The ‘bright' side of innovation management for international new ventures

All businesses are finding it difficult to figure out how to enhance the environment and society. Following the co-generation of social, ethical, and corporate aims, new sustainable inventions have evolved since the COVID-19 pandemic event, similar to new solutions into a workable, viable, and ethical business. The positive and negative aspects of inventions are a topic of discussion among innovation management academics. In particular, how innovation may be more sustainable even when job inequities caused by automation have sparked a feeling of the importance of upholding human rights. Despite that, the innovation management literature is still far from being pedantic in studying automation and human rights towards sustainable innovations in the context of international new ventures (INVs). The article challenges a pessimistic view of innovations by examining automation and human rights for 3000 INVs through the perspective of the micro-foundations. Multiple linear regression analysis is used to evaluate hypotheses, demonstrating how social entrepreneurship can play a constructive mediating role in upholding human rights and promoting automation. This demonstrates the necessity for additional research on a business's individual level to create social breakthroughs. The study encourages policymakers and the government to support sustainable innovations by utilizing technology to boost job quality, uphold human rights, and foster global entrepreneurship.

A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.

Bacterial coinfections in COVID: Prevalence, antibiotic sensitivity patterns and clinical outcomes from a tertiary institute of Northern India.

Purpose: Bacterial coinfections are a leading cause of morbidity and mortality during viral infections including corona virus disease (COVID-19). The COVID-19 pandemic has highlighted the need to comprehend the complex connection between bacterial and viral infections. During the current pandemic, systematic testing of the COVID-19 patients having bacterial coinfections is essential to choose the correct antibiotics for treatment and prevent the spread of antimicrobial resistance (AMR). This study was planned to study the prevalence, demographic parameters, comorbidities, antibiotic sensitivity patterns, and outcomes in hospitalized COVID-19 patients with bacterial coinfections. Material and Methods: The COVID-19 patients having bacterial coinfections were selected for the study and analyzed for the prevalence, antibiotic sensitivities, comorbidities, and clinical outcomes. The bacterial isolates were identified and the antibiotic susceptibility testing was performed according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Results: Of the total 1,019 COVID-19 patients screened, 5.2% (n = 53) demonstrated clinical signs of bacterial coinfection. Escherichia coli were the most common isolate followed by Pseudomonas aeruginosa and Klebsiella spp. among the gram-negative bacterial infections. Coagulase-negative Staphylococcus species (CONS) and Staphylococcus aureus were most common among the gram-positive bacterial infections. The antibiotic sensitivity profiling revealed that colistin (99%), imipenem (78%), and fosfomycin (95%) were the most effective drugs against the gram-negative isolates while vancomycin (100%), teicoplanin (99%), and doxycycline (71%) were most potent against the gram-positive isolates. The analysis of the clinical parameters and outcomes revealed that among the COVID-19 patients with bacterial coinfections, the mortality rate was higher (39%) than the control group (17%) (P-value < 0.001). Conclusion: This study reveals the significantly increased rates of bacterial coinfections among COVID-19 patients which may lead to an increase in mortality. This study will guide the physicians at the primary level on the rational and correct usage of antibiotics in such COVID cases. Hence, systematic testing of COVID-19 patients with bacterial coinfections is the need of the hour to decrease the mortality rate and limit the spread of AMR.

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Reshaping the tumor microenvironment with oncolytic viruses, positive regulation of the immune synapse, and blockade of the immunosuppressive oncometabolic circuitry.

BACKGROUND: Oncolytic viruses are considered part of immunotherapy and have shown promise in preclinical experiments and clinical trials. Results from these studies have suggested that tumor microenvironment remodeling is required to achieve an effective response in solid tumors. Here, we assess the extent to which targeting specific mechanisms underlying the immunosuppressive tumor microenvironment optimizes viroimmunotherapy. METHODS: We used RNA-seq analyses to analyze the transcriptome, and validated the results using Q-PCR, flow cytometry, and immunofluorescence. Viral activity was analyzed by replication assays and viral titration. Kyn and Trp metabolite levels were quantified using liquid chromatography-mass spectrometry. Aryl hydrocarbon receptor (AhR) activation was analyzed by examination of promoter activity. Therapeutic efficacy was assessed by tumor histopathology and survival in syngeneic murine models of gliomas, including Indoleamine 2,3-dioxygenase (IDO)-/- mice. Flow cytometry was used for immunophenotyping and quantification of cell populations. Immune activation was examined in co-cultures of immune and cancer cells. T-cell depletion was used to identify the role played by specific cell populations. Rechallenge experiments were performed to identify the development of anti-tumor memory. RESULTS: Bulk RNA-seq analyses showed the activation of the immunosuppressive IDO-kynurenine-AhR circuitry in response to Delta-24-RGDOX infection of tumors. To overcome the effect of this pivotal pathway, we combined Delta-24-RGDOX with clinically relevant IDO inhibitors. The combination therapy increased the frequency of CD8+ T cells and decreased the rate of myeloid-derived suppressor cell and immunosupressive Treg tumor populations in animal models of solid tumors. Functional studies demonstrated that IDO-blockade-dependent activation of immune cells against tumor antigens could be reversed by the oncometabolite kynurenine. The concurrent targeting of the effectors and suppressors of the tumor immune landscape significantly prolonged the survival in animal models of orthotopic gliomas. CONCLUSIONS: Our data identified for the first time the in vivo role of IDO-dependent immunosuppressive pathways in the resistance of solid tumors to oncolytic adenoviruses. Specifically, the IDO-Kyn-AhR activity was responsible for the resurface of local immunosuppression and resistance to therapy, which was ablated through IDO inhibition. Our data indicate that combined molecular and immune therapy may improve outcomes in human gliomas and other cancers treated with virotherapy.

Work experience on algorithm-based platforms: The bright and dark sides of turking

The prevalent use of digital labor platforms has transformed the nature of work globally. Such algorithm-based platforms have triggered many technological, legal, ethical, and human resource management challenges. Despite some benefits (i.e., flexibility), the precarious conditions and commodification of jobs are major concerns in these platform-based employment conditions. The remote-work paradigm shift during the COVID-19 pandemic has made the interplay between technology, digitalization, and precarious workers' well-being a critical issue to address. This paper focuses on microtask platforms by examining overall well-being associated with turking as a work experience. Using a sample of 401 Amazon Mechanical Turk workers during the early stage of the COVID-19 pandemic, data were collected on individual conditions affecting the overall quality of workers' lives. The results from two structural equation models demonstrated the direct and mediating effects of task characteristics, excessive working, and financial pressure, mirroring the bright and dark sides of turking. Greater turking task significance and meaningfulness increase personal growth opportunities, ultimately improving workers' perceived quality of life. However, excessive work and greater financial pressure decrease self-acceptance and overall quality of life. This study examines the complicated nature of work experience on algorithm-based platforms by unpacking individual factors that affect workers' well-being.

Correlation between placental histopathology and perinatal outcome in COVID-19.

Objectives: An alarming rate of adverse perinatal outcomes as well as maternal deaths has been reported worldwide during this pandemic. It would be prudent to start thinking on the lines of acute or chronic intrauterine fetal hypoxia due to placental microvascular pathology or villitis caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection. Autopsy studies of deceased patients with severe COVID-19 have revealed the presence of diffuse pulmonary alveolar damage, thrombosis, and microvascular injuries. It is expected that similar pathological features such as microvascular injuries could be found in the placenta of infected pregnant women. Materials and Methods: Placentas of singleton pregnancies from 42 SARS-CoV-2 positive mothers delivered at term were submitted for histopathological examination. Those with multifetal gestation, hypertensive disorder, fetal growth restriction, structural or chromosomal anomalies in the fetus, thrombophilia, prolonged prelabor rupture of membranes, and placenta accreta spectrum were excluded from the study. Histopathological examination was done by two pathologists independently and only those results concurred by both were reported. Histopathological features and corresponding neonatal outcome were analyzed. Results: Reports of 42 placentas from patients with SARS-CoV-2, delivered at term (37-40 weeks) were analyzed in our study. Features of maternal vascular malperfusions (MVM) were present in 45% (n = 19) cases. Features of fetal vascular malperfusions (FVM) were present in 23.8% (n = 10) cases. There were 47.6% (n = 20) cases showing at least one feature of acute inflammatory pathology (AIP) and 42.8% (n = 18) showing features of chronic inflammatory pathology (CIP). Neonatal respiratory distress syndrome was found in 19% (n = 8) of the neonates. Correspondingly, nearly all placentas (n = 7) of these neonates showed features of MVM, FVM, AIP and CIP. There was no maternal or neonatal mortality in our study group. Conclusion: The main findings of our study include maternal as well as fetal vascular malperfusions and placental inflammatory pathology. These findings provide an outline for better understanding of etiological factors and pathogenesis of adverse perinatal outcomes in SARS-CoV-2 infection.

Low expression of TMPRSS2-a SARS-CoV-2 internalization protease-associates with basal subtype of head and neck squamous cell carcinoma.

SARS-CoV-2 is a single-stranded RNA virus that has caused the ongoing COVID-19 pandemic. ACE2 and other genes utilized by SARS-CoV-2 to enter human cells have been shown to express in Head and Neck Squamous Cell Carcinoma (HNSCC) patients. However, their expression pattern in different subtypes has not been investigated. Hence, in the current study, we have analyzed the expression of ACE2, TMPRSS2 and FURIN in 649 HNSCC patients from two independent cohorts. Our analysis showed significantly lower expression of TMPRSS2 while significantly increased expression of ACE2 and FURIN in HPV-negative HNSCC. Comparison of expression of these genes in the three subtypes of HNSCC patients (basal, classical and inflamed/mesenchymal) showed no significant difference in the expression of ACE2 among the three subtypes; however, the basal subtype showed significantly reduced expression of TMPRSS2 but significantly increased expression of FURIN. Comparison of expression of these genes between the HPV-negative patients of basal subtype vs all others confirmed significantly lower expression of TMPRSS2 in HPV-negative patients of basal subtype as compared to all others. Our study shows that the different subtypes of HNSCC patients have different expression patterns of genes utilized by the SARS-CoV-2 to enter human cells, and hence, their susceptibility to SARS-CoV-2 may also be different. As the expression of TMPRSS2 is significantly lower in the HNSCC patients of the basal subtype, we predict that these patients would be less susceptible to SARS-CoV-2 infection than the patients of other subtypes. However, these findings need to be further validated.

Time versus tissue: Timely identification of Scedosporium Rhinosinusitis in a post-COVID-19 case by MALDI-TOF MS leading to successful management.

COVID-19 (Coronavirus Disease 2019), illness with associated comorbidities and corticosteroid therapy makes the host immunocompromised and prone to opportunistic microbial infections. As the world continues to struggle with the pandemic of COVID-19, an increase in cases of opportunistic fungal infections have been reported from all over the world during the second wave of COVID-19 like aspergillosis, mucormycosis, and candidiasis. Scedosporium apiospermum is an emerging pathogen that is usually associated with mycetoma, pulmonary infection, and central nervous infections. It has been rarely associated with fungal rhinosinusitis (FRS). In this study, a rare case of FRS caused by S.apiospermum in an immunocompromised post-Covid-19 diabetic woman is reported.

Mastering digital transformation: The nexus between leadership, agility, and digital strategy

Drawing upon new institutional theory, we developed and tested a model on how digital transformational leadership and organizational agility influence digital transformation with digital strategy as a moderator. We found that digital transformational leadership and organizational agility positively influence digital transformation, and digital transformational leadership influences organizational agility. The finding of our study also indicates organizational agility to mediate the relationship between digital transformational leadership and digital transformation. Our findings offer an advanced understanding of the impact of transformational leadership and organizational agility on digital transformation and the role of digital strategy. Our study's findings address critical questions about how leadership style and promoting organizational agility in the public sector can enhance digital transformation.

Three-dimensional DenseNet self-attention neural network for automatic detection of student’s engagement

Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. The students’ understanding of the course and their emotional engagement, as indicated via facial expressions, are intertwined. This paper attempts to present a three-dimensional DenseNet self-attention neural network (DenseAttNet) used to identify and evaluate student participation in modern and traditional educational programs. With the Dataset for Affective States in E-Environments (DAiSEE), the proposed DenseAttNet model outperformed all other existing methods, achieving baseline accuracy of 63.59% for engagement classification and 54.27% for boredom classification, respectively. Besides, DenseAttNet trained on all four multi-labels, namely boredom, engagement, confusion, and frustration has registered an accuracy of 81.17%, 94.85%, 90.96%, and 95.85%, respectively. In addition, we performed a regression experiment on DAiSEE and obtained the lowest Mean Square Error (MSE) value of 0.0347. Finally, the proposed approach achieves a competitive MSE of 0.0877 when validated on the Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset.

Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data

In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism's intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal-Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.

Resource integration and dynamic capability of frontline employee during COVID-19 pandemic: From value creation and engineering management perspectives.

The cornerstone of any successful organizations is the frontline employees. Frontline employees (FLEs) are always in action at the frontline of the business. They do not operate from the office space or from the corporate setting. Frontline employees directly interact with their customers. During the COVID-19 pandemic, many frontline employees experienced numerous challenges as most of the places there were full or partial lockdown imposed by the government agencies and the frontline employees could not be able to directly connect with their customers. Not many studies are there which investigated the issue of resource integration, dynamic capabilities, and engineering management abilities of the frontline employees such as technological capability, emotional intelligence, and psychological capability which perceived to influence the frontline employee adaptability and organization performance. In this background, the purpose of this study is to examine the relationship between frontline employee adaptability and organization performance during COVID-19 pandemic from technological, emotional, and psychological perspectives. With the help of dynamic capability view and different adaptability theories, a theoretical model has been developed conceptually. Later the conceptual model has been validated using partial least square - structural equation modeling technique considering 412 respondents from frontline employees of different organizations in Asia and EMEA. The study found that frontline employees' dynamic capabilities and engineering management abilities significantly and positively impact employee adaptability which in turn impact the performance of the organization mediating through employee job satisfaction and employee performance.

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- α family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

Second Wave of the COVID-19 Pandemic in Delhi, India: High Seroprevalence Not a Deterrent?

Background We report the findings of a large follow-up, community-based, cross-sectional serosurvey and correlate it with the coronavirus disease (COVID-19) test-positivity rate and the caseload observed between the peaks of the first and the second wave of the COVID-19 pandemic in Delhi, India. Methodology Individuals aged five and above were recruited from 274 wards of the state (population approximately 19.6 million) from January 11 to January 22, 2021. A total of 100 participants each were included from all wards for a net sample size of approximately 28,000. A multistage sampling technique was employed to select participants for the household serosurvey. Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin (IgG) antibodies were detected by using the VITROS® (Ortho Clinical Diagnostics, Raritan, NJ, USA) assay (90% sensitivity, 100% specificity). Results Antibody positivity was observed in 14,298 (50.76%) of 28,169 samples. The age, sex, and district population-weighted seroprevalence of the SARS-CoV-2 IgG was 50.52% (95% confidence interval [CI] = 49.94-51.10), and after adjustment for assay characteristics, it was 56.13% (95% CI = 55.49-56.77). On adjusted analysis, participants aged ≥50 years, of female gender, housewives, having ever lived in containment zones, urban slum dwellers, and diabetes or hypertensive patients had significantly higher odds of SARS-CoV-2 antibody positivity. The peak infection rate and the test-positivity rate since October 2020 were initially observed in mid-November 2020, with a subsequent steep declining trend, followed by a period of persistently low case burden lasting until the first week of March 2021. This was followed by a steady increase followed by an exponential surge in infections from April 2021 onward culminating in the second wave of the pandemic. Conclusions The presence of infection-induced immunity from SARS-CoV-2 even in more than one in two people can be ineffective in protecting the population. Despite such high seroprevalence, population susceptibility to COVID-19 can be accentuated by variants of concern having the ability for rapid transmission and depletion of antibody levels with the threat of recurrent infections, signifying the need for mass vaccination.

Longer-run Economic Consequences of Pandemics

What are the medium- to long-term effects of pandemics? How do they differ from other economic disasters? We study major pandemics using the rates of return on assets stretching back to the 14th century. Significant macroeconomic after-effects of pandemics persist for about decades, with real rates of return substantially depressed, in stark contrast to what happens after wars. Our findings are consistent with the neoclassical growth model: capital is destroyed in wars, but not in pandemics;pandemics instead may induce relative labor scarcity and/or a shift to greater precautionary savings.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM.

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.