An air-cooled design for an air gap membrane distillation (AGMD) process offers potential for significantly lowering energy requirements and reducing cost for desalination by eliminating or reducing ...the balance of plant components associated with the coolant flow system. Experiments were conducted on an air-cooled AGMD module to study the effects of air gap, support mesh conductivity and hydrophobicity, and the condensing surface hydrophobicity. A novel modular design was developed in which modules could be arranged in a series configuration to increase the distillate flux. The output from the series configuration was found to yield about three times the distillate water compared to a single pass water-cooled system with the same temperature difference (ΔT). The results also indicate that the mesh conductivity has a favorable effect on the flux value whereas the hydrophobicity of the mesh has no significant effect. The hydrophobicity of the condensing surface is favorable on two accounts: first, it increases the distillate flux at temperatures below 60 °C and secondly, the temperature difference of the saline feed when it enters and leaves the module is lower, which can lead to energy savings and higher yield when used in a series configuration.
•Presents a novel air-cooled air gap membrane distillation (AGMD) design•Presents systematic experimental studies on the effects of various design and operating parameters•The novel AGMD system can purify water by a factor of over 20 and is a factor of 10 purer than common drinking water
Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized ...resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.
Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the ...work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70-0.78 and 0.72-0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.
In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and ...quantitative means of passively monitoring the impact and response of these interventions at a local level is needed.
We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19.
We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background.
We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.
RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.
Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated ...biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD.
We have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection.
We have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587.
Our results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.
Resource sharing by means of load balancing in cloud computing environments helps for efficient utilization of cloud resources and higher overall throughput. However, implementation of poor load ...balancing algorithms may cause some virtual machines starving for additional cloud resources. Employing meagre crafted mechanism for priority-oriented load balancing may leave low-level priority virtual machines starving. We suggest an improved resource sharing mechanism for load balancing in the cloud computing environments. The suggested mechanism helps to provide efficient load balancing by avoiding starvation. In order to cater efficient load balancing, the proposed resource sharing technique takes respective virtual machines’ priority levels into consideration. An implementation of the suggested load balancing algorithm in cloud environment provides reduction in waiting time of the starving virtual machines which are looking for additional resources in cloud platform. The implementation of our proposed algorithm has been deployed on a prototype cloud computing infrastructure testbed established with open source software OpenStack. The prototype cloud testbed is supported in backend by the open source CentOS Linux operating system’s minimal setup. Experimental results of proposed load balancing mechanism in the prototype cloud computing infrastructure setup designate reduction in the waiting time of overloaded starving virtual machines. The proposed mechanism is beneficial to accomplish priority-oriented and starvation free resource sharing for load balancing in cloud computing environments. In future, the proposed technique can be further enhanced for implementing load balancing in collaborated cloud computing environments.
The motion of an infinitesimal mass in the restricted four body problem is studied assuming both the orbits of motion of the three primaries to be elliptical. It is assumed that the forces governing ...the motion of the bodies is the mutual gravitational attractions of the primaries, however the problem is restricted in the sense that the effect of the fourth body of infinitesimal mass is assumed to be negligible. We have derived the equations of motion. The location and stability of the planar equilibrium points is studied for Sun–Earth–Moon system and graphically presented. The pulsating ZVC and the basin of attraction for the system are analyzed.
Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that ...have garnered interest are "melancholic depression" and "anxious depression". Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression.
With the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress.
We find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted to analyze data from many other biomedical applications where the data has similar characteristics.
Proteolytic fragments of amyloid and post-translational modification of tau species in Cerebrospinal fluid (CSF) as well as cerebral amyloid deposition are important biomarkers for Alzheimer's ...Disease. We conducted genome-wide association study to identify genetic factors influencing CSF biomarker level, cerebral amyloid deposition, and disease progression. The genome-wide association study was performed via a meta-analysis of two non-overlapping discovery sample sets to identify genetic variants other than APOE ε4 predictive of the CSF biomarker level (Aβ1-42, t-Tau, p-Tau181P, t-Tau:Aβ1-42 ratio, and p-Tau181P:Aβ1-42 ratio) in patients enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Loci passing a genome-wide significance threshold of P < 5 x 10-8 were followed-up for replication in an independent sample set. We also performed joint meta-analysis of both discovery sample sets together with the replication sample set. In the discovery phase, we identified variants in FRA10AC1 associated with CSF Aβ1-42 level passing the genome-wide significance threshold (directly genotyped SNV rs10509663 PFE = 1.1 x 10-9, imputed SNV rs116953792 PFE = 3.5 x 10-10), rs116953792 (Pone-sided = 0.04) achieved replication. This association became stronger in the joint meta-analysis (directly genotyped SNV rs10509663 PFE = 1.7 x 10-9, imputed SNV rs116953792 PFE = 7.6 x 10-11). Additionally, we identified locus 15q21 (imputed SNV rs1503351 PFE = 4.0 x 10-8) associated with CSF Aβ1-42 level. No other variants passed the genome-wide significance threshold for other CSF biomarkers in either the discovery sample sets or joint analysis. Gene set enrichment analyses suggested that targeted genes mediated by miR-33, miR-146, and miR-193 were enriched in various GWAS analyses. This finding is particularly important because CSF biomarkers confer disease susceptibility and may be predictive of the likelihood of disease progression in Alzheimer's Disease.
Abstract
Background
The AgResults initiative tests the efficacy of results-based prizes to scale-up smallholder technology adoption. In Nigeria the project awarded a $18.75/ton prize for private ...sector actors who aggregated maize from smallholders that was treated by Aflasafe—a biocontrol that addresses liver cancer-causing aflatoxin contamination in maize. This paper examines the impact of AgResults initiative on smallholder farmers.
Methods
This evaluation estimates the causal effect of the AgResults program on farmer outcomes by comparing survey data from AgResults farmers to survey data from a matched comparison group of farmers. To improve balance, we use propensity score weights. In considering inestimable selection bias, we describe several key considerations, including the inclusion of comparison areas for treatment post-evaluation.
Results
The project increased Aflasafe adoption by 56% points, farmers earned 16% more net maize income on average. However, the majority of farmers in villages engaged by the project did not know about Aflasafe’s health benefits. This suggests that complimentary donor-directed efforts may still be needed to generate general awareness about the technologies whose benefit is not immediately visible to the smallholders. With the prize focused on aggregation, private sector actors may have reduced incentive to raise awareness about Aflasafe’s health benefits in case farmers held back Aflasafe-treated maize for consumption.
Conclusions
This paper highlights the potential of results-based prizes to engage the private sector in solving development problems. However, it also equally highlights the gaps that such an approach may have, arguing for the need for having complimentary efforts to address those gaps. This is particularly the case when the technology’s benefits are not perceived by the consumer (aflatoxins are not visible to the eye, and the health benefits are not immediate), or when the technology results in positive externalities (final consumers of Aflasafe-treated maize also benefit). Broader consumer awareness needs to be raised to promote continued development of the market for Aflasafe-treated maize, aflatoxin standards need to be enforced, and aflatoxin testing needs to be more easily available.
PDF
