Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study.

Background: Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown. Methods: FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria. Findings: The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group. Interpretation: With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Adherence to COVID-19 preventive measures among residents in selected townships, Yangon Region, Myanmar: a community-based cross-sectional study.

BACKGROUND: To fight the current coronavirus disease (COVID-19) pandemic, many countries have implemented various mitigation measures to contain the spread of the disease. By engaging with health service providers, the community's participation in adherence to preventive measures is certainly required in the implementation of COVID-19 mitigation strategies. Therefore, this study aimed to assess the level of adherence to COVID-19 preventive measures and its associated factors among the residents, Yangon Region, Myanmar. METHODS: A community-based cross-sectional study was carried out among 636 residents in Yangon Region, Myanmar, from October to December 2021. A multistage non-probability sampling method, purposively selected for three townships in Yangon Region and convenience sampling for 212 participants from each township, was applied and the data were collected by face-to-face interviews using structured and pretested questionnaires. Data were entered, coded, and analyzed using IBM SPSS version 25.0. Simple and multiple logistic regression analysis were performed to identify the significant variables of adherence to COVID-19 preventive measures. RESULTS: As a level of adherence to COVID-19 preventive measures, the proportion of residents who had good adherence was 39.3% (95% CI 35.5-43.2%), moderate adherence was 37.6% (95% CI 33.8-41.5%), and poor adherence was 23.1% (95% CI 19.9-26.6%). The age group of 31-40 years (AOR: 3.13, 95% CI 1.62-6.05), 30 years and younger (AOR: 3.22, 95% CI 1.75-5.92), Burmese ethnicity (AOR: 2.52, 95% CI 1.44-4.39), own business (AOR: 3.19, 95% CI 1.15-8.87), high school education level and below (AOR: 1.64, 95% CI 1.02-2.69), less than 280.90 USD of monthly family income (AOR: 1.51, 95% CI 1.01-2.29), low knowledge about COVID-19 (AOR: 1.90, 95% CI 1.26-2.88) were significantly associated with poor adherence to COVID-19 preventive measures. CONCLUSIONS: In this study, nearly one-fourth of the residents were experiencing poor adherence to COVID-19 preventive measures. Therefore, building up the risk communication through the community using widely used mainstream media, the continuation of disease surveillance and announcement of updated information or advice for the public to increase awareness towards COVID-19, and enforcement to follow the recommended directions and regulations of health institutions are vital to consider for improving the adherence to preventive measures against COVID-19 among the residents.

Modeling the effects of treatment resistance and anticholinergic burden on cognitive function domains in patients with schizophrenia.

The contribution of anticholinergic burden to cognitive function in patients with treatment resistant schizophrenia (TRS) is uncertain. This case-control study aims to comprehensively examine the association between treatment resistance and cognitive functions and the contribution of anticholinergic burden in patients with schizophrenia. Anticholinergic burden of all patients was calculated using the Anticholinergic Cognitive Burden scale. Exploratory Factor Analysis of 11 cognitive assessments identified four cognitive domains: verbal memory, attention and general cognitive functions, visual memory and processing speed, and executive function. Two structural equation models (SEM) examined the relationship of TRS and these cognitive functions with, and without considering anticholinergic burden. A total of 288 participants were included (TRS N=111, non-TRS N=177). Patients with TRS performed poorer than the non-TRS group only in the executive function domain. Anticholinergic burden contributed significantly to the attention and general cognitive functions, visual memory and processing speed, and executive function. The impact of TRS on executive function was no longer significant after adding anticholinergic burden to the SEM. Results suggested that anticholinergic burden contributes to a wide range of cognitive function impairment in patients with schizophrenia and is likely to be part of the apparent differences of cognitive function between TRS and non-TRS.

Tsukamurella Bacteremia in a Surgical Patient: Case Report and Review of the Literature.

Background: Tsukamurella species were first isolated in 1941. Since then, 48 cases of Tsukamurella bacteremia have been reported, a majority of which were immunosuppressed patients with central venous catheters.A case is described and previous cases of Tsukamurella bacteremia are reviewed. Patients and Methods: A 70-year-old total parenteral nutrition (TPN)-dependent female with recurrent enterocutaneous fistula (ECF), developed leukocytosis one week after a challenging ECF takedown. After starting broad-spectrum antibiotic agents, undergoing percutaneous drainage of intra-abdominal abscess, and subsequent repositioning of the drain, her leukocytosis resolved. Blood and peripherally inserted central catheter (PICC) cultures grew Tsukamurella spp. The patient was discharged to home with 14 days of daily 2 g ceftriaxone, with resolution of bacteremia. Conclusions: Tsukamurella spp. are a rare opportunistic pathogen predominantly affecting immunocompromised patients, with central venous catheters present in most cases. However, there have been few reported cases in immunocompetent individuals with predisposing conditions such as end-stage renal disease and uncontrolled diabetes mellitus.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator.

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

Renal relapse in children and adolescents with childhood-onset lupus nephritis: a 20-year study.

OBJECTIVES: There is little data on renal relapse in childhood-onset LN (cLN). We investigate the incidence, predictive factors and outcomes related to renal relapse. METHODS: We conducted a retrospective cohort study of all cLN diagnosed at ≤18 years between 2001-2021 to investigate the incidence and outcomes related to renal relapse. RESULTS: Ninety-five Chinese cLN patients (91% proliferative LN) were included. Induction immunosuppression was prednisolone and CYC [n = 36 (38%)] or MMF [n = 33 (35%)]. Maintenance immunosuppression was prednisolone and MMF [n = 53 (54%)] or AZA [n = 29 (31%)]. The rates of complete remission/partial remission (CR/PR) at 12 months were 78.9%/7.4%. Seventy renal relapses occurred in 39 patients over a follow-up of 10.2 years (s.d. 5.9) (0.07 episode/patient-year). Relapse-free survival was 94.7, 86.0, 80.1, 71.2, 68.3, 50.3 and 44.5% at 1, 2, 3, 4, 5, 10 and 20 years, respectively. Multivariate analysis showed that LN diagnosis <13.1 years [adjusted hazard ratio (HRadj) 2.59 995% CI 1.27, 5.29), P = 0.01], AZA maintenance [HRadj 2.20 (95% CI 1.01, 4.79), P = 0.05], PR [HRadj 3.9 (95% CI 1.03, 9.19), P = 0.01] and non-remission [HRadj 3.08 (95% CI 1.35, 11.3), P = 0.04] at 12 months were predictive of renal relapse. Renal relapse was significantly associated with advanced chronic kidney disease (stages 3-5) and end-stage kidney disease (17.9% vs 1.8%, P < 0.01). Furthermore, patients with renal relapse showed an increased incidence of infections (30.8% vs 10.7%, P = 0.02), osteopenia (38.5% vs 17.9%, P = 0.04) and hypertension (30.8% vs 7.1%, P < 0.01). CONCLUSION: Renal relapse is common among cLN, especially among young patients, and is associated with an increased incidence of morbidity and mortality. Attaining CR and the use of MMF appear to decrease the incidence of renal relapse.

Clozapine dosing patterns and clinical outcomes in patients with treatment resistant schizophrenia.

Clozapine is the only medication found to be effective for patients with treatment resistant schizophrenia-spectrum disorders (TRS) and its prescription patterns may impact on their outcomes. The study aims to explore the impact of clozapine dosing frequency, dose level and presence of pharmacological augmentation on the clinical, social and cognitive outcomes in patients with TRS. Patients with TRS and on clozapine were interviewed. Daily defined dose (DDD) and anticholinergic burden were calculated. Patients were categorized in three ways: the single daily dose (SDD) and multiple daily dose (MDD), ≤300 mg/day (LD) and >300 mg/day (HD) of clozapine, and clozapine monotherapy (MT) and augmentation therapy (AT). The impact of these clozapine prescription patterns and their interaction on patient outcomes were examined with ANOVA. Of 124 patients on clozapine, 98 patients (79%) had SDD, 59 patients (47.6%) received LD, and 58 patients (46.8%) had MT. Patients in the LD group had significantly better cognitive functions. Though no significant effect of clozapine dosing frequency on outcomes, among patients on LD, those on MDD had better processing speed, short-term and visual memory. Patients with MT had better motivation. Among patients on HD, those with MT had better motivation and vocational functioning. These results provide guidance to the clozapine prescription in a naturalistic setting to achieve optimizing outcomes for patients with TRS in social and cognitive functions. Further longitudinal studies are needed to verify the results.

Long-Term Outcomes of Children and Adolescents With Biopsy-Proven Childhood-Onset Lupus Nephritis.

Introduction: Long-term data pertaining to childhood-onset lupus nephritis (cLN) remain extremely scarce. Methods: We conducted a retrospective cohort study of biopsy-proven cLN with onset age <18 years diagnosed from 2001 to 2020 to ascertain the long-term patient and kidney survival rates, and the incidence of advanced chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR] <60 ml/min per 1.73 m2). Results: A total of 92 subjects (78 female; age 13.7 ± 3.3 years; all Chinese) were included, with follow-up duration of 10.3 years (interquartile range, 5.8-15.9). Of these, 83 children (90%) had proliferative lupus nephritis (LN) (Class III/IV ± V). Mycophenolate was used for induction in 36%, whereas 34% received cyclophosphamide (CYC); 55% received mycophenolate as maintenance immunosuppression. The rates of complete remission (CR) and partial remission (PR) at 6 months and 12 months, respectively, were 65% and 20% and 78% and 8%. Two patients died (mortality rate 2.1/1000 patient-years), with a standardized mortality ratio of 22.3. Three patients (3.2%) developed end-stage kidney disease (ESKD), and advanced CKD occurred in 5 patients (5.4%). Survival rates without advanced CKD, ESKD, or death were 96.7%, 94.2%, 92.7%, 83.2% and 83.2% at 1 year, 5 years, 10 years, 15 years, and 20 years, respectively. Multivariate analysis revealed that severe kidney failure necessitating dialysis at presentation (adjusted hazard ratio 37.7, 95% confidence interval [CI] 4.0-355.6, P = 0.002), nonresponse (NR) after 12 months of treatment (adjusted hazard ratio 11.2, 95% CI 2.3-54.9, P = 0.003), and multiple nephritis flares (adjusted hazard ratio 2.6, 95% CI 1.1-6.2, P = 0.03) were predictive of advanced CKD, ESKD, or death. Other adverse outcomes included infections (2.9 episodes/100 patient-years), osteopenia (32%), hypertension (17%), short stature (14%), and avascular necrosis (7%). Conclusion: The long-term outcomes of cLN appeared to have improved in the present era with effective immunosuppression, cautious drug tapering, and assurance of medication adherence. There is still an unacceptably high prevalence of adverse outcomes.

Impact of containment measures on community mobility, daily confirmed cases, and mortality in the third wave of COVID-19 epidemic in Myanmar.

In Myanmar, third wave of COVID-19 epidemic began with a surge of confirmed cases in the last week of May 2021. The laboratory-confirmed cases and deaths distinctly increased within 9 weeks. The government and the Ministry of Health adopted containment measures to flatten the peak of the epidemic and to suppress the disease transmission. The strictly containment measures: stay-at-home restrictions, school closure, and office closure have reduced the community mobility, confirmed cases and mortality. Therefore, the timely containment measures implemented by the government were important to reduce the transmission as observed in the third wave of COVID-19 epidemic in Myanmar.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Sex differences in symptom severity, cognition and psychosocial functioning among individuals with at-risk mental state for psychosis.

AIM: Sex differences are well documented in schizophrenia, but have been much less studied in at-risk mental state (ARMS) for psychosis. We aimed to examine sex differences in symptomatology, cognition, social and role functioning in individuals with ARMS, with specific focus on clarifying relationships between sex, negative symptoms and functioning. METHODS: One hundred and seventy-seven Chinese participants aged 15-40 years with ARMS were recruited from a specialized early intervention service in Hong Kong. ARMS status was verified by Comprehensive Assessment of At-Risk Mental State. Assessments encompassing symptom profiles, a brief battery of cognitive tests and social and role functioning were conducted. Brief Negative Symptom Scale was adapted to measure negative symptoms at the level of five core domains. RESULTS: Males with ARMS exhibited significantly poorer social functioning and more severe asociality of negative symptoms than female counterparts. Mediation analysis revealed that sex difference in social functioning became statistically insignificant when asocality was included in the model, indicating that asociality mediated the relationship between sex and social functioning. No sex differences were observed in other core domains of negative symptoms, other symptom dimensions, cognitive measures and role functioning. CONCLUSIONS: This study suggests that sex differences in ARMS may be less pronounced that those observed in established psychotic disorders. Our findings of differential pattern of asociality between sexes and its mediating role on sex difference in social functioning underscore the importance in investigating negative symptoms at a separable domain-level. Further research is required to identify sex-specific predictors of longitudinal outcomes in at-risk populations.

Psychiatric comorbidity in individuals at-risk for psychosis: Relationships with symptoms, cognition and psychosocial functioning.

AIM: Psychiatric comorbidity frequently occurs with at-risk mental state (ARMS) for psychosis. Its relationships with psychopathology, cognition and functioning, however, remain to be further clarified. We aimed to examine prevalence and correlates of psychiatric comorbidity, and its associations with psychosocial functioning and subjective quality-of-life (QoL) in a representative sample of Chinese ARMS individuals. METHODS: One hundred ten help-seeking participants aged 15 to 40 years with ARMS were recruited from a specialized early psychosis service in Hong Kong. ARMS status was verified by comprehensive assessment of at-risk mental state (CAARMS). Comorbid Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition non-psychotic psychiatric disorders at baseline were ascertained using diagnostic interview and medical record review. Assessments encompassing symptom profiles, psychosocial functioning, subjective QoL and a brief cognitive battery were conducted. RESULTS: Forty-nine (44.5%) ARMS participants were diagnosed as having comorbid non-psychotic psychiatric disorders at baseline, primarily depressive and anxiety disorders. Binary multiple logistic regression analysis revealed that female gender, more severe depressive symptoms, higher suicidality and poorer global cognitive functioning were independently associated with comorbid diagnosis status. ARMS participants with psychiatric comorbidity displayed significantly more limited extended social networks and poorer subjective QoL than those without psychiatric comorbidity. CONCLUSION: Comorbid disorders were frequently observed in Chinese ARMS individuals, and were linked to poorer cognition and higher suicide risk. Our findings underscore a potential critical role of psychiatric comorbidity in determining social functioning and subjective QoL in at-risk individuals. Further longitudinal research is required to clarify trajectories of comorbid disorder status and its prospective impact on clinical and functional outcomes in ARMS populations.

Detection of waste dumping locations in landfill using multi-temporal Landsat thermal images.

The disposal of solid waste in a conventional landfill is inevitably associated with potential adverse environmental impacts, resulting in the migration of landfill gas and offensive odors on the surrounding areas. In addition to the obnoxious fumes and hazardous leachate, heat generation is continuously observed within the landfill during the aerobic and anaerobic phases. Despite the negative impacts, such "heat generation" phenomenon can turn into valuable information to aid in detecting unauthorized landfill activities and tracing unrecorded dumping regions. The spatial distribution of waste buried under the ground can be approximated and revealed through measuring the ground surface heat flux. In this study, we demonstrate how to utilize thermal remote sensing techniques to measure the land surface temperature (LST), which can aid in outlining the waste dumping regions within a landfill. The Jeleeb Al-Shuyoukh landfill, located in Kuwait, was used to demonstrate the proposed method, where the record of the exact dumping location was missing during the Gulf war. Ten-year Landsat Thematic Mapper(TM)/Enhanced Thematic Mapper Plus (ETM+) images (1985-1994) were acquired and processed in order to compute the LST within the landfill. Through combining the multi-temporal LST contours and overlay analysis, the most probable dumping regions within the landfill were outlined. A probability map was created to indicate the possible location of waste dumping within the studied landfill. With reference to the 50 boreholes drilled by the Environmental Protection Authority of Kuwait, our results derived during the summer and winter seasons both yielded an overall accuracy of 72%.

Oral D-galactose supplementation in PGM1-CDG.

PurposePhosphoglucomutase-1 deficiency is a subtype of congenital disorders of glycosylation (PGM1-CDG). Previous casereports in PGM1-CDG patients receiving oral D-galactose (D-gal) showed clinical improvement. So far no systematic in vitro and clinical studies have assessed safety and benefits of D-gal supplementation. In a prospective pilot study, we evaluated the effects of oral D-gal in nine patients.MethodsD-gal supplementation was increased to 1.5 g/kg/day (maximum 50 g/day) in three increments over 18 weeks. Laboratory studies were performed before and during treatment to monitor safety and effect on serum transferrin-glycosylation, coagulation, and liver and endocrine function. Additionally, the effect of D-gal on cellular glycosylation was characterized in vitro.ResultsEight patients were compliant with D-gal supplementation. No adverse effects were reported. Abnormal baseline results (alanine transaminase, aspartate transaminase, activated partial thromboplastin time) improved or normalized already using 1 g/kg/day D-gal. Antithrombin-III levels and transferrin-glycosylation showed significant improvement, and increase in galactosylation and whole glycan content. In vitro studies before treatment showed N-glycan hyposialylation, altered O-linked glycans, abnormal lipid-linked oligosaccharide profile, and abnormal nucleotide sugars in patient fibroblasts. Most cellular abnormalities improved or normalized following D-gal treatment. D-gal increased both UDP-Glc and UDP-Gal levels and improved lipid-linked oligosaccharide fractions in concert with improved glycosylation in PGM1-CDG.ConclusionOral D-gal supplementation is a safe and effective treatment for PGM1-CDG in this pilot study. Transferrin glycosylation and ATIII levels were useful trial end points. Larger, longer-duration trials are ongoing.

Defining the Phenotype and Assessing Severity in Phosphoglucomutase-1 Deficiency.

OBJECTIVE: To define phenotypic groups and identify predictors of disease severity in patients with phosphoglucomutase-1 deficiency (PGM1-CDG). STUDY DESIGN: We evaluated 27 patients with PGM1-CDG who were divided into 3 phenotypic groups, and group assignment was validated by a scoring system, the Tulane PGM1-CDG Rating Scale (TPCRS). This scale evaluates measurable clinical features of PGM1-CDG. We examined the relationship between genotype, enzyme activity, and TPCRS score by using regression analysis. Associations between the most common clinical features and disease severity were evaluated by principal component analysis. RESULTS: We found a statistically significant stratification of the TPCRS scores among the phenotypic groups (P < .001). Regression analysis showed that there is no significant correlation between genotype, enzyme activity, and TPCRS score. Principal component analysis identified 5 variables that contributed to 54% variance in the cohort and are predictive of disease severity: congenital malformation, cardiac involvement, endocrine deficiency, myopathy, and growth. CONCLUSIONS: We established a scoring algorithm to reliably evaluate disease severity in patients with PGM1-CDG on the basis of their clinical history and presentation. We also identified 5 clinical features that are predictors of disease severity; 2 of these features can be evaluated by physical examination, without the need for specific diagnostic testing and thus allow for rapid assessment and initiation of therapy.

Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites.

This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites' land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring.

Cytotoxic activity of kenaf (Hibiscus cannabinus L.) seed extract and oil against human cancer cell lines

Objective: To examine the cytotoxic properties of both the kenaf (Hibiscus cannabinus L.) seed extract and kenaf seed oil on human cervical cancer, human breast cancer, human colon cancer and human lung cancer cell lines.Methods:kenaf seed oil on human cancer cell lines was evaluated by using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and sulforhodamine B assays. Cell morphological changes were observed by using an inverted light microscope.Results:The in vitro cytotoxic activity of the kenaf (Hibiscus cannabinus L.) seed extract and cancer cell lines. Morphological alterations in the cell lines after KSE and KSO treatment were observed. KSE and KSO possessed effective cytotoxic activities against all the cell lines been selected.Conclusions:KSE and KSO could be potential sources of natural anti-cancer agents. Further The kenaf seed extract (KSE) exhibited a lower IC50 than kenaf seed oil (KSO) in all of the investigations on using kenaf seeds for anti-proliferative properties are warranted.

Mutational analysis supports a core role for Drosophila α-catenin in adherens junction function.

α-catenin associates the cadherin-catenin complex with the actin cytoskeleton. α-catenin binds to ß-catenin, which links it to the cadherin cytoplasmic tail, and F-actin, but also to a multitude of actin-associated proteins. These interactions suggest a highly complex cadherin-actin interface. Moreover, mammalian αE-catenin has been implicated in a cadherin-independent cytoplasmic function in Arp2/3-dependent actin regulation, and in cell signaling. The function and regulation of individual molecular interactions of α-catenin, in particular during development, are not well understood. We have generated mutations in Drosophila α-Catenin (α-Cat) to investigate α-Catenin function in this model, and to establish a setup for testing α-Catenin-related constructs in α-Cat-null mutant cells in vivo. Our analysis of α-Cat mutants in embryogenesis, imaginal discs and oogenesis reveals defects consistent with a loss of cadherin function. Compromising components of the Arp2/3 complex or its regulator SCAR ameliorate the α-Cat loss-of-function phenotype in embryos but not in ovaries, suggesting negative regulatory interactions between α-Catenin and the Arp2/3 complex in some tissues. We also show that the α-Cat mutant phenotype can be rescued by the expression of a DE-cadherin::α-Catenin fusion protein, which argues against an essential cytosolic, cadherin-independent role of Drosophila α-Catenin.

Geometric calibration and radiometric correction of LiDAR data and their impact on the quality of derived products.

LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data.