Engineered algal systems for the treatment of anaerobic digestate: A meta-analysis

The objective of this review was to provide quantitative insights into algal growth and nutrient removal in anaerobic digestate. To synthesize the relevant literature, a meta-analysis was conducted using data from 58 articles to elucidate key factors that impact algal biomass productivity and nutrient removal from anaerobic digestate. On average, algal biomass productivity in anaerobic digestate was significantly lower than that in synthetic control media (p < 0.05) but large variation in productivity was observed. A mixed-effects multiple regression model across study revealed that biological or chemical pretreatment of digestate significantly increase productivity (p < 0.001). In contrast, the commonly used practice of digestate dilution was not a significant factor in the model. High initial total ammonia nitrogen suppressed algal growth (p = 0.036) whereas initial total phosphorus concentration, digestate sterilization, CO2 supplementation, and temperature were not statistically significant factors. Higher growth corresponded with significantly higher NH4-N and phosphorus removal with a linear relationship of 6.4 mg NH4-N and 0.73 mg P removed per 100 mg of algal biomass growth (p < 0.001). The literature suggests that suboptimal algal growth in anaerobic digestate could be due to factors such as turbidity, high free ammonia, and residual organic compounds. This analysis shows that non-dilution approaches, such as biological or chemical pretreatment, for alleviating algal inhibition are recommended for algal digestate treatment systems.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy

OBJECTIVE

To evaluate the alterations of language and memory functions using dynamic causal modeling, in order to identify the epileptogenic hemisphere in temporal lobe epilepsy (TLE).

METHODS

Twenty-two patients with left TLE and 13 patients with right TLE underwent functional magnetic resonance imaging (fMRI) during four memory and four language mapping tasks. Dynamic causal modeling (DCM) was employed on fMRI data to examine effective directional connectivity in memory and language networks and the alterations in people with TLE compared to healthy individuals.

RESULTS

DCM analysis suggested that TLE can influence the memory network more widely compared to the language network. For memory mapping, it demonstrated overall hyperconnectivity from the left hemisphere to the other cranial regions in the picture encoding, and from the right hemisphere to the other cranial regions in the word encoding tasks. On the contrary, overall hypoconnectivity was seen from the brain hemisphere contralateral to the seizure onset in the retrieval tasks. DCM analysis further manifested hypoconnectivity between the brain's hemispheres in the language network in patients with TLE compared to controls. The CANTAB® neuropsychological test revealed a negative correlation for the left TLE and a positive correlation for the right TLE cohorts for the connections extracted by DCM that were significantly different between the left and right TLE cohorts.

INTERPRETATION

In this study, dynamic causal modeling evidenced the reorganization of language and memory networks in TLE that can be used for a better understanding of the effects of TLE on the brain's cognitive functions.

Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy

Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation method is proposed for the fully automated grading of brain tumors in magnetic resonance (MR) images. First, the tumor is segmented based on two characteristics of the tumor appearance (intensity and edges information). Second, the features of the tumor region are extracted. Then, the online support vector machine with the kernel (OSVMK) by dynamic fuzzy rule-based optimization of the parameters is used for the grading of tumors. The performance evaluation of the proposed tumor segmentation method was performed by manual segmentation using similarity criteria. Also, tumor grading results compared the proposed online method, the conventional online method, and the batch SVM with the kernel (batch SVMK) in terms of accuracy, precision, recall, specificity, and execution times. The segmentation results show a good correlation between the tumor segmented by the proposed method and by experts manually. Also, the grading results based on the accuracy, precision, recall, and specificity, 95.20%, 97.87%, 96.48%, and 96.45%, respectively, indicate the acceptable performance of the proposed method. The execution times of the introduced online method are much less than the batch SVMK. The method demonstrates the potential of fully automated tumor grading to provide a non-invasive diagnosis in order to determine the treatment strategy for the disease. So the physicians, according to the tumor's grade, can match the treatment of the brain tumor to the patient's individual needs and thus make the best course of treatment for each patient.

Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging

BACKGROUND

Temporal lobe epilepsy (TLE) is the most common type of epilepsy associated with changes in the cerebral cortex throughout the brain. Magnetic resonance imaging (MRI) is widely used for detecting such anomalies; nevertheless, it produces spatially correlated data that cannot be considered by the usual statistical models. This study aimed to compare cortical thicknesses between patients with TLE and healthy controls by considering the spatial dependencies across different regions of the cerebral cortex in MRI.

METHODS

In this study, T1-weighted MRI was performed on 20 healthy controls and 33 TLE patients. Nineteen patients had a left TLE and 14 had a right TLE. Cortical thickness was measured for all individuals in 68 regions of the cerebral cortex based on images. Fully Bayesian spectral method was utilized to compare the cortical thickness of different brain regions between groups. Neural networks model was used to classify the patients using the identified regions.

RESULTS

For the left TLE patients, cortical thinning was observed in bilateral caudal anterior cingulate, lateral orbitofrontal (ipsilateral), the bilateral rostral anterior cingulate, frontal pole and temporal pole (ipsilateral), caudal middle frontal and rostral middle frontal (contralateral side). For the right TLE patients, cortical thinning was only observed in the entorhinal area (ipsilateral). The AUCs of the neural networks for classification of left and right TLE patients versus healthy controls were 0.939 and 1.000, respectively.

CONCLUSION

Alteration of cortical gray matter thickness was evidenced as common effect of epileptogenicity, as manifested by the patients in this study using the fully Bayesian spectral method by taking into account the complex structure of the data.

Delayed cervical cancer diagnosis: a systematic review

OBJECTIVE

Cervical cancer (CC) is a preventable women's cancer. Vaccination and routine Pap smear screening have reduced cervical cancer-related mortality by 70-80% in the world. The eradication of CC depends on identifying the disease early and removing barriers to its timely detection. This review study was designed to determine diagnostic delay and factors related to delayed CC diagnosis in the world.

MATERIALS AND METHODS

A comprehensive search was carried out in databases including Medline, Web of Science, Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan), and Scopus for articles published up to December 2021. Publications were included if they reported data on the delayed CC, and factors related to diagnosis of CC in women. There was no time restriction in this review.

RESULTS

In total, 45 articles were entered into the study. In studies, advanced stages of CC (IIB to IV) varied from 10.2% to 87.9% due to delayed diagnosis. A delayed CC diagnosis was reported in 4.3%-89.1% of patients. The median and mean days of delayed diagnosis were 59-210 days and 2.92-10.5 months, respectively. Factors related to delayed CC diagnosis were categorized into three components including patient, medical history, and health system delay. Patient delay included socio-demographic, husband/ partner, and knowledge. Medical history included medical issues, obstetrics, and family history. Health system delays included health facilities and levels of accessibility.

CONCLUSIONS

There is an urgent need to shorten the diagnostic journey of CC patients by addressing all the components of diagnostic delay and developing strategies to modify the factors associated with these delays.

Interactions of microalgae-bacteria consortia for nutrient removal from wastewater: A review

Nitrogen and phosphorus pollution can cause eutrophication, resulting in ecosystem disruption. Wastewater treatment systems employing microalgae-bacteria consortia have the potential to enhance the nutrient removal efficiency from wastewater through mutual interaction and synergetic effects. The knowledge and control of the mechanisms involved in microalgae-bacteria interaction could improve the system's ability to transform and recover nutrients. In this review, a critical evaluation of recent literature was carried out to synthesize knowledge related to mechanisms of interaction between microalgae and bacteria consortia for nutrient removal from wastewater. It is now established that microalgae can produce oxygen through photosynthesis for bacteria and, in turn, bacteria supply the required metabolites and inorganic carbon source for algae growth. Here we highlight how the interaction between microalgae and bacteria is highly dependent on the nitrogen species in the wastewater. When the nitrogen source is ammonium, the generated oxygen by microalgae has a positive influence on nitrifying bacteria. When the nitrogen source is nitrate, the oxygen can have an inhibitory effect on denitrifying bacteria. However, some strains of microalgae have the capability to supply hydrogen gas for hydrogenotrophic denitrifiers as an energy source. Recent literature on biogranulation of microalgae and bacteria and its application for nutrient removal and biomass recovery is also discussed as a promising approach. Significant research challenges remain for the integration of microalgae-bacteria consortia into wastewater treatment processes including microbial community control and process stability over long time horizons.

Cultivation of Mixed Microalgae Using Municipal Wastewater: Biomass Productivity, Nutrient Removal, and Biochemical Content

BACKGROUND

Microalgal biotechnology has gained much attention previously. Monoculture algae cultivation has been carried out extensively in the last decades. However, although the mixed microalgae cultivation has some advantageous over pure cultures, there is still a lack of knowledge about the performance of mixed cultures.

OBJECTIVE

In this study, it has been tried to investigate all growth aspects of marine and freshwater microalgal species in a mixed culture and their biological effects on biomass growth and composition based on wastewater nutrient consumption.

MATERIAL AND METHODS

Three algal species of Chlorella vulgaris, Scenedesmus obliquus, and Nannochloropsis sp. were cultivated in saline wastewater individually, then the effects of mixing the three strains on biomass productivity, nutrient removal efficiency, chlorophyll, carotenoid, and lipid content were investigated.

RESULTS

The obtained results revealed that the mixed culture of three strains showed the highest biomass productivity of 191 mg. L^-1.d^-1. Also, while there were no significant differences between the performance of mono and mixed culture of algal species in the removal efficiency of wastewater nutrients, the three-strain microalgal mixed culture showed the highest values of 3.5 mg.L^-1.d^-1 and 5.75 mg.L^-1.d^-1 in the removal rate of phosphate and nitrate, respectively. In terms of total chlorophyll and carotenoid per produced biomass, however, the mixed culture of three species showed the lowest values of 4.08 and 0.6 mg. g biomass^-1, respectively.

CONCLUSIONS

The finding proves the potential of attractive and economically feasible mixed microalgae cultivation for high percentage nutrient removal and microalgal biomass production.

Application of a robust data envelopment analysis model for performance evaluation of electricity distribution companies

Purpose

This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran’s 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions.

Design/methodology/approach

The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model’s robustness.

Findings

The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels.

Practical implications

By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions.

Originality/value

The paper’s novel concept applies DEA to Iran’s electricity distribution companies and analyzes them by examining geographic classification, size and the type of the companies. In addition, a stability test is conducted and productivity changes are estimated.

Electrocardiogram signal generation using electrical model of cardiac cell: application in cardiac ischemia

One of the most common causes of heart failure is ischaemia. In this disease, the heart muscles die due to the lack or insufficiency of the blood in the cardiac veins. As a result of such a phenomenon, the action potential in that part of the heart would fade. In this article, using the electric model of the cardiac cell and the mechanism of producing an ECG signal in the heart, the process of producing cardiac electrical potential has been modelled. In this regard, the basic constituent signals of the ECG are generated. Afterward, by accumulating these signals, the final ECG is reproduced. In addition, by variation of the presented model parameters, the cardiac ischaemic signal is simulated in a way that the influence of ventricle ischaemia on the ventricular tissues is considered. The results of such a simulation demonstrate a sufficient match between the model output and the reported changes of the cardiac arrhythmia including ischaemic failures. Here, we report the 91% match between the simulated signal and the considered clinical data.

Evolution of Graph Theory in Dynamic Functional Connectivity for Lateralization of Temporal Lobe Epilepsy

Resting-state functional magnetic resonance imaging (rsfMRI) has described the functional architecture of the human brain in the absence of any task or stimulus. Since the functional connectivity (FC), has non-stationary nature, it is evidenced to be varying over time. Using dynamic functional connectivity, six graph theoretical characteristics were measured and compared between left and right temporal lobe epilepsy (TLE). We also obtain a trend for each characteristic in the time course of experiments. The results demonstrated that the static connectivity analysis failed to fully separate the left and right TLE patients for some characteristics, whereby the dynamic analysis has been shown capable of identifying the laterality. Furthermore, the results suggest that the temporal trend of some graph theoretical characteristics can be exploited as a novel marker for TLE laterality.

AXSIS: Exploring the frontiers in attosecond X-ray science, imaging and spectroscopy

X-ray crystallography is one of the main methods to determine atomic-resolution 3D images of the whole spectrum of molecules ranging from small inorganic clusters to large protein complexes consisting of hundred-thousands of atoms that constitute the macromolecular machinery of life. Life is not static, and unravelling the structure and dynamics of the most important reactions in chemistry and biology is essential to uncover their mechanism. Many of these reactions, including photosynthesis which drives our biosphere, are light induced and occur on ultrafast timescales. These have been studied with high time resolution primarily by optical spectroscopy, enabled by ultrafast laser technology, but they reduce the vast complexity of the process to a few reaction coordinates. In the AXSIS project at CFEL in Hamburg, funded by the European Research Council, we develop the new method of attosecond serial X-ray crystallography and spectroscopy, to give a full description of ultrafast processes atomically resolved in real space and on the electronic energy landscape, from co-measurement of X-ray and optical spectra, and X-ray diffraction. This technique will revolutionize our understanding of structure and function at the atomic and molecular level and thereby unravel fundamental processes in chemistry and biology like energy conversion processes. For that purpose, we develop a compact, fully coherent, THz-driven atto-second X-ray source based on coherent inverse Compton scattering off a free-electron crystal, to outrun radiation damage effects due to the necessary high X-ray irradiance required to acquire diffraction signals. This highly synergistic project starts from a completely clean slate rather than conforming to the specifications of a large free-electron laser (FEL) user facility, to optimize the entire instrumentation towards fundamental measurements of the mechanism of light absorption and excitation energy transfer. A multidisciplinary team formed by laser-, accelerator,- X-ray scientists as well as spectroscopists and biochemists optimizes X-ray pulse parameters, in tandem with sample delivery, crystal size, and advanced X-ray detectors. Ultimately, the new capability, attosecond serial X-ray crystallography and spectroscopy, will be applied to one of the most important problems in structural biology, which is to elucidate the dynamics of light reactions, electron transfer and protein structure in photosynthesis.

Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model

Assessment of cardiac right-ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right-ventricle functional parameters from cardiac MRI images contains segmentation of right-ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right-ventricle in short axis MRI images. After segmentation of right-ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right-ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root-mean-square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤ 10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right-ventricle led us to use a shape prior based method and this work can develop by four-dimensional processing for determining the first ventricular slices.

Coronary artery disease in a large renal transplant population: implications for management

Coronary artery disease (CAD) accounts for approximately one-half of the sizable mortality in patients with end-stage renal disease who have undergone transplantation. The study was a retrospective review of 1460 patients who underwent renal transplantation at the Mount Sinai Medical Center from January 1, 2000 to October 31, 2009. Noninvasive stress testing was performed in 848 patients (88.1%) with 278 patients (32.8%) having abnormal results. Cardiac catheterization was performed in 357 patients (37.1%) and of these, 212 patients had obstructive disease (59.4%). At 5 years posttransplant, there was no statistically significant difference between those with nonobstructive CAD and those who required percutaneous or surgical interventions (adjusted hazard ratio [aHR], 1.243; CI 95%, 0.513-3.010; p = 0.630). Those with medically managed obstructive CAD had significantly higher rates of death at the 5-year period when compared to those who received percutaneous intervention (aHR, 3.792; CI 95%, 1.320-10.895; p = 0.013) or those who received coronary artery bypass grafting (aHR, 6.691; CI 95%, 1.200-37.323). Because noninvasive imaging is poorly predictive of coronary disease in this high-risk population, an anatomic diagnosis is recommended. Revascularization may result in improved long-term outcomes.

Uterine segmentation and volume measurement in uterine fibroid patients' MRI using fuzzy C-mean algorithm and morphological operations

BACKGROUND

Uterine fibroids are common benign tumors of the female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine fibroids by shrinkage of the size of these tumors. Segmentation of the uterine region is essential for an accurate treatment strategy.

OBJECTIVES

In this paper, we will introduce a new method for uterine segmentation in T1W and enhanced T1W magnetic resonance (MR) images in a group of fibroid patients candidated for UAE in order to make a reliable tool for uterine volumetry.

PATIENTS AND METHODS

Uterine was initially segmented using Fuzzy C-Mean (FCM) method in T1W-enhanced images and some morphological operations were then applied to refine the initial segmentation. Finally redundant parts were removed by masking the segmented region in T1W-enhanced image over the registered T1W image and using histogram thresholding. This method was evaluated using a dataset with ten patients' images (sagittal, axial and coronal views).

RESULTS

We compared manually segmented images with the output of our system and obtained a mean similarity of 80%, mean sensitivity of 75.32% and a mean specificity of 89.5%. The Pearson correlation coefficient between the areas measured by the manual method and the automated method was 0.99.

CONCLUSIONS

The quantitative results illustrate good performance of this method. By uterine segmentation, fibroids in the uterine may be segmented and their properties may be analyzed.