New product introduction and diffusion with costly search

Will the low search cost in the new economy help speed up new product introduction? The usual model of product market search suggests that a low search cost can turn out to have detrimental incentives on innovation and new product introduction as the low search cost erodes firms' market power, attenuating the profit from innovation. This usual model, however, misses the important dimension of product market search that how often it pays to search depends on the magnitude of the search cost. This paper studies a model of monopolistic competition with costly search, where the point of departure is that of a fixed cost of a shopping trip. With this fixed cost, the optimal search frequency is tied to the magnitude of the search cost. In this environment, a low search cost could turn out to be favorable to innovation. At a low search cost, consumers search more often, speeding up the diffusion of new products and possibly resulting in higher profits for firms, despite the erosion of market power.product market search, innovation, new product introduction

The association of dimethylarginine dimethylaminohydrolase 1 gene polymorphism with type 2 diabetes: a cohort study

<p>Abstract</p> <p>Background</p> <p>Elevated plasma levels of asymmetric dimethylarginine (ADMA) has been reported to be associated with insulin resistance and micro/macrovascular diabetic complications, and may predict cardiovascular events in type 2 diabetic patients. Dimethylarginine dimethylaminohydrolase 1 (DDAH1) is the major enzyme eliminating ADMA in humans, but the effect of genetic variations in <it>DDAH1 </it>on type 2 diabetes and its long-term outcome are unknown.</p> <p>Methods</p> <p>From July 2006 to June 2009, we assessed the association between polymorphisms in <it>DDAH1 </it>and type 2 diabetes in 814 consecutive unrelated subjects, including 309 type 2 diabetic patients and 505 non-diabetic individuals. Six single nucleotide polymorphisms (SNPs) in <it>DDAH1</it>, rs233112, rs1498373, rs1498374, rs587843, rs1403956, and rs1241321 were analyzed. Plasma ADMA levels were determined by high performance liquid chromatography. Insulin sensitivity was assessed by the homeostasis model assessment of insulin resistance (HOMA-IR).</p> <p>Results</p> <p>Among the 6 SNPs, only rs1241321 was significantly associated with a decreased risk of type 2 diabetes (AA <it>vs </it>GG+AG, OR = 0.64, 95% CI 0.47-0.86, p = 0.004). The association remained unchanged after adjustment for plasma ADMA level. The fasting plasma glucose and log HOMA-IR tended to be lower in subjects carrying the homozygous AA genotype of rs1241321 compared with the GG+AG genotypes. Over a median follow-up period of 28.2 months, there were 44 all-cause mortality and 50 major adverse cardiovascular events (MACE, including cardiovascular death, non-fatal myocardial infarction and stroke). Compared with the GG and AG genotypes, the AA genotype of rs1241321 was associated with reduced risk of MACE (HR = 0.31, 95% CI: 0.11-0.90, p = 0.03) and all-cause mortality (HR = 0.18, 95% CI: 0.04-0.80, p = 0.02) only in subgroup with type 2 diabetes. One common haplotype (GGCAGC) was found to be significantly associated with a decreased risk of type 2 diabetes (OR = 0.67, 95% CI = 0.46-0.98, p = 0.04).</p> <p>Conclusions</p> <p>Our results provide the first evidence that SNP rs1241321 in <it>DDAH1 </it>is associated with type 2 diabetes and its long-term outcome.</p

Towards General-Purpose Text-Instruction-Guided Voice Conversion

This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results.Comment: Accepted to ASRU 202

Chinese Social Media Reaction to the MERS-Cov and Avian Influenza A (H7N9) Outbreaks

Background: As internet and social media use have skyrocketed, epidemiologists have begun to use online data such as Google query data and Twitter trends to track the activity levels of influenza and other infectious diseases. In China, Weibo is an extremely popular microblogging site that is equivalent to Twitter. Capitalizing on the wealth of public opinion data contained in posts on Weibo, this study used Weibo as a measure of the Chinese people’s reactions to two different outbreaks: the 2012 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak, and the 2013 outbreak of human infection of avian influenza A(H7N9) in China. Methods: Keyword searches were performed in Weibo data collected by The University of Hong Kong’s Weiboscope project. Baseline values were determined for each keyword and reaction values per million posts in the days after outbreak information was released to the public. Results: The results show that the Chinese people reacted significantly to both outbreaks online, where their social media reaction was two orders of magnitude stronger to the H7N9 influenza outbreak that happened in China than the MERS-CoV outbreak that was far away from China. Conclusions: These results demonstrate that social media could be a useful measure of public awareness and reaction to disease outbreak information released by health authorities

A 9 bp cis-element in the promoters of class I small heat shock protein genes on chromosome 3 in rice mediates L-azetidine-2-carboxylic acid and heat shock responses

In rice, the class I small heat shock protein (sHSP-CI) genes were found to be selectively induced by L-azetidine-2-carboxylic acid (AZC) on chromosome 3 but not chromosome 1. Here it is shown that a novel cis-responsive element contributed to the differential regulation. By serial deletion and computational analysis, a 9 bp putative AZC-responsive element (AZRE), GTCCTGGAC, located between nucleotides –186 and –178 relative to the transcription initiation site of Oshsp17.3 was revealed. Deletion of this putative AZRE from the promoter abolished its ability to be induced by AZC. Moreover, electrophoretic mobility shift assay (EMSA) revealed that the AZRE interacted specifically with nuclear proteins from AZC-treated rice seedlings. Two AZRE–protein complexes were detected by EMSA, one of which could be competed out by a canonical heat shock element (HSE). Deletion of the AZRE also affected the HS response. Furthermore, transient co-expression of the heat shock factor OsHsfA4b with the AZRE in the promoter of Oshsp17.3 was effective. The requirement for the putative AZRE for AZC and HS responses in transgenic Arabidopsis was also shown. Thus, AZRE represents an alternative form of heat HSE, and its interaction with canonical HSEs through heat shock factors may be required to respond to HS and AZC

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results

Rhamnose Binding Protein as an Anti-Bacterial Agent-Targeting Biofilm of Pseudomonas aeruginosa

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Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results

PacBio But Not Illumina Technology Can Achieve Fast, Accurate and Complete Closure of the High GC, Complex Burkholderia pseudomallei Two-Chromosome Genome

Although PacBio third-generation sequencers have improved the read lengths of genome sequencing which facilitates the assembly of complete genomes, no study has reported success in using PacBio data alone to completely sequence a two-chromosome bacterial genome from a single library in a single run. Previous studies using earlier versions of sequencing chemistries have at most been able to finish bacterial genomes containing only one chromosome with de novo assembly. In this study, we compared the robustness of PacBio RS II, using one SMRT cell and the latest P6-C4 chemistry, with Illumina HiSeq 1500 in sequencing the genome of Burkholderia pseudomallei, a bacterium which contains two large circular chromosomes, very high G+C content of 68–69%, highly repetitive regions and substantial genomic diversity, and represents one of the largest and most complex bacterial genomes sequenced, using a reference genome generated by hybrid assembly using PacBio and Illumina datasets with subsequent manual validation. Results showed that PacBio data with de novo assembly, but not Illumina, was able to completely sequence the B. pseudomallei genome without any gaps or mis-assemblies. The two large contigs of the PacBio assembly aligned unambiguously to the reference genome, sharing &gt;99.9% nucleotide identities. Conversely, Illumina data assembled using three different assemblers resulted in fragmented assemblies (201–366 contigs), sharing only 92.2–100% and 92.0–100% nucleotide identities to chromosomes I and II reference sequences, respectively, with no indication that the B. pseudomallei genome consisted of two chromosomes with four copies of ribosomal operons. Among all assemblies, the PacBio assembly recovered the highest number of core and virulence proteins, and housekeeping genes based on whole-genome multilocus sequence typing (wgMLST). Most notably, assembly solely based on PacBio outperformed even hybrid assembly using both PacBio and Illumina datasets. Hybrid approach generated only 74 contigs, while the PacBio data alone with de novo assembly achieved complete closure of the two-chromosome B. pseudomallei genome without additional costly bench work and further sequencing. PacBio RS II using P6-C4 chemistry is highly robust and cost-effective and should be the platform of choice in sequencing bacterial genomes, particularly for those that are well-known to be difficult-to-sequence

Impact of Visual Repetition Rate on Intrinsic Properties of Low Frequency Fluctuations in the Visual Network

BACKGROUND: Visual processing network is one of the functional networks which have been reliably identified to consistently exist in human resting brains. In our work, we focused on this network and investigated the intrinsic properties of low frequency (0.01-0.08 Hz) fluctuations (LFFs) during changes of visual stimuli. There were two main questions to be discussed in this study: intrinsic properties of LFFs regarding (1) interactions between visual stimuli and resting-state; (2) impact of repetition rate of visual stimuli. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed scanning sessions that contained rest and visual stimuli in various repetition rates with a novel method. The method included three numerical approaches involving ICA (Independent Component Analyses), fALFF (fractional Amplitude of Low Frequency Fluctuation), and Coherence, to respectively investigate the modulations of visual network pattern, low frequency fluctuation power, and interregional functional connectivity during changes of visual stimuli. We discovered when resting-state was replaced by visual stimuli, more areas were involved in visual processing, and both stronger low frequency fluctuations and higher interregional functional connectivity occurred in visual network. With changes of visual repetition rate, the number of areas which were involved in visual processing, low frequency fluctuation power, and interregional functional connectivity in this network were also modulated. CONCLUSIONS/SIGNIFICANCE: To combine the results of prior literatures and our discoveries, intrinsic properties of LFFs in visual network are altered not only by modulations of endogenous factors (eye-open or eye-closed condition; alcohol administration) and disordered behaviors (early blind), but also exogenous sensory stimuli (visual stimuli with various repetition rates). It demonstrates that the intrinsic properties of LFFs are valuable to represent physiological states of human brains