExPreS in this cohort was 0.971. The multiparameter score that we propose, ExPreS, shows good accuracy to predict extubation outcome in patients receiving IMV in the ICU. In the prospective validation, the use of ExPreS decreased the extubation failure rate from 8.2% to 2.4%, even in a cohort of more severe patients.

Neuropharmacology in traumatic brain injury: from preclinical to clinical neuroprotection?

Traumatic brain injury (TBI) constitutes a major health problem worldwide and is a leading cause of death and disability in individuals, contributing to devastating socioeconomic consequences. Despite numerous promising pharmacological strategies reported as neuroprotective in preclinical studies, the translation to clinical trials always failed, albeit the great diversity of therapeutic targets evaluated. In this review, first, we described epidemiologic features, causes, and primary and secondary injuries of TBI. Second, we outlined the current literature on animal models of TBI, and we described their goals, their advantages and disadvantages according to the species used, the type of injury induced, and their clinical relevance. Third, we defined the concept of neuroprotection and discussed its evolution. We also identified the reasons that might explain the failure of clinical translation. Then, we reviewed post‐TBI neuroprotective treatments with a focus on the following pleiotropic drugs, considered “low hanging fruit” with high probability of success: glitazones, glibenclamide, statins, erythropoietin, and progesterone, that were largely tested and demonstrated efficient in preclinical models of TBI. Finally, our review stresses the need to establish a close cooperation between basic researchers and clinicians to ensure the best clinical translation for neuroprotective strategies for TBI.

Quantum Speedup for Protein Structure Prediction.

Protein structure prediction (PSP) predicts the native conformation for a given protein sequence. Classically, the problem has been shown to belong to the NP-complete complexity class. Its applications range from physics, through bioinformatics to medicine and quantum biology. It is possible however to speed it up with quantum computational methods, as we show in this paper. Here we develop a fast quantum algorithm for PSP in three-dimensional hydrophobic-hydrophilic model on body-centered cubic lattice with quadratic speedup over its classical counterparts. Given a protein sequence of n amino acids, our algorithm reduces the temporal and spatial complexities to, respectively, [Formula: see text] and O(n2 logn) . With respect to oracle-related quantum algorithms for the NP-complete problems, we identify our algorithm as optimal. To justify the feasibility of the proposed algorithm we successfully solve the problem on IBM quantum simulator involving 21 and 25 qubits. We confirm the experimentally obtained high probability of success in finding the desired conformation by calculating the theoretical probability estimations.

Intelligent Secure Networking in In-band Full-duplex Dynamic Access Networks: Spectrum Management and Routing Protocol

Recent developments in self-interference cancellation (SIC) techniques have made in-band full-duplex (IBFD) transmission systems possible. IBFD technology can enhance, or theoretically speaking double, spectrum utilization by allowing simultaneous transmission and reception using the same radio-frequency channel. Integrating the IBFD technology in cognitive radio (CR) systems can further enhance spectrum utilization and provide huge spectrum opportunities for efficient deployment of next-generation multi-hop wireless systems. Routing protocols for typical CR networks (CRNs) assume that CR users are equipped with half-duplex receivers. Limited number of protocols have been designed for FD-based CRNs. However, these protocols do not account for security threats in wireless environment, which could significantly affect system performance. In this paper, we develop a security-aware intelligent routing scheme that aims at mitigating the effects of jamming attacks on IBFD CRNs. This scheme considers the unique characteristics of the CRN environment while being IBFD-aware. Specifically, it considers the primary user’s channel-availability time, channel quality and jamming strategy. For a source-destination pair, our protocol collects a set of loop-free paths. Then, it attempts to assign channels for the hops along each path with the goal of achieving the highest Probability of Success (PoS) of each path. The per-hop channel selection for each path is derived as an optimization problem, for which sub-optimal solutions are found using a polynomial-time approximate method. Based on the computed channel assignment and PoS over each path, the path with the highest PoS between the source and destination is selected. Simulation results show that our secure-aware IBFD-based routing can significantly mitigate the jamming effects on CRN performance by improving the packet-delivery ratio, and consequently network throughput compared with existing FD-based routing protocols.

A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression

Multiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. Our objective is to select the most promising individuals for further backcrossing or selfing. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches. Our proposed selection method can assist breeders to efficiently design trait introgression projects.

Optimization-Time Analysis for Cybersecurity

A mathematical framework to reason about time resilience in cybersecurity is here introduced. We first consider an attacker who is able to mount several multi-stage attacks on the organization: the defender’s objective is to select an optimal portfolio of security controls, within a given budget, to withstand the highest number of attacks. The mathematical model is a Markov chain with an initial state called the safe state, intermediate states for all possible attacks (each attack state denoting a probabilistic attack graph), and a sink state denoting a successful attack. The overall defence problem is formulated as a bi-level multi-objective optimization, i.e., the defender selects an optimal portfolio of security controls to mitigate an optimal attacker. In order to determine the probability of success of an attack two cases will be considered: (a) the expected probability of success and (b) the highest probability of success. We refer to these two cases as expected-time analysis and worst-case time analysis, respectively. To solve precisely these bi-level optimizations strong duality and Mixed Integer Linear Programming are used. We then extend the framework to investigate resilience in terms of the total duration of the attacks; variations of the previous optimizations are presented to this purpose. Finally numerical evaluations are provided to compare the results obtained from the expected-time analysis and the worst-case time analysis.

Evaluation of the adequacy of the antimicrobial therapy of invasive Haemophilus influenzae infections: A pharmacokinetic/pharmacodynamic perspective

IntroductionIn Europe, non-typeable H. influenzae (NTHi) is the leading cause of invasive H. influenzae disease in adults and is associated with high mortality. The goal of this study was to determine whether current antimicrobial treatments for H. influenzae infection in Spain are suitable based on their probability of achieving pharmacokinetic/pharmacodynamic (PK/PD) targets. MethodsPharmacokinetic parameters for the antibiotics studied (amoxicillin, amoxicillin/clavulanic acid, ampicillin, cefotaxime, ceftriaxone, imipenem and ciprofloxacin) and susceptibility data for H. influenzae were obtained from literature. A Monte Carlo simulation was used to estimate the probability of target attainment (PTA), defined as the probability that at least a specific value of a PK/PD index is achieved at a certain MIC, and the cumulative fraction of response (CFR), defined as the expected population PTA for a specific drug dose and a specific microorganism population. ResultsRegardless of dosing regimen, all antibiotics yielded CFR values of 100% or nearly 100% for all strains, including BL+, BL− and BLNAR, except amoxicillin and ampicillin for BL+. Thus, if an infection is caused by BL+ strains, treatment with amoxicillin and ampicillin has a high probability of failure (CFR≤8%). For standard doses of amoxicillin, amoxicillin/clavulanic acid and imipenem, PK/PD breakpoints were consistent with EUCAST clinical breakpoints. For the other antimicrobials, PK/PD breakpoints were higher than EUCAST clinical breakpoints. ConclusionsOur study confirms by PK/PD analysis that, with the antimicrobials used as empirical treatment of invasive H. influenzae disease, a high probability of therapeutic success can be expected.

Investigation of MicroRNA and transcription factor mediated regulatory network for silicosis using systems biology approach

Silicosis is a major health issue among workers exposed to crystalline silica. Genetic susceptibility has been implicated in silicosis. The present research demonstrates key regulatory targets and propagated network of gene/miRNA/transcription factor (TF) with interactions responsible for silicosis by integrating publicly available microarray data using a systems biology approach. Array quality is assessed with the Quality Metrics package of Bioconductor, limma package, and the network is constructed using Cytoscape. We observed and enlist 235 differentially expressed genes (DEGs) having up-regulation expression (85 nos) and down-regulation expression (150 nos.) in silicosis; and 24 TFs for the regulation of these DEGs entangled with thousands of miRNAs. Functional enrichment analysis of the DEGs enlighten that, the maximum number of DEGs are responsible for biological process viz, Rab proteins signal transduction (11 nos.) and Cellular Senescence (20 nos.), whereas IL-17 signaling pathway (16 nos.) and Signalling by Nuclear Receptors (14 nos.) etc. are Biological Pathway involving more DEGs. From the identified 1100 high target microRNA (miRNA)s involved in silicosis, 1055 miRNAs are found to relate with down-regulated genes and 847 miRNAs with up-regulated genes. The CDK19 gene (Up-regulated) is associated with 617 miRNAs whereas down-regulated gene ARID5B is regulated by as high as 747 high target miRNAs. In Prediction of Small-molecule signatures, maximum scoring small-molecule combinations for the DEGs have shown that CGP-60774 (with 20 combinations), alvocidib (with 15 combinations) and with AZD-7762 (24 combinations) with few other drugs having the high probability of success.

A Multidimensional Virtual Reality Neurorehabilitation Approach to Improve Functional Memory: Who Is the Ideal Candidate?

Aims: We aimed to identify the significant predictors of ecological memory amelioration after the Human Empowerment Aging and Disability (HEAD) rehabilitation program, a multidimensional treatment for chronic neurological diseases.Materials and Methods: Ninety-three patients with Parkinson disease (n = 29), multiple sclerosis (n = 26), and stroke (n = 38) underwent a multidimensional rehabilitation. We focused on changes after treatment on ecological memory (outcome measure) evaluated by Rivermead Behavioral Memory Test, Third Edition (RBMT-3). Minimal clinically important difference (MCID) after treatment were calculated for RBMT-3. The change score on RBMT-3 was categorized in positive effect, stabilization, or no effect of the treatment. Random forest classification identified who significantly benefited from treatment against who did not in terms of ecological memory functioning. Accordingly, logistic regression models were created to identify the best predictors of the treatment effect. A predicted probability value was derived, and the profile of the ideal candidate of HEAD protocol was shown by combining different ranks of significant predictors in a 3 × 3 matrix for each pair of predictors.Results: A significant number of cases reported positive effect of the treatment on ecological memory, with an amelioration over the MCID or a stabilization. The random forest analysis highlighted a discrete accuracy of prediction (>0.60) for all the variables considered at baseline for identifying participants who significantly benefited and who did not from the treatment. Significant logistic regression model (Wald method) showed a predictive role of Montreal Cognitive Assessment (MoCA; p = 0.007), 2-Minute Walk Test (2MWT; p = 0.038), and RBMT-3 (p < 0.001) at baseline on HEAD treatment effect. Finally, we observed a high probability of success in people with higher residual cognitive functioning (MoCA; odds ratio = 1.306) or functional mobility (2MWT; odds ratio = 1.013).Discussion: The HEAD program is a rehabilitation with effects on multiple domains, including ecological memory. Residual level of cognitive and/or motor functioning is a significant predictor of the treatment success. These findings confirm the intrinsic relationship subsisting between motor and cognitive functions and suggest the beneficial effects of physical activity on cognitive functions and vice versa.