The Rise of Dough: Mid-Pandemic Baked Desserts Startups

Since the Philippines implemented the COVID-19 community quarantine, many Filipinos either lost their jobs or stopped going to work. Due to lack of a stable income source, many Filipinos resorted to selling various items, including baked goods, via online platforms. This study aimed to explore the phenomenon and its corresponding factors on the emergence of startups that mainly sell baked desserts on Facebook and Instagram during the quarantine period in Metro Manila. The researchers investigated whether external factors (i.e., social- cultural circumstances and economic circumstances) and internal factors (i.e., intrinsic motivations and attitudes toward baked desserts) affected the rise of baked desserts startups. The researchers also investigated the consumer’s decision to establish an online baked desserts startup during the pandemic. In the end, the researchers found out that the consumers’ intrinsic motivation played the most significant role in influencing consumer decision to begin an online baked dessert startup, which links to external factors. Additionally, attitudes toward baked desserts only affected the menu and business planning; but did not motivate consumers to start selling due to their love for eating baked desserts. Despite such, external and internal factors contribute to the emergence of baked desserts businesses during the pandemic. The findings of this study provide insight into the mid-pandemic state of the online baked desserts industry

Technology Adoption of Computer-Aided Instruction in Healthcare: A Structured Review

Computer-Aided Instruction (CAI) is one of the interactive teaching methods that electronically presents instructional resources and enhances learner performance. In health settings, using CAI is one of the important ways to improve learners\u27 knowledge and usefulness in their healthcare specialization yet there is still a lack of research that offers a comprehensive synthesis of investigating into the adoption of CAI in healthcare. This research aims to provide a comprehensive review of related literatures on the enablers and barriers for technology adoption of CAI in healthcare. 31 journals were analyzed and revealed that several studies were utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT). The researchers then conducted qualitative coding for thematic analysis and categorized the qualitative data to find themes and patterns. Enablers as well as barriers to CAI adoption in healthcare were then discussed along with the common conclusions, limitations and recommendations for future studies. Results shows that key enablers were perceived ease of use, ease of usefulness, performance expectancy, social influence, user experience, and effort expectancy while identified key barriers were government support, funding constraints, and interactivity. The majority of the research articles highlighted the benefits of CAI in healthcare education as an innovative method for boosting the effectiveness of both teaching and learning

Technology Adoption of Computer-Aided Instruction in Healthcare: A Structured Review

Computer-Aided Instruction (CAI) is one of the interactive teaching methods that electronically presents instructional resources and enhances learner performance. In health settings, using CAI is one of the important ways to improve learners’ knowledge and usefulness in their healthcare specialization yet there is still a lack of research that offers a comprehensive synthesis of investigating into the adoption of CAI in healthcare. This research aims to provide a comprehensive review of related literatures on the enablers and barriers for technology adoption of CAI in healthcare. 31 journals were analyzed and revealed that several studies were utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT). The researchers then conducted qualitative coding for thematic analysis and categorized the qualitative data to find themes and patterns. Enablers as well as barriers to CAI adoption in healthcare were then discussed along with the common conclusions, limitations and recommendations for future studies. Results shows that key enablers were perceived ease of use, ease of usefulness, performance expectancy, social influence, user experience, and effort expectancy while identified key barriers were government support, funding constraints, and interactivity. The majority of the research articles highlighted the benefits of CAI in healthcare education as an innovative method for boosting the effectiveness of both teaching and learning

Environmental pleiotropy and demographic history direct adaptation under antibiotic selection

Evolutionary rescue following environmental change requires mutations permitting population growth in the new environment. If change is severe enough to prevent most of the population reproducing, rescue becomes reliant on mutations already present. If change is sustained, the fitness effects in both environments, and how they are associated-termed 'environmental pleiotropy'-may determine which alleles are ultimately favoured. A population's demographic history-its size over time-influences the variation present. Although demographic history is known to affect the probability of evolutionary rescue, how it interacts with environmental pleiotropy during severe and sustained environmental change remains unexplored. Here, we demonstrate how these factors interact during antibiotic resistance evolution, a key example of evolutionary rescue fuelled by pre-existing mutations with pleiotropic fitness effects. We combine published data with novel simulations to characterise environmental pleiotropy and its effects on resistance evolution under different demographic histories. Comparisons among resistance alleles typically revealed no correlation for fitness-i.e., neutral pleiotropy-above and below the sensitive strain's minimum inhibitory concentration. Resistance allele frequency following experimental evolution showed opposing correlations with their fitness effects in the presence and absence of antibiotic. Simulations demonstrated that effects of environmental pleiotropy on allele frequencies depended on demographic history. At the population level, the major influence of environmental pleiotropy was on mean fitness, rather than the probability of evolutionary rescue or diversity. Our work suggests that determining both environmental pleiotropy and demographic history is critical for predicting resistance evolution, and we discuss the practicalities of this during in vivo evolution

Initial Mutations Direct Alternative Pathways of Protein Evolution

Whether evolution is erratic due to random historical details, or is repeatedly directed along similar paths by certain constraints, remains unclear. Epistasis (i.e. non-additive interaction between mutations that affect fitness) is a mechanism that can contribute to both scenarios. Epistasis can constrain the type and order of selected mutations, but it can also make adaptive trajectories contingent upon the first random substitution. This effect is particularly strong under sign epistasis, when the sign of the fitness effects of a mutation depends on its genetic background. In the current study, we examine how epistatic interactions between mutations determine alternative evolutionary pathways, using in vitro evolution of the antibiotic resistance enzyme TEM-1 β-lactamase. First, we describe the diversity of adaptive pathways among replicate lines during evolution for resistance to a novel antibiotic (cefotaxime). Consistent with the prediction of epistatic constraints, most lines increased resistance by acquiring three mutations in a fixed order. However, a few lines deviated from this pattern. Next, to test whether negative interactions between alternative initial substitutions drive this divergence, alleles containing initial substitutions from the deviating lines were evolved under identical conditions. Indeed, these alternative initial substitutions consistently led to lower adaptive peaks, involving more and other substitutions than those observed in the common pathway. We found that a combination of decreased enzymatic activity and lower folding cooperativity underlies negative sign epistasis in the clash between key mutations in the common and deviating lines (Gly238Ser and Arg164Ser, respectively). Our results demonstrate that epistasis contributes to contingency in protein evolution by amplifying the selective consequences of random mutations

Markers of Dysglycaemia and Risk of Coronary Heart Disease in People without Diabetes: Reykjavik Prospective Study and Systematic Review

BACKGROUND: Associations between circulating markers of dysglycaemia and coronary heart disease (CHD) risk in people without diabetes have not been reliably characterised. We report new data from a prospective study and a systematic review to help quantify these associations. METHODS AND FINDINGS: Fasting and post-load glucose levels were measured in 18,569 participants in the population-based Reykjavik study, yielding 4,664 incident CHD outcomes during 23.5 y of mean follow-up. In people with no known history of diabetes at the baseline survey, the hazard ratio (HR) for CHD, adjusted for several conventional risk factors, was 2.37 (95% CI 1.79-3.14) in individuals with fasting glucose > or = 7.0 mmol/l compared to those or = 7 mmol/l at baseline were excluded, relative risks for CHD, adjusted for several conventional risk factors, were: 1.06 (1.00-1.12) per 1 mmol/l higher fasting glucose (23 cohorts, 10,808 cases, 255,171 participants); 1.05 (1.03-1.07) per 1 mmol/l higher post-load glucose (15 cohorts, 12,652 cases, 102,382 participants); and 1.20 (1.10-1.31) per 1% higher HbA(1c) (9 cohorts, 1639 cases, 49,099 participants). CONCLUSIONS: In the Reykjavik Study and a meta-analysis of other Western prospective studies, fasting and post-load glucose levels were modestly associated with CHD risk in people without diabetes. The meta-analysis suggested a somewhat stronger association between HbA(1c) levels and CHD risk

Quantifying the Adaptive Potential of an Antibiotic Resistance Enzyme

For a quantitative understanding of the process of adaptation, we need to understand its “raw material,” that is, the frequency and fitness effects of beneficial mutations. At present, most empirical evidence suggests an exponential distribution of fitness effects of beneficial mutations, as predicted for Gumbel-domain distributions by extreme value theory. Here, we study the distribution of mutation effects on cefotaxime (Ctx) resistance and fitness of 48 unique beneficial mutations in the bacterial enzyme TEM-1 β-lactamase, which were obtained by screening the products of random mutagenesis for increased Ctx resistance. Our contributions are threefold. First, based on the frequency of unique mutations among more than 300 sequenced isolates and correcting for mutation bias, we conservatively estimate that the total number of first-step mutations that increase Ctx resistance in this enzyme is 87 [95% CI 75–189], or 3.4% of all 2,583 possible base-pair substitutions. Of the 48 mutations, 10 are synonymous and the majority of the 38 non-synonymous mutations occur in the pocket surrounding the catalytic site. Second, we estimate the effects of the mutations on Ctx resistance by determining survival at various Ctx concentrations, and we derive their fitness effects by modeling reproduction and survival as a branching process. Third, we find that the distribution of both measures follows a Fréchet-type distribution characterized by a broad tail of a few exceptionally fit mutants. Such distributions have fundamental evolutionary implications, including an increased predictability of evolution, and may provide a partial explanation for recent observations of striking parallel evolution of antibiotic resistance

Network Models of TEM β-Lactamase Mutations Coevolving under Antibiotic Selection Show Modular Structure and Anticipate Evolutionary Trajectories

Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design

The gender specific frequency of risk factor and CHD diagnoses prior to incident MI: A community study

BACKGROUND: CHD is a chronic disease often present years prior to incident AMI. Earlier recognition of CHD may be associated with higher levels of recognition and treatment of CHD risk factors that may delay incident AMI. To assess timing of CHD and CHD risk factor diagnoses prior to incident AMI. METHODS: This is a 10-year population based medical record review study that included all medical care providers in Olmsted County, Minnesota for all women and a sample of men residing in Olmsted County, MN with confirmed incident AMI between 1995 and 2000. RESULTS: All medical care for the 10 years prior to incident AMI was reviewed for 150 women and 148 men (38% sample) in Olmsted County, MN. On average, women were older than men at the time of incident AMI (74.7 versus 65.9 years, p < 0.0001). 30.4% of the men and 52.0% of the women received diagnoses of CHD prior to incident AMI (p = 0.0002). Unrecognized and untreated CHD risk factors were present in both men (45% of men 5 years prior to AMI) and women (22% of women 5 years prior to first AMI), more common in men and those without a diagnosis of CHD prior to incident AMI (p < 0.0001). CONCLUSION: A CHD diagnosis prior to incident AMI is associated with higher rates of recognition and treatment of CHD risk factors suggesting that diagnosing CHD prior to AMI enhances opportunities to lower the risk of future CHD events