Caenorhabditis elegans as a model system for studying drug induced mitochondrial toxicity

Today HIV-1 infection is recognized as a chronic disease with obligatory lifelong treatment to keep viral titers below detectable levels. The continuous intake of antiretroviral drugs however, leads to severe and even life-threatening side effects, supposedly by the deleterious impact of nucleoside-analogue type compounds on the functioning of the mitochondrial DNA polymerase. For detailed investigation of the yet partially understood underlying mechanisms, the availability of a versatile model system is crucial. We therefore set out to develop the use of Caenorhabditis elegansto study drug induced mitochondrial toxicity. Using a combination of molecular-biological and functional assays, combined with a quantitative analysis of mitochondrial network morphology, we conclude that anti-retroviral drugs with similar working mechanisms can be classified into distinct groups based on their effects on mitochondrial morphology and biochemistry. Additionally we show that mitochondrial toxicity of antiretroviral drugs cannot be exclusively attributed to interference with the mitochondrial DNA polymerase

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning

Reinforcement Learning (RL) can be used to fit a mapping from patient state to a medication regimen. Prior studies have used deterministic and value-based tabular learning to learn a propofol dose from an observed anesthetic state. Deep RL replaces the table with a deep neural network and has been used to learn medication regimens from registry databases. Here we perform the first application of deep RL to closed-loop control of anesthetic dosing in a simulated environment. We use the cross-entropy method to train a deep neural network to map an observed anesthetic state to a probability of infusing a fixed propofol dosage. During testing, we implement a deterministic policy that transforms the probability of infusion to a continuous infusion rate. The model is trained and tested on simulated pharmacokinetic/pharmacodynamic models with randomized parameters to ensure robustness to patient variability. The deep RL agent significantly outperformed a proportional-integral-derivative controller (median absolute performance error 1.7% +/- 0.6 and 3.4% +/- 1.2). Modeling continuous input variables instead of a table affords more robust pattern recognition and utilizes our prior domain knowledge. Deep RL learned a smooth policy with a natural interpretation to data scientists and anesthesia care providers alike.Comment: International Conference on Artificial Intelligence in Medicine 202

A risk profile for identifying community-dwelling elderly with a highrisk of recurrent falling: results of a 3-year prospective study

Introduction: The aim of the prospective study reported here was to develop a risk profile that can be used to identify community-dwelling elderly at a high risk of recurrent falling. Materials and methods: The study was designed as a 3-year prospective cohort study. A total of 1365 community-dwelling persons, aged 65 years and older, of the population-based Longitudinal Aging Study Amsterdam participated in the study. During an interview in 1995/1996, physical, cognitive, emotional and social aspects of functioning were assessed. A follow-up on the number of falls and fractures was conducted during a 3-year period using fall calendars that participants filled out weekly. Recurrent fallers were identified as those who fell at least twice within a 6-month period during the 3-year follow-up. Results: The incidence of recurrent falls at the 3-year follow-up point was 24.9% in women and 24.4% in men. Of the respondents, 5.5% reported a total of 87 fractures that resulted from a fall, including 20 hip fractures, 21 wrist fractures and seven humerus fractures. Recurrent fallers were more prone to have a fall-related fracture than those who were not defined as recurrent fallers (11.9% vs. 3.4%; OR: 3.8; 95% CI: 2.3-6.1). Backward logistic regression analysis identified the following predictors in the risk profile for recurrent falling: two or more previous falls, dizziness, functional limitations, weak grip strength, low body weight, fear of falling, the presence of dogs/cats in the household, a high educational level, drinking 18 or more alcoholic consumptions per week and two interaction terms (high educationx18 or more alcohol consumptions per week and two or more previous falls x fear of falling) (AUC=0.71). Discussion: At a cut-off point of 5 on the total risk score (range 0-30), the model predicted recurrent falling with a sensitivity of 59% and a specificity of 71%. At a cut-off point of 10, the sensitivity and specificity were 31% and 92%, respectively. A risk profile including nine predictors that can easily be assessed seems to be a useful tool for the identification of community-dwelling elderly with a high risk of recurrent falling. © International Osteoporosis Foundation and National Osteoporosis Foundation 2006

Symptoms and quality of life in late stage Parkinson syndromes: a longitudinal community study of predictive factors

BACKGROUND Palliative care is increasingly offered earlier in the cancer trajectory but rarely in Idiopathic Parkinson's Disease(IPD), Progressive Supranuclear Palsy(PSP) or Multiple System Atrophy(MSA). There is little longitudinal data of people with late stage disease to understand levels of need. We aimed to determine how symptoms and quality of life of these patients change over time; and what demographic and clinical factors predicted changes. METHODS We recruited 82 patients into a longitudinal study, consenting patients with a diagnosis of IPD, MSA or PSP, stages 3-5 Hoehn and Yahr(H&Y). At baseline and then on up to 3 occasions over one year, we collected self-reported demographic, clinical, symptom, palliative and quality of life data, using Parkinson's specific and generic validated scales, including the Palliative care Outcome Scale (POS). We tested for predictors using multivariable analysis, adjusting for confounders. FINDINGS Over two thirds of patients had severe disability, over one third being wheelchair-bound/bedridden. Symptoms were highly prevalent in all conditions - mean (SD) of 10.6(4.0) symptoms. More than 50% of the MSA and PSP patients died over the year. Over the year, half of the patients showed either an upward (worsening, 24/60) or fluctuant (8/60) trajectory for POS and symptoms. The strongest predictors of higher levels of symptoms at the end of follow-up were initial scores on POS (AOR 1.30; 95%CI:1.05-1.60) and being male (AOR 5.18; 95% CI 1.17 to 22.92), both were more predictive than initial H&Y scores. INTERPRETATION The findings point to profound and complex mix of non-motor and motor symptoms in patients with late stage IPD, MSA and PSP. Symptoms are not resolved and half of the patients deteriorate. Palliative problems are predictive of future symptoms, suggesting that an early palliative assessment might help screen for those in need of earlier intervention

Generalized Holographic Quantum Criticality at Finite Density

We show that the near-extremal solutions of Einstein-Maxwell-Dilaton theories, studied in ArXiv:1005.4690, provide IR quantum critical geometries, by embedding classes of them in higher-dimensional AdS and Lifshitz solutions. This explains the scaling of their thermodynamic functions and their IR transport coefficients, the nature of their spectra, the Gubser bound, and regulates their singularities. We propose that these are the most general quantum critical IR asymptotics at finite density of EMD theories.Comment: v4: Corrected the scaling equation for the conductivity in section 9.

Cardiac arrest patients have an impaired immune response, which is not influenced by induced hypothermia

Induced hypothermia is increasingly applied as a therapeutic intervention in ICUs. One of the underlying mechanisms of the beneficial effects of hypothermia is proposed to be reduction of the inflammatory response. However, a fear of reducing the inflammatory response is an increased infection risk. Therefore, we studied the effect of induced hypothermia on immune response after cardiac arrest. A prospective observational cohort study in a mixed surgical-medical ICU. Patients admitted at the ICU after surviving cardiac arrest were included and during 24 hours body temperature was strictly regulated at 33°C or 36°C. Blood was drawn at three time points: after reaching target temperature, at the end of the target temperature protocol and after rewarming to 37°C. Plasma cytokine levels and response of blood leucocytes to stimulation with toll-like receptor (TLR) ligands lipopolysaccharide (LPS) from Gram-negative bacteria and lipoteicoic acid (LTA) from Gram-positive bacteria were measured. Also, monocyte HLA-DR expression was determined. In total, 20 patients were enrolled in the study. Compared to healthy controls, cardiac arrest patients kept at 36°C (n = 9) had increased plasma cytokines levels, which was not apparent in patients kept at 33°C (n = 11). Immune response to TLR ligands in patients after cardiac arrest was generally reduced and associated with lower HLA-DR expression. Patients kept at 33°C had preserved ability of immune cells to respond to LPS and LTA compared to patients kept at 36°C. These differences disappeared over time. HLA-DR expression did not differ between 33°C and 36°C. Patients after cardiac arrest have a modest systemic inflammatory response compared to healthy controls, associated with lower HLA-DR expression and attenuated immune response to Gram-negative and Gram-positive antigens, the latter indicative of an impaired immune response to bacteria. Patients with a body temperature of 33°C did not differ from patients with a body temperature of 36°C, suggesting induced hypothermia does not affect immune response in patients with cardiac arrest. ClinicalTrials.gov NCT01020916, registered 25 November 200

Corner contributions to holographic entanglement entropy

The entanglement entropy of three-dimensional conformal field theories contains a universal contribution coming from corners in the entangling surface. We study these contributions in a holographic framework and, in particular, we consider the effects of higher curvature interactions in the bulk gravity theory. We find that for all of our holographic models, the corner contribution is only modified by an overall factor but the functional dependence on the opening angle is not modified by the new gravitational interactions. We also compare the dependence of the corner term on the new gravitational couplings to that for a number of other physical quantities, and we show that the ratio of the corner contribution over the central charge appearing in the two-point function of the stress tensor is a universal function for all of the holographic theories studied here. Comparing this holographic result to the analogous functions for free CFT's, we find fairly good agreement across the full range of the opening angle. However, there is a precise match in the limit where the entangling surface becomes smooth, i.e., the angle approaches $\pi$, and we conjecture the corresponding ratio is a universal constant for all three-dimensional conformal field theories. In this paper, we expand on the holographic calculations in our previous letter arXiv:1505.04804, where this conjecture was first introduced.Comment: 62 pages, 6 figures, 1 table; v2: minor modifications to match published version, typos fixe