Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers

Determinants of immunization status of children aged 1-5 years attending a tertiary health care facility: A cross-sectional study

Background: Childhood immunization is one of the key health interventions, which is useful in decreasing the under-five mortality. There has been a recent decline in immunization coverage rates in India as shown by the National Family Health Survey 4. Objective: This study was undertaken with the objective of determining the socio-economic and demographic risk factors associated with partial immunization of children aged 1-5 years attending a tertiary care center in South India. Materials and Methods: This cross-sectional study was done at the outpatient department of Institute of Child Health and Hospital for Children, Chennai, from May 2015 to October 2015. Children aged 1-5 years attending the outpatient department were included by convenient sampling. A sample size of 1100 was calculated based on a pilot study. After obtaining parental informed consent, data were collected on immunization and socio-demographic factors, namely, gender, place of residence, parental education and occupation, family income, number of children, birth order, place of delivery, and awareness of the type of vaccines to be given. The WHO definitions were used to classify immunization status as full, partial, and delayed immunization. Univariate and multivariate logistic regression analyses were performed to determine independent risk factors for partial immunization. Results: Totally, 1104 children were included in the study. 1056 (95.7% [95% confidence interval 94.5-96.9]) were fully immunized, whereas 41 (3.7% [2.59-4.81]) were partially immunized and 7 (0.6% [0.14-1.06]) had delayed immunization. None of the children were totally unimmunized. Univariate analysis identified rural residence, home delivery, having more than 2 children, birth order more than 2, lack of maternal and paternal education beyond primary school level, and lack of awareness on immunization as significant risk factors for partial immunization. After multivariate logistic regression, rural residence, having more than 2 children, and lack of awareness on immunization emerged as independent risk factors for partial immunization. Conclusion: Residing in rural areas, having more than 2 children, and lack of awareness on immunization are significant independent risk factors for a child not being fully immunized

Biomass and Carbon Stock Estimation in Woody Grass (\u3cem\u3eDendrocalamus strictus\u3c/em\u3e L.) in Doon Valley, India

Bamboos commonly kown as woody grass are one of the most important species particularly in Asia, where it is frequently considered as the ―timber of the poor‖ (Rao et al., 1985). With about 23 genera and 136 species, India is the second largest reservoir of bamboos, next only to China (SFR, 2013 and Nath et al., 2009). Bamboos occur extensively in the managed ecosystems of India—both as plantations (and in agroforestry (scattered clumps, hedgerows on farm boundaries etc. Dendrocalamus strictus L. is most commonly found bamboo in India. It is widely distributed in dry deciduous forests and grows rapidly in all climatic conditions and occupies about 53 % of total bamboo area in India. It grows better in the drier parts and on sandstone, granite and coarse grained soils with low moisture- retaining capacity and soils with pH range 5.5–7.6. It grows more than 8 feet in 6–8 months. The species is used widely for as raw material in paper mills and also for variety of purposes such as construction, agricultural implements, musical instruments, furniture etc. The species is also suitable for reclamations of degraded and ravine lands. The accurate assessment of biomass estimates of a forest is important for many applications (Brown, 2002; Chave et al., 2004; Arora et al., 2014; Verma et al., 2014). In recent years, the carbon cycle has become an important issue in the world and plants play a major role in carbon storage. Biomass estimation enables us to estimate the amount of carbon dioxide that can be sequestered from the atmosphere. However, most of the carbon and biomass studies focus on assessing the capability of trees viz., poplar, eucalyptus, shisham, chir teak, subabul etc. The studies related to biomass and carbon stock estimation in bamboos is limited. The present study examine specifically the above ground stand biomass, biomass structure and C storage in D. strictus

Losing Control:Sleep Deprivation Impairs the Suppression of Unwanted Thoughts

Unwanted memories often enter conscious awareness when individuals confront reminders. People vary widely in their talents at suppressing such memory intrusions; however, the factors that govern suppression ability are poorly understood. We tested the hypothesis that successful memory control requires sleep. Following overnight sleep or total sleep deprivation, participants attempted to suppress intrusions of emotionally negative and neutral scenes when confronted with reminders. The sleep-deprived group experienced significantly more intrusions (unsuccessful suppressions) than the sleep group. Deficient control over intrusive thoughts had consequences: Whereas in rested participants suppression reduced behavioral and psychophysiological indices of negative affect for aversive memories, it had no such salutary effect for sleep-deprived participants. Our findings raise the possibility that sleep deprivation disrupts prefrontal control over medial temporal lobe structures that support memory and emotion. These data point to an important role of sleep disturbance in maintaining and exacerbating psychiatric conditions characterized by persistent, unwanted thoughts

Losing Control: Sleep Deprivation Impairs the Suppression of Unwanted Thoughts

Unwanted memories often enter conscious awareness when individuals confront reminders. People vary widely in their talents at suppressing such memory intrusions; however, the factors that govern suppression ability are poorly understood. We tested the hypothesis that successful memory control requires sleep. Following overnight sleep or total sleep deprivation, participants attempted to suppress intrusions of emotionally negative and neutral scenes when confronted with reminders. The sleep-deprived group experienced significantly more intrusions (unsuccessful suppressions) than the sleep group. Deficient control over intrusive thoughts had consequences: Whereas in rested participants suppression reduced behavioral and psychophysiological indices of negative affect for aversive memories, it had no such salutary effect for sleep-deprived participants. Our findings raise the possibility that sleep deprivation disrupts prefrontal control over medial temporal lobe structures that support memory and emotion. These data point to an important role of sleep disturbance in maintaining and exacerbating psychiatric conditions characterized by persistent, unwanted thoughts

An unexpected absence of queuosine modification in the tRNAs of an Escherichia coli B strain

The post-transcriptional processing of tRNAs decorates them with a number of modified bases important for their biological functions. Queuosine, found in the tRNAs with GUN anticodons (Asp, Asn, His, Tyr), is an extensively modified base whose biosynthetic pathway is still unclear. In this study, it was observed that the $tRNA^{Tyr}$ from Escherichia coli B105 (a B strain) migrated faster than that from E. coli CA274 (a K-12 strain) on acid urea gels. The organization of $tRNA^{Tyr}$ genes in E. coli B105 was found to be typical of the B strains. Subsequent analysis of $tRNA^{Tyr}$ and $tRNA^{His}$ from several strains of E. coli on acid urea gels, and modified base analysis of tRNA preparations enriched for $tRNA^{Tyr}$, showed that E. coli B105 lacked queuosine in its tRNAs. However, the lack of queuosine in tRNAs was not a common feature of all E. coli B strains. The tgt and queA genes in B105 were shown to be functional by their ability to complement tgt and queA mutant strains. These observations suggested a block at the step of the biosynthesis of $preQ_1$(or $preQ_0$) in the B105 strain. Interestingly, a multicopy vector harbouring a functional tgt gene was toxic to E. coli B105 but not to CA274. Also, in mixed cultures, E. coli B105 was readily competed out by the CA274 strain. The importance of these observations and this novel strain (E. coli B105) in unravelling the mechanism of $preQ_1$ or $preQ_0$ biosynthesis is discussed