How and when adversity breeds ingenuity in an emerging market:Environmental threats, co-innovation, and frugal innovation

Despite burgeoning research on frugal innovation, there is limited understanding of how environmental threats shape frugal innovation, the mechanisms underlying this relationship and its boundary conditions. To address these gaps, we propose a moderated mediation model based on the strategy tripod perspective to examine the impact of environmental threats on frugal innovation through the mediating mechanism of co-innovationcapability. Moreover, we investigate how legal incompleteness can moderate this relationship. We tested our model empirically with data from 301 manufacturing firms in an emerging market of Ghana, using a time-lagged research design. The results of our analysis largely support the proposed hypotheses in the model, revealing a more nuanced understanding of the indirect impact of environmental threats on frugal product innovation to contribute to the existing body of knowledge in the field of innovation

How and when adversity breeds ingenuity in an emerging market:Environmental threats, co-innovation, and frugal innovation

Despite burgeoning research on frugal innovation, there is limited understanding of how environmental threats shape frugal innovation, the mechanisms underlying this relationship and its boundary conditions. To address these gaps, we propose a moderated mediation model based on the strategy tripod perspective to examine the impact of environmental threats on frugal innovation through the mediating mechanism of co-innovationcapability. Moreover, we investigate how legal incompleteness can moderate this relationship. We tested our model empirically with data from 301 manufacturing firms in an emerging market of Ghana, using a time-lagged research design. The results of our analysis largely support the proposed hypotheses in the model, revealing a more nuanced understanding of the indirect impact of environmental threats on frugal product innovation to contribute to the existing body of knowledge in the field of innovation

Neoadjuvant sipuleucel-T induces both Th1 activation and immune regulation in localized prostate cancer.

Sipuleucel-T is the only FDA-approved immunotherapy for metastatic castration-resistant prostate cancer. The mechanism by which this treatment improves survival is not fully understood. We have previously shown that this treatment can induce the recruitment of CD4 and CD8 T cells to the tumor microenvironment. In this study, we examined the functional state of these T cells through gene expression profiling. We found that the magnitude of T cell signatures correlated with the frequency of T cells as measured by immunohistochemistry. Sipuleucel-T treatment was associated with increased expression of Th1-associated genes, but not Th2-, Th17 - or Treg-associated genes. Post-treatment tumor tissues with high CD8+T cell infiltration was associated with high levels of CXCL10 expression. On in situ hybridization, CXCL10+ cells colocalized with CD8+T cells in post-treatment prostatectomy tumor tissue. Neoadjuvant sipuleucel-T was also associated with upregulation of immune inhibitory checkpoints, including CTLA4 and TIGIT, and downregulation of the immune activation marker, dipeptidylpeptidase, DPP4. Treatment-associated declines in serum PSA were correlated with induction of Th1 response. In contrast, rises in serum PSA while on treatment were associated with the induction of multiple immune checkpoints, including CTLA4, CEACAM6 and TIGIT. This could represent adaptive immune resistance mechanisms induced by treatment. Taken together, neoadjuvant sipuleucel-T can induce both a Th1 response and negative immune regulation in the prostate cancer microenvironment

Supervised classification via constrained subspace and tensor sparse representation

SRC, a supervised classifier via sparse representation, has rapidly gained popularity in recent years and can be adapted to a wide range of applications based on the sparse solution of a linear system. First, we offer an intuitive geometric model called constrained subspace to explain the mechanism of SRC. The constrained subspace model connects the dots of NN, NFL, NS, NM. Then, inspired from the constrained subspace model, we extend SRC to its tensor-based variant, which takes as input samples of high-order tensors which are elements of an algebraic ring. A tensor sparse representation is used for query tensors. We verify in our experiments on several publicly available databases that the tensor-based SRC called tSRC outperforms traditional SRC in classification accuracy. Although demonstrated for image recognition, tSRC is easily adapted to other applications involving underdetermined linear systems

Predicting managers' mental health across countries: using country-level COVID-19 statistics

Background: There is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic because they face particular hardship. Objective: We aim to predict mental disorder (anxiety and depression) symptoms of managers across countries using country-level COVID-19 statistics. Methods: A two-wave online survey of 406 managers from 26 countries was performed in May and July 2020. We used logistic panel regression models for our main analyses and performed robustness checks using ordinary least squares regressions. In the sample, 26.5% of managers reached the cut-off levels for anxiety (General Anxiety Disorder-7; GAD-7) and 43.5% did so for depression (Patient Health Questionnaire-9; PHQ-9) symptoms. Findings: We found that cumulative COVID-19 statistics (e.g., cumulative cases, cumulative cases per million, cumulative deaths, and cumulative deaths per million) predicted managers' anxiety and depression symptoms positively, whereas daily COVID-19 statistics (daily new cases, smoothed daily new cases, daily new deaths, smoothed daily new deaths, daily new cases per million, and smoothed daily new cases per million) predicted anxiety and depression symptoms negatively. In addition, the reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor. Individually, we found that the cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Conclusions: Cumulative COVID-19 statistics predicted managers' anxiety and depression symptoms positively, while non-cumulative daily COVID-19 statistics predicted anxiety and depression symptoms negatively. Cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor

Single and multiple object tracking using a multi-feature joint sparse representation

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms

Hyperspectral image spectral-spatial feature extraction via tensor principal component analysis

We consider the tensor-based spectral-spatial feature\ud extraction problem for hyperspectral image classification.\ud First, a tensor framework based on circular convolution is proposed.\ud Based on this framework, we extend the traditional PCA to\ud its tensorial version TPCA, which is applied to the spectral-spatial\ud features of hyperspectral image data. The experiments show\ud that the classification accuracy obtained using TPCA features\ud is significantly higher than the accuracies obtained by its rivals

Drosophila olfactory receptors as classifiers for volatiles from disparate real world applications

Olfactory receptors evolved to provide animals with ecologically and behaviourally relevant information. The resulting extreme sensitivity and discrimination has proven useful to humans, who have therefore co-opted some animals' sense of smell. One aim of machine olfaction research is to replace the use of animal noses and one avenue of such research aims to incorporate olfactory receptors into artificial noses. Here, we investigate how well the olfactory receptors of the fruit fly, Drosophila melanogaster, perform in classifying volatile odourants that they would not normally encounter. We collected a large number of in vivo recordings from individual Drosophila olfactory receptor neurons in response to an ecologically relevant set of 36 chemicals related to wine ('wine set') and an ecologically irrelevant set of 35 chemicals related to chemical hazards ('industrial set'), each chemical at a single concentration. Resampled response sets were used to classify the chemicals against all others within each set, using a standard linear support vector machine classifier and a wrapper approach. Drosophila receptors appear highly capable of distinguishing chemicals that they have not evolved to process. In contrast to previous work with metal oxide sensors, Drosophila receptors achieved the best recognition accuracy if the outputs of all 20 receptor types were used

Early evidence and predictors of mental distress of adults one month in the COVID-19 epidemic in Brazil

Objective: We aim to provide early evidence of mental distress and its associated predictors among adults one month into the COVID-19 crisis in Brazil. Methods: We conducted an online survey of 638 adults in Brazil on March 25–28, 2020, about one month (32 days) cross-sectionally after the first COVID-19 case in South America was confirmed in São Paulo. The 638 adults were in 25 states out of the 26 Brazilian states, with the only exception being Roraima, the least populated state in the Amazon. Of all the participating adults, 24%, 20%, and 18% of them were located in Rio de Janeiro state, Santa Catarina state, and São Paulo state respectively. Results: In Brazil, 52% (332) of the sampled adults experienced mild or moderate distress, and 18.8% (120) suffered severe distress. Adults who were female, younger, more educated, and exercised less reported higher levels of distress. Each individual's distance from the Brazilian epicenter of São Paulo interacted with age and workplace attendance to predict the level of distress. The “typhoon eye effect” was stronger for people who were older or attended their workplace less. The most vulnerable adults were those who were far from the epicenter and did not go to their workplace in the week before the survey. Conclusion: Identifying the predictors of distress enables mental health services to better target finding and helping the more mentally vulnerable adults during the ongoing COVID-19 crisis