bluuebunny/crossref_metadata_2025 huggingface dataset:
The glycan CA19-9 promotes pancreatitis and pancreatic cancer in mice
Authors: Engle Dannielle D. , Tiriac Hervé , Rivera Keith D. , Pommier Arnaud , Whalen Sean , Oni Tobiloba E. , Alagesan Brinda , Lee Eun Jung , Yao Melissa A. , Lucito Matthew S. , Spielman Benjamin , Da Silva Brandon , Schoepfer Christina , Wright Kevin , Creighton Brianna , Afinowicz Lauren , Yu Kenneth H. , Grützmann Robert , Aust Daniela , Gimotty Phyllis A. , Pollard Katherine S. , Hruban Ralph H. , Goggins Michael G. , Pilarsky Christian , Park Youngkyu , Pappin Darryl J. , Hollingsworth Michael A. , Tuveson David A.
Published: June 2019

Sweet bystander becomes a villain

Patients with pancreatic cancer often have elevated blood levels of CA19-9, a carbohydrate antigen present on many proteins. CA19-9 is thus commonly used as a biomarker for diagnosing and monitoring disease progression. In a study of mice, Engle
et al.
found that CA19-9 may be more than an innocent bystander that marks the presence of pancreatic disease; it may play a causal role in disease (see the Perspective by Halbrook and Crawford). Transgenic mice expressing the human enzymes that add CA19-9 to proteins developed severe pancreatitis that could be reversed by treatment with CA19-9 antibodies. When the transgenic mice also harbored a
Kras
oncogene, they went on to develop pancreatic cancer. These unexpected observations suggest new avenues for the treatment of pancreatic disease.

Science
, this issue p.
1156
; see also p.
1132
Akkermansia muciniphila
induces intestinal adaptive immune responses during homeostasis
Authors: Ansaldo Eduard , Slayden Leianna C. , Ching Krystal L. , Koch Meghan A. , Wolf Natalie K. , Plichta Damian R. , Brown Eric M. , Graham Daniel B. , Xavier Ramnik J. , Moon James J. , Barton Gregory M.
Published: June 2019

Context shapes anticommensal immunity

The gut bacterium
Akkermansia muciniphila
is associated with protection from obesity, enhanced wound healing, and augmented antitumor responses. Ansaldo
et al.
found that this microbe induces antigen-specific immunoglobulin G1 (IgG1) antibodies generated by B cells with CD4
+
T cell help. This is in contrast to most anticommensal responses, which involve the T cell–independent production of IgA antibodies. In a gnotobiotic setting in which all components of the microbiome are defined,
A. muciniphila
–specific T cells expanded only when
A. muciniphila
was present. The T cells primarily displayed a phenotype associated with B cell help. However, in mice with a conventional gut microbiota, other proinflammatory
A. muciniphila
–specific T cell populations also expanded. Thus, anti–
A. muciniphila
immunity is context dependent, which may explain the variable immune responses to this microbe reported in patients.

Science
, this issue p.
1179
Structural identification of a hotspot on CFTR for potentiation
Authors: Liu Fangyu , Zhang Zhe , Levit Anat , Levring Jesper , Touhara Kouki K. , Shoichet Brian K. , Chen Jue
Published: June 2019

Keeping the gate open

Cystic fibrosis is a progressive disease that affects lung function and is often fatal. It is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR). One class of mutants impairs ion conductance, and the drug ivacaftor acts by increasing the ion flux. Liu
et al.
describe high-resolution structures of CFTR bound to ivacaftor and to an investigational drug GLPG1837 that also potentiates ion flow. The two drugs bind at the same site in the transmembrane region. This site coincides with a hinge involved in channel gating, suggesting that the drugs may stabilize the open conformation of the channel.

Science
, this issue p.
1184
Particle analogs of electrons in colloidal crystals
Authors: Girard Martin , Wang Shunzhi , Du Jingshan S. , Das Anindita , Huang Ziyin , Dravid Vinayak P. , Lee Byeongdu , Mirkin Chad A. , Olvera de la Cruz Monica
Published: June 2019

Mobile particles in colloidal crystals

The crystallization of nanoparticles can be controlled by functionalizing them with DNA strands that direct assembly through hybridization. The design rules for interactions between pairs of particles resemble those for ionic compounds. Inspired by molecular dynamics simulations, Girard
et al.
show that larger particles (∼10 nanometers in diameter) that have mutual repulsive interactions can form a stable lattice only if much smaller conjugate particles (∼1.5 nanometers in diameter) are present. These smaller particles are mobile and diffuse through the lattice, so the bonding interaction resembles the classical picture of electrons in metals.

Science
, this issue p.
1174
Structural Insights into the Inhibition of the Extended-Spectrum β-Lactamase PER-2 by Avibactam
Authors: Ruggiero Melina , Papp-Wallace Krisztina M. , Brunetti Florencia , Barnes Melissa D. , Bonomo Robert A. , Gutkind Gabriel , Klinke Sebastián , Power Pablo
Published: June 2019

The diazabicyclooctane (DBO) avibactam (AVI) reversibly inactivates most serine-β-lactamases.
Blockade of PD-1 and LAG-3 Immune Checkpoints Combined with Vaccination Restores the Function of Antiviral Tissue-Resident CD8+TRMCells and Reduces Ocular Herpes Simplex Infection and Disease in HLA Transgenic Rabbits
Authors: Roy Soumyabrata , Coulon Pierre-Gregoire , Prakash Swayam , Srivastava Ruchi , Geertsema Roger , Dhanushkodi Nisha , Lam Cynthia , Nguyen Vivianna , Gorospe Elyssa , Nguyen Angela M. , Salazar Stephanie , Alomari Nuha I. , Warsi Wasay R. , BenMohamed Lbachir
Published: June 2019

HSV-specific tissue-resident memory CD8+TRMcells play a critical role in preventing virus reactivation from latently infected TG and subsequent virus shedding in tears that trigger the recurrent corneal herpetic disease. In this report, we determined how the dual blockade of PD-1 and LAG-3 immune checkpoints, combined with vaccination, improved the function of CD8+TRMcells associated with a significant reduction in recurrent ocular herpes in HLA transgenic (Tg) rabbit model. The combined blockade of PD-1 and LAG-3 appeared to have a synergistic effect in generating frequent polyfunctional CD8+TRMcells that infiltrated both the cornea and the TG. The preclinical findings using the established HLA Tg rabbit model of recurrent herpes highlight that blocking immune checkpoints combined with a T cell-based vaccine would provide an important strategy to combat recurrent ocular herpes in the clinic.
Prevalence of lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH) in China: results from the China Health and Retirement Longitudinal Study
Authors: Zhang Weiyu , Zhang Xiaopeng , Li Haibin , Wu Feng , Wang Huanrui , Zhao Meishan , Hu Hao , Xu Kexin
Published: June 2019

Objective

Rapid population ageing in China is increasing the prevalence of lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH) among older people. The associated economic burden is increasing as well. Relevant data from China are currently insufficient.

Design

Secondary analysis of a cohort sample.

Setting

A nationally representative, cross-sectional survey—the China Health and Retirement Longitudinal Study (CHARLS)—was conducted in 2011 in mainland China.

Participants

The study included individuals in the community selected from CHARLS by multistage probability sampling. A total of 5888 participants aged 50 years and above were included.

Outcome measures

Self-reported morbid state was derived from a structured questionnaire. The weighted prevalence of LUTS/BPH was estimated and stratified by age group, marital status, education level, economic level, residential area and geographical region. Multivariable weighted logistic regression was used to examine the association of socioeconomic status with the odds of BPH.

Results

The weighted overall prevalence of LUTS/BPH was 10.66% (95% CI 9.36 to 12.12). Among individuals aged over 70 years, the prevalence was 14.67% (95% CI 11.80 to 18.09) and it increased with ageing (p<0.05). The prevalence of LUTS/BPH among subjects residing in urban areas was higher (13.55%, 95% CI 10.95 to 16.64) than those living in rural areas (8.38%, 95% CI 6.90 to 10.15). The prevalence of LUTS/BPH was lowest in the South-Central and South-West regions and highest in the North-West region.

Conclusions

We found an increasing trend of prevalence of LUTS/BPH with ageing. It varied according to marital status, socioeconomic status and geographical region.
Predictors of an early death in patients diagnosed with colon cancer: a retrospective case–control study in the UK
Authors: Donnelly Conan , Hart Nigel , McCrorie Alan David , Donnelly Michael , Anderson Lesley , Ranaghan Lisa , Gavin Anna
Published: June 2019

Objective

Despite considerable improvements, 5-year survival rates for colon cancer in the UK remain poor when compared with other socioeconomically similar countries. Variation in 5-year survival can be partly explained by higher rates of death within 3 months of diagnosis in the UK. This study investigated the characteristics of patients who died within 3 months of a diagnosis of colon cancer with the aim of identifying specific patient factors that can be addressed or accounted for to improve survival outcomes.

Design

A retrospective case–control study design was applied with matching on age, sex and year diagnosed. Patient, disease, clinical and service characteristics of patients diagnosed with colon cancer in a UK region (2005–2010) who survived less than 3 months from diagnosis (cases) were compared with patients who survived between 6 and 36 months (controls). Patient and clinical data were sourced from general practice notes and hospital databases 1–3 years prediagnosis.

Results

Being older (aged ≥78 years) and living in deprivation quintile 5 (OR=2.64, 95% CI 1.15 to 6.06), being unmarried and living alone (OR=1.64, 95% CI 1.07 to 2.50), being underweight compared with normal weight or obese (OR=3.99, 95% CI 1.14 to 14.0), and being older and living in a rural as opposed to urban area (OR=1.96, 95% CI 1.21 to 3.17) were all independent predictors of early death from colon cancer. Missing information was also associated with early death, including unknown stage, histological type and marital/accommodation status after accounting for other factors.

Conclusion

Several factors typically associated with social isolation were a recurring theme in patients who died early from colon cancer. This association is unexplained by clinical or diagnostic pathway characteristics. Socially isolated patients are a key target group to improve outcomes of the worst surviving patients, but further investigation is required to determine if being isolated itself is actually a cause of early death from colon cancer.
Investigating the natural history and prognostic factors of ASD in children: the multicEntric Longitudinal study of childrEN with ASD - the ELENA study protocol
Authors: Baghdadli Amaria , Miot Stéphanie , Rattaz Cécile , Akbaraly Tasnime , Geoffray Marie-Maude , Michelon Cécile , Loubersac Julie , Traver Sabine , Mortamais Marion , Sonié Sandrine , Pottelette Julien , Robel Laurence , Speranza Mario , Vesperini Stéphanie , Maffre Thierry , Falissard Bruno , Picot Marie-Christine
Published: June 2019

Introduction

There is global concern about the increasing prevalence of autism spectrum disorders (ASDs), which are early-onset and long-lasting disorders. Although ASDs are considered to comprise a unique syndrome, their clinical presentation and outcome vary widely. Large-scale and long-term cohort studies of well-phenotyped samples are needed to better understand the course of ASDs and their determinants. The primary objective of the multicEntric Longitudinal study of childrEN with ASD (ELENA) study is to understand the natural history of ASD in children and identify the risk and prognostic factors that affect their health and development.

Methods and analysis

This is a multicentric, longitudinal, prospective, observational cohort in which 1000 children with ASD diagnosed between 2 and 16 years of age will be recruited by 2020 and followed over 6 years. The baseline follow-up starts with the clinical examination to establish the ASD diagnosis. A battery of clinical tools consisting of the Autism Diagnostic Observation Schedule, the revised version of the Autism Diagnostic Interview, measures of intellectual functioning, as well as large-scale behavioural and developmental measurements will allow us to study the heterogeneity of the clinical presentation of ASD subtypes. Subsequent follow-up at 18 months and at 3, 4.5 and 6 years after the baseline examination will allow us to explore the developmental trajectories and variables associated with the severity of ASD. In addition to the children’s clinical and developmental examinations, parents are invited to complete self-reported questionnaires concerning perinatal and early postnatal history, congenital anomalies, genetic factors, lifestyle factors, medical and psychiatric comorbidities, and the socioeconomic environment. As of 1 November 2018, a total of 766 participants have been included.

Ethics and dissemination

Ethical approval was obtained through the Marseille Mediterranean Ethics Committee (ID RCB: 2014-A01423-44), France. We aim to disseminate the findings through national and international conferences, international peer-reviewed journals, and social media.

Trial registration number

NCT02625116; Pre-results.
Different potencies of topical corticosteroids for a better treatment strategy in children with atopic dermatitis (the Rotterdam Eczema study): protocol for an observational cohort study with an embedded randomised open-label controlled trial
Authors: van Halewijn Karlijn F , Bohnen Arthur M , van den Berg Pieter J , Pasmans Suzanne G M A , Bindels Patrick J E , Elshout Gijs
Published: June 2019

Introduction

Topical corticosteroids (TCS) of different potencies are the main treatment to control atopic dermatitis (AD). The Dutch guideline on AD for general practitioners (GPs) recommends a stepwise approach in which treatment steps are tailored to the severity of the disease, starting with the lowest possible potency of TCS. However, it remains unclear whether the recommended stepwise approach is most efficient. This randomised open-label controlled trial aims to determine whether a potent TCS is more effective than a low-potency TCS in the initial treatment of children with a moderate flare-up of AD in primary care. In the observational cohort, the overall aim is to determine the frequency, burden and determinants of flare-ups of AD during follow-up.

Methods and analysis

The study is an observational cohort study with an embedded pragmatic randomised controlled, open-label trial. Eligible are patients diagnosed with AD (aged 12 weeks to 18 years) who visited the GP for AD or received repeated prescriptions for AD in the previous 12 months; follow-up of the cohort is 1 year. Children are enrolled in the trial if they have a flare-up of AD during follow-up in the cohort. Eligible children are randomised to the intervention group (with a potent TCS once daily) or to the GP guideline group (with a low potency TCS once daily). Primary outcome is the difference in average subjective disease severity over 24 weeks follow-up in the trial, measured with the patient-oriented eczema measure. As secondary outcome, the Eczema Area and Severity Index is measured.

Ethics and dissemination

This study tests the hypothesis that immediate treatment with a potent TCS during a flare-up of AD leads to faster and more efficacious results as compared with starting with a TCS with low potency with less overall use of TCS. The study protocol is approved by the Medical Ethics Committee (MEC) of the Erasmus Medical Center Rotterdam, the Netherlands (MEC-2017–328). The results of the study will be published in international peer-reviewed journals and presented at national and international conferences.

Trial registration number

NTR: 6679; Pre-results.
Novel insights of overtraining syndrome discovered from the EROS study
Authors: Cadegiani Flavio A , Kater Claudio Elias
Published: June 2019

Background

Excessive training and inadequate recovery could cause ‘overtraining syndrome’ (OTS), which is characterised by underperformance and fatigue. The pathophysiology of OTS is unclear. We aimed to describe novel mechanisms and risk factors associated with OTS, and thereby facilitate its early identification and prevention, from a comprehensive joint qualitative analysis of the findings from all the four arms of the Endocrine and Metabolic Responses on Overtraining Syndrome (EROS) study.

Methods

We compared the types and proportions of behavioural patterns of 67 evaluated parameters of OTS from 51 participants—athletes with OTS (OTS, n=14), healthy athletes (n=25) and healthy non-physically active controls (n=12). We performed overall and pairwise comparisons for statistically significant differences between the three groups (p<0.05).

Results

A total of 44 (65.7%) markers exhibited significant differences between the three groups: 32 (72.7%) showed a loss of the conditioning effect of exercise (‘deconditioning’), 7 (15.9%) showed changes exclusive to OTS, 3 (6.8%) maintained the exercise-induced conditioning effects and 2 (4.5%) revealed an exacerbation of the adaptive changes to exercises.

Conclusion

Our findings suggest that OTS is likely triggered by multiple factors, not restricted to excessive training, resulted from a chronic energy deprivation, leading to multiple losses in the conditioning processes typically observed in healthy athletes, as a combination of ‘paradoxical deconditioning’ processes, which explains the gradual and marked loss of physical conditioning found in OTS. We, therefore, suggest that the term ‘paradoxical deconditioning syndrome’ better represents the features of this syndrome.
Cataract surgery for falls prevention and improving vision: modelling the health gain, health system costs and cost-effectiveness in a high-income country
Authors: Boyd Matt , Kvizhinadze Giorgi , Kho Adeline , Wilson Graham , Wilson Nick
Published: June 2019

Aim

To estimate the health gain, health system costs and cost-effectiveness of cataract surgery when expedited as a falls prevention strategy (reducing the waiting time for surgery by 12 months) and as a routine procedure.

Methods

An established injurious falls model designed for the New Zealand (NZ) population (aged 65+ years) was adapted. Key parameters relating to cataracts were sourced from the literature and the NZ Ministry of Health. A health system perspective with discounting at 3% was used.

Results

Expedited cataract surgery for 1 year of incident cases was found to generate a total 240 quality-adjusted life years (QALYs) (95% uncertainty interval (UI) 161 to 360) at net health system costs of NZ$2.43 million (95% UI 2.02 to 2.82 million) over the remaining lifetimes of the surgery group. This intervention was cost-effective by widely accepted standards with an incremental cost-effectiveness ratio (ICER) of NZ$10 600 (US$7540) (95% UI NZ$6030 to NZ$15 700) per QALY gained. The level of cost-effectiveness did not vary greatly by sex, ethnicity and previous fall history, but was higher for the 65–69 age group compared with the oldest age group of 85–89 years (NZ$7000 vs NZ$14 200 per QALY gained). Comparing cataract surgery with no surgery, the ICER was even more favourable at NZ$4380 (95% UI 2410 to 7210) per QALY. Considering only the benefits for vision improvement and excluding the benefits of falls prevention, it was still favourable at NZ$9870 per QALY.

Conclusions

Expedited cataract surgery appears very cost-effective. Routine cataract surgery is itself very cost-effective, and its value appears largely driven by the falls prevention benefits.
The truth behind conscientious objection in medicine
Authors: Ben-Moshe Nir
Published: June 2019

Answers to the questions of what justifies conscientious objection in medicine in general and which specific objections should be respected have proven to be elusive. In this paper, I develop a new framework for conscientious objection in medicine that is based on the idea that conscience can express true moral claims. I draw on one of the historical roots, found in Adam Smith’s impartial spectator account, of the idea that an agent’s conscience can determine the correct moral norms, even if the agent’s society has endorsed different norms. In particular, I argue that when a medical professional is reasoning from the standpoint of an impartial spectator, his or her claims of conscience are true, or at least approximate moral truth to the greatest degree possible for creatures like us, and should thus be respected. In addition to providing a justification for conscientious objection in medicine by appealing to the potential truth of the objection, the account advances the debate regarding the integrity and toleration justifications for conscientious objection, since the standard of the impartial spectator specifies the boundaries of legitimate appeals to moral integrity and toleration. The impartial spectator also provides a standpoint of shared deliberation and public reasons, from which a conscientious objector can make their case in terms that other people who adopt this standpoint can and should accept, thus offering a standard fitting to liberal democracies.
Mystery or Mycobacterium? Lessons learned from a challenging incision site infection
Authors: Weisent Jennifer , May Elizabeth
Published: June 2019

An eight‐month‐old female domestic short hair cat underwent routine ovariohysterectomy and was adopted from a shelter nine days later. A refractory infection associated with the abdominal incision site proved unresponsive to surgical debridement and multiple courses of oral antibiotic treatment over 10 weeks, resulting in relinquishment of the cat. Initial diagnostic test samples submitted by the shelter veterinarian failed to identify a causative agent for a deep pyogranulomatous dermatitis and panniculitis. The lesions resolved following treatment with oral pradofloxacin, and the cat was adopted but subsequently lost to follow‐up. This case highlights the importance of generating a differential diagnoses list and outlines difficulties obtaining appropriate and timely diagnostic testing and treatment, especially in cases involving multiple practitioners and financial constraints. The report also emphasises how a challenging and potentially zoonotic infection might be overlooked and under‐reported, specifically in low‐income and shelter settings.
Simulating switchgrass aboveground biomass and production costs in eastern Canada with the Integrated Farm System Model
Authors: Duchemin Marc , Jégo Guillaume , Morissette René
Published: June 2019

Switchgrass (Panicum virgatum L.) is a herbaceous perennial grass that can be used as bedding for livestock, planted in buffer strips, and used as biofuel, but it is still not widely grown in eastern Canada. The objectives of this study were to verify the performance of the Integrated Farm System Model (IFSM) in simulating switchgrass growth and to estimate its yield potential and production costs in eastern Canada. The performance of IFSM was assessed with dry matter (DM) yield of switchgrass (‘Cave-in-Rock’) measured over three growing seasons (2015–2017) in southern Quebec, Canada. The model performed reasonably well, with normalized root mean square errors of 19.5% for calibration and 27.9% for validation. Simulation results of potential yield and economic management over the long term (1986–2015) for five switchgrass production sites in eastern Canada indicated that average DM yields in Quebec City and Fredericton (9.6 and 9.7 t ha−1, respectively) were significantly lower than average DM yields in Saint-Hubert, Ottawa, and London (10.8, 10.4, and 11.0 t ha−1, respectively). Average annual production costs per tonne of DM for the spring harvest were higher at low-yield sites (CAD$66.67 and $64.50 for Fredericton and Quebec City, respectively) than at high-yield sites ($60.10, $62.82, and $60.08 for Saint-Hubert, Ottawa, and London, respectively). The IFSM-estimated production costs were within the range of the calculated values reported in other agro-economic analyses conducted in Ontario and Quebec.
Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search
Authors: Zhan Peng , Sun Changchang , Hu Yupeng , Luo Wei , Zheng Jiecai , Li Xueqing
Published: June 2019

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.
Research on Path Tracking Algorithm of Autopilot Vehicle Based on Image Processing
Authors: Rao Yutai , Yang Fan
Published: June 2019

Smart cars are the result of the combination of the latest technological achievements in the fields of artificial intelligence, sensors, control science, computer, and network technology with the modern automobile industry. Intelligent cars usually have functions, such as automatic shifting, automatic driving, and automatic road condition recognition. The research of intelligent car technology involves many disciplines. This thesis focuses on the field of smart car visual navigation, focusing on image denoising, image information recognition, extraction, and pattern recognition control algorithms. The traditional trajectory tracking algorithm is mainly used in industrial computer or high-performance computer. The computational complexity leads to poor real-time control, and it is easily interfered by external complex terrain environment and internal disordered electromagnetic environment during vehicle driving. In general, on a regular basis, by the image analysis of the driver or the driver information, the image information is proposed using way trace processing technology, vehicle tracking control method and automatic driving rules. The simulation and experimental results show that the proposed control methods and rules used to carry out automatic driving vehicle are feasible. The algorithm reduces the complexity of the algorithm, improves the real-time and stability of the control and finally achieves a good trajectory tracking effect of the car on high-speed automatic driving.
Rado’s Conjecture and its Baire version
Authors: Zhang Jing
Published: June 2019

Rado’s Conjecture is a compactness/reflection principle that says any nonspecial tree of height [Formula: see text] has a nonspecial subtree of size [Formula: see text]. Though incompatible with Martin’s Axiom, Rado’s Conjecture turns out to have many interesting consequences that are also implied by certain forcing axioms. In this paper, we obtain consistency results concerning Rado’s Conjecture and its Baire version. In particular, we show that a fragment of [Formula: see text], which is the forcing axiom for Baire Indestructibly Proper forcings, is compatible with the Baire Rado’s Conjecture. As a corollary, the Baire Rado’s Conjecture does not imply Rado’s Conjecture. Then we discuss the strength and limitations of the Baire Rado’s Conjecture regarding its interaction with stationary reflection principles and some families of weak square principles. Finally, we investigate the influence of Rado’s Conjecture on some polarized partition relations.
Adaptive power management strategy-based optimization and estimation of a renewable energy storage system in stand-alone microgrid with machine learning and data monitoring
Authors: Sathishkumar D. , Karthikeyan C.
Published: June 2019

This work is carried out with the optimum design of a stand-alone hybrid energy storage system (HESS) based on solar, wind and super capacitor (SC) with battery storage system which is effectively optimized in the grid power system. The distribution of the power source is mainly considered on the Hybrid renewable energy power sources. This discourse presents an adaptive power optimizing the three-phase inverter and grid-connected hybrid renewable energy resources efficiently. In this analysis, the similar parameters are taken for the compensation such as voltage fluctuation, harmonics and Frequency imbalance by implementing Adaptive Power Management Strategy (APMS) and the obtained issues are synchronized by inverter control. All these comparative activities of the inverter are done either discretely or combined to stabilize the unbalanced impacts of a wide range of adjusted, uneven, power loss at the circulation level. A battery and SC energy management are essential for maintaining the energy sustainability in renewable energy system. Combination of solar and wind with the battery and SC is used to test the proposed stand-alone grid management. The proposed hybrid power system is designed to work under classical-based energy management and this performance is monitored with the help of the Internet of Things (IoT) and machine learning based on Polynomial Linear Regression Algorithm. The focus of the suggested HESS is reduced by the loss in stand-alone grid system with an economic performances.
ORGANISATIONAL CHANGE AND BUSINESS MODEL INNOVATION: AN EXPLORATORY STUDY OF AN ENERGY UTILITY
Authors: LATILLA VITO MANFREDI , FRATTINI FEDRICO , FRANZO SIMONE , CHIESA VITTORIO
Published: June 2019

We study the intricate relationship between business model innovation and the relevant organisational changes that can facilitate the renewal of a traditional business model. To do so, we build on the inductive, longitudinal single case study of an energy utility, describing the mechanisms through which the business model has been innovated over time, and the organisational changes that enabled and fostered such innovation. The innovation itself was a result of the need to face the current digital transformation that is compelling energy utilities to renew their traditional business models and offer customers a new value proposition. This study therefore contributes to the ongoing academic debate on business model innovation and its practical application, adding to the broad discussion on organisational ambidexterity and the analysis of the most relevant organisational changes adopted to implement effective business model innovation.
Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier
Authors: Subudhi Asit , Jena Subhransu S. , Sabut Sukanta
Published: June 2019

Neuroimaging investigation is an essential parameter to detect infarct lesion in stroke patients. Precise detection of brain lesions is an important task related to impaired behavior. In this paper, we aimed to develop an automatic method to segment and classify infarct lesion in diffusion-weighted imaging (DWI) of brain MRI. The method includes hybrid fuzzy [Formula: see text]-means (HFCM) clustering in which the structure of [Formula: see text]-means clustering is modified with rough sets and fuzzy sets to improve the segmentation performance with self-adjusted intensity thresholds. Quantitative evaluation was carried out on 128 MRI slices of brain image collected from ischemic stroke patients at the Department of Radiology, IMS and SUM Hospital, Bhubaneswar. The informative statistical features have been extracted using gray-level co-occurrence matrix (GLCM) and used to classify the types of stroke infarct according to the Oxfordshire Community Stroke Project (OCSP) classification. The parameters such as accuracy, Dice similarity index (DSI) and Jaccard index (JI) were utilized to evaluate the effectiveness of the proposed method in detecting the stroke lesions. The segmentation method achieved the average accuracy, DSI and JI of 96.8%, 95.8% and 92.2%, respectively, in support vector machine (SVM) classifier. The obtained results are higher in terms of random forest (RF) classification. With a high Dice coefficient of 0.958 and other evaluated parameters, the proposed method outperforms earlier published results.
A family of vector-valued quantum modular forms of depth two
Authors: Males Joshua
Published: June 2019

We introduce and investigate an infinite family of functions which are shown to have generalized quantum modular properties. We realize their “companions” in the lower half plane both as double Eichler integrals and as non-holomorphic theta functions with coefficients given by double error functions. Further, we view these Eichler integrals in a modular setting as parts of certain weight two indefinite theta series.
Balancing Prediction Errors for Robust Sentiment Classification
Authors: Iqbal Mohsin , Karim Asim , Kamiran Faisal
Published: June 2019

Sentiment classification is a popular text mining task in which textual content (e.g., a message) is assigned a polarity label (typically positive or negative) reflecting the sentiment expressed in it. Sentiment classification is used widely in applications like customer feedback analysis where robustness and correctness of results are critical. In this article, we highlight that prediction accuracy alone is not sufficient for assessing the performance of a sentiment classifier; it is also important that the classifier is not biased toward positive or negative polarity, thus distorting the distribution of positive and negative messages in the predictions. We propose a measure, called Polarity Bias Rate, for quantifying this bias in a sentiment classifier. Second, we present two methods for removing this bias in the predictions of unsupervised and supervised sentiment classifiers. Our first method, called Bias-Aware Thresholding (BAT), shifts the decision boundary to control the bias in the predictions. Motivated from cost-sensitive learning, BAT is easily applicable to both lexicon-based unsupervised and supervised classifiers. Our second method, called Balanced Logistic Regression (BLR) introduces a bias-remover constraint into the standard logistic regression model. BLR is an automatic bias-free supervised sentiment classifier.

We evaluate our methods extensively on seven real-world datasets. The experiments involve two lexicon-based and two supervised sentiment classifiers and include evaluation on multiple train-test data sizes. The results show that bias is controlled effectively in predictions. Furthermore, prediction accuracy is also increased in many cases, thus enhancing the robustness of sentiment classification.
A Distributed Algorithm to Calculate Max-Min Fair Rates Without Per-Flow State
Authors: Jose Lavanya , Ibanez Stephen , Alizadeh Mohammad , McKeown Nick
Published: June 2019

Most congestion control algorithms, like TCP, rely on a reactive control system that detects congestion, then marches carefully towards a desired operating point (e.g. by modifying the window size or adjusting a rate). In an effort to balance stability and convergence speed, they often take hundreds of RTTs to converge; an increasing problem as networks get faster, with less time to react.

This paper is about an alternative class of congestion control algorithms based on proactive-scheduling: switches and NICs "pro-actively" exchange control messages to run a \em distributed algorithm to pick "explicit rates for each flow. We call these Proactive Explicit Rate Control (PERC) algorithms. They take as input the routing matrix and link speeds, but not a congestion signal. By exploiting information such as the number of flows at a link, they can converge an order of magnitude faster than reactive algorithms.

Our main contributions are (1) s-PERC ("stateless" PERC), a new practical distributed PERC algorithm without per-flow state at the switches, and (2) a proof that s-PERC computes exact max-min fair rates in a known bounded time, the first such algorithm to do so without per-flow state. To analyze s-PERC, we introduce a parallel variant of standard waterfilling, 2-Waterfilling. We prove that s-PERC converges to max-min fair in 6N rounds, where N is the number of iterations 2-Waterfilling takes for the same routing matrix.

We describe how to make s-PERC practical and robust to deploy in real networks. We confirm using realistic simulations and an FPGA hardware testbed that s-PERC converges 10-100x faster than reactive algorithms like TCP, DCTCP and RCP in data-center networks and 1.3--6x faster in wide-area networks (WANs). Long flows complete in close to the ideal time, while short-lived flows are prioritized, making it appropriate for data-centers and WANs.
Global Attraction of ODE-based Mean Field Models with Hyperexponential Job Sizes
Authors: Van Houdt Benny
Published: June 2019

Mean field modeling is a popular approach to assess the performance of large scale computer systems. The evolution of many mean field models is characterized by a set of ordinary differential equations that have a unique fixed point. In order to prove that this unique fixed point corresponds to the limit of the stationary measures of the finite systems, the unique fixed point must be a global attractor. While global attraction was established for various systems in case of exponential job sizes, it is often unclear whether these proof techniques can be generalized to non-exponential job sizes. In this paper we show how simple monotonicity arguments can be used to prove global attraction for a broad class of ordinary differential equations that capture the evolution of mean field models with hyperexponential job sizes. This class includes both existing as well as previously unstudied load balancing schemes and can be used for systems with either finite or infinite buffers. The main novelty of the approach exists in using a Coxian representation for the hyperexponential job sizes and a partial order that is stronger than the componentwise partial order used in the exponential case.
What-If Analysis of Page Load Time in Web Browsers Using Causal Profiling
Authors: Pourghassemi Behnam , Amiri Sani Ardalan , Chandramowlishwaran Aparna
Published: June 2019

Web browsers have become one of the most commonly used applications for desktop and mobile users. Despite recent advances in network speeds and several techniques to speed up web page loading such as speculative loading, smart caching, and multi-threading, browsers still suffer from relatively long page load time (PLT). As web applications are receiving widespread attention owing to their cross-platform support and comparatively straightforward development process, they need to have higher performance to compete with native applications. Recent studies have investigated the bottleneck of the modern web browser's performance and conclude that network connection is not the browser's bottleneck anymore. Even though there is still no consensus on this claim, no subsequent analysis has been conducted to inspect which parts of the browser's computation contribute to the performance overhead. In this paper, we apply comprehensive and quantitative what-if analysis on the web browser's page loading process. Unlike conventional profiling methods, we applycausal profiling to precisely determine the impact of each computation stage such as HTML parsing and Layout on PLT. For this purpose, we develop COZ+, a high-performance causal profiler capable of analyzing large software systems such as the Chromium browser. COZ+ highlights the most influential spots for further optimization, which can be leveraged by browser developers and/or website designers. For instance, COZ+ shows that optimizing JavaScript by 40% is expected to improve the Chromium desktop browser's page loading performance by more than 8.5% under typical network conditions.
A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics
Authors: Kumar Dhruv , Li Jian , Chandra Abhishek , Sitaraman Ramesh
Published: June 2019

Streaming analytics require real-time aggregation and processing of geographically distributed data streams continuously over time. The typical analytics infrastructure for processing such streams follow a hub-and-spoke model, comprising multiple edges connected to a center by a wide-area network (WAN). The aggregation of such streams often require that the results be available at the center within a certain acceptable delay bound. Further, the WAN bandwidth available between the edges and the center is often scarce or expensive, requiring that the traffic between the edges and the center be minimized. We propose a novel Time-to-Live (TTL-)based mechanism for real-time aggregation that provably optimizes both delay and traffic, providing a theoretical basis for understanding the delay-traffic tradeoff that is fundamental to streaming analytics. Our TTL-based optimization model provides analytical answers to how much aggregation should be performed at the edge versus the center, how much delay can be incurred at the edges, and how the edge-to-center bandwidth must be apportioned across applications with different delay requirements. To evaluate our approach, we implement our TTL-based aggregation mechanism in Apache Flink, a popular stream analytics framework. We deploy our Flink implementation in a hub-and-spoke architecture on geo-distributed Amazon EC2 data centers and a WAN-emulated local testbed, and run aggregation tasks for realistic workloads derived from extensive Akamai and Twitter traces. The delay-traffic tradeoff achieved by our Flink implementation agrees closely with theoretical predictions of our model. We show that by deriving the optimal TTLs using our model, our system can achieve a "sweet spot" where both delay and traffic are minimized, in comparison to traditional aggregation schemes such as batching and streaming.
mRSC
Authors: Amjad Muhammad , Misra Vishal , Shah Devavrat , Shen Dennis
Published: June 2019

When evaluating the impact of a policy (e.g., gun control) on a metric of interest (e.g., crime-rate), it may not be possible or feasible to conduct a randomized control trial. In such settings where only observational data is available, synthetic control (SC) methods [2-4] provide a popular data-driven approach to estimate a "synthetic" or "virtual" control by combining measurements of "similar" alternatives or units (called "donors"). Recently, robust synthetic control (RSC) [7] was proposed as a generalization of SC to overcome the challenges of missing data and high levels of noise, while removing the reliance on expert domain knowledge for selecting donors. However, both SC and RSC (and its variants) suffer from poor estimation when the pre-intervention period is too short. As the main contribution of this work, we propose a generalization of unidimensional RSC to multi-dimensional Robust Synthetic Control, mRSC. Our proposed mechanism, mRSC, incorporates multiple types of measurements (or metrics) in addition to the measurement of interest for estimating a synthetic control, thus overcoming the challenge of poor inference due to limited amounts of pre-intervention data. We show that the mRSC algorithm, when using K relevant metrics, leads to a consistent estimator of the synthetic control for the target unit of interest under any metric. Our finite-sample analysis suggests that the mean-squared error (MSE) of our predictions decays to zero at a rate faster than the RSC algorithm by a factor of K and √K for the training (pre-intervention) and testing (post-intervention) periods, respectively. Additionally, we propose a principled scheme to combine multiple metrics of interest via a diagnostic test that evaluates if adding a metric can be expected to result in improved inference. Our mechanism for validating mRSC performance is also an important and related contribution of this work: time series prediction. We propose a method to predict the future evolution of a time series based on limited data when the notion of time is relative and not absolute, i.e., where we have access to a donor pool that has already undergone the desired future evolution. We conduct extensive experimentation to establish the efficacy of mRSC in three different scenarios: predicting the evolution of a metric of interest using synthetically generated data from a known factor model, and forecasting weekly sales and score trajectories of a Walmart store and Cricket game, respectively.
Architecture-Aware Approximate Computing
Authors: Karakoy Mustafa , Kislal Orhan , Tang Xulong , Kandemir Mahmut Taylan , Arunachalam Meenakshi
Published: June 2019

Deliberate use of approximate computing has been an active research area recently. Observing that many application programs from different domains can live with less-than-perfect accuracy, existing techniques try to trade off program output accuracy with performance-energy savings. While these works provide point solutions, they leave three critical questions regarding approximate computing unanswered, especially in the context of dropping/skipping costly data accesses: (i) what is the maximum potential of skipping (i.e., not performing) data accesses under a given inaccuracy bound?; (ii) can we identify the data accesses to drop randomly, or is being architecture aware (i.e., identifying the costliest data accesses in a given architecture) critical?; and (iii) do two executions that skip the same number of data accesses always result in the same output quality (error)? This paper first provides answers to these questions using ten multithreaded workloads, and then, motivated by the negative answer to the third question, presents a program slicing-based approach that identifies the set of data accesses to drop such that (i) the resulting performance/energy benefits are maximized and (ii) the execution remains within the error (inaccuracy) bound specified by the user. Our slicing-based approach first uses backward slicing and then forward slicing to decide the set of data accesses to drop. Our experimental evaluations using ten multithreaded workloads show that, when averaged over all benchmark programs we have, 8.8% performance improvement and 13.7% energy saving are possible when we set the error bound to 2%, and the corresponding improvements jump to 15% and 25%, respectively, when the error bound is raised to 4%.
Learning to Control Renewal Processes with Bandit Feedback
Authors: Cayci Semih , Eryilmaz Atilla , Srikant R.
Published: June 2019

We consider a bandit problem with K task types from which the controller activates one task at a time. Each task takes a random and possibly heavy-tailed completion time, and a reward is obtained only after the task is completed. The task types are independent from each other, and have distinct and unknown distributions for completion time and reward. For a given time horizon τ, the goal of the controller is to schedule tasks adaptively so as to maximize the reward collected until τ expires. In addition, we allow the controller to interrupt a task and initiate a new one. In addition to the traditional exploration-exploitation dilemma, this interrupt mechanism introduces a new one: should the controller complete the task and get the reward, or interrupt the task for a possibly shorter and more rewarding alternative? We show that for all heavy-tailed and some light-tailed completion time distributions, this interruption mechanism improves the reward linearly over time. Applications of this model include server scheduling, optimal free sampling strategies in advertising and adaptive content selection. From a learning perspective, the interrupt mechanism necessitates learning the whole arm distribution from truncated observations. For this purpose, we propose a robust learning algorithm named UCB-BwI based on median-of-means estimator for possibly heavy-tailed reward and completion time distributions. We show that, in a K-armed bandit setting with an arbitrary set of L possible interrupt times, UCB-BwI achieves O(Kłog(τ)+KL) regret. We also prove that the regret under any admissible policy is Ømega(Kłog(τ)), which implies that UCB-BwI is order optimal.