Impact of Interest Rate on Investment in Nigeria

The study investigates the Impact of Interest rate on Investment in Nigeria.  Multipleregressionswas used as the statistical method for the study which reveals that high interest rate negatively affect investment.  In line with the findings, the study made the following suggestions; that relevant monetary authority should evolve policies that will encourage savings and reduce prime lending rate to genuine investors, among others.   It further recommend that since there is a direct relationship between income and savings, relevant authority should consider economic policies that will increase income level of the people in order to mobilize investment. Keywords: Interest rate, Investment, lending rate

Government Expenditure, Foreign Direct Investment and Economic Growth in Nigeria

Government expenditure and Foreign Direct Investment (FDI) are vital macroeconomic variables of any economy as they are strong propellant of economic growth. The need to control and monitoring government spending and the FDI so as to achieve a steady economic growth necessitated this study. The study seeks to determine the impact of government expenditure and FDI on the Nigeria economic growth. A multiple regression analysis was used to test the relationship between government expenditure (capital and recurrent expenditure) and FDI as the explanatory variables on GDP (proxy for economic growth) as the dependent variable. Our result revealed that the explanatory variables: CEXP, REXP and FDI had significant relationship with economic growth. However CEXP did not conform to expectation. Some recommendations such as a thorough and accountable management of capital and recurrent expenditures in Nigeria, adequate planning, an effective macroeconomic framework and conducive economic environment to encourage foreign direct investment is require

Eye-tracking the time‐course of novel word learning and lexical competition in adults and children

Lexical competition is a hallmark of proficient, automatic word recognition. Previous research suggests that there is a delay before a new spoken word becomes engaged in this process, with sleep playing an important role. However, data from one method--the visual world paradigm--consistently show competition without a delay. We trained 42 adults and 40 children (aged 7-8) on novel word-object pairings, and employed this paradigm to measure the time-course of lexical competition. Fixations to novel objects upon hearing existing words (e.g., looks to the novel object biscal upon hearing “click on the biscuit”) were compared to fixations on untrained objects. Novel word-object pairings learned immediately before testing and those learned the previous day exhibited significant competition effects, with stronger competition for the previous day pairings for children but not adults. Crucially, this competition effect was significantly smaller for novel than existing competitors (e.g., looks to candy upon hearing “click on the candle”), suggesting that novel items may not compete for recognition like fully-fledged lexical items, even after 24 hours. Explicit memory (cued recall) was superior for words learned the day before testing, particularly for children; this effect (but not the lexical competition effects) correlated with sleep-spindle density. Together, the results suggest that different aspects of new word learning follow different time courses: visual world competition effects can emerge swiftly, but are qualitatively different from those observed with established words, and are less reliant upon sleep. Furthermore, the findings fit with the view that word learning earlier in development is boosted by sleep to a greater degree

Unbalanced synaptic inhibition can create intensity-tuned auditory cortex neurons

Intensity-tuned auditory cortex neurons may be formed by intensity-tuned synaptic excitation. Synaptic inhibition has also been shown to enhance, and possibly even create intensity-tuned neurons. Here we show, using in vivo whole cell recordings in pentobarbital-anesthetized rats, that some intensity-tuned neurons are indeed created solely through disproportionally large inhibition at high intensities, without any intensity-tuned excitation. Since inhibition is essentially cortical in origin, these neurons provide examples of auditory feature-selectivity arising de novo at the cortex.Comment: 22 pages, 5 figure

Second Language Processing Shows Increased Native-Like Neural Responses after Months of No Exposure

Although learning a second language (L2) as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2—particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP) study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes—including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research program with potentially important consequences for second language acquisition and related fields

Palladium–mediated organofluorine chemistry

Producción CientíficaThe substitution of fluorine for hydrogen in a molecule may result in profound changes in its properties and behaviour. Fluorine does not introduce special steric constraints since the F atom has a small size. However, the changes in bond polarity and the possibility of forming hydrogen bonds with other hydrogen donor fragments in the same or other molecules, may change the solubility and physical properties of the fluorinated compound when compared to the non-fluorinated one. Fluorine forms strong bonds to other elements and this ensures a good chemical stability. Altogether, fluorinated compounds are very attractive in materials chemistry and in medicinal chemistry, where many biologically active molecules and pharmaceuticals do contain fluorine in their structure and this has been shown to be essential for their activityJunta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA302U13)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA256U13

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval

Within the last 15 years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or ameliorate multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current approaches: expressiveness and flexibility are obtained to the expense of robustness and vice versa; available multimodal sources of information are little exploited; modeling multi-faceted and strongly interrelated musical information is limited with current architectures; models are typically restricted to short-term analysis that does not account for the hierarchical temporal structure of musical signals. Dealing with music data requires the ability to tackle both uncertainty and complex relational structure at multiple levels of representation. Traditional approaches have generally treated these two aspects separately, probability and learning being the usual way to represent uncertainty in knowledge, while logical representation being the usual way to represent knowledge and complex relational information. We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic and (deep) learning. We show that existing approaches used in MIR find powerful extensions and unifications in StarAI, and we explain why we think it is time to consider the new perspectives offered by this promising research field