The Sentinel: A proposed potential intruder alert system based on human behaviours

Petty crimes such as robbery and burglary are common in Malaysia, especially in urban areas and major cities. There are many reports of such cases happening right in front of the victim’s premise, posing a safety concern where one can’t even be safe around his/her home.Therefore, this paper proposes a Potential Intruder Alert System based on Human Behaviors (The Sentinel) utilizing image processing techniques and sophisticated machine learning to identify suspicious human behaviors.The Sentinel will then alert the users should the system detect any suspicious movements or threats lurking outside the house

Sleep problems in children with autism spectrum disorder in Hong Kong: a cross-sectional study

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder with a growing prevalence of sleep problems associated with significant behavioral problems and more severe autism clinical presentation. Little is known about the relationships between autism traits and sleep problems in Hong Kong. Therefore, this study aimed to examine whether children with autism have increased sleep problems than non-autistic children in Hong Kong. The secondary objective was to examine the factors associated with sleep problems in an autism clinical sample.MethodsThis cross-sectional study recruited 135 children with autism and 102 with the same age range of non-autistic children, aged between 6 and 12 years. Both groups were screened and compared on their sleep behaviors using the Children's Sleep Habits Questionnaire (CSHQ).ResultsChildren with autism had significantly more sleep problems than non-autistic children [t(226.73) = 6.20, p < 0.001]. Bed -sharing [beta = 0.25, t(165) = 2.75, p = 0.07] and maternal age at birth [beta = 0.15, t(165) = 2.05, p = 0.043] were significant factors associated with CSHQ score on the top of autism traits. Stepwise linear regression modeling identified that only separation anxiety disorder (beta = 4.83, t = 2.40, p = 0.019) best-predicted CSHQ.ConclusionIn summary, autistic children suffered from significantly more sleep problems and co-occurring separation anxiety disorder brings greater sleep problems as compared to non-autistic children. Clinicians should be more aware of sleep problems to provide more effective treatments to children with autism

MicroRNA profiling study reveals MIR-150 in association with metastasis in nasopharyngeal carcinoma

© 2017 The Author(s). MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in pathogenesis of human cancers. Several miRNAs have been shown to involve in nasopharyngeal carcinoma (NPC) pathogenesis through alteration of gene networks. A global view of the miRNA expression profile of clinical specimens would be the best way to screen out the possible miRNA candidates that may be involved in disease pathogenesis. In this study, we investigated the expression profiles of miRNA in formalin-fixed paraffin-embedded tissues from patients with undifferentiated NPC versus non-NPC controls using a miRNA real-time PCR platform, which covered a total of 95 cancer-related miRNAs. Hierarchical cluster analysis revealed that NPC and non-NPC controls were clearly segregated. Promisingly, 10 miRNA candidates were differentially expressed. Among them, 9 miRNAs were significantly up-regulated of which miR-205 and miR-196a showed the most up-regulated in NPC with the highest incidence percentage of 94.1% and 88.2%, respectively, while the unique down-regulated miR-150 was further validated in patient sera. Finally, the in vitro gain-of-function and loss-of-function assays revealed that miR-150 can modulate the epithelial-mesenchymal-transition property in NPC/HK-1 cells and led to the cell motility and invasion. miR-150 may be a potential biomarker for NPC and plays a critical role in NPC tumourigenesis.Link_to_subscribed_fulltex

Impact and relationship of anterior commissure and time-dose factor on the local control of T1N0 glottic cancer treated by 6 MV photons

<p>Abstract</p> <p>Background</p> <p>To evaluate prognostic factors that may influence local control (LC) of T1N0 glottic cancer treated by primary radiotherapy (RT) with 6 MV photons.</p> <p>Methods</p> <p>We retrospectively reviewed the medical records of 433 consecutive patients with T1N0 glottic cancer treated between 1983 and 2005 by RT in our institution. All patients were treated with 6 MV photons. One hundred and seventy seven (41%) patients received 52.5 Gy in 23 fractions with 2.5 Gy/fraction, and 256 (59%) patients received 66 Gy in 33 fractions with 2 Gy/fraction.</p> <p>Results</p> <p>The median follow-up time was 10.5 years. The 10-year LC rates were 91% and 87% for T1a and T1b respectively. Multivariate analysis showed LC rate was adversely affected by poorly differentiated histology (Hazard Ratio [HR]: 7.5, <it>p </it>= 0.035); involvement of anterior commissure (HR: 2.34, <it>p </it>= 0.011); fraction size of 2.0 Gy (HR: 2.17, <it>p </it>= 0.035) and tumor biologically effective dose (BED) < 65 Gy₁₅(HR: 3.38, <it>p </it>= 0.017).</p> <p>Conclusions</p> <p>The negative impact of anterior commissure involvement could be overcome by delivering a higher tumor BED through using fraction size of > 2.0 Gy. We recommend that fraction size > 2.0 Gy should be utilized, for radiation schedules with five daily fractions each week.</p

Método híbrido para categorización de texto basado en aprendizaje y reglas

En este artículo se presenta un nuevo método híbrido de categorización automática de texto, que combina un algoritmo de aprendizaje computacional, que permite construir un modelo base de clasificación sin mucho esfuerzo a partir de un corpus etiquetado, con un sistema basado en reglas en cascada que se emplea para filtrar y reordenar los resultados de dicho modelo base. El modelo puede afinarse añadiendo reglas específicas para aquellas categorías difíciles que no se han entrenado de forma satisfactoria. Se describe una implementación realizada mediante el algoritmo kNN y un lenguaje básico de reglas basado en listas de términos que aparecen en el texto a clasificar. El sistema se ha evaluado en diferentes escenarios incluyendo el corpus de noticias Reuters-21578 para comparación con otros enfoques, y los modelos IPTC y EUROVOC. Los resultados demuestran que el sistema obtiene una precisión y cobertura comparables con las de los mejores métodos del estado del arte

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Protein Tyrosine Phosphatase 1B Inhibitors from the Roots of Cudrania tricuspidata

A chemical investigation of the methanol extract from the roots of Cudrania tricuspidata resulted in the isolation of 16 compounds, including prenylated xanthones 1–9 and flavonoids 10–16. Their structures were identified by NMR spectroscopy and mass spectrometry and comparisons with published data. Compounds 1–9 and 13–16 significantly inhibited PTP1B activity in a dose dependent manner, with IC50 values ranging from 1.9–13.6 μM. Prenylated xanthones showed stronger PTP1B inhibitory effects than the flavonoids, suggesting that they may be promising targets for the future discovery of novel PTP1B inhibitors. Furthermore, kinetic analyses indicated that compounds 1 and 13 inhibited PTP1B in a noncompetitive manner; therefore, they may be potential lead compounds in the development of anti-obesity and -diabetic agents

Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability