Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

A coding variant in RARG confers susceptibility to anthracycline-induced cardiotoxicity in childhood cancer

Anthracyclines are used in over 50% of childhood cancer treatment protocols, but their clinical usefulness is limited by anthracycline-induced cardiotoxicity (ACT) manifesting as asymptomatic cardiac dysfunction and congestive heart failure in up to 57% and 16% of patients, respectively. Candidate gene studies have reported genetic associations with ACT, but these studies have in general lacked robust patient numbers, independent replication or functional validation. Thus, the individual variability in ACT susceptibility remains largely unexplained. We performed a genome-wide association study in 280 patients of European ancestry treated for childhood cancer, with independent replication in similarly treated cohorts of 96 European and 80 non-European patients. We identified a nonsynonymous variant (rs2229774, p.Ser427Leu) in RARG highly associated with ACT (P = 5.9 × 10(-8), odds ratio (95% confidence interval) = 4.7 (2.7-8.3)). This variant alters RARG function, leading to derepression of the key ACT genetic determinant Top2b, and provides new insight into the pathophysiology of this severe adverse drug reaction.

Content-Aware Delivery of Scalable Video in Network Coding Enabled Named Data Networks

In this work, we propose a novel network coding enabled NDN architecture for the delivery of scalable video. Our scheme utilizes network coding in order to address the problem that arises in the original NDN protocol, where optimal use of the bandwidth and caching resources necessitates the coordination of the forwarding decisions. To optimize the performance of the proposed network coding based NDN protocol and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interest messages sent by clients and intermediate nodes. This algorithm guarantees that the achieved flow of Data objects will maximize the average quality of the video delivered to the client population. To support the handling of Interest messages and Data objects when intermediate nodes perform network coding, we modify the standard NDN protocol and introduce the use of Bloom filters, which store efficiently additional information about the Interest messages and Data objects. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme performs very close to the optimal performance

Quantifying the improvement in sepsis diagnosis, documentation, and coding: the marginal causal effect of year of hospitalization on sepsis diagnosis.

PURPOSE To quantify the coinciding improvement in the clinical diagnosis of sepsis, its documentation in the electronic health records, and subsequent medical coding of sepsis for billing purposes in recent years. METHODS We examined 98,267 hospitalizations in 66,208 patients who met systemic inflammatory response syndrome criteria at a tertiary care center from 2008 to 2012. We used g-computation to estimate the causal effect of the year of hospitalization on receiving an International Classification of Diseases, Ninth Revision, Clinical Modification discharge diagnosis code for sepsis by estimating changes in the probability of getting diagnosed and coded for sepsis during the study period. RESULTS When adjusted for demographics, Charlson-Deyo comorbidity index, blood culture frequency per hospitalization, and intensive care unit admission, the causal risk difference for receiving a discharge code for sepsis per 100 hospitalizations with systemic inflammatory response syndrome, had the hospitalization occurred in 2012, was estimated to be 3.9% (95% confidence interval [CI], 3.8%-4.0%), 3.4% (95% CI, 3.3%-3.5%), 2.2% (95% CI, 2.1%-2.3%), and 0.9% (95% CI, 0.8%-1.1%) from 2008 to 2011, respectively. CONCLUSIONS Patients with similar characteristics and risk factors had a higher of probability of getting diagnosed, documented, and coded for sepsis in 2012 than in previous years, which contributed to an apparent increase in sepsis incidence.

Temporal trends in the systemic inflammatory response syndrome, sepsis, and medical coding of sepsis.

BACKGROUND Recent reports using administrative claims data suggest the incidence of community- and hospital-onset sepsis is increasing. Whether this reflects changing epidemiology, more effective diagnostic methods, or changes in physician documentation and medical coding practices is unclear. METHODS We performed a temporal-trend study from 2008 to 2012 using administrative claims data and patient-level clinical data of adult patients admitted to Barnes-Jewish Hospital in St. Louis, Missouri. Temporal-trend and annual percent change were estimated using regression models with autoregressive integrated moving average errors. RESULTS We analyzed 62,261 inpatient admissions during the 5-year study period. 'Any SIRS' (i.e., SIRS on a single calendar day during the hospitalization) and 'multi-day SIRS' (i.e., SIRS on 3 or more calendar days), which both use patient-level data, and medical coding for sepsis (i.e., ICD-9-CM discharge diagnosis codes 995.91, 995.92, or 785.52) were present in 35.3 %, 17.3 %, and 3.3 % of admissions, respectively. The incidence of admissions coded for sepsis increased 9.7 % (95 % CI: 6.1, 13.4) per year, while the patient data-defined events of 'any SIRS' decreased by 1.8 % (95 % CI: -3.2, -0.5) and 'multi-day SIRS' did not change significantly over the study period. Clinically-defined sepsis (defined as SIRS plus bacteremia) and severe sepsis (defined as SIRS plus hypotension and bacteremia) decreased at statistically significant rates of 5.7 % (95 % CI: -9.0, -2.4) and 8.6 % (95 % CI: -4.4, -12.6) annually. All-cause mortality, SIRS mortality, and SIRS and clinically-defined sepsis case fatality did not change significantly during the study period. Sepsis mortality, based on ICD-9-CM codes, however, increased by 8.8 % (95 % CI: 1.9, 16.2) annually. CONCLUSIONS The incidence of sepsis, defined by ICD-9-CM codes, and sepsis mortality increased steadily without a concomitant increase in SIRS or clinically-defined sepsis. Our results highlight the need to develop strategies to integrate clinical patient-level data with administrative data to draw more accurate conclusions about the epidemiology of sepsis.

NetCodCCN: a Network Coding approach for Content-Centric Networks

Content-Centric Networking (CCN) naturally supports multi-path communication, as it allows the simultaneous use of multiple interfaces (e.g. LTE and WiFi). When multiple sources and multiple clients are considered, the optimal set of distribution trees should be determined in order to optimally use all the available interfaces. This is not a trivial task, as it is a computationally intense procedure that should be done centrally. The need for central coordination can be removed by employing network coding, which also offers improved resiliency to errors and large throughput gains. In this paper, we propose NetCodCCN, a protocol for integrating network coding in CCN. In comparison to previous works proposing to enable network coding in CCN, NetCodCCN permit Interest aggregation and Interest pipelining, which reduce the data retrieval times. The experimental evaluation shows that the proposed protocol leads to significant improvements in terms of content retrieval delay compared to the original CCN. Our results demonstrate that the use of network coding adds robustness to losses and permits to exploit more efficiently the available network resources. The performance gains are verified for content retrieval in various network scenarios.

An intergenic non-coding RNA targets and regulates the ribosome in H. volcanii

As translation is the final step in gene expression it is particularly important to understand the processes involved in translation regulation. It was shown in the last years that a class of RNA, the non-protein-coding RNAs (ncRNAs), is involved in regulation of gene expression via various mechanisms (e.g. gene silencing by microRNAs). Almost all of these ncRNA discovered so far target the mRNA in order to modulate protein biosynthesis, this is rather unexpected considering the crucial role of the ribosome during gene expression. However, recent data from our laboratory showed that there is a new class of ncRNAs, which target the ribosome itself [Gebetsberger et al., 2012/ Pircher et al, 2014]. These so called ribosome-associated ncRNAs (rancRNAs) have an impact on translation regulation, mainly by interfering / modulating the rate of protein biosynthesis. The main goal of this project is to identify and describe novel potential regulatory rancRNAs in H. volcanii with the focus on intergenic candidates. Northern blot analyses already revealed interactions with the ribosome and showed differential expression of rancRNAs during different growth phases or under specific stress conditions. To investigate the biological relevance of these rancRNAs, knock-outs were generated in H. volcanii which were used for phenotypic characterization studies. The rancRNA s194 showed association with the 50S ribosomal subunit in vitro and in vivo and was capable of inhibiting peptide bond formation and seems to inhibit translation in vitro. These preliminary data for the rancRNA s194 make it an interesting candidate for further functional studies to identify the molecular mechanisms by which rancRNAs can modulate protein biosynthesis. Characterization of further rancRNA candidates are also underway.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Physician coding for mental illness reimbursement and the need for mental health parity

Many patients with anxiety and depression initially seek treatment from their primary care physicians. Changes in insurance coverage and current mental parity laws, make reimbursement for services a problem. This has led to a coding dilemma for physicians seeking payment for their services. This study seeks to determine first the frequency at which primary care physicians use alternative coding, and secondly, if physicians would change their coding practices, provided reimbursement was assured through changes in mental parity laws. A mail survey was sent to 260 randomly selected primary care physicians, who are family practice, internal medicine, and general practice physicians, and members of the Harris County Medical Society. The survey evaluated the physicians' demographics, the number of patients with psychiatric disorders seen by primary care physicians, the frequency with which physicians used alternative coding, and if mental parity laws changed, the rate at which physicians would use a psychiatric illness diagnosis as the primary diagnostic code. The overall response rate was 23%. Only 47 of the 59 physicians, who responded, qualified for the study and of those 45% used a psychiatric disorder to diagnose patients with a primary psychiatric disorder, 47% used a somatic/symptom disorder, and 8% used a medical diagnosis. From the physicians who would not use a psychiatric diagnosis as a primary ICD-9 code, 88% were afraid of not being reimbursed and 12% were worried about stigma or jeopardizing insurability. If payment were assured using a psychiatric diagnostic code, 81% physicians would use a psychiatric diagnosis as the primary diagnostic code. However, 19% would use an alternative diagnostic code in fear of stigmatizing and/or jeopardizing patients' insurability. Although the sample size of the study design was adequate, our survey did not have an ideal response rate, and no significant correlation was observed. However, it is evident that reimbursement for mental illness continues to be a problem for primary care physicians. The reformation of mental parity laws is necessary to ensure that patients receive mental health services and that primary care physicians are reimbursed. Despite the possibility of improved mental parity legislation, some physicians are still hesitant to assign patients with a mental illness diagnosis, due to the associated stigma, which still plays a role in today's society. ^

Dynamic population coding in primary visual cortex

More than a century ago Ramon y Cajal pioneered the description of neural circuits. Currently, new techniques are being developed to streamline the characterization of entire neural circuits. Even if this 'connectome' approach is successful, it will represent only a static description of neural circuits. Thus, a fundamental question in neuroscience is to understand how information is dynamically represented by neural populations. In this thesis, I studied two main aspects of dynamical population codes. ^ First, I studied how the exposure or adaptation, for a fraction of a second to oriented gratings dynamically changes the population response of primary visual cortex neurons. The effects of adaptation to oriented gratings have been extensively explored in psychophysical and electrophysiological experiments. However, whether rapid adaptation might induce a change in the primary visual cortex's functional connectivity to dynamically impact the population coding accuracy is currently unknown. To address this issue, we performed multi-electrode recordings in primary visual cortex, where adaptation has been previously shown to induce changes in the selectivity and response amplitude of individual neurons. We found that adaptation improves the population coding accuracy. The improvement was more prominent for iso- and orthogonal orientation adaptation, consistent with previously reported psychophysical experiments. We propose that selective decorrelation is a metabolically inexpensive mechanism that the visual system employs to dynamically adapt the neural responses to the statistics of the input stimuli to improve coding efficiency. ^ Second, I investigated how ongoing activity modulates orientation coding in single neurons, neural populations and behavior. Cortical networks are never silent even in the absence of external stimulation. The ongoing activity can account for up to 80% of the metabolic energy consumed by the brain. Thus, a fundamental question is to understand the functional role of ongoing activity and its impact on neural computations. I studied how the orientation coding by individual neurons and cell populations in primary visual cortex depend on the spontaneous activity before stimulus presentation. We hypothesized that since the ongoing activity of nearby neurons is strongly correlated, it would influence the ability of the entire population of orientation-selective cells to process orientation depending on the prestimulus spontaneous state. Our findings demonstrate that ongoing activity dynamically filters incoming stimuli to shape the accuracy of orientation coding by individual neurons and cell populations and this interaction affects behavioral performance. In summary, this thesis is a contribution to the study of how dynamic internal states such as rapid adaptation and ongoing activity modulate the population code accuracy. ^

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.

Post-transcriptional Regulation of Mammalian Gene Expression in Non-coding Region of Target RNA

Tumor Suppressor Candidate 2 (TUSC2) is a novel tumor suppressor gene located in the human chromosome 3p21.3 region. TUSC2 mRNA transcripts could be detected on Northern blots in both normal lung and some lung cancer cell lines, but no endogenous TUSC2 protein could be detected in a majority of lung cancer cell lines. Mechanisms regulating TUSC2 protein expression and its inactivation in primary lung cancer cells are largely unknown. We investigated the role of the 5’- and 3’-untranslated regions (UTRs) of the TUSC2 gene in the regulation of TUSC2 protein expression. We found that two small upstream open-reading frames (uORFs) in the 5’UTR of TUSC2 could markedly inhibit the translational initiation of TUSC2 protein by interfering with the “scanning” of the ribosome initiation complexes. Site-specific stem-loop array reverse transcription-polymerase chain reaction (SLA-RT-PCR) verified several micoRNAs (miRNAs) targeted at 3’UTR and directed TUSC2 cleavage and degradation. In addition, we used the established let-7-targeted high mobility group A2 (Hmga2) mRNA as a model system to study the mechanism of regulation of target mRNA by miRNAs in mammalian cells under physiological conditions. There have been no evidence of direct link between mRNA downregulation and mRNA cleavages mediated by miRNAs. Here we showed that the endonucleolytic cleavages on mRNAs were initiated by mammalian miRNA in seed pairing style. Let-7 directed cleavage activities among the eight predicted potential target sites have varied efficiency, which are influenced by the positional and the structural contexts in the UTR. The 5’ cleaved RNA fragments were mostly oligouridylated at their 3’-termini and accumulated for delayed 5’–3’ degradation. RNA fragment oligouridylation played important roles in marking RNA fragments for delayed bulk degradation and in converting RNA degradation mode from 3’–5’ to 5’–3’ with cooperative efforts from both endonucleolytic and non-catalytic miRNA-induced silencing complex (miRISC). Our findings point to a mammalian miRNA-mediated mechanism for the regulation of mRNA that miRNA can decrease target mRNA through target mRNA cleavage and uridine addition