An Optimal Integral Controller for Adaptive Optics Systems.

Integral controllers are commonly employed in astronomical adaptive optics. This work presents a novel tuning procedure for integral controllers in adaptive optics systems which relies on information about the measured disturbances. This tuning procedure consists of two main steps. First, it models and identifies measured disturbances as continuous-time-damped oscillators using Whittles´s likelihood and the wavefront sensor output signal. Second, it determines the integral controller gain of the adaptive optics system by minimizing the output variance. The effectiveness of this proposed method is evaluated through theoretical examples and numerical simulations conducted using the Object-Oriented Matlab Adaptive Optics toolbox. The simulation results demonstrate that this approach accurately estimates the disturbance model and can reduce the output variance. Our proposal results in improved performance and better astronomical images even in challenging atmospheric conditions. These findings significantly contribute to adaptive optics system operations in astronomical observatories and establish our procedure as a promising tool for fine-tuning integral controllers in astronomical adaptive optics systems.

Frequency of diminished ovarian reserve in women with multiple sclerosis in Chile: An exploratory study.

INTRODUCTION: Multiple Sclerosis (MS) is a chronic disease affecting around 2.8 million people worldwide. Two-thirds are women, and the mean age at diagnosis is about 30 years old. Social trends are moving towards older age at first pregnancy, both in women with and without MS. OBJECTIVES: To determine the frequency of diminished ovarian reserve (DOR) through anti-Mullerian Hormone (AMH) measurement in women with MS at fertile age and Healthy Females (HF) in Chile. METHODS: Case-control, multicentric, cross-sectional study including relapsing-remitting people with MS (pwMS) between 18 and 40 years and sex and age-matched HF. We obtained a blood sample to determine AMH levels. We defined DOR as AMH <1.5 ng/mL and very-low AMH levels as <0.5 ng/mL. Also, we performed questions regarding reproductive decision-making. RESULTS: We included 79 sex and age-matched HF and 92 pwMS, median age 32(19-40) years, median disease duration 6 (1-17)years, median EDSS 1.0 (0-6), 95% were receiving disease-modifying therapy (DMT), 70% high-efficacy DMT and 37% with a treatment that contraindicates pregnancy. DOR was observed in 24% (n = 22) of the pwMS, compared to 14% (n = 11) of the HF (p = 0.09), while very-low AMH levels were observed in 7.6% (n = 7) of pwMS and none of the HF (p = 0.0166). We observed an inverse correlation between age and AMH levels. Age was the only significant risk factor for low AMH levels in pwMS (OR 1.14 95%CI(1.00-1-31), p = 0.04), including smoking, body mass index (BMI), hormonal contraception, autoimmune comorbidity, high/low-moderate efficacy DMT, and active disease as covariables. We did not find statistically significant differences in age at diagnosis, BMI, disease duration, EDSS, autoimmune comorbidity, use of hormonal contraception, or percentage of active disease between MS women with normal vs DOR. Over 70% of pwMS desired to become pregnant in the future, while 60% considered that the diagnosis of MS was a limitation for pregnancy planning. CONCLUSIONS: No differences in DOR, measured by levels of AMH, were observed between pwMS MS and HF in Chile. As expected, AMH levels were correlated only with ageing. This information may be evaluated early during the disease course to help patients and neurologists with fertility counselling and family planning considerations regarding DMT use.

Antimicrobial, Antioxidant, and Anti-Inflammatory Properties of Monofloral Honeys from Chile.

Honey is a mixture of compounds produced by bees that has been appreciated by humanity since the creation of the oldest civilizations. It has multiple uses and can be a highly nutritional and healing substance. It has been used in traditional medicine as a natural alternative for the treatment of diverse clinical conditions. This is due to its reported bioactive properties. The objective of this article is to exhibit and analyze the biological properties of different types of honey originating from Chile based on their antimicrobial, antioxidant, and anti-inflammatory activities, focusing primarily on recompiling experimental studies made on monofloral honey of plant species present in the Chilean territory. The result of this bibliographical review shows that Chilean honey possesses remarkable bioactive properties, mainly antimicrobial and antioxidant activities, with a few studies on its anti-inflammatory activity. Most of these results were attributed to monofloral honey belonging to ulmo (Eucryphia cordifolia) and quillay (Quillaja saponaria Molina) plant species. These properties are related to the presence of several bioactive components, such as phenolic components (mainly flavonoids), hydrogen peroxide (H2O2), enzymes, proteins, and carbohydrates. The biodiversity of the flora and the environmental conditions of the Chilean territory are responsible for the wide range of bioactive compounds and biological properties found in Chilean honey. Further studies must be made to uncover the medicinal potential of these native honeys.

A randomized clinical trial of precision prevention materials incorporating MC1R genetic risk to improve skin cancer prevention activities among Hispanics.

Purpose: Skin cancer incidence is increasing among Hispanics, who experience worse outcomes than non-Hispanic Whites. Precision prevention incorporating genetic testing for MC1R, a skin cancer susceptibility marker, may improve prevention behavior. Patients and Methods: Hispanic participants (n=920) from Tampa, FL and Ponce, PR, were block-randomized within MC1R higher- and average-risk groups to precision prevention or generic prevention arms. We collected baseline information on demographics, family history of cancer, phenotypic characteristics, health literacy, health numeracy, and psychosocial measures. Participants reported weekday and weekend sun exposure (in hours), number of sunburns, frequency of five sun protection behaviors, intentional outdoor and indoor tanning, and skin examinations at baseline, three months, and nine months. Participants also reported these outcomes for their eldest child ≤10 years old. Results: Among MC1R higher-risk participants, precision prevention increased sunscreen use (OR=1.74, p=0.03) and receipt of a clinical skin exam (OR=6.51, p=0.0006); and it decreased weekday sun exposure hours (ß=-0.94, p=0.005) and improved sun protection behaviors (ß=0.93, p=0.02) in their children. There were no significant intervention effects among MC1R average risk participants. The intervention did not elevate participant cancer worry. We also identified moderators of the intervention effect among both average- and higher-risk participants. Conclusions: Receipt of MC1R precision prevention materials improved some skin cancer prevention behaviors among higher-risk participants and their children and did not result in reduced prevention activities among average-risk participants. Despite these encouraging findings, levels of sun protection behaviors remained suboptimal among participants, warranting more awareness and prevention campaigns targeted to Hispanics.

Longitudinal Collection of Patient-Reported Outcomes and Activity Data during CAR-T Therapy: Feasibility, Acceptability, and Data Visualization.

Background: Clinicians must closely monitor patients for toxicities after chimeric antigen receptor T-cell therapy (CAR-T). Patient-reported outcomes (PROs) (e.g., toxicities, quality of life) and activity data (e.g., steps, sleep) may complement clinicians' observations. This study tested the feasibility and acceptability of collecting PROs and activity data from patients with hematologic malignancies during CAR-T and explored preliminary data patterns. Methods: Participants wore a Fitbit tracker and completed PROs at several timepoints through 90-days post-infusion. Feasibility was assessed with a priori benchmarks for recruitment (≥50%), retention (≥70%), PRO completion (≥70%), and days wearing the Fitbit (≥50%). Acceptability was assessed with participant satisfaction (a priori benchmark > 2 on a 0−4 scale). Results: Participants (N = 12) were M = 66 years old (SD = 7). Rates of recruitment (68%), retention (83%), PRO completion (85%), and days wearing the Fitbit (85%) indicated feasibility. Satisfaction with completing the PROs (M = 3.2, SD = 0.5) and wearing the Fitbit (M = 2.9, SD = 0.5) indicated acceptability. Preliminary data patterns suggested that participants with better treatment response (vs. progressive disease) had a higher toxicity burden. Conclusions: Longitudinal PRO and activity data collection was feasible and acceptable. Data collected on a larger scale may be used to specify risk prediction models to identify predictors of severe CAR-T-related toxicities and inform early interventions.

Assessment of melanoma precision prevention materials incorporating MC1R genetic risk information.

Few studies have examined cognitive responses to mailed precision prevention materials. MC1R is a robust, well-described melanoma susceptibility marker. The purpose was to assess cognitive responses to generic or precision prevention materials incorporating MC1R genetic risk. Non-Hispanic White participants (n = 1134) enrolled in a randomized controlled trial received either precision prevention materials incorporating MC1R genetic risk (higher/average) or generic prevention (standard) materials. Six months after baseline, 808 (71.3%) participants reported on the amount of prevention materials read (5-point scale); believability and clarity of materials; intention to change preventive behaviors (7-point Likert scale); and recall of their MC1R genetic risk. Comparisons were conducted using Kruskal-Wallis and chi-squared tests. Overall, participants read most to all (Mdn = 4, IQR = 2) of the prevention materials, reported high believability (Mdn = 7, IQR = 1) and clarity (Mdn = 7, IQR = 1), and moderate intention to change preventive behaviors (Mdn = 5, IQR = 2). Higher-risk participants reported slightly less clarity (Mdn = 6, IQR = 2) than either average-risk (Mdn = 6, IQR = 1, p = 2.50 × 10-3) or standard participants (Mdn = 7, IQR = 1, p = 2.30 × 10-5); and slightly less believability (Mdn = 6, IQR = 1) than standard participants (Mdn = 7, IQR = 1, p = .005). Higher-risk participants were 2.21 times as likely (95% CI = 1.43-3.43) to misremember or forget their risk compared to average-risk participants; misremembering was observed only among higher-risk participants (14%). Mailed precision prevention information were mostly read, highly believable and clear, and resulted in moderate levels of intention to change sun protection behaviors, bolstering the feasibility of population-level precision prevention. Defensive reactions may explain lower clarity, believability, and higher incorrect risk recall among higher-risk participants.

Precision prevention uses an individual's genetics, environment, and/or lifestyle to promote prevention behaviors. However, if materials incorporating precision prevention information are not easily accessible, individuals may misinterpret or distrust findings. Few studies have examined participant-reported believability and clarity of mailed precision prevention materials, how much they read, and whether they intend to change preventive behaviors. We assessed genetic risk for melanoma by determining DNA variation at the MC1R gene, a known melanoma risk marker. Participants were mailed either precision prevention materials conveying their MC1R genetic risk or generic (without genetic risk information) prevention materials. Overall, participants read most of the materials, gave high believability and clarity scores, and reported moderate levels of intention to change preventive behavior. However, participants at higher genetic risk had slightly lower believability and clarity scores than the generic group and were more likely to forget or misremember their genetic risk than participants at average genetic risk. Among participants who correctly recalled their genetic risk, differences in believability diminished, while differences in clarity remained. We conclude that precision prevention materials are highly believable and clear, but additional strategies may be necessary to maximize believability, clarity, and risk recall for individuals at a higher genetic risk.

Metformin Treatment Regulates the Expression of Molecules Involved in Adiponectin and Insulin Signaling Pathways in Endometria from Women with Obesity-Associated Insulin Resistance and PCOS.

Polycystic ovary syndrome (PCOS) is an endocrine/metabolic disorder associated with insulin resistance (IR) and obesity. Endometria from women with PCOS present failures in insulin action, glucose uptake and signaling of insulin-sensitizing molecules, such as adiponectin, with consequences for reproduction. Metformin (MTF) treatment improves insulin signaling in endometrial tissues, but its mechanism is not fully understood. This study addresses the MTF effect, as well as adiponectin agonist action, on levels of molecules associated with insulin and adiponectin signaling pathways in endometrial tissue and cells, as assessed by immunohistochemistry and immunocytochemistry, respectively. Endometrial tissues were obtained from women and divided into five groups: Normal Weight (control); Obesity + IR; Obesity + IR + PCOS; Obesity + IR + MTF; Obesity + IR + PCOS + MTF. Endometrial cells stimulated with TNFα (as obesity-marker) were also used to partially emulate an obesity environment. The results showed low levels of insulin/adiponectin signaling in the endometria from women with obesity, IR and PCOS compared with the control group. MTF re-established these levels, independently of PCOS. TNFα-associated molecules were elevated in pathologic endometria, whereas MTF diminished these levels. The low levels of insulin/adiponectin molecules in endometrial cells treated with TNFα were reverted by MTF, similar to what was observed in the case of the adiponectin agonist. Therefore, independently of PCOS, MTF can re-establish levels of molecules involved in insulin/adiponectin signaling in endometrial cells, suggesting an improvement in insulin action and reproductive failures observed in endometria from women with obesity/PCOS.

LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile.

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.

A multilevel, low literacy dual language intervention to promote colorectal cancer screening in community clinics in Florida: A randomized controlled trial.

One of the largest disparities in cancer mortality in the United States occurs with colorectal cancer (CRC). The objectives of this multilevel two-arm intervention trial were to compare the efficacy of two interventions to promote CRC screening (CRCS) with fecal immunochemical test (FIT) and examine sociodemographic and psychosocial predictors of FIT screening. Individuals ages 50-75 (n = 326) who were not up-to-date with CRCS, could understand English or Spanish, and were at average CRC risk were recruited from two federally qualified health centers (FQHCs) in Florida. Prior to intervention, CRCS rates in the FQHCs were 27.1% and 32.9%, respectively. Study enrollment occurred April 2018-November 2019. System-level intervention components included leveraging electronic medical record (EMR) systems and delivering patient reminders. Participants were randomized to C-CARES (education+FIT) or C-CARES Plus (C-CARES+personalized coaching [for those not completing FIT within 90 days]). Primary outcome was completed FIT returned <1 year. Primary outcome analyses were performed using logistic regression. 225 participants completed FIT (69.0% [95% CI: 64.0-74.0%]), with no significant difference in FIT uptake by intervention arm (67.3% C-CARES Plus vs. 70.8% C-CARES; p = .49). FIT uptake was significantly higher among patients who received intervention materials in Spanish (77.2%) compared to those who received materials in English (63.2%, p < .01). The personalized coaching in the C-CARES Plus arm did not appear to provide added benefit beyond the C-CARES intervention. Multilevel approaches that include EMR prompts, reminders, FIT access, and provision of low-literacy, language-concordant education can support efforts to improved community clinics' CRCS rates. Future efforts should focus on repeat FIT screening. Trial registration: The trial was registered at ClinicalTrials.gov (NCT03906110).

A Two-Filter Approach for State Estimation Utilizing Quantized Output Data.

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss-Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.

Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices.

Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.

A Randomized Trial of Precision Prevention Materials to Improve Primary and Secondary Melanoma Prevention Activities among Individuals with Limited Melanoma Risk Phenotypes.

Inherited variation at MC1R is associated with elevated melanoma risk among non-Hispanic whites (NHWs). MC1R genetic testing may unmask previously unrecognized disease risk, especially among individuals with few melanoma phenotypic risk factors. We recruited NHW individuals with limited phenotypic risk factors from two primary care clinics in west-central Florida. Participants (n = 1134) were randomized within MC1R genotype risk group (average/higher) to receive mailed precision prevention (i.e., intervention) or generic prevention materials. Participants reported hours of weekday and weekend sun exposure, frequency of intentional outdoor tanning and sun protection behaviors, number of sunburns, indoor tanning episodes, and skin examinations at baseline, and after 6 and 12 months. Among MC1R higher-risk participants, the intervention increased the likelihood of often or always wearing a shirt with sleeves (OR = 1.49, p = 0.03) and seeking shade or using an umbrella (OR = 1.42, p = 0.046), and it decreased the number of sunburns among their young children (ß = -0.13, p = 0.03). Intervention effects were not noted among MC1R average-risk participants. Moderation analyses identified intervention effects within subgroups in average-risk and higher-risk participants. Precision prevention information conveying MC1R testing results can increase the practice of some sun protection behaviors among at-risk individuals with limited melanoma risk phenotypes and may provide a cross-generational tool to counteract increasing incidence of melanoma.

On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models.

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience.

PURPOSE: The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS: Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS: The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION: The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

TIMEx: tumor-immune microenvironment deconvolution web-portal for bulk transcriptomics using pan-cancer scRNA-seq signatures.

SUMMARY: The heterogeneous cell types of the tumor-immune microenvironment (TIME) play key roles in determining cancer progression, metastasis and response to treatment. We report the development of TIMEx, a novel TIME deconvolution method emphasizing on estimating infiltrating immune cells for bulk transcriptomics using pan-cancer single-cell RNA-seq signatures. We also implemented a comprehensive, user-friendly web-portal for users to evaluate TIMEx and other deconvolution methods with bulk transcriptomic profiles. AVAILABILITY AND IMPLEMENTATION: TIMEx web-portal is freely accessible at http://timex.moffitt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Disturbance Modelling for Minimum Variance Control in Adaptive Optics Systems Using Wavefront Sensor Sampled-Data.

Modern large telescopes are built based on the effectiveness of adaptive optics systems in mitigating the detrimental effects of wavefront distortions on astronomical images. In astronomical adaptive optics systems, the main sources of wavefront distortions are atmospheric turbulence and mechanical vibrations that are induced by the wind or the instrumentation systems, such as fans and cooling pumps. The mitigation of wavefront distortions is typically attained via a control law that is based on an adequate and accurate model. In this paper, we develop a modelling technique based on continuous-time damped-oscillators and on the Whittle's likelihood method to estimate the parameters of disturbance models from wavefront sensor time-domain sampled-data. On the other hand, when the model is not accurate, the performance of the minimum variance controller is affected. We show that our modelling and identification techniques not only allow for more accurate estimates, but also for better minimum variance control performance. We illustrate the benefits of our proposal via numerical simulations.

Exogenous Application of Brassinosteroid 24-Norcholane 22(S)-23-Dihydroxy Type Analogs to Enhance Water Deficit Stress Tolerance in Arabidopsis thaliana.

Brassinosteroids (BRs) are plant hormones that play an essential role in plant development and have the ability to protect plants against various environmental stresses, such as low and high temperature, drought, heat, salinity, heavy metal toxicity, and pesticides. Mitigation of stress effects are produced through independent mechanisms or by interaction with other important phytohormones. However, there are few studies in which this property has been reported for BRs analogs. Thus, in this work, the enhancement of drought stress tolerance of A. thaliana was assessed for a series of 2-deoxybrassinosteroid analogs. In addition, the growth-promoting activity in the Rice Lamina Inclination Test (RLIT) was also evaluated. The results show that analog 1 exhibits similar growth activity as brassinolide (BL; used as positive control) in the RLIT bioassay. Interestingly, both compounds increase their activities by a factor of 1.2-1.5 when they are incorporated to polymer micelles formed by Pluronic F-127. On the other hand, tolerance to water deficit stress of Arabidopsis thaliana seedlings was evaluated by determining survival rate and dry weight of seedlings after the recovery period. In both cases, the effect of analog 1 is higher than that exhibited by BL. Additionally, the expression of a subset of drought stress marker genes was evaluated in presence and absence of exogenous applied BRs. Results obtained by qRT-PCR analysis, indicate that transcriptional changes of AtDREBD2A and AtNCED3 genes were more significant in A. thaliana treated with analog 1 in homogeneous solution than in that treated with BL. These changes suggest the activation of alternative pathway in response to water stress deficit. Thus, exogenous application of BRs synthetic analogs could be a potential tool for improvement of crop production under stress conditions.

Cohort Identification for Translational Bioinformatics Studies.

Translational studies for therapeutic development require cohort identification to identify appropriate biological materials from patients that can be utilized to test a specific hypothesis. Robust health information systems exist, but there are numerous challenges in accessing the information to select appropriate biological specimens needed for translational experiments. This chapter on methods describes the current standard process for cohort identification utilized by the Cutaneous Oncology Program and the Collaborative Data Services Core (CDSC) at Moffitt Cancer Center. The methods include utilization of graphical user interfaces coupled with database querying. As such, this chapter outlines the regulatory and procedural processes needed to utilize a health information management system to filter patients for cohort identification.

PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients.

BACKGROUND: Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient's tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients' drug responses based on gene expression and drug activity data from PDX models. RESULTS: Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials. CONCLUSION: Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org .

A negative storage model for precise but compact storage of genetic variation data.

Falling sequencing costs and large initiatives are resulting in increasing amounts of data available for investigator use. However, there are informatics challenges in being able to access genomic data. Performance and storage are well-appreciated issues, but precision is critical for meaningful analysis and interpretation of genomic data. There is an inherent accuracy vs. performance trade-off with existing solutions. The most common approach (Variant-only Storage Model, VOSM) stores only variant data. Systems must therefore assume that everything not variant is reference, sacrificing precision and potentially accuracy. A more complete model (Full Storage Model, FSM) would store the state of every base (variant, reference and missing) in the genome thereby sacrificing performance. A compressed variation of the FSM can store the state of contiguous regions of the genome as blocks (Block Storage Model, BLSM), much like the file-based gVCF model. We propose a novel approach by which this state is encoded such that both performance and accuracy are maintained. The Negative Storage Model (NSM) can store and retrieve precise genomic state from different sequencing sources, including clinical and whole exome sequencing panels. Reduced storage requirements are achieved by storing only the variant and missing states and inferring the reference state. We evaluate the performance characteristics of FSM, BLSM and NSM and demonstrate dramatic improvements in storage and performance using the NSM approach.