bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semi-solid rheology characterization of sludge conditioned with inorganic coagulants
Authors: Feng Guohong , Hu Zhi , Ma He , Bai Tiantian , Guo Yabing , Hao Yiran 
Published: January 2020

Abstract
Rheology measurement, a state-of-the-art technology in a multitude of engineering disciplines, has often been used for computational fluid dynamic simulation of wastewater treatment processes, especially in anaerobic digestion and dewatering. In this work, rheological tests were used to study the semi-solid characteristics of sludge and a good result was obtained. The inorganic coagulants polyaluminum chloride (PAC) and ferric chloride (FC) both increased the floc strength of sludge, leading to higher rheology parameters such as elastic modulus, viscous modulus and specific thixotropy area. Curiously, the shape of all rheological curves exhibited little change with increasing coagulant dosage. The results indicated that various physical and chemical actions among coagulants and sludge flocs relate only to rigid structure, not to the nature of rheology behavior. Frequency sweep tests clearly showed that elastic modulus was a logarithmic function of frequency, suggesting that sludge could not properly be called a soft material due to its inorganic particles. An improved viscoelastic model was successfully developed to predict the experimental data of creep and recovery tests in the linear viscoelastic region. Furthermore, complicated viscoelastic behavior of sludge was also observed, and all the rheology tests could provide the optimum dosage of PAC but not the optimum dosage of FC.
Technical-economic comparison of chemical precipitation and ion exchange processes for the removal of phosphorus from wastewater
Authors: Chiavola Agostina , Bongirolami Simona , Di Francesco Giorgia 
Published: January 2020

Abstract
Chemical precipitation with the addition of ferric chloride is commonly used to remove phosphorus from wastewater. However, since its application also involves several disadvantages, alternative solutions are required. The present paper shows the results of a full-scale experimental work aimed at evaluating the efficiency of the ion exchange process using a polymeric anion exchange resin impregnated with aluminum ions in the removal of phosphorus from wastewater. The study compared the results obtained through this process with chemical precipitation, considering both technical and economic issues. At the same dosage of 6 L/hour and influent concentration (about 6 mg/L), total removal efficiency of 95% and 78% (including also that occurring in the mechanical and biological processes) was achieved by means of the anion exchange process and chemical precipitation, respectively. However, in the latter case, this value was insufficient to ensure consistent compliance with the limit of 2 mg/L Ptot set on the effluent; to achieve this goal, the ferric chloride dosage had to be raised to 12 L/hour, thus increasing the related costs. Furthermore, the anion exchange process generated a lower sludge production. Therefore, the ion exchange process represents a valid alternative to chemical precipitation for P removal from wastewater.
Endoplasmic Reticulum Stress Increases Multidrug-resistance Protein 2 Expression and Mitigates Acute Liver Injury
Authors: Huang Wen-Ge , Wang Jun , Liu Yu-Juan , Wang Hong-Xia , Zhou Si-Zhen , Chen Huan , Yang Fang-Wan , Li Ying , Yi Yu , He Yi-Huai 
Published: January 2020

Background:
Multidrug-resistance protein (MRP) 2 is a key membrane
transporter that is expressed on hepatocytes and regulated by nuclear factor kappa B
(NF-κB). Interestingly, endoplasmic reticulum (ER) stress is closely associated with liver
injury and the activation of NF-κB signaling.

Objective:
Here, we investigated the impact of ER stress on MRP2 expression and the
functional involvement of MRP2 in acute liver injury.

Methods:
ER stress, MRP2 expression, and hepatocyte injury were analyzed in a
carbon tetrachloride (CCl4)-induced mouse model of acute liver injury and in a
thapsigargin (TG)-induced model of ER stress.

Results:
CCl4 and TG induced significant ER stress, MRP2 protein expression and NF-
κB activation in mice and LO2 cells (P<0.05). Pretreatment with ER stress inhibitor 4-
phenyl butyric acid (PBA) significantly mitigated CCl4 and TG-induced ER stress and
MRP2 protein expression (P<0.05). Moreover, pretreatment with pyrrolidine
dithiocarbamic acid (PDTC; NF-κB inhibitor) significantly inhibited CCl4-induced NF-κB
activation and reduced MRP2 protein expression (1±0.097 vs. 0.623±0.054; P<0.05).
Furthermore, hepatic downregulation of MRP2 expression significantly increased CCl4-
induced ER stress, apoptosis, and liver injury.

Conclusion:
ER stress enhances intrahepatic MRP2 protein expression by activating
NF-κB. This increase in MRP2 expression mitigates ER stress and acute liver injury.
Current and Future Neuropharmacological Options for the Treatment of Essential Tremor
Authors: Alonso-Navarro Hortensia , García-Martín Elena , Agúndez José A.G. , Jiménez-Jiménez Félix J. 
Published: January 2020

Background:Essential Tremor (ET) is likely the most frequent movement disorder. In this review, we have summarized the current pharmacological options for the treatment of this disorder and discussed several future options derived from drugs tested in experimental models of ET or from neuropathological data.Methods:A literature search was performed on the pharmacology of essential tremors using PubMed Database from 1966 to July 31, 2019.Results:To date, the beta-blocker propranolol and the antiepileptic drug primidone are the drugs that have shown higher efficacy in the treatment of ET. Other drugs tested in ET patients have shown different degrees of efficacy or have not been useful.Conclusion:Injections of botulinum toxin A could be useful in the treatment of some patients with ET refractory to pharmacotherapy. According to recent neurochemical data, drugs acting on the extrasynaptic GABAA receptors, the glutamatergic system or LINGO-1 could be interesting therapeutic options in the future.
Association between Regional Cerebral Blood Flow and Mini-Mental State Examination Score in Patients with Alzheimer’s Disease
Authors: Saito Hitoshi , Kashiwakura Ikuo , Tsushima Megumi , Mariya Yasushi 
Published: January 2020

Background:
In patients with Alzheimer’s disease (AD), cerebral blood flow (CBF) is
decreased from the early stages. CBF in AD is currently estimated from Z-scores using statistical
analysis. However, the Z-score is not considered the impaired area ratio.

Methods:
In the present study, a novel indicator, ΣzS, associated with brain surface area and Zscores,
is defined and the association with regional CBF has been estimated using Mini-Mental State
Examination (MMSE) scores, which indicate the severity of cognitive impairment in patients with
AD.

Results:
A negative correlation was detected between ΣzS in the posterior cingulate gyrus and the
subset numbers 1, 2, and 5 of the total MMSE scores. Furthermore, a negative correlation was detected
between the total MMSE score and ΣzS in Brodmann area 30, which is a subdivided area of
the brain.

Conclusion:
These results suggest that ΣzS may be a useful indicator of CBF metabolism, and thus
may improve the current understanding of cognitive function in patients with AD.
Continuous Measurement of Reconnaissance Marines in Training With Custom Smartphone App and Watch: Observational Cohort Study
Authors: Saxon Leslie , DiPaula Brooks , Fox Glenn R , Ebert Rebecca , Duhaime Josiah , Nocera Luciano , Tran Luan , Sobhani Mona 
Published: January 2020

Background
Specialized training for elite US military units is associated with high attrition due to intense psychological and physical demands. The need to graduate more service members without degrading performance standards necessitates the identification of factors to predict success or failure in targeted training interventions.

Objective
The aim of this study was to continuously quantify the mental and physical status of trainees of an elite military unit to identify novel predictors of success in training.

Methods
A total of 3 consecutive classes of a specialized training course were provided with an Apple iPhone, Watch, and specially designed mobile app. Baseline personality assessments and continuous daily measures of mental status, physical pain, heart rate, activity, sleep, hydration, and nutrition were collected from the app and Watch data.

Results
A total of 115 trainees enrolled and completed the study (100% male; age: mean 22 years, SD 4 years) and 64 (55.7%) successfully graduated. Most training withdrawals (27/115, 23.5%) occurred by day 7 (mean 5.5 days, SD 3.4 days; range 1-22 days). Extraversion, positive affect personality traits, and daily psychological profiles were associated with course completion; key psychological factors could predict withdrawals 1-2 days in advance (P=.009).

Conclusions
Gathering accurate and continuous mental and physical status data during elite military training is possible with early predictors of withdrawal providing an opportunity for intervention.
Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study
Authors: Ding Huitong , An Ning , Au Rhoda , Devine Sherral , Auerbach Sanford H , Massaro Joseph , Joshi Prajakta , Liu Xue , Liu Yulin , Mahon Elizabeth , Ang Ting FA , Lin Honghuang 
Published: January 2020

Background
Although some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests.

Objective
This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors.

Methods
Our study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation.

Results
A total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests.

Conclusions
We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.
Effect of a Mobile Phone–Based Glucose-Monitoring and Feedback System for Type 2 Diabetes Management in Multiple Primary Care Clinic Settings: Cluster Randomized Controlled Trial
Authors: Yang Yeoree , Lee Eun Young , Kim Hun-Sung , Lee Seung-Hwan , Yoon Kun-Ho , Cho Jae-Hyoung 
Published: January 2020

Background
Recent evidence of the effectiveness of mobile phone–based diabetes management systems is generally based on studies conducted in tertiary hospitals or professional diabetes clinics.

Objective
This study aimed to evaluate the clinical efficacy and applicability of a mobile phone–based glucose-monitoring and feedback system for the management of type 2 diabetes mellitus (T2DM) in multiple primary care clinic settings.

Methods
In this multicenter, cluster-randomized controlled, open trial, 13 primary care clinics in Seoul and other large cities in South Korea were voluntarily recruited. Overall, 150 (9 clinics) and 97 (4 clinics) participants with T2DM were assigned to the intervention and control groups, respectively (2:1 allocation). Every month, participants in both groups attended face-to-face physicians’ consultation for the management of diabetes in the clinic. For the intervention group, participants were required to upload their daily self-monitoring of blood glucose (SMBG) results using the mobile phone app in addition to outpatient care for 3 months. The results were automatically transmitted to the main server. Physicians had to check their patients’ SMBG results through an administrator’s website and send a short feedback message at least once a week. At baseline and 3 months, both groups had anthropometry and blood tests, including hemoglobin A1c (HbA1c), and responded to questionnaires about treatment satisfaction and compliance.

Results
At 3 months, participants in the intervention group showed significantly more improvement in HbA1c (adjusted mean difference to control −0.30%, 95% CI −0.50 to −0.11; P=.003) and fasting plasma glucose (−17.29 mg/dL, 95% CI −29.33 to −5.26; P=.005) than those in the control group. In addition, there was significantly more reduction in blood pressure, and the score regarding treatment satisfaction and motivation for medication adherence increased more in the intervention group than in the control group. In the subgroup analyses, the effect on glycemic control was more significant among younger patients and higher baseline HbA1c levels.

Conclusions
The mobile phone–based glucose-monitoring and feedback system was effective in glycemic control when applied in primary care clinic settings. This system could be utilized effectively with diverse institutions and patients.

Trial Registration
Clinical Research Information Service (CRIS) https://tinyurl.com/tgqawbz
Analysis of Massive Online Medical Consultation Service Data to Understand Physicians’ Economic Return: Data Mining Study
Authors: Jiang Jinglu , Cameron Ann-Frances , Yang Ming 
Published: January 2020

Background
Online health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services.

Objective
This study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the relative importance of these features; and (3) understand how these features interact, linearly or nonlinearly, in relation to payment.

Methods
The dataset is from the largest China-based online medical consultation platform, which covers 1,582,564 consultation records between patient-physician pairs from 2009 to 2018. ML techniques (ie, hyperparameter tuning, model training, and validation) were applied with four classifiers—logistic regression, decision tree (DT), random forest, and gradient boost—to identify the most important features and their relative importance for predicting paid vs free-only appointments.

Results
After applying the ML feature selection procedures, we identified 11 key features on the platform, which are potentially useful to predict payment. For the binary ML classification task (paid vs free services), the 11 features as a whole system achieved very good prediction performance across all four classifiers. DT analysis further identified five distinct subgroups of patients delineated by five top-ranked features: previous offline connection, total dialog, physician response rate, patient privacy concern, and social return. These subgroups interact with the physician differently, resulting in different payment outcomes.

Conclusions
The results show that, compared with features related to physician reputation, service-related features, such as service delivery quality (eg, consultation dialog intensity and physician response rate), patient source (eg, online vs offline returning patients), and patient involvement (eg, provide social returns and reveal previous treatment), appear to contribute more to the patient’s payment decision. Promoting multiple timely responses in patient-provider interactions is essential to encourage payment.
Patient-Reported Outcome Measures of Utilizing Person-Generated Health Data in the Case of Simulated Stroke Rehabilitation: Development Method
Authors: Dimaguila Gerardo Luis , Gray Kathleen , Merolli Mark 
Published: January 2020

Background
Person-generated health data (PGHD) are health data that people generate, record, and analyze for themselves. Although the health benefits of PGHD use have been reported, there is no systematic way for patients to measure and report the health effects they experience from using their PGHD. Patient-reported outcome measures (PROMs) allow patients to systematically self-report their outcomes of a health care service. They generate first-hand evidence of the impact of health care services and are able to reflect the real-world diversity of actual patients and management approaches. Therefore, this paper argues that a PROM of utilizing PGHD, or PROM-PGHD, is necessary to help build evidence-based practice in clinical work with PGHD.

Objective
This paper aims to describe a method for developing PROMs for people who are using PGHD in conjunction with their clinical care—PROM-PGHD, and the method is illustrated through a case study.

Methods
The five-step qualitative item review (QIR) method was augmented to guide the development of a PROM-PGHD. However, using QIR as a guide to develop a PROM-PGHD requires additional socio-technical consideration of the PGHD and the health technologies from which they are produced. Therefore, the QIR method is augmented for developing a PROM-PGHD, resulting in the PROM-PGHD development method.

Results
A worked example was used to illustrate how the PROM-PGHD development method may be used systematically to develop PROMs applicable across a range of PGHD technology types used in relation to various health conditions.

Conclusions
This paper describes and illustrates a method for developing a PROM-PGHD, which may be applied to many different cases of health conditions and technology categories. When applied to other cases of health conditions and technology categories, the method could have broad relevance for evidence-based practice in clinical work with PGHD.
Locating Medical and Recreational Cannabis Outlets for Research Purposes: Online Methods and Observational Study
Authors: Pedersen Eric R , Firth Caislin , Parker Jennifer , Shih Regina A , Davenport Steven , Rodriguez Anthony , Dunbar Michael S , Kraus Lisa , Tucker Joan S , D'Amico Elizabeth J 
Published: January 2020

Background
An increasing number of states have laws for the legal sale of recreational and medical cannabis out of brick-and-mortar storefront locations. Given the proliferation of cannabis outlets and their potential for impact on local economies, neighborhood structures, and individual patterns of cannabis use, it is essential to create practical and thorough methods to capture the location of such outlets for research purposes. However, methods used by researchers vary greatly between studies and often do not include important information about the retailer’s license status and storefront signage.

Objective
The aim of this study was to find methods for locating and observing cannabis outlets in Los Angeles County after the period when recreational cannabis retailers were granted licenses and allowed to be open for business.

Methods
The procedures included searches of online cannabis outlet databases, followed by methods to verify each outlet’s name, address, license information, and open status. These procedures, conducted solely online, resulted in a database of 531 outlets. To further verify each outlet’s information and collect signage data, we conducted direct observations of the 531 identified outlets.

Results
We found that 80.9% (430/531) of these outlets were open for business, of which 37.6% (162/430) were licensed to sell cannabis. Unlicensed outlets were less likely to have signage indicating the store sold cannabis, such as a green cross, which was the most prevalent form of observed signage. Co-use of cannabis and tobacco/nicotine has been found to be a substantial health concern, and we observed that 40.6% (175/430) of cannabis outlets had a tobacco/nicotine outlet within sight of the cannabis outlet. Most (350/430, 81.4%) cannabis outlets were located within the City of Los Angeles, and these outlets were more likely to be licensed than outlets outside the city.

Conclusions
The findings of this study suggest that online searches and observational methods are both necessary to best capture accurate and detailed information about cannabis outlets. The methods described here can be applied to other metropolitan areas to more accurately capture the availability of cannabis in an area.
Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
Authors: Wang Chaofan , Sarsenbayeva Zhanna , Chen Xiuge , Dingler Tilman , Goncalves Jorge , Kostakos Vassilis 
Published: January 2020

Background
Hand hygiene is a crucial and cost-effective method to prevent health care–associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation.

Objective
We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing.

Methods
We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping.

Results
Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance.

Conclusions
A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.