Suboptimal Combination Research Articles

Condition-number-based measurement configuration optimization for nanostructure reconstruction by optical scatterometry

The quality of the measured signature is influenced not only by the instrument’s precision but also by the selected measurement configuration. In optical scatterometry, the purpose of measurement configuration optimization (MCO) is to select an optimal or suboptimal combination of measurement conditions, such as the angles of incidence, azimuth, polarization and wavelength, to achieve higher measurement accuracy. This analysis not only requires an effective optimization strategy but is also time-consuming. In this work, we propose a general MCO method that incorporates error propagation theory and condition-number-based error estimation technique, by which the MCO problem can be formulated as an optimization problem for the condition number of the coefficient matrix in the linear estimation of parameter deviations. The method is demonstrated on a multi-wavelength Mueller matrix scatterometry measuring a Si grating. With the help of the neural-network-based surrogate model, the feasibility of the method is verified by making a comparison with Latin hypercube sampling. Fitting results of the measured and calculated Mueller matrix spectra obtained at the selected optimal measurement configuration show a good agreement. The proposed method is promising to provide an alternate solution to globally evaluate the MCO problem in optical scatterometry and other measurement scenarios.

Abstract 1358: The selective HDAC6 inhibitor ITF3756 stimulates an antitumor immune response and leads to tumor regression in combination with anti CTLA-4 antibody in a colon carcinoma murine model

Abstract Stimulation of tumor immune response with antibodies blocking the repressive activity of immune checkpoints gives impressive results with long-lasting tumor regression that is achieved regrettably only in a fraction of patients. To overcome these suboptimal results, different targets and combination therapies are the aim of active non-clinical and clinical research. Importantly, clinical trials of combination therapies have demonstrated better efficacy but also an increase of adverse events. HDAC6 is a member of the Zn-dependent histone deacetylase family of enzymes with peculiar structure and functions. In contrast to what is observed with many other HDAC family members, HDAC6 KO mice are viable and have no peculiar phenotype indicating that selective HDAC6 inhibitors should be well tolerated. Since HDAC6 is an obligate regulator of cytokine-induced PD-L1 expression, HDAC6 inhibitors might have a role in modulating tumor immune responses. We here report the in vivo efficacy of ITF3756, a potent and selective HDAC6 inhibitor that reduces in vitro the expression of PD-L1 on human monocytes and on CD8 T cells, counters immune exhaustion in CD8 T cells but shows no direct cytotoxic effects on a panel of murine tumor cell lines. In syngeneic tumor models ITF3756 showed anti-tumor activity that was comparable to the efficacy of an anti PD1 antibody and that went along with increased immune cell infiltration and the generation of tumor-reactive CD8 and CD4 cells. ITF3756 was inactive in immune-deficient animals and selective depletion of either CD8 or CD4 cells severely blunted the antitumor activity of ITF3756. In combination with an anti CTLA-4 antibody, ITF3756 lead to a complete tumor eradication in 50% of the animals. Re-challenge of these animals did not result in tumor growth indicating that the combination therapy had elicited tumor immunity. Blocking the PD-1/PD-L1 axis either alone or in combination with inhibition of CTLA4 in NOD mice strongly accelerated the development of auto-immune diabetes that was lethal in some treatment groups. In contrast neither ITF3756 alone nor the combination with anti CTLA4 lead to any significant induction of diabetes despite the activity of ITF3756 on the PD-1/PD-L1 axis. We conclude that ITF3756 induces an in vivo antitumor immune response and a durable antitumor activity when combined with an anti CTLA-4 antibody. Neither ITF3756 monotherapy nor the combination with anti CTLA4 accelerated the induction of autoimmune diabetes in NOD mice suggesting a favorable safety profile that was confirmed in preclinical GLP toxicology studies. Phase I clinical trials with ITF3756 will be initiated this year. Citation Format: Gianluca Fossati, Chiara Ripamonti, Gianluca Caprini, Pietro Pozzi, Elisabetta Galbiati, Paola Cordella, Mattia Marchini, Barbara Vergani, Giovanni Sandrone, Andrea Stevenazzi, Christian Steinkuhler. The selective HDAC6 inhibitor ITF3756 stimulates an antitumor immune response and leads to tumor regression in combination with anti CTLA-4 antibody in a colon carcinoma murine model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1358.

The vexing triad of obesity, alcohol, and coagulopathy predicts the need for multiple operations in liver transplantation

IntroductionOne in four liver transplants (LT) require return to the operating room(R-OR) within 48 h of surgery. We hypothesize that donor, recipient, and intraoperative factors will predict R-OR. MethodsLT recipients were enrolled in an observational study to measure coagulation with thrombelastography (TEG) were assessed with transplant recipient and donor variables for risk of R-OR. Results160 recipients with a median age of 55 years and a MELD-Na of 22 were analyzed. R-OR occurred in 22%. Recipient BMI (p = 0.006), donor heavy alcohol use (p = 0.017), TEG MA (p = 0.013) during the anhepatic phase of surgery, TEG MA at anhepatic and 30-min after reperfusion (p < 0.05), and red blood cell transfusions (p < 0.001) were associated with R-OR. ConclusionThe vexing triad of recipient obesity, heavy donor alcohol use, and low TEG MA were associated with a high rate of R-OR. Strategies to reduce this sub-optimal combination of risk factors could reduce the frequency of unplanned re-operations.

Combinatorial nanococktails via self-assembling lipid prodrugs for synergistically overcoming drug resistance and effective cancer therapy

BackgroundCombinatorial systemic chemotherapy is a powerful treatment paradigm against cancer, but it is fraught with problems due to the emergence of chemoresistance and additive systemic toxicity. In addition, coadministration of individual drugs suffers from uncontrollable pharmacokinetics and biodistribution, resulting in suboptimal combination synergy.MethodsToward the goal of addressing these unmet medical issues, we describe a unique strategy to integrate multiple structurally disparate drugs into a self-assembling nanococktail platform. Conjugation of a polyunsaturated fatty acid (e.g., linoleic acid) with two chemotherapies generated prodrug entities that were miscible with tunable drug ratios for aqueous self-assembly. In vitro and in vivo assays were performed to investigate the mechanism of combinatorial nanococktails in mitigating chemoresistance and the efficacy of nanotherapy.ResultsThe coassembled nanoparticle cocktails were feasibly fabricated and further refined with an amphiphilic matrix to form a systemically injectable and PEGylated nanomedicine with minimal excipients. The drug ratio incorporated into the nanococktails was optimized and carefully examined in lung cancer cells to maximize therapeutic synergy. Mechanistically, subjugated resistance by nanococktail therapy was achieved through the altered cellular uptake pathway and compromised DNA repair via the ATM/Chk2/p53 cascade. In mice harboring cisplatin-resistant lung tumor xenografts, administration of the nanococktail outperformed free drug combinations in terms of antitumor efficacy and drug tolerability.ConclusionOverall, our study provides a facile and cost-effective approach for the generation of cytotoxic nanoparticles to synergistically treat chemoresistant cancers.

How the phased array coil elements placement influences the image quality in cardiac magnetic resonance

Phased array coils are used for signal reception in cardiac magnetic resonance (CMR) for their large spatial coverage and for their high signal-to-noise ratio (SNR). However, SNR loss and image quality degradation may arise in cases of incorrect placement of coil arrays, due to sub-optimal combination of the individual coil signals. Analytical simulation of the magnetic fields produced by each individual receiver of a commercial 8-elements cardiac array is proposed. A 2D cross-correlation between array elements magnetic field maps is also proposed to evaluate coil shift. Phantom and in vivo experiments were performed at 1.5 T to evaluate the effect of array coil misalignment in terms of SNR losses and image degradation. Results obtained from phantom and in vivo experiments with different coil shifting are presented. A SNR reduction up to 70% was observed in the cardiac region in case of misalignment between the anterior and the posterior coil portions. Providing tools to prevent coil misalignment could be beneficial to minimize image degradation in MRI exams. In this paper, we proposed a straightforward method based on 2D cross-correlation to estimate the coil shift from images collected from a homogeneous phantom. Providing knowledge of the B1 field in a region of interest would help technologists to have information on the expected image quality and to potentially give indication whether a coil repositioning is needed.

An open label study of the safety and efficacy of a single dose of weekly chloroquine and azithromycin administered for malaria prophylaxis in healthy adults challenged with 7G8 chloroquine-resistant Plasmodium falciparum in a controlled human malaria infection model

BackgroundMalaria remains the top infectious disease threat facing the U.S. military in many forward operating environments. Compliance with malaria chemoprophylaxis remains a critical component in preventing malaria in the deployed Service Member. Studies of previous military operations show that compliance is consistently higher with weekly versus daily dosing regimens. Current FDA approved weekly chemoprophylaxis options have contraindications that can limit prescribing. The combination of chloroquine (CQ) with azithromycin (AZ) has previously been shown to be an efficacious treatment option for malaria, has pharmacokinetics compatible with weekly dosing, and has shown synergy when combined in vitro.MethodsIn this open label study, 18 healthy volunteers, aged 18–50 years (inclusive), were randomly assigned to receive either 300 mg CQ or 300 mg CQ and 2 gm azithromycin (CQAZ) of directly observed therapy, weekly for 3 weeks prior to undergoing mosquito bite challenge with chloroquine-resistant Plasmodium falciparum. Volunteers that remained asymptomatic and had no evidence of parasitaemia continued to receive weekly post-exposure chemoprophylaxis for 3 weeks following malaria challenge. The primary endpoint was the number of volunteers that remained asymptomatic and had no evidence of parasitaemia 28 days after the malaria challenge.ResultsAll 6 (100%) volunteers randomized to the CQ control group became symptomatic with parasitaemia during the 28-day post-challenge period. Only 1/12 (8.3%) of volunteers in the CQAZ group developed symptoms and parasitaemia during the 28-day post-challenge period. However, after chemoprophylaxis was discontinued an additional 6 volunteers developed parasitaemia between days 28–41 after challenge, with 4 of 6 experiencing symptoms. 80% of subjects in the CQAZ group experienced treatment related gastrointestinal adverse events (including 13% that experienced severe nausea) compared to 38% in the CQ group. A comparison of the pharmacokinetics in the CQAZ group demonstrated higher azithromycin Cmax (p = 0.03) and AUC (p = 0.044) levels in those volunteers who never became parasitaemic compared to those who did.ConclusionGiven the high rate of side effects and poor efficacy when administered for 3 weeks before and after challenge, the combination of weekly chloroquine and azithromycin is a suboptimal regimen combination for weekly malaria chemoprophylaxis.Trial registration ClinicalTrials.gov NCT03278808

Pan-resistant HIV-1 emergence in the era of integrase strand-transfer inhibitors: a case report

In 2019, WHO reported that the prevalence of HIV-1 drug resistance to first-line regimens of non-nucleoside reverse transcriptase inhibitors is increasing in countries with a low number of therapeutic options. This increasing prevalence of drug resistance is an important threat to ending the AIDS pandemic, as it compromises individual clinical outcomes and increases the risk of transmission. In countries with a high number of therapeutic options, little global information is available regarding the prevalence of multidrug-resistant HIV-1 infections, which presents a potential challenge for the clinical management of people with HIV. Even after the approval of two new antiretroviral drug classes in 2007, which were intended to help alleviate this problem, some cases of infection with HIV-1 that show limited susceptibility to the five available antiretroviral classes have been described. We did a thorough in-vitro evaluation of the drug resistance profile of HIV-1 from an observational case to show that five-class pan-resistant clinical cases do exist. We investigated a case of a highly treatment-experienced Caucasian male with HIV-1 who had a poor virological response to previous combination antiretroviral therapy (ART) and who was not responding to a dolutegravir-based regimen. For the complete panel of approved antiretrovirals, we examined genotypic resistance using the Stanford HIV Drug Resistance Database interpretation algorithm, and we examined phenotypic resistance using the PhenoSense and Trofile assays. Using viral gp160 sequence analysis, we also explored the potential susceptibility of this virus to novel therapeutic drugs targeting viral envelope binding. The individual was diagnosed with HIV-1 on Sept 29, 1989. Since starting antiretroviral treatment in 1995, the individual had received more than 14 different antiretroviral drugs over the course of his illness. In November, 2017, a blood sample was collected from the individual and we did drug resistance analysis tests. We found that this individual was infected with a pan-resistant HIV-1 subtype B strain that showed broad genotypic and phenotypic cross-resistance to all approved antiretroviral drugs, including the newest second-generation integrase inhibitors, dolutegravir and bictegravir. With no remaining clinical options available, except for investigational drugs, this case provides evidence that new antiretroviral drugs with different mechanisms of action are needed. Our case report shows that HIV-1 multidrug cross-resistance remains an important concern, despite the extensive number of antiretroviral drugs currently available. Highly treatment-experienced patients, especially those who have been given suboptimal combination ART, can develop highly complex resistance-associated mutation patterns, conferring cross-resistance to all widely available ARTs. The identification and reporting of cases, such as the one described in this report, are needed to increase awareness of emerging trends that have the potential to affect patient management. None.

A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties.

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

Triple-Frequency Code-Phase Combination Determination: A Comparison with the Hatch-Melbourne-Wübbena Combination Using BDS Signals

Considering the influence of the ionosphere, troposphere, and other systematic errors on double-differenced ambiguity resolution (AR), we present an optimal triple-frequency code-phase combination determination method driven by both the model and the real data. The new method makes full use of triple-frequency code measurements (especially the low-noise of the code on the B3 signal) to minimize the total noise level and achieve the largest AR success rate (model-driven) under different ionosphere residual situations (data-driven), thus speeding up the AR by directly rounding. With the triple-frequency Beidou Navigation Satellite System (BDS) data collected at five stations from a continuously-operating reference station network in Guangdong Province of China, different testing scenarios are defined (a medium baseline, whose distance is between 20 km and 50 km; a medium-long baseline, whose distance is between 50 km and 100 km; and a long baseline, whose distance is larger than 100 km). The efficiency of the optimal code-phase combination on the AR success rate was compared with that of the geometry-free and ionosphere-free (GIF) combination and the Hatch-Melbourne-Wübbena (HMW) combination. Results show that the optimal combinations can always achieve better results than the HMW combination with B2 and B3 signals, especially when the satellite elevation angle is larger than 45°. For the wide-lane AR which aims to obtain decimeter-level kinematic positioning service, the standard deviation (STD) of ambiguity residuals for the suboptimal combination are only about 0.2 cycles, and the AR success rate by directly rounding can be up to 99%. Compared with the HMW combinations using B1 and B2 signals and using B1 and B3 signals, the suboptimal combination achieves the best results in all baselines, with an overall improvement of about 40% and 20%, respectively. Additionally, the STD difference between the optimal and the GIF code-phase combinations decreases as the baseline length increases. This indicates that the GIF combination is more suitable for long baselines. The proposed optimal code-phase combination determination method can be applied to other multi-frequency global navigation satellite systems, such as new-generation BDS, Galileo, and modernized GPS.

BP101 Peptide Promotes Female Sexual Receptivity in the Rat

IntroductionLow sexual desire is a frequent sexual problem in women, with only one drug for the condition approved by the Food and Drug Administration. AimTo evaluate the ability of a novel synthetic peptide, BP101, to facilitate sexual behavior after intranasal administration or infusion into certain brain areas in female rats. MethodsBilaterally ovariectomized female rats, primed with a suboptimal combination of estradiol benzoate (EB) and progesterone, were used as a model of low sexual motivation. Sexual behavior was tested with stud male rats after acute (experiment 1) or long-term (experiment 2) intranasal administration of BP101 or peptide infusion into the olfactory bulb, medial preoptic area, ventromedial hypothalamic nucleus, or ventral tegmental area (experiment 3). Main Outcome MeasuresFrequency of solicitations (SF), as an indicator of sexual motivation in female rats, and lordosis frequency and ratio, as measurements of female consummatory sexual behavior. ResultsAcute intranasal BP101 administration moderately increased SF, with the highest tested dose of 300 μg/kg causing an 80% increase. Female rats receiving BP101 75 or 300 μg/kg daily on days 6 to 16 of the peptide administration displayed twofold higher SF compared with the placebo-treated animals, an increase comparable to optimally hormone-primed female rats. Infusion of BP101 1 and 5 μg per rat into the medial preoptic area, but not into the olfactory bulb, ventromedial hypothalamic nucleus, or ventral tegmental area, increased SF in female rats supplemented with EB 10 or 20 μg. The effect was relatively more pronounced in female rats receiving EB 10 μg (≈300%) compared with EB 20 μg (≈50%) with direct brain infusions. ConclusionBP101 displays a potent stimulatory effect on sexual motivation in the female rat, and the medial preoptic area seems to be the site of its action. BP101 is effective in female rats receiving different hormone supplementations, making the present data generalizable to pre- and postmenopausal women with hypoactive sexual desire.Andreev-Andrievskiy A, Lomonosov M, Popova A, et al. BP101 Peptide Promotes Female Sexual Receptivity in the Rat. J Sex Med 2017;14:336–346.

Towards a framework for terrain attribute selection in environmental studies

Terrain attributes (e.g. slope, rugosity) derived from digital terrain models are commonly used in environmental studies. The increasing availability of GIS tools that generate those attributes can lead users to select a sub-optimal combination of terrain attributes for their applications. Our objectives were to identify sets of terrain attributes that best capture terrain properties and to assess how they vary with surface complexity. 230 tools from 11 software packages were used to derive terrain attributes from nine surfaces of different topographic complexity levels. Covariation and independence of terrain attributes were explored using three multivariate statistical methods. Distinct groups of correlated terrain attributes were identified, and their importance in describing a surface varied with surface complexity. Terrain attributes were highly covarying and sometimes ambiguously defined within software documentation. We found that a combination of six to seven particular terrain attributes always captures more than 70% of the topographic structure of surfaces.

Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes.

Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes–recommended in a related study and that includes a measure of relative position, slope, two measures of orientation, topographic mean and a measure of rugosity–yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.

A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ=0.87) to 97.4% (κ=0.94) against 85.3% (κ=0.69) to 93.4% (κ=0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems naturally with its original formulation.

Algorithmic co-optimization of genetic constructs and growth conditions: application to 6-ACA, a potential nylon-6 precursor.

Optimizing bio-production involves strain and process improvements performed as discrete steps. However, environment impacts genotype and a strain that is optimal under one set of conditions may not be under different conditions. We present a methodology to simultaneously vary genetic and process factors, so that both can be guided by design of experiments (DOE). Advances in DNA assembly and gene insulation facilitate this approach by accelerating multi-gene pathway construction and the statistical interpretation of screening data. This is applied to a 6-aminocaproic acid (6-ACA) pathway in Escherichia coli consisting of six heterologous enzymes. A 32-member fraction factorial library is designed that simultaneously perturbs expression and media composition. This is compared to a 64-member full factorial library just varying expression (0.64 Mb of DNA assembly). Statistical analysis of the screening data from these libraries leads to different predictions as to whether the expression of enzymes needs to increase or decrease. Therefore, if genotype and media were varied separately this would lead to a suboptimal combination. This is applied to the design of a strain and media composition that increases 6-ACA from 9 to 48 mg/l in a single optimization step. This work introduces a generalizable platform to co-optimize genetic and non-genetic factors.

Pap Smear Images Classification for Early Detection of Cervical Cancer

In this presents the analyses of the Pap smear cervical cell images for cervical screening and detection. Initially preprocessed the cell images to remove unwanted noises, Followed by extraction of the cell from the background to obtain the cytoplasm and nucleus of the cell which is the region of interest. It is the only parts of the cell which can be used to differentiate normal cell from abnormal one.20 salient features were extracted for training of support vector machine. SVM-RFE is used for features selection; the RFE algorithm removes unimportant features based on backward sequential selection by iteratively deleting one feature at a time, resulting in suboptimal combination of r(r<n) features with best predictive performance. Normalisation of the data was done to the feature so as to obtain a uniform distinct range – the feature values were normalised to 0 and 1. We conducted an experiment for evaluation of classifier; we adopted 10 cross validation and left one out for the testing of the classifier. Radial basis function and polynomial kernel function were used. In the first phase, we varied the value of gamma for training and evaluation, and the best classification accuracy was obtained at 9; where accuracy was 95.33% and sensitivity was 93.21% the performance of the classifier was increased as the value of gamma increased. In the second phase, we used polynomial kernel function, showing better performance of 97.02% accuracy and 95.50% of the sensitivity of the classifier. The classifier gives promising results with average accuracy of 92.961%, sensitivity 90.833% and specificity 80.39%. General Terms Support Vector Machine, Polynomial Kernel Function, Radial Basis Function.

English

The most favorable technique for high speed data transmission is Multicarrier Code Division Multiple Access. The MC-CDMA signals, which are characterized through PAPR, which can reduce the efficiency of the system. In this paper, we have to reduce the PAPR of an MC-CDMA system using PTS (Partial Transmit Sequence) through suboptimal combination algorithms (BPSO&BFO), which uses binary phase factors, for different number of subcarriers and for different length of sub blocks of PTS. I. I n tro d uct io n Mainly, the transmission of multi-carrier is to divide the entire bandwidth into smaller bandwidths. Each bandwidth with a different sub-carrier frequency, such that each of these narrow- band signals is protected from frequency selective fading and the data-rates are improved as compared to single-carrier system as we say that the total bandwidth can be increased significantly.MC-CDMA is a combination of both OFDM and CDMA that can provide protection from frequency selective fading and time dispersion. The CDMA part that provides both multiple access ability as well as spread each user signal over the frequency domain provide protection cover from the impact of frequency selective fading. The OFDM provides spreading across time domain of each spreading code's chip which decreases the effect of inter-symbol interference. So that we are able to get high data rate for transmission purpose. As we see, MC-CDMA is a powerful technique which has multiple accesses, but not free from problem. MC-CDMA signal has large peak to average ratio (PAPR) which largely limits its applications. High PAPR values causes a critical problem to the power amplifier (PA) used at transmitter. The efficiency performance of power amplifier decreases as PAPR increases. At transmitter side, the signal suffers from non-linear distortion. At receiver side, it degrades Bit Error Rate performance. This implements the use of power amplifier with large linear range so that the cost increases. Therefore it is necessary to reduce the impact of PAPR by means of PAPR reduction schemes. PTS is one of them most suitable probabilistic distortion less technique due to its less complexity.

An approach to simulation-based parameter and structure optimization of MATLAB/Simulink models using evolutionary algorithms

In engineering, a broad range of environments exist for modeling and simulation with integrated parameter optimization. The established techniques only optimize model parameter values, the model structure is considered to be fixed. As system performance is optimized, one may have to redesign the model structure. The redesign is done manually by an analyst. The suboptimal combination of automatic parameter optimization and manual structural changes leads to an optimization task that is prone to error. This paper details an approach that provides optimization through automatic reconfiguration of both the model structure and model parameters. An optimization method that uses an evolutionary algorithm is supported by a model management method. This method is based on the system entity structure/model base framework. The admissible model structures and their associated model parameter sets are specified using the system entity structure ontology. Basic dynamic model components are organized in a model base. In addition to this, new algorithms are introduced. These map knowledge coded in the system entity structure to a set of numerical (structure) parameters, and also perform this mapping in reverse. In this manner a combined structure and parameter optimization problem is derived. Since both methods – evolutionary algorithm and model management – work together concurrently, different system configurations can be evaluated automatically. The objective is to provide an optimal solution; a model optimized for both parameter and structure.

PAPR Distribution Analysis of OFDM signals with Partial Transmit Sequence

Third Generation Partnership Project (3GPP) LTE has adopted OFDMA as the uplink multiple access scheme. One of the major drawbacks is the high PAPR. Not only is the performance of PAPR with PTS technique influenced by the number of subblocks and the phase vector but also by the subblock partitioning. The Partial Transmit Sequence (PTS) technique suffers from the search complexity of finding the optimum set of phase vectors. We propose a suboptimal combination algorithm that reduces the search complexity. The number of commutations in the suboptimal combination algorithm is much lower than the required by the original PTS technique. In this paper, we propose a suboptimal combination algorithm to reduce the searching complexity of finding the optimum set of vectors to minimize PAPR. The performance of PAPR utilizing the PTS technique improves by the use of the proposed suboptimal combination algorithm. We also show that a SC- FDMA system with Interleaved-FDMA or Localized-FDMA performs better than Orthogonal- FDMA in the uplink direction where transmitter power efficiency is of great importance.

A systematic review of antiretroviral adherence interventions for HIV-infected people who use drugs.

HIV-infected persons who use drugs (PWUDs) are particularly vulnerable for suboptimal combination antiretroviral therapy (cART) adherence. A systematic review of interventions to improve cART adherence and virologic outcomes among HIV-infected PWUDs was conducted. Among the 45 eligible studies, randomized controlled trials suggested directly administered antiretroviral therapy, medication-assisted therapy (MAT), contingency management, and multi-component, nurse-delivered interventions provided significant improved short-term adherence and virologic outcomes, but these effects were not sustained after intervention cessation. Cohort and prospective studies suggested short-term increased cART adherence with MAT. More conclusive data regarding the efficacy on cART adherence and HIV treatment outcomes using cognitive behavioral therapy, motivational interviewing, peer-driven interventions and the integration of MAT into HIV clinical care are warranted. Of great concern was the virtual lack of interventions with sustained post-intervention adherence and virologic benefits. Future research directions, including the development of interventions that promote long-term improvements in adherence and virologic outcomes, are discussed.

Meta-Model-based Estimation of Optimal System Variants with MATLAB/Simulink

The idea that the efficient usage of all necessary production resources has not only an ecological im- portance but is also an overwhelming economical compet- itive factor becomes accepted since the 1980s. Modeling and simulation with integrated parameter optimization is used routinely to improve process performance. In engi- neering a well known environment for this task is the MATLAB/Simulink programming system. Using this or similar, established techniques only model parameter of a single model structure is optimized. Model structure is considered to be fixed as the relationships between model elements are defined during model development. Until now no tools and methods are known which can optimize product design and production processes utilizing all exist- ing degrees of freedom. As process performance is opti- mized it may be necessary to redesign the model struc- ture. The redesign is normally carried out manually by an analyst but not automatically by the optimization method. This suboptimal combination of automatic parameter optimization and manual structure changes leads to a time consuming and error-prone optimization task. The system theoretical approach of the System Entity Struc- ture/Model Base framework (SES/MB) is able to define alternative model structures and parameter sets in a single meta-model, called System Entity Structure (SES). Moreover, atomic models are stored in a model base (MB). Using both, SES and MB, it is possible to generate modular, hierarchical models with different structures and parameters. Evolutionary Algorithms are a subtopic of Artificial Intelli- gence that are involved in combinatorial optimization problems. These algorithms are based on ideas inspired by biological evolution. They often perform well for many problem types because they do not make assumptions about the problem specific search space. The research reported in this paper details an approach providing optimization through automatic reconfiguration of both: model structure and model parameters. An evolutionary algorithm based optimization method is assisted by an SES/MB based model management. It searches for an optimal solution with repeated, combined model parameter and model structure changes resulting in a combined parameter and structure optimized model