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The bladder scanner (BS), a portable ultrasound device specialized in bladder volume measurement, has been developed and applied to clinical assessment of postvoid residual urine, which is a requisite in evaluating patients with voiding dysfunction. However, experiences in the application of the BS to the pediatric population remain limited despite commonly encountered reluctance to catheterization. This prospective observational study aimed to evaluate the correlation and accuracy of the newly developed pediatric module of the BS (BioCon-900) in measuring bladder volume in children 0-6 years old.This study included 29 patients scheduled to undergo preventive untethering for their spinal dysraphism. When they undergo cystometry for the confirmation of normal neurologic function, bladder volume was measured by BS when recorded volume infusion reached each quartile of the age-adjusted estimated bladder capacity (EBC). The difference (bias) between measured and infused volume was expressed as a percentage of EBC (%EBC). The correlation coefficient and the Bland-Altman plot were obtained to determine the discriminating power and accuracy, respectively. The acceptable limit was set as 30%EBC.A strong correlation between the measured and infused volume (r = 0.95, P 0.001) was found for the entire age range. This excellent correlation remained comparable between children less than three years and the older ones. Bladder volume tended to be overestimated, and the mean bias was 33 ± 22.3%EBC, and it became higher with increasing quartiles. The accuracy was acceptable in all ranges of measurement in the older group and first and second quartiles in the younger one.We have first evaluated the potential use of BS in 0-3 years old children and compared the results with 4-6-year-old children in whom the accuracy of BS has been demonstrated. The strong point of our study was the inclusion of data spanning all quartiles of bladder volume. The use of infused volume as reference enabled us to assess the accuracy in a more precise way than the use of ultrasound. Despite the good discriminating power, the accuracy was not acceptable in higher quartiles in the younger group. If the trend of overestimation especially higher volume, could be understood prior to measurement, it would be helpful to assume the real val.The children's module in BS showed excellent discriminating power and generally acceptable accuracy in more than four-year-old children. This may lose accuracy in higher quartiles among less than three years old children.

Background: Cranial dural arteriovenous fistulas with cortical venous drainage are rare lesions that can present with hemorrhage. A high rate of rebleeding in the early period following hemorrhage has been reported, but published long-term rates are much lower. No study has examined how risk of rebleeding changes over time. Our objective was to quantify the relative incidence of rebleeding in the early and later periods following hemorrhage. Methods: Patients with dural arteriovenous fistula and cortical venous drainage presenting with hemorrhage were identified from the multinational CONDOR (Consortium for Dural Fistula Outcomes Research) database. Natural history follow-up was defined as time from hemorrhage to first treatment, rebleed, or last follow-up. Rebleeding in the first 2 weeks and first year were compared using incidence rate ratio and difference. Results: Of 1077 patients, 250 met the inclusion criteria and had 95 cumulative person-years natural history follow-up. The overall annualized rebleed rate was 7.3% (95% CI, 3.2–14.5). The incidence rate of rebleeding in the first 2 weeks was 0.0011 per person-day; an early rebleed risk of 1.6% in the first 14 days (95% CI, 0.3–5.1). For the remainder of the first year, the incidence rate was 0.00015 per person-day; a rebleed rate of 5.3% (CI, 1.7–12.4) over 1 year. The incidence rate ratio was 7.3 (95% CI, 1.4–37.7; P , 0.026). Conclusions: The risk of rebleeding of a dural arteriovenous fistula with cortical venous drainage presenting with hemorrhage is increased in the first 2 weeks justifying early treatment. However, the magnitude of this increase may be considerably lower than previously thought. Treatment within 5 days was associated with a low rate of rebleeding and appears an appropriate timeframe.

BackgroundTools predicting intracranial dural arteriovenous fistulas (dAVFs) treatment outcomes remain scarce. This study aimed to use a multicenter database comprising more than 1000 dAVFs to develop a practical scoring system that predicts treatment outcomes.MethodsPatients with angiographically confirmed dAVFs who underwent treatment within the Consortium for Dural Arteriovenous Fistula Outcomes Research-participating institutions were retrospectively reviewed. A subset comprising 80% of patients was randomly selected as training dataset, and the remaining 20% was used for validation. Univariable predictors of complete dAVF obliteration were entered into a stepwise multivariable regression model. The components of the proposed score (VEBAS) were weighted based on their ORs. Model performance was assessed using receiver operating curves (ROC) and areas under the ROC.ResultsA total of 880 dAVF patients were included. Venous stenosis (presence vs absence), elderly age (ConclusionThe VEBAS score is a practical grading system that can guide patient counseling when considering dAVF intervention by predicting the likelihood of treatment success, with higher scores portending a greater likelihood of complete obliteration.

Over the last decade, there is tremendous amount of technological advances in micro fabrication technology to manufacture devices of high complexities and compact devices to capture global market in semiconductor industry. As the device size shrinks, process integration gets more complicated and interaction between the dielectric layer and metallic layers becomes extremely important. This work presents an integration of a high quality Nitride film to copper. Native copper oxide layer was removed by ammonia plasma pre-treatment using a typical PE CVD (Plasma Enhanced Chemical Vapor Deposition) chamber and a high quality Nitride film deposition was followed on the surface of copper. The copper films exhibited various degrees of haze and discoloration such as dark greenish or dark gold, caused by the copper silicide layer formation at the interface between copper and Nitride. The silicide formation was able to be prevented by maintaining a stable Nitrogen plasma environment prior to introducing SiH4 gas flow into a PE CVD chamber. Results show that Nitride film combined with an in-stiu pre-treatment has excellent film properties in its integration with Copper.

Background and Purpose: Epidemiological studies have shown racial and ethnic minorities to have higher stroke risk and worse outcomes than non-Hispanic Whites. In this cohort study, we analyzed the STAR (Stroke Thrombectomy and Aneurysm Registry) database, a multi-institutional database of patients who underwent mechanical thrombectomy for acute large vessel occlusion stroke to determine the relationship between mechanical thrombectomy outcomes and race. Methods: Patients who underwent mechanical thrombectomy between January 2017 and May 2020 were analyzed. Data included baseline characteristics, vascular risk factors, complications, and long-term outcomes. Functional outcomes were assessed with respect to Hispanic status delineated as non-Hispanic White (NHW), non-Hispanic Black (NHB), or Hispanic patients. Multivariate analysis was performed to identify variables associated with unfavorable outcome or modified Rankin Scale ≥3 at 90 days. Results: Records of 2115 patients from the registry were analyzed. Median age of Hispanic patients undergoing mechanical thrombectomy was 60 years (72–84), compared with 63 years (54–74) for NHB, and 71 years (60–80) for NHW patients ( P P P P Conclusions: Hispanic patients are less likely to have favorable outcome at 90 days following mechanical thrombectomy compared to NHW or NHB patients. Further prospective studies are required to validate our findings.

BACKGROUND: Low-grade intracranial dural arteriovenous fistulas (dAVF) have a benign natural history in the majority of cases. The benefit from treatment of these lesions is controversial.OBJECTIVE: To compare the outcomes of observation versus intervention for low-grade dAVFs.METHODS: We retrospectively reviewed dAVF patients from institutions participating in the CONsortium for Dural arteriovenous fistula Outcomes Research (CONDOR). Patients with low-grade (Borden type I) dAVFs were included and categorized into intervention or observation cohorts. The intervention and observation cohorts were matched in a 1:1 ratio using propensity scores. Primary outcome was modified Rankin Scale (mRS) at final follow-up. Secondary outcomes were excellent (mRS 0-1) and good (mRS 0-2) outcomes, symptomatic improvement, mortality, and obliteration at final follow-up.RESULTS: The intervention and observation cohorts comprised 230 and 125 patients, respectively. We found no differences in primary or secondary outcomes between the 2 unmatched cohorts at last follow-up (mean duration 36 mo), except obliteration rate was higher in the intervention cohort (78.5% vs 24.1%, P < .001). The matched intervention and observation cohorts each comprised 78 patients. We also found no differences in primary or secondary outcomes between the matched cohorts except obliteration was also more likely in the matched intervention cohort (P < .001). Procedural complication rates in the unmatched and matched intervention cohorts were 15.4% and 19.2%, respectively.CONCLUSION: Intervention for low-grade intracranial dAVFs achieves superior obliteration rates compared to conservative management, but it fails to improve neurological or functional outcomes. Our findings do not support the routine treatment of low-grade dAVFs.

Supplemental Digital Content is available in the text., Background and Purpose: Stent-assisted coil embolization using the new generation Neuroform Atlas Stent System has shown promising safety and efficacy. The primary study results of the anterior circulation aneurysm cohort of the treatment of wide-neck, saccular, intracranial, aneurysms with the Neuroform Atlas Stent System (ATLAS trial [Safety and Effectiveness of the Treatment of Wide Neck, Saccular Intracranial Aneurysms With the Neuroform Atlas Stent System]) are presented. Methods: ATLAS IDE trial (Investigational Device Exemption) is a prospective, multicenter, single-arm, open-label study of wide-neck (neck ≥4 mm or dome-to-neck ratio 50%) at the target location. The primary safety end point was any major stroke or ipsilateral stroke or neurological death within 12 months. Adjudication of the primary end points was performed by an independent Imaging Core Laboratory and the Clinical Events Committee. Results: A total of 182 patients with wide-neck anterior circulation aneurysms at 25 US centers were enrolled. The mean age was 60.3±11.4 years, 73.1% (133/182) women, and 80.8% (147/182) white. Mean aneurysm size was 6.1±2.2 mm, mean neck width was 4.1±1.2 mm, and mean dome-to-neck ratio was 1.2±0.3. The most frequent aneurysm locations were the anterior communicating artery (64/182, 35.2%), internal carotid artery ophthalmic artery segment (29/182, 15.9%), and middle cerebral artery bifurcation (27/182, 14.8%). Stents were placed in the anticipated anatomic location in all patients. The study met both primary safety and efficacy end points. The composite primary efficacy end point of complete aneurysm occlusion (Raymond-Roy 1) without parent artery stenosis or aneurysm retreatment was achieved in 84.7% (95% CI, 78.6%–90.9%) of patients. Overall, 4.4% (8/182, 95% CI, 1.9%–8.5%) of patients experienced a primary safety end point of major ipsilateral stroke or neurological death. Conclusions: In the ATLAS IDE anterior circulation aneurysm cohort premarket approval study, the Neuroform Atlas stent with adjunctive coiling met the primary end points and demonstrated high rates of long-term complete aneurysm occlusion at 12 months, with 100% technical success and

AIM Ultrasound shear wave elastography (SWE) has been used to measure elasticity (Young's modulus: YM) in solid organs. It was reported to show a better correlation with intravesical pressure (Pves) than with compliance, supporting its potential use in noninvasive cystometry. Contrariwise, conceptually, YM should be more correlated with compliance than with Pves. To optimize the potential use of YM as a noninvasive urodynamic study, the relationship between YM, Pves, and compliance was reassessed in this study. METHOD YM was serially measured using SWE along with bladder filling. To overcome problems inherent to current compliance measurements, modified dynamic compliance was developed from cystometry by a locally weighted scatter plot smoothing algorithm. Then it was matched with YM from SWE. YM was also correlated with Pves. Furthermore, to understand the nature of YM, which was measured by ultrasound, the bladder wall's modulus, which was the mathematical assessment of YM derived from cystometric data, was also calculated and compared. RESULTS Thirty-two neurogenic bladder patients were included in this study. YM correlated with Pves (r = .72, p

Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread. Electronic supplementary material The online version of this article (doi:10.1007/s10479-017-2480-9) contains supplementary material, which is available to authorized users.

Background Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models. Methods We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990–2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew’s Correlation Coefficient (MCC). Results For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61. Conclusions Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models., Author summary Accurate and timely forecasts of dengue fever can help mitigate the impact of the disease. Currently, regression and time series models are frequently used to predict dengue cases and outbreaks. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) models offer an appealing alternative as they have a nonlinear framework and can be applied to high dimensional data. In this study, we compared the performance of ML algorithms with that of regression and time series models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance in 3 dengue-endemic regions. Model predictions were based upon local dengue surveillance (e.g., case counts), population, temporal, and weather data. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of the models. Our results identified 2 scenarios when ML models performed better than conventional models: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. This research suggests that ML models can be a beneficial tool for dengue early warning systems.

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers., Accepted for publication at The 13th International Conference on Computational Semantics (IWCS 2019)

BACKGROUND Though the Pipeline Embolization Device (Medtronic) is approved for use in adults 22 yr and older, the high efficacy and long-term durability of the device is attractive for treatment of intracranial aneurysms in younger patients who often have aneurysms less amenable to traditional endovascular treatments. OBJECTIVE To report technical, angiographic, and clinical outcomes in patients aged 21 or below undergoing flow-diversion treatment for intracranial aneurysms. METHODS Retrospective review across 16 institutions identified 39 patients aged 21 or below undergoing 46 treatment sessions with Pipeline Embolization Device placement between 2012 and 2018. A total of 50 intracranial aneurysms were treated. Details regarding patient demographics, aneurysm characteristics, treatment considerations, clinical outcomes, and aneurysm occlusion were obtained and analyzed in a multicenter database. RESULTS A total of 70% of patients were male. Nonsaccular morphology was seen in half of identified aneurysms. Six aneurysms were giant, and five patients were treated acutely after ruptured presentation. Eight patients were younger than 10 yr of age. Complete aneurysm occlusion was seen in 74% of treated aneurysms. Three aneurysms (6%) were retreated. A total of 83% of patients had a modified Rankin Scale scores of ≤2 at last clinical follow-up. There were 2 early mortalities (4.3%) in the immediate postprocedure period because of rerupture of a treated ruptured aneurysm. No recanalization of a previously occluded aneurysm was observed. CONCLUSION Flow-diversion treatment is a safe and effective treatment for intracranial aneurysms in patients younger than 22 yr. Rates of complete aneurysm occlusion and adverse events are comparable for rates seen in older patients.

Generating “commonsense’’ knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. The idea of our approach to this problem is to provide a learning system with a “head start” consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess. We match these initial schemas to simple children’s stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot-and-filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language-like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children’s story to these schemas and generating inferences from these matches.

Understanding the effect of media on disease spread can help improve epidemic forecasting and uncover preventive measures to slow the spread of disease. Most previously introduced models have approximated media effect through disease incidence, making media influence dependent on the size of epidemic. We propose an alternative approach, which relies on real data about disease coverage in the news, allowing us to model low incidence/high interest diseases, such as SARS, Ebola or H1N1. We introduce a network-based model, in which disease is transmitted through local interactions between individuals and the probability of transmission is affected by media coverage. We assume that media attention increases self-protection (e.g. hand washing and compliance with social distancing), which, in turn, decreases disease model. We apply the model to the case of H1N1 transmission in Mexico City in 2009 and show how media influence-measured by the time series of the weekly count of news articles published on the outbreak-helps to explain the observed transmission dynamics. We show that incorporating the media attention based on the observed media coverage of the outbreak better estimates the disease dynamics from what would be predicted by using media function that approximate the media impact using the number of cases and rate of spread. Finally, we apply the model to a typical influenza season in Washington, DC and estimate how the transmission pattern would have changed given different levels of media coverage.

Advances in integrated circuit fabrication technology over the past two decades have resulted in integrated circuits with smaller device dimensions, larger area, and complexity. As the device size shrinks, process integration gets more complicated and interaction between the layers becomes very important. In this work, the silicon nitride layer is deposited on test wafers with different percentage of NH3 to study the effect of NH3 on film profile like step coverage and conformity. A stack of 5000 A oxide was deposited on test wafers with low and H hydrogen nitride to study the effect of Cu diffusion and adhesion properties of nitride layer. The deposited wafers were etched to calculate the etch rate and etch rate dependence on hydrogen content. Furthermore, the low pressure CVD (LPCVD) process is used to deposit the nitride layer, and characteristics of low nitride with LPCVD layers are done using SEM analysis. Low H nitride developed in this work gives excellent copper diffusion resistance. Low H nitride gives good adhesion to the FSG layer. Low-H nitride film increases etch selectivity up to 30%. In addition, it also provides a larger process window for trench and via etching. In situ process developed in this work gives the highest productivity.

We propose a computational modeling framework for inducing combinatory categorial grammars from arbitrary behavioral data. This framework provides the analyst fine-grained control over the assumptions that the induced grammar should conform to: (i) what the primitive types are; (ii) how complex types are constructed; (iii) what set of combinators can be used to combine types; and (iv) whether (and to what) the types of some lexical items should be fixed. In a proof-of-concept experiment, we deploy our framework for use in distributional analysis. We focus on the relationship between s(emantic)-selection and c(ategory)-selection, using as input a lexicon-scale acceptability judgment dataset focused on English verbs' syntactic distribution (the MegaAcceptability dataset) and enforcing standard assumptions from the semantics literature on the induced grammar., 18 pages, 2 figures, to be published in SALT 30

176 Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect that manifests in the hands or feet primarily as numbness or tingling but can also have motor or painful components. CIPN monitoring is typically conducted by patients self-reporting symptoms during appointments or via questionnaires. Objective CIPN testing may improve early detection but is not typical during treatment, perhaps due to the need for specialized equipment and personnel. The objective of this pilot study was to conduct initial testing of a smartphone app that collects patient-reported and objective CIPN data. Methods: NeuroDetect V1.0 (available in iOS app store) includes a patient-reported CIPN questionnaire (EORTC CIPN20) and two functional assessments available within ResearchKit, the Gat and Balance and 9-Hole Peg tests. Patients who had completed neurotoxic chemotherapy were enrolled to complete NeuroDetect assessments one time. Study participants were classified as CIPN cases if they answered ≥ 3 for at least one of the first four CIPN20 questions, which ask about numbness or tingling in the hands or feet on a 1-4 scale. User acceleration data features were extracted with two open source tools, mhealthtools and pdkit. The results of the functional assessments were compared between CIPN cases and controls in principal component analysis and partial least squares discriminant analysis. Results: Twenty-four patients who had completed neurotoxic chemotherapy enrolled and completed the NeuroDetect assessments. Integration across all 87 features measured in the gait and balance test explained 42% of the difference between CIPN cases (n = 14) and controls (n = 10) and 9 individual features were significantly different between cases and controls (all p < 0.05). In the 9-hole Peg Test, hand speed explained 77% of the difference between CIPN cases and controls. CIPN cases took longer to complete the 9-hole peg test though the difference was not statistically significant (dominant hand mean 407.3 vs. 406.2 ms, p = 0.075). Conclusions: App-based functional assessment may detect evidence of CIPN. We are developing additional CIPN functional assessments for NeuroDetect V2.0 to test CIPN detection in a longitudinal study of patients undergoing neurotoxic chemotherapy.

Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source code available to develop a large-scale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. The labeled dataset is available at: https://osf.io/d45bw/. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code. We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.

Cerebral cavernous malformations (CCM) are vascular abnormalities of the central nervous system with an incidence of 0.4-0.5% and an annual rate of hemorrhage ranging from 0.7% to 1%. Most lesions are located in the cerebral hemisphere but some occur in deeper locations such as the basal ganglia and pons. The most common symptoms during presentation are headache, seizures, and focal neurologic deficits. Surgery remains the most effective treatment modality for symptomatic CCM, while the management of incidental CCM remains controversial. Factors associated with increased risk of hemorrhage include being female and less than 40 years old. This finding, however, is not consistent in all natural history studies evaluated. During follow-up, the most important and consistent risk factor for rebleed was a prior hemorrhage. Here, we provide an indepth but concise review of the literature regarding the natural history of CCMs.

Spinal arteriovenous malformations and fistulas comprise spinal vascular malformations (sVMs), a rare but challenging neurosurgic entity. A number of mechanisms have been proposed as explanations of neurologic decline in the setting of arteriovenous shunting, including venous hypertension, venous congestion, hemorrhage, vascular steal, or mass effect, which can be worsened with venous varices or aneurysmal dilations. Anatomic location and features dictate the categorization of sVMs. Two major systems are in place for classification of sVMs - (1) the American/French/English connection and (2) the Spetzler system - as well as a number of systems that have been described over the past century.

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.

Background/Objective: The effect of pre-morbid lipid levels on neurologic outcome after Acute Ischemic Stroke (AIS) is variable in the published literature. In this study, we retrospectively use a cohort to evaluate the relationship of cholesterol subfractions to outcome. Methods: The cohort consists of AIS patients treated with IA therapy between September 2008 and December 2010. Favorable outcome for these analyses was defined as a modified Rankin Scale of ≤ 3 at time of discharge. Univariate associations with favorable outcome were sought for demographic, co-morbidity, stroke risk factor, and procedure related variables. Independent associations were identified by relative risk regression using generalized linear models with a logit link, a Poisson distribution for variance and robust standard errors. All analyses were performed in STATA. Results: The cohort included 42 patients; mean age was 62 years, 62% were women, median pre-procedure NIHSS was 16, 70% of patients achieved a mRS ≤ 3. Univariate analysis suggested significance of a number of variables with favorable outcome. In multivariate analysis only three variables-previous tobacco use, highest procedural SBP, and history of atrial fibrillation-remained significant. Adding LDL to the multivariate model did not show association, though adding HDL as a continuous variable did show an association. Results are shown in the table. Conclusions: In our cohort there was no association between favorable outcome at time of discharge and LDL, but higher HDL was associated with an increased chance of good outcome. While this association between HDL and outcome is reported for patients treated with intravenous tPA, it is novel for patients treated with IA therapy and warrants further study. Our data collection is ongoing and we aim to double the size of our dataset; the updated analyses will be presented at the meeting.

This paper presents a method for comparative life cycle assessment (LCA) of two distinctively different systems based on industrial heavy-duty off-road equipment. The study demonstrates how to compare the environmental impact performance of two different machines when they perform the same types of operations, but have different levels of operational productivity. Considering the entire life cycle of a machine from material extraction to end-of-life treatment, the two machines are analyzed and compared in terms of their life cycle impact score. To make a fair comparison between the two machines, the impact generated by the same amount of production is used as the basis for comparison. The results of the study can quantify the relative life cycle impact across different machine architectures for design and market support purposes.Copyright © 2012 by ASME

This paper presents a comprehensive life cycle assessment (LCA) study of heavy duty off-road equipment. The machine studied here is a typical piece of diesel construction machinery equipped with the iT4 (interim Tier 4) certified diesel engine. Two life cycle impact assessment methods, Eco-Indicator 99 and IPCC 2007, are used to calculate the environmental impact and global warming potential associated with the machine’s life cycle, from material extraction to end-of-life recycling and disposal. Due to fuel consumption and emissions, machine utilization during the usage phase is expected to account for most of the total environmental impact. However, the impact from usage can vary greatly, depending on how customers use the machine. To take into account various machine usage patterns, this LCA study performs two sensitivity analyses, varying the load factor and varying the fuel consumption rate, respectively.