Activity Monitoring Research Articles

Discovery of anti-tumor agent targeted MetAP-2 using a special turn-on fluorescent probe

Methionine aminopeptidase 2 (MetAP-2) catalyzes the removal of methionine from the amino-terminus of proteins or peptides and is a known target for anti-tumor therapy. In this study, we developed an enzyme-activated fluorescent probe, DDAO-BMT, specifically for the real-time detection of endogenous MetAP-2. This probe enables the determination of extracellular MetAP-2 activity and visual monitoring of intracellular MetAP-2. Additionally, Sappan Lignum was identified as an active herbal medicine against MetAP-2, with brazilin identified as its key active constituent. Brazilin also exhibited a significant anti-tumor effect, including the inhibition on proliferation, migration, and invasion of tumor cells, and the preliminary mechanism of its activity against cancer has been elucidated. The use of this probe facilitates the monitoring of endogenous MetAP-2 in complex biological system, aiding in the discovery of novel active compounds for cancer treatment.

Development of a compact active neutron monitor to measure the H*(10): Design, Simulation and Validation

Development of a compact active neutron monitor to measure the H*(10): Design, Simulation and Validation

Integrating active demand into the distribution system using metaheuristic techniques

AbstractIntegrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring

According to the World Health Organization, healthy communities rely on well-functioning ecosystems. Clean air, fresh water, and nutritious food are inextricably linked to ecosystem health. Changes in biological activity convey important information about ecosystem dynamics, and understanding such changes is crucial for the survival of our species. Scientific edge cyberinfrastructures collect distributed data and process it in situ, often using machine learning algorithms. Most current machine learning algorithms deployed on edge cyberinfrastructures, however, are trained on data that does not accurately represent the real stream of data collected at the edge. In this work we explore the applicability of two new self-supervised learning algorithms for characterizing an insufficiently curated, imbalanced, and unlabeled dataset collected by using a set of nine microphones at different locations at the Morton Arboretum, an internationally recognized tree-focused botanical garden and research center in Lisle, IL. Our implementations showed completely autonomous characterization capabilities, such as the separation of spectrograms by recording location, month, week, and hour of the day. The models also showed the ability to discriminate spectrograms by biological and atmospheric activity, including rain, insects, and bird activity, in a completely unsupervised fashion. We validated our findings using a supervised deep learning approach and with a dataset labeled by experts, confirming competitive performance in several features. Toward explainability of our self-supervised learning approach, we used acoustic indices and false color spectrograms, showing that the topology and orientation of the clouds of points in the output space over a 24-h period are strongly linked to the unfolding of biological activity. Our findings show that self-supervised learning has the potential to learn from and process data collected at the edge, characterizing it with minimal human intervention. We believe that further research is crucial to extending this approach for complete autonomous characterization of raw data collected on distributed sensors at the edge.

Remote Patient Monitoring Using Mobile Health Technology in Cancer Care and Research: Patients' Views and Preferences.

There is an increasing interest in studying the potential of mobile health (mHealth) technologies, such as smartphone apps and wearables, as monitoring tools for patients with cancer during or after their treatment. However, little research is dedicated to exploring the opinions and concerns of patients regarding the adoption of these technologies. This study aimed to gain insight into patients' perspectives and preferences for participating in mHealth-based monitoring in cancer care. A qualitative study comprising semistructured interviews was conducted in the Netherlands between April and June 2023. Participants were eligible if they were 18 years or older with a current or past diagnosis of cancer. The interview guide was developed on the basis of the technology acceptance model, with main themes being use, communication, trust, privacy, and expectations. Thirteen participants with urologic primary cancer were interviewed. Most patients had already some familiarity with the use of digital monitoring devices or wearables. Main barriers included persistent reminders of the illness, receiving notifications deemed unnecessary or unwanted, and the acknowledgment that mHealth technology does not serve as a substitute for human doctors. Conversely, patients recognized the potential for time-savings through the utilization of mHealth, viewed active monitoring as nonburdensome, considered mHealth a tool for reducing the communication threshold with their doctor, and expressed willingness to adopt such a platform if they perceived personal or societal relevance. This study has elucidated which factors are important for successful development of mHealth for patients with cancer. While both barriers and facilitators play a role, patients' attitudes were positive toward the implementation of remote digital monitoring, showing promising prospects for future research of mHealth in oncology.

A Study on A Hybrid Water Quality Prediction Model Using Sequence to Sequence Learning Based LSTM And Machine Learning

In the year 2022, around 99.4% of the population of South Korea is using tap water for drinking purposes. However, most of the sources are large seasonal turbidity and frequent algal blooms, which may pose a high risk to the treatment processes. Besides this, the facilities are getting aged and deteriorating day by day, raising concerns over WQ contamination and the need for stringent WQ management. The set point method is being applied in South Korea for monitoring WQ as well as pollution detection. This approach considers legally set WQ criteria for each process as threshold and sends out alerts for pollution when real-time WQ measurements exceed the threshold. This threshold base approach can trigger lots of false alarms. This study focuses on developing an efficient prediction strategy for WQ pollution active monitoring in South Korea with the help of LSTM Seq2Seq model. We further suggest hybrid WQ prediction model that integrates the LSTM Seq2Seq predictions with further machine learning models in order to improve the performance of the predictions. By comparing the short-term and long-term predictive performance of these models, we aim to develop an optimal WQ prediction model.

Daily and Peak Monitor Independent Movement Summary (MIMS) Values Associated With Metabolic Syndrome: NHANES 2011-12 and 2013-14.

The U.S. National Health and Nutrition Examination Survey (NHANES) physical activity monitor datasets for 2011-12 and 2013-14 were released in late 2020. To date, there has been limited interpretation of these nationally representative wrist-worn accelerometer data (summarized and reported in Monitor Independent Movement Summary [MIMS] units) and their relationships with health-related outcomes. This study examined the associations between free-living DailyMIMS (volume), Peak 1MIMS and Peak 30MIMS (intensity), and risk of Metabolic Syndrome (MetS). Data from adults (N=3787; 18-80+ years) in the 2011-12 and 2013-14 NHANES cycles with health examination and accelerometry data were included. Accelerometer data were processed into Peak 1MIMS and Peak 30MIMS (MIMS/min), and DailyMIMS (MIMS/day). Design-based generalized linear and logistic regressions, and a sample-weighted decision tree, were used to examine associations between MIMS variables and MetS risk factors. Lower Peak 1MIMS, Peak 30MIMS, and DailyMIMS were observed for every one-unit increase in the number of risk factors -3.9 [95% CI: -4.3, -3.4] and -2.3 [-2.6, -2.1] MIMS/min, (-672.1 [-772.7, -571.5] MIMS/day, respectively, all p< 0.001). The Decision Tree classified individuals ≥ 46.5 years with a DailyMIMS ≥ 12 245 MIMS/day and a Peak 30MIMS < 45.1 MIMS/min as having MetS (≥ 3 risk factors). Individuals < 46.5 years with a Peak 1MIMS ≥ 62.9 MIMS/min were classified with 0 risk factors. Higher DailyMIMS and PeakMIMS were associated with an absence of MetS risk factors, with a progressive decline as the number of risk factors increased. These findings may be considered as preliminary benchmarks for DailyMIMS and PeakMIMS associated with cardiometabolic risk.

Mobility Abilities Mediate the Association of a More Active Lifestyle With Mobility Disability in Older Adults.

Few studies have analyzed sensor-derived metrics of mobility abilities and total daily physical activity (TDPA). We tested whether sensor-derived mobility metrics and TDPA indices are independently associated with mobility disabilities. This cohort study derived mobility abilities from a belt-worn sensor that recorded annual supervised gait testing. TDPA indices were obtained from a wrist-worn activity monitor. Mobility disability was determined by self-report and inability to perform an 8-feet walk task. Baseline associations of mobility metrics and TDPA (separately and together) were examined with logistic regressions and incident associations (average 7 years follow-up) with Cox models. Mediation analysis quantified the extent mobility metrics mediate the association of TDPA with mobility disability. 724 ambulatory older adults (mean age 82 years, 77.4% female) were studied. In separate models, mobility abilities (eg, step time variability, turning angular velocity) and TDPA were related to mobility disabilities. Examined together in a single model, mobility abilities remained associated with mobility disabilities, while TDPA was attenuated. This attenuation of TDPA could be explained by mediation analysis that showed about 50% of TDPA associations with mobility disabilities is mediated via mobility abilities (prevalent mobility disability 54%, incident mobility disability 40%, incident loss of ambulation 50%; all p's < .001). Sensor-derived mobility metrics assess more diverse facets of mobility. These metrics mediate approximately half of the association of higher levels of daily physical activity with reduced mobility disability in older adults. Findings may inform the design of targeted interventions to reduce mobility disability in late life.

The Impact of Internal Control Systems on Corporate Performance among Listed Banks in Ghana: The Moderating Role of Information Technology

In today's fast-paced and unpredictable business landscape, companies need to embrace new strategies and innovations rooted in adaptable and flexible frameworks. This study examined how Internal Control Systems (ICS) and information technology influences corporate performance of the listed Banks in Ghana on the Ghana Stock Exchange. The study followed a positivist approach and utilized quantitative methods to meet its objectives. Surveys were conducted using self-administered questionnaires, and data was collected through convenience and purposive sampling. Partial least squares regression with structural equation modelling (PLS-SEM) was utilized to analyze the complex structural relationships between the variables. Additionally, the study explored the moderating role of Information Technology in enhancing the relationships between various dimensions of ICS and corporate performance. The results emphasize the importance of Information Technology and control activities within Internal Control Systems (ICS), evidenced by their positive correlation with corporate performance. It also highlights the significance of monitoring activities in detecting and preventing fraudulent activities, errors, and inefficiencies. Our findings recommend policymakers, industry stakeholders, and entrepreneurs in the banking sector to prioritize and create a conducive regulatory environment for robust ICS implementation in listed banks in Ghana.

Real-World Evidence of Indapamide-Induced Rhabdomyolysis: A Retrospective Analysis of Electronic Health Records.

Previous research and pharmacovigilance monitoring activities have suggested a potential association between indapamide use and rhabdomyolysis. This study aims to investigate the potential causal relationship between the use of indapamide and rhabdomyolysis. A case-control study conducted using electronic health records data, between July 1, 2016 and December 31, 2022. Patients who have rhabdomyolysis event (cases) were matched to four controls bases on age, gender, and date. We examined the odds for indapamide exposure through three risk periods: current use, recent use, and former. The study outcome was ascertained through the presence of CK level over 1000 U/L (i.e., rhabdomyolysis event). Subsequently, a multivariable conditional logistic regression analysis was utilized to assess the causal association of indapamide exposure on the likelihood of developing rhabdomyolysis, while accounting for potential confounding variables. The study population consisted of 2965 cases and 11 860 controls. The results of the conditional logistic regression analysis indicated a lack of association between exposure to indapamide for the current users with an odds ratio (OR) of 0.6 (95% confidence intervals (CI): 0.39-1.05). The odds of recent indapamide use among cases was lower than controls (OR 0.2; 95% CI: 0.14-0.47). Lastly, the OR for former use of indapamide was 0.1 (95% CI: 0.07-0.23). In this study, we did not find association between indapamide use and rhabdomyolysis regardless timing of exposure.

Measuring transboundary disease spread - ASF in wild boars straddling Piedmont and Liguria

The aim of this paper is to describe the impact, dynamics and risk factors of the incursion of the African Swine Fever (ASF) virus detected in early 2022 in the wild boar population straddling Piedmont and Liguria, 24 months after its emergence.A study area of 4,162.2 square kilometres, comprising 234 municipalities in Liguria and Piedmont (north-western Italy), along with an external buffer zone, was considered. The epidemiological units were wild boars tested for ASF using real-time PCR between late December 2021 and December 31, 2023. The data were obtained from passive and active surveillance. Conventional methodologies were applied in the analysis of time series of tests, cases, or prevalence rates of positive animals. Maps were used to visualise and compare the monitoring activities, the location of cases and municipal standardised prevalence ratios. Clustering of high and low trends was studied by semester using a space-time permutation model. Risk factors analysis was based on multivariate Poisson regression modelling.Over the 2-year study period, the epidemic's spread was closely monitored by testing 10,412 wild boars (25% of them from passive surveillance). Probability of disease detection was 10.9 times higher among found dead animals compared with animals tested in active surveillance. Difficulties in carcass searching led to heterogeneities in surveillance sensitivity achieved locally and inherent uncertainties. A total of 1,165 wild boars that tested positive for ASF have been detected in 125 municipalities across three provinces. The outbreak has expanded over time and exhibited an increasing trend, with an epidemic doubling time of 10.7 months. There was seasonality, with an increasing trend from summer to spring. Prevalence rates followed the same temporal pattern. Significant low and high clusters respectively indicated endemic episodes in the interior areas of virus circulation and an ongoing invasion of surrounding regions.The characteristics of this outbreak align with the cycle of wild boar habitat transmission observed in other outbreaks across Europe. The measures implemented were not sufficient to stop the epidemic: however, targeted interventions such as depopulation campaigns, reinforcement of physical barriers and increased biosecurity measures have prevented local spillover to domestic pigs and partially hindered the advance of the disease. In addition, the shown seasonality of the disease, likely can enhance control measures. The ongoing studies of the local wild boar populations and the evolution the epidemic along with the lessons learned so far will improve the efficiency and effectiveness of efforts to limit and ultimately eradicate the disease.

Body Fluid Collection Devices for Ostomy Patients: A Review.

Background/Objectives: Abdominal ostomy surgery has a severe impact on individuals' daily lives. These procedures are typically indicated for conditions such as cancer, inflammatory bowel disease, or traumatic injuries. They involve creating an artificial opening, denominated the stoma, in the abdominal area to divert feces or urine, establishing a connection between the affected organs and the body's exterior. Thus, specialized products to collect the body fluids are required, being effective and tailored products crucial to enhance the quality of life of such patients. Methods: This paper presents a review of fecal fluid collection devices and advanced technologies designed to assist patients with ostomies. The study aims to identify the known bags/devices and evaluate their attributed performance in enhancing the population's physical and social quality of life. This review is based on a systematic search conducted between 20 February and 2 March 2024, in the PubMed, Scopus, Web of Science, Google Scholar, and Google Patents databases. Articles published within the last eight years from this period were included in the analysis. Results: The devices found in the study were classified as passive, requiring active monitoring by the user, and active, providing automated assistance. Three main categories were identified, reflecting the most significant concerns of patients: (1) devices that control fluid leakage, reducing peristomal dermatological problems; (2) devices that minimize odors and noise, reducing social embarrassment; and (3) devices that monitor fluid volume, helping with electrolyte balance, especially in patients with ileostomies. Conclusions: This study revealed that the existing devices meet primary collection and disposal needs. However, introducing smart devices could offer greater control and confidence to users, providing real-time information on gas pressure, stool texture, and accumulated volume. Thus, overall, the development of advanced technologies can significantly improve patients' quality of life, restore social confidence, and enable a more effective management of the condition by sharing information with medical teams.

Daily activity patterns in agoutis (Dasyprocta spp) in response to relaxed predation

Animals' fitness is determined in a large proportion by the balance in energetic requirements maintained during daily activities, in response to environmental factors. Predation is a major environmental factor influencing the activity patterns of prey, and the deployment of adaptive responses to predation represents a significant cost to prey populations and communities. Experimental removal of predators to study the effect on activity patterns of prey is impractical for vertebrate species. However, islands are often deprived of predators and provide an excellent arena to study prey's responses in the absence of any cue related to predation risk. Here, we investigated whether natural absence of predators in islands has influenced the activity patterns of diurnal agoutis in Panama, by monitoring activity in three sites in each habitat type (predator-free vs predator). We predicted that agoutis in predator-free sites can expand their activity patterns towards the night, which is the period of highest predation risk, in sites with predators. One of our predator-free sites showed relative high activity at night, with no evidence of nocturnality in sites with predators. A clear pattern across our three predator-free sites was that agoutis started their daily activity earlier, before sunrise, which is a period with significant predation risk as well. Our study highlights the role that felids play in regulating agoutis' daily activity patterns and we discuss the implications of our finding. Finally, we also offer a review on agoutis' activity pattern in the Neotropics.

Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data

Cardiovascular disease (CVD) is the most important cause of morbidity and mortality worldwide. Early detection, prevention or even prediction is of pivotal importance to reduce the burden of cardiovascular disease and its associated costs. Low cost, consumer-grade smartwatches have the potential to revolutionize cardiovascular medicine by enabling continuous monitoring of heart rate and activity. When combined with machine learning(ML), the resulting large amounts of time series data hold the potential of detection, or exclusion of CVD. However, analyzing such large datasets is challenging due to the sparse presence of informative segments. Efficient selection of these segments is essential for developing predictive models for clinical deployment. The objective of this paper was to investigate the potential of an acceleration-deceleration curvebased ML model as a novel clinical indicator for the detection of cardiovascular diseases. We used data from the ME-TIME study; 42 participants from which 21 have a cardiovascular disease and 21 are health controls. Data from each subject was normalized to decrease inter-subject variability. A neural network model aggregated predictions per week. We showed that per-subject normalization by the peak value of curves during inactivity, aggregation of model predictions over a week, and using a contrastive loss, resulted in a predictive model with 99 % ± 3 % specificity and 40 % ± 49 % sensitivity on the development set, and 100 % specificity with 67 % ± 47 % sensitivity on the test set. Acceleration-deceleration curves are effective patterns for ruling out the presence of cardiovascular disease, but caution must be taken to properly pre-process the curves and carefully choosing a model that reduces the variability in the extracted curves.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

Label-Free SERS Sensors for Real-Time Monitoring of Tyrosine Phosphorylation.

Dysregulation of receptor tyrosine kinases (RTKs) has been shown to correlate with cancer cell proliferation and drug resistance. Thus, monitoring the activity of RTKs at a chemical level could provide new biomedical insights and methods to assess the drug efficacy. However, direct monitoring of kinase activity is challenging and most commonly relies on in vitro techniques such as Western blotting and ELISAs. Herein, we report the development of a gold nanoparticle-based surface-enhanced Raman scattering (SERS) sensor, which allows the real-time monitoring of tyrosine phosphorylation of a reporter peptide (Axltide) by the Axl enzyme. We demonstrate that our sensor shows strong signal localization, and we are able to detect tyrosine phosphorylation of the reporter peptide through chemical phosphorylation and enzymatically with similar peak changes to those observed in the spontaneous Raman spectra. Through monitoring the SERS spectrum, we can observe changes in phosphorylation in real time.

Templates-Built Structural Designs for Piezoelectrochemical Pressure Sensors.

Self-powered sensors, capable of detecting static and dynamic pressure without an external power source, are pivotal for advancements in human-computer interaction, health monitoring, and artificial intelligence. Current sensing technologies, however, often fall short of meeting the growing needs for precise and timely pressure monitoring. This article introduces a novel self-powered pressure sensor utilizing electrochemical reactions. The sensor's ion conduction path and internal resistance adjust in response to external stress across a broad range. Its three-dimensional structure, crafted by using a simple template on the electrolyte, enables the efficient and cost-effective detection of various mechanical stimuli. This device not only achieves an optimized power density of approximately 2.34 mW cm-2─surpassing most existing technologies─but also features excellent flexibility, quick response, and recovery times (0.15 and 0.19 s respectively); high durability (2000 cycles); and a broad sensing range (0.23-20 kPa). Moreover, it serves as an ionic touchpad, enhancing data collection and recognition, and integrates seamlessly with a mouthpiece for accurate, real-time monitoring of respiratory activities. This innovative sensor offers minimal cost and simple process requirements while providing multifunctional capabilities for energy harvesting and pressure sensing, marking a significant step forward in the design of next-generation sensors.

Does radiofrequency radiation impact sleep? A double-blind, randomised, placebo-controlled, crossover pilot study.

The most common source of Radiofrequency Electromagnetic Field (RF-EMF) exposures during sleep includes digital devices, yet there are no studies investigating the impact of multi-night exposure to electromagnetic fields emitted from a baby monitor on sleep under real-world conditions in healthy adults. Given the rise in the number of people reporting to be sensitive to manmade electromagnetic fields, the ubiquitous use of Wi-Fi enabled digital devices and the lack of real-world data, we investigated the effect of 2.45 GHz radiofrequency exposure during sleep on subjective sleep quality, and objective sleep measures, heart rate variability and actigraphy in healthy adults. This pilot study was a 4-week randomised, double-blind, crossover trial of 12 healthy adults. After a one-week run-in period, participants were randomised to exposure from either an active or inactive (sham) baby monitor for 7 nights and then crossed over to the alternate intervention after a one-week washout period. Subjective and objective assessments of sleep included the Pittsburgh Insomnia Rating Scale (PIRS-20), electroencephalography (EEG), actigraphy and heart rate variability (HRV) derived from electrocardiogram. Sleep quality was reduced significantly (p < 0.05) and clinically meaningful during RF-EMF exposure compared to sham-exposure as indicated by the PIRS-20 scores. Furthermore, at higher frequencies (gamma, beta and theta bands), EEG power density significantly increased during the Non-Rapid Eye Movement sleep (p < 0.05). No statistically significant differences in HRV or actigraphy were detected. Our findings suggest that exposure to a 2.45 GHz radiofrequency device (baby monitor) may impact sleep in some people under real-world conditions however further large-scale real-world investigations with specified dosimetry are required to confirm these findings.

MRI and CT compatible asymmetric bilayer hydrogel electrodes for EEG-based brain activity monitoring

The exploration of multi-dimensional brain activity with high temporal and spatial resolution is of great significance in the diagnosis of neurological disease and the study of brain science. Although the integration of electroencephalogram (EEG) with magnetic resonance imaging (MRI) and computed tomography (CT) provides a potential solution to achieve a brain-functional image with high spatiotemporal resolution, the critical issues of interface stability and magnetic compatibility remain challenging. Therefore, in this research, we proposed a conductive hydrogel EEG electrode with an asymmetrical bilayer structure, which shows the potential to overcome the challenges. Benefiting from the bilayer structure with different moduli, the hydrogel electrode exhibits high biological and mechanical compatibility with the heterogeneous brain-electrode interface. As a result, the impedance can be reduced compared with conventional metal electrodes. In addition, the hydrogel-based ionic conductive electrodes, which are free from metal conductors, are compatible with MRI and CT. Therefore, they can obtain high spatiotemporal resolution multi-dimensional brain information in clinical settings. The research outcome provides a new approach for establishing a platform for early diagnosis of brain diseases and the study of brain science.

Association of Aquatic Invertebrates with the Water Quality in the Ponds of Bhaktapur

Aquatic invertebrates are representative of the health of habitats they live in. This study was conducted to evaluate aquatic invertebrate diversity and relate them with physico-chemical factors in the ponds of Bhaktapur. Rani pokhari, Bhajya pokhari, Na pokhari and Kamal pokhari were selected for the study. Plankton net was swiped out for approximately one minute in each corner of all the ponds. Five subsequent sweeps were taken repeatedly at each site. Invertebrates collected in the net were kept in a plastic container covered with a lid. The collected species were preserved in 70% alcohol. For physico-chemical analysis, water samples were collected from 10cm below the surface water in BOD bottles from each site and taken to the laboratory of Department of Zoology, Bhaktapur Multiple Campus for the further identification and laboratory work. Total 16 species of aquatic invertebrates belonging to five phyla, 10 orders and 16 families were recorded from the ponds of Bhaktapur. Species richness of aquatic invertebrates was found to be 13, 11, four and five in Rani pokhari, Bhajya pokhari, Na pokhari and Kamal pokhari respectively. Phylum Arthropoda was most abundant in all four ponds. The correlation of species richness with physico-chemical parameters showed a positive correlation with water temperature (r = 0.96), electric conductivity (r = 0.98), free carbon dioxide (r = 0.99) and dissolved oxygen (r = 0.51) while the negative correlation with pH (r = -0.83) and alkalinity (r = -0.83). The result obtained is valuable and useful for continuous water quality monitoring activities and the conservation of freshwater fauna in the ponds of Bhaktapur. Further detailed studies on aquatic invertebrates were highly recommended.