Transformation of speech sequences in human sensorimotor circuits.

After we listen to a series of words, we can silently replay them in our mind. Does this mental replay involve a reactivation of our original perceptual dynamics? We recorded electrocorticographic (ECoG) activity across the lateral cerebral cortex as people heard and then mentally rehearsed spoken sentences. For each region, we tested whether silent rehearsal of sentences involved reactivation of sentence-specific representations established during perception or transformation to a distinct representation. In sensorimotor and premotor cortex, we observed reliable and temporally precise responses to speech; these patterns transformed to distinct sentence-specific representations during mental rehearsal. In contrast, we observed less reliable and less temporally precise responses in prefrontal and temporoparietal cortex; these higher-order representations, which were sensitive to sentence semantics, were shared across perception and rehearsal of the same sentence. The mental rehearsal of natural speech involves the transformation of stimulus-locked speech representations in sensorimotor and premotor cortex, combined with diffuse reactivation of higher-order semantic representations.

Green ultrasonication-assisted extraction of microalgae Chlorella sp. for polysaturated fatty acid (PUFA) rich lipid extract using alternative solvent mixture.

Conventionally, microalgal lipid extraction uses volatile organic compounds as an extraction solvent. However, these solvents are harmful to human and environmental health. Therefore, this study evaluated the feasibility of alternative green solvents, namely, ethanol, dimethyl carbonate (DMC), cyclopentyl methyl ether (CPME), and 2-methyltetrahydrofuran (2-MeTHF) in lipid extraction from Chlorella sp. via ultrasound-assisted extraction (UAE). This study indicated that extraction parameters, such as ethanol-to-2-MeTHF ratio, solvent-to-biomass ratio, temperature, and time, significantly affected the crude lipid yield (P < 0.05). The highest crude lipid yield of 25.05 ± 0.924% was achieved using ethanol-2-MeTHF mixture (2:1, v/v) with a solvent-to-biomass ratio of 20:1 (v/w) at 60 °C for 25 min accompanying 100 W and 40 kHz. Ethanol-2-MeTHF-extracted lipids showed dominance in linoleic acid, α-linolenic acid, and palmitic acid. Overall this findings supported UAE using ethanol and 2-MeTHF as extraction solvents is a promising green alternative to conventional solvent extraction of lipids from microalgae.

Smoothed Quantile Regression with Large-Scale Inference.

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. This paper focuses on statistical inference for quantile regression in the "increasing dimension" regime. We provide a comprehensive analysis of a convolution smoothed approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as conquer, turns the non-differentiable check function into a twice-differentiable, convex and locally strongly convex surrogate, which admits fast and scalable gradient-based algorithms to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on dimensionality than needed for conventional quantile regression. The validity of multiplier bootstrap is also provided. Numerical studies confirm conquer as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the R package conquer.

Analysis of microalgal growth kinetic model and carbohydrate biosynthesis cultivated using agro-industrial waste residuals as carbon source.

Microalgal carbohydrate is considered one of the potential feedstock for biofuel produced via the bioconversion process. However, the current cultivation process using commercial medium exhibited low biomass production and its carbohydrate productivity which become a bottleneck for sustainable microalgal-carbohydrate-based biofuel production. Thus, the objective of this study is to assess the utilization of industrial waste including molasses and glycerol on the Halochlorella rubescens and Tetraselmis suecica growth as well as its carbohydrate content under different cultivation modes such as autotrophic, heterotrophic and photoheterotrophic conditions. From this study, the highest maximum biomass of H. rubenscens and T. suecica of 0.653 ± 0.009 and 0.669 ± 0.01gL-1 were obtained when the cultivation was performed under photoheterotrophic using molasses. High carbohydrate content of H. rubescens and T. seucica of 56.81 ± 0.39% and 71.52 ± 0.03% with glucose represent the dominant sugar was observed under this condition. The growth kinetic model of the analysis indicated that Huang and Gompertz Models described well the growth of H. rubescens and T. suecica under photoheteroptroph condition with a high significant R2 of 0.99. The information generated could be beneficial for the future development of low-cost microalgal cultivation media formulation for future microalgal carbohydrate-based products such as bioethanol.

1-Hydroxy-2(1H)-pyridinone-Based Chelators with Potential Catechol O-Methyl Transferase Inhibition and Neurorescue Dual Action against Parkinson's Disease.

Two analogues of tolcapone where the nitrocatechol group has been replaced by a 1-hydroxy-2(1H)-pyridinone have been designed and synthesised. These compounds are expected to have a dual mode of action both beneficial against Parkinson's disease: they are designed to be inhibitors of catechol O-methyl transferase, which contribute to the reduction of dopamine in the brain, and to protect neurons against oxidative damage. To assess whether these compounds are worthy of biological assessment to demonstrate these effects, measurement of their pKa and stability constants for Fe(III), in silico modelling of their potential to inhibit COMT and blood-brain barrier scoring were performed. These results demonstrate that the compounds may indeed have the desired properties, indicating they are indeed promising candidates for further evaluation.

Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks.

Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surroundings. To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story. The main challenge with this approach is that the measured signal consists of both the stimulus-induced signal, as well as intrinsic-neural and nonneuronal signals. By exploiting the experimental design, we propose to estimate stimulus-locked brain networks by treating nonstimulus-induced signals as nuisance parameters. In many neuroscience applications, it is often important to identify brain regions that are connected to many other brain regions during cognitive process. We propose an inferential method to test whether the maximum degree of the estimated network is larger than a prespecific number. We prove that the type I error can be controlled and that the power increases to one asymptotically. Simulation studies are conducted to assess the performance of our method. Finally, we analyze a functional magnetic resonance imaging dataset obtained under the Sherlock Holmes movie stimuli.

Propagation of Information Along the Cortical Hierarchy as a Function of Attention While Reading and Listening to Stories.

How does attention route information from sensory to high-order areas as a function of task, within the relatively fixed topology of the brain? In this study, participants were simultaneously presented with 2 unrelated stories-one spoken and one written-and asked to attend one while ignoring the other. We used fMRI and a novel intersubject correlation analysis to track the spread of information along the processing hierarchy as a function of task. Processing the unattended spoken (written) information was confined to auditory (visual) cortices. In contrast, attending to the spoken (written) story enhanced the stimulus-selective responses in sensory regions and allowed it to spread into higher-order areas. Surprisingly, we found that the story-specific spoken (written) responses for the attended story also reached secondary visual (auditory) regions of the unattended sensory modality. These results demonstrate how attention enhances the processing of attended input and allows it to propagate across brain areas.

The fitness consequences of aneuploidy are driven by condition-dependent gene effects.

Aneuploidy is a hallmark of tumor cells, and yet the precise relationship between aneuploidy and a cell's proliferative ability, or cellular fitness, has remained elusive. In this study, we have combined a detailed analysis of aneuploid clones isolated from laboratory-evolved populations of Saccharomyces cerevisiae with a systematic, genome-wide screen for the fitness effects of telomeric amplifications to address the relationship between aneuploidy and cellular fitness. We found that aneuploid clones rise to high population frequencies in nutrient-limited evolution experiments and show increased fitness relative to wild type. Direct competition experiments confirmed that three out of four aneuploid events isolated from evolved populations were themselves sufficient to improve fitness. To expand the scope beyond this small number of exemplars, we created a genome-wide collection of >1,800 diploid yeast strains, each containing a different telomeric amplicon (Tamp), ranging in size from 0.4 to 1,000 kb. Using pooled competition experiments in nutrient-limited chemostats followed by high-throughput sequencing of strain-identifying barcodes, we determined the fitness effects of these >1,800 Tamps under three different conditions. Our data revealed that the fitness landscape explored by telomeric amplifications is much broader than that explored by single-gene amplifications. As also observed in the evolved clones, we found the fitness effects of most Tamps to be condition specific, with a minority showing common effects in all three conditions. By integrating our data with previous work that examined the fitness effects of single-gene amplifications genome-wide, we found that a small number of genes within each Tamp are centrally responsible for each Tamp's fitness effects. Our genome-wide Tamp screen confirmed that telomeric amplifications identified in laboratory-evolved populations generally increased fitness. Our results show that Tamps are mutations that produce large, typically condition-dependent changes in fitness that are important drivers of increased fitness in asexually evolving populations.

Assessing Airflow Limitation among Smokers in a Primary Care Setting.

BACKGROUND: Many smokers have undiagnosed chronic obstructive pulmonary disease (COPD), and yet screening for COPD is not recommended. Smokers who know that they have airflow limitation are more likely to quit smoking. This study aims to identify the prevalence and predictors of airflow limitation among smokers in primary care. METHODS: Current smokers ≥ 40 years old who were asymptomatic clinic attendees in a primary care setting were recruited consecutively for two months. We used a two-step strategy. Step 1: participants filled in a questionnaire. Step 2: Assessment of airflow limitation using a pocket spirometer. Multiple logistic regression was utilised to determine the best risk predictors for airflow limitation. RESULTS: Three hundred participants were recruited. Mean age was 58.35 (SD 10.30) years old and mean smoking history was 34.56 pack-years (SD 25.23). One in two smokers were found to have airflow limitation; the predictors were Indian ethnicity, prolonged smoking pack-year history and Lung Function Questionnaire score ≤ 18. Readiness to quit smoking and the awareness of COPD were low. CONCLUSIONS: The high prevalence of airflow limitation and low readiness to quit smoking imply urgency with helping smokers to quit smoking. Identifying airflow limitation as an additional motivator for smoking cessation intervention may be considered. A two-step case-finding method is potentially feasible.

The cluster graphical lasso for improved estimation of Gaussian graphical models.

The task of estimating a Gaussian graphical model in the high-dimensional setting is considered. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to a lasso penalty, is a well-studied approach for this task. A surprising connection between the graphical lasso and hierarchical clustering is introduced: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) a penalized log likelihood is maximized on the subset of variables within each connected component. Thus, the graphical lasso determines the connected components of the estimated network via single linkage clustering. The single linkage clustering is known to perform poorly in certain finite-sample settings. Therefore, the cluster graphical lasso, which involves clustering the features using an alternative to single linkage clustering, and then performing the graphical lasso on the subset of variables within each cluster, is proposed. Model selection consistency for this technique is established, and its improved performance relative to the graphical lasso is demonstrated in a simulation study, as well as in applications to a university webpage and a gene expression data sets.

Sparse Reduced Rank Huber Regression in High Dimensions.

We propose a sparse reduced rank Huber regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank- and sparsity-constrained nonconvex optimization problem, which is then solved using a block coordinate descent and an alternating direction method of multipliers algorithm. We establish nonasymptotic estimation error bounds under both Frobenius and nuclear norms in the high-dimensional setting. This is a major contribution over existing results in reduced rank regression, which mainly focus on rank selection and prediction consistency. Our theoretical results quantify the tradeoff between heavy-tailedness of the random noise and statistical bias. For random noise with bounded (1+δ) th moment with δ∈(0,1), the rate of convergence is a function of δ, and is slower than the sub-Gaussian-type deviation bounds; for random noise with bounded second moment, we obtain a rate of convergence as if sub-Gaussian noise were assumed. We illustrate the performance of the proposed method via extensive numerical studies and a data application. Supplementary materials for this article are available online.

Mobility and Hip Function Among Geriatric Patients With Displaced Neck of Femur Fractures Treated With Arthroplasty.

Background: Neck of femur fractures result in impaired function for older people. Despite surgery, many patients experience a decrease in functional level and poorer health status after the injury. The objectives of this study were (1) to determine the short-term mobility and hip function of geriatric patients who underwent hip replacement surgery for a displaced neck of femur fracture in our local population and (2) to identify factors which affect the functional outcome of these patients. Methods: Patients aged 60 years and above, who were admitted for neck of femur fracture from January 2017 to December 2020, and treated surgically with arthroplasty, were included. Information on patient demography, comorbidities, perioperative data, mobility, hip function and complications were retrospectively collected. Outcome measures used were independent ambulation and recovery of pre-fracture mobility at 1 year after surgery while hip function was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total, pain, stiffness, and physical function scores. Factors associated with these outcomes were analyzed. Results: 168 patients with a mean age of 75.2 (SD 8.4) years met the inclusion criteria. 32.1% of patients regained their pre-fracture mobility and 59.6% remained independent 1 year after surgery. Logistic regression analysis identified age, gender, surgical procedure, and time to surgery as significant contributors to recovery of pre-fracture mobility. Older age and increasing requirement for postoperative ambulatory aid resulted in worse WOMAC total and physical function scores. No significant differences were observed in patient-reported hip function between those who had a total hip arthroplasty and those who had a hemiarthroplasty. Conclusion: Most geriatric patients with displaced neck of femur fractures did not regain pre-fracture mobility despite surgical treatment with arthroplasty.

A Comparative Study of Respiratory Quality of Life among Firefighters, Traffic Police and Other Occupations in Malaysia.

BACKGROUND: Despite its excellent psychometric properties, St George's Respiratory Questionnaire (SGRQ) has not been previously used in measuring respiratory quality of life (RQoL) among traffic police and firefighters who are at risk of poor respiratory health by virtue of their occupations. OBJECTIVE: To assess and compare the RQoL of the occupationally exposed (firefighters and traffic police) and the occupationally unexposed populations in Penang, Malaysia. METHODS: We recruited male traffic police and firefighters from 5 districts of Penang by convenient sampling during June to September 2018. Participants completed the SGRQ. Scores (symptoms, activity, impacts, total) were derived using a scoring calculator. Higher scores indicate poorer RQoL. Univariate and multivariate linear regression models were fitted to explore the relationship of the independent predictive factors with participants' RQoL. RESULTS: We recruited 706 participants---211 firefighters, 198 traffic police, and 297 from general population. Smokers had significantly higher scores than non-smokers in all SGRQ domains. Regardless of smoking status, the "occupationally exposed group" had higher symptoms score than the "occupationally unexposed group," who had higher activity and impact scores. Smoking status, comorbidity status and monthly income were significant independent predictors of SGRQ total score. CONCLUSION: In comparison with the general population, firefighters and traffic police reported poorer RQoL; smoking further deteriorated their respiratory health. There is a need to strengthen preventive health measures against occupational disease and smoking cessation among firefighters and traffic police.

Enhanced gastrointestinal survivability of recombinant Lactococcus lactis using a double coated mucoadhesive film approach.

Vaccine administration via the oral route is preferable to parenteral routes due to ease of administration. To date, most available oral vaccines comprises of live attenuated pathogens as oppose to peptide-based vaccines due to its low bioavailability within the gastrointestinal (GI) tract. Over the years, probiotic-based peptide delivery vehicles comprising of lactic acid bacteria such as Lactococcus lactis has emerged as an interesting alternative due to its generally recognized as safe (GRAS) status, a fully sequenced genome, transient gut colonization time, and is an efficient cellular factory for heterologous protein production. However, its survivability through the GI tract is low, thus better delivery approaches are being explored to improve its bioavailability. In this study, we employ the incorporation of a double coated mucoadhesive film consisting of sodium alginate and Lycoat RS 720 film as the inner coat. The formulated film exhibits good mechanical properties of tensile strength and percent elongation for manipulation and handling with an entrapment yield of 93.14±2.74%. The formulated mucoadhesive film is subsequently loaded into gelatin capsules with an outer enteric Eudragit L100-55 coating capable of a pH-dependent breakdown above pH 5.5 to protect against gastric digestion. The final product and unprotected controls were subjected to in vitro simulated gastrointestinal digestions to assess its survivability. The product demonstrated enhanced protection with an increase of 69.22±0.67% (gastric) and 40.61±8.23% (intestinal) survivability compared to unprotected controls after 6 hours of sequential digestion. This translates to a 3.5 fold increase in overall survivability. Owing to this, the proposed oral delivery system has shown promising potential as a live gastrointestinal vaccine delivery host. Further studies involving in vivo gastrointestinal survivability and mice immunization tests are currently being carried out to assess the efficacy of this novel oral delivery system in comparison to parenteral routes.

Barriers to smoking cessation: a qualitative study from the perspective of primary care in Malaysia.

OBJECTIVES: This qualitative study aims to construct a model of the barriers to smoking cessation in the primary care setting. DESIGN: Individual in-depth, semistructured interviews were audio-taped, then verbatim transcribed and translated when necessary. The data were first independently coded and then collectively discussed for emergent themes using the Straussian grounded theory method. PARTICIPANTS AND SETTING: Fifty-seven current smokers were recruited from a previous smoking related study carried out in a primary care setting in Malaysia. Current smokers with at least one failed quit attempts were included. RESULTS: A five-theme model emerged from this grounded theory method. (1) Personal and lifestyle factors: participants were unable to resist the temptation to smoke; (2) Nicotine addiction: withdrawal symptoms could not be overcome; (3) Social cultural norms: participants identified accepting cigarettes from friends as a token of friendship to be problematic; (4) Misconception: perception among smokers that ability to quit was solely based on one's ability to achieve mind control, and perception that stopping smoking will harm the body and (5) Failed assisted smoking cessation: smoking cessation services were not felt to be user-friendly and were poorly understood. The themes were organised into five concentric circles based on time frame: those actionable in the short term (themes 1 and 2) and the long term (themes 3, 4, 5). CONCLUSIONS: Five themes of specific beliefs and practices prevented smokers from quitting. Clinicians need to work on these barriers, which can be guided by the recommended time frames to help patients to succeed in smoking cessation.

Replicates in high dimensions, with applications to latent variable graphical models.

In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting. We consider learning the structure of a graphical model with latent variables, under the assumption that these variables take a constant value across replicates within each subject. By collecting multiple replicates for each subject, we are able to estimate the conditional dependence relationships among the observed variables given the latent variables. To test the null hypothesis of conditional independence between two observed variables, we propose a pairwise decorrelated score test. Theoretical guarantees are established for parameter estimation and for this test. We show that our proposal is able to estimate latent variable graphical models more accurately than some existing proposals, and apply the proposed method to a brain imaging dataset.

Statistical properties of convex clustering.

In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and k-means clustering. In addition, we derive the range of the tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimator of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to some traditional clustering methods in simulation studies.

Knowledge of Parkinson's Disease in a Multiethnic Urban Asian Setting.

BACKGROUND: Public knowledge regarding Parkinson's disease (PD) is important to facilitate good health-seeking behavior, but the literature on this topic is scarce. OBJECTIVE: We aimed to explore the level of public knowledge regarding PD in a large multiethnic urban Asian cohort, and (as a secondary aim) in a smaller cohort of PD patients and caregivers. METHODS: A Knowledge of PD Questionnaire (KPDQ) was developed and administered to members of the Malaysian general public, and to PD patients and caregivers. The KPDQ tests recognition of PD symptoms and general knowledge regarding PD. RESULTS: 1,258 members of the general public completed the KPDQ. Tremor was the most widely recognized symptom (recognized by 79.0% of respondents); however, 83.7% incorrectly believed that all PD patients experience tremor. Memory problem was the most widely recognized NMS. Overall, motor symptoms were better recognized than NMS. Common misperceptions were that there is a cure for PD (49.8%) and that PD is usually familial (41.4%). Female gender, Chinese ethnicity, tertiary education, healthcare-related work, and knowing someone with PD were independently associated with higher KPDQ scores. PD patients (n = 116) and caregivers (n = 135) demonstrated superior knowledge compared with the general public group, but one-third of them believed that PD is currently curable. CONCLUSIONS: This is the only study on public knowledge regarding PD in Asia. Important gaps in knowledge were evident, which could present a barrier to early diagnosis and appropriate treatment of PD. This highlights the need for targeted education campaigns and further research in this area.

Sparse Biclustering of Transposable Data.

We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online.

Learning Graphical Models With Hubs.

We consider the problem of learning a high-dimensional graphical model in which there are a few hub nodes that are densely-connected to many other nodes. Many authors have studied the use of an ℓ1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ1 penalty implicitly assumes that each edge is equally likely and independent of all other edges. We propose a general framework to accommodate more realistic networks with hub nodes, using a convex formulation that involves a row-column overlap norm penalty. We apply this general framework to three widely-used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. An alternating direction method of multipliers algorithm is used to solve the corresponding convex optimization problems. On synthetic data, we demonstrate that our proposed framework outperforms competitors that do not explicitly model hub nodes. We illustrate our proposal on a webpage data set and a gene expression data set.