| 1. |
- Bai, Wensong
(författare)
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The Best of Both Worlds : The Effects of Knowledge and Network Relationships on Performance of Returnee Entrepreneurial Firms
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In an increasingly globalized business world, international human mobility and its association with entrepreneurship presents attractive opportunities for business research. One increasingly important phenomenon in the field is returnee entrepreneurship, which is the reverse flow of migrants who acquired skills in developed countries back to their emerging-market home countries. In this way, knowledge from developed countries transfers back to, and fills technological and entrepreneurial gaps in, emerging countries. This thesis aims to explain returnee entrepreneurship by examining what factors differentiate innovation, internationalization and business performance of returnee entrepreneurial firms.The empirical investigation uses a mixed method approach comprising a qualitative single case study and four quantitative studies of a recent sample of 200 Chinese returnee entrepreneurial firms, and focusses on factors such as knowledge and network relationships, which are two advantages that returnee entrepreneurial firms are thought to have. By distinguishing among types of knowledge from various sources, and network relationships from both international and domestic origins, the findings show that international opportunity knowledge and domestic business relationships influence the innovation performance of returnee entrepreneurial firms. The structural characteristics of the international network of a returnee entrepreneurial firm are related to that firm’s international networking capability, which in turn influence its opportunity-driven internationalization. Additionally, the international experiential knowledge of returnee entrepreneurs nurtures their firms’ international market knowledge, and these experiences also indirectly affect firm internationalization by being transmitted through that market knowledge. Finally, international networking capability and innovation performance positively influence the business performance of returnee entrepreneurial firms, whereas the international business knowledge has a negative impact on business performance.The thesis challenges the enthusiastic view of returnee entrepreneurial firms by refining the heterogeneity of both the returnee entrepreneurs themselves, and the performance variances of their ventures. Empirically, this thesis suggests a combination of knowledge and resources from both ‘worlds’ that can best benefit the performance of a returnee entrepreneurial firm. This thesis also contributes to theory by providing new insights into the combined effect of (1) an individual entrepreneur’s international experience, and (2) a new venture’s knowledge base and capabilities; furthermore it also describes what that combined effect will be on the new venture’s performance.
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| 2. |
- Felton, Adam, et al.
(författare)
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The tree species matters : Biodiversity and ecosystem service implications of replacing Scots pine production stands with Norway spruce.
- 2020
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Ingår i: Ambio. - : Springer. - 0044-7447 .- 1654-7209. ; 49:5, s. 1035-1049
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Forskningsöversikt (refereegranskat)abstract
- The choice of tree species used in production forests matters for biodiversity and ecosystem services. In Sweden, damage to young production forests by large browsing herbivores is helping to drive a development where sites traditionally regenerated with Scots pine (Pinus sylvestris) are instead being regenerated with Norway spruce (Picea abies). We provide a condensed synthesis of the available evidence regarding the likely resultant implications for forest biodiversity and ecosystem services from this change in tree species. Apart from some benefits (e.g. reduced stand-level browsing damage), we identified a range of negative outcomes for biodiversity, production, esthetic and recreational values, as well as increased stand vulnerability to storm, frost, and drought damage, and potentially higher risks of pest and pathogen outbreak. Our results are directly relevant to forest owners and policy-makers seeking information regarding the uncertainties, risks, and trade-offs likely to result from changing the tree species in production forests.
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| 3. |
- Holmström, Emma, et al.
(författare)
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Productivity of Scots pine and Norway spruce in central Sweden and competitive release in mixtures of the two species
- 2018
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 0378-1127 .- 1872-7042. ; 429, s. 287-293
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Tidskriftsartikel (refereegranskat)abstract
- A 53 year old growth and yield study of monocultures and mixture of two species, Scots pine and Norway spruce, was analysed. The replacement design of the experiment (at the time of establishment same density in all treatments and 50/50 proportion in the mixture) in ten blocks enables the comparison of site and species specific growth, as well as the species specific response to competition. In monocultures Scots pine, produced 126% more stem wood biomass than Norway spruce. Scots pine benefitted from the mixture and tended to grow as in a reduced spacing. Norway spruce, the subdominant tree species, suffered from the competition, and produced less than expected, with a lower mean diameter compared to the monoculture. Hence, no facilitative or complementary effects was possible to prove when growing the species in mixture. However, the experiment demonstrates that if the uncertainties in choice of species at the time of regeneration is high, then the mixture of two species could be an option. The lower density of the highest yielding species in the mixture compared to the monoculture, could be compensated in growth by the competition release.
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| 4. |
- Holmström, Oscar, et al.
(författare)
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A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy.
- 2020
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.METHODS: Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.RESULTS: Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.CONCLUSION: Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.
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| 5. |
- Holmström, Oscar, et al.
(författare)
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Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting
- 2021
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Ingår i: JAMA Network Open. - : American Medical Association (AMA). - 2574-3805. ; 4:3
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Tidskriftsartikel (refereegranskat)abstract
- Importance: Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas.Objective: To determine whether artificial intelligence-supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests.Design, Setting, and Participants: In this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya.Exposures: Detection of squamous cell atypia in the digital samples by analysis with the DLS.Main Outcomes and Measures: The accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images.Results: Papanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 x 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (kappa = 0.72) and fair compared with the assessment of glass slides (kappa = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS.Conclusions and Relevance: In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.
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| 6. |
- Holmström, Oscar, et al.
(författare)
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Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium
- 2017
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Ingår i: Global Health Action. - : Informa UK Limited. - 1654-9716 .- 1654-9880. ; 10:sup3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis.OBJECTIVE: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images.METHODS: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs.RESULTS: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3-100%) in the test set (n = 217) of manually labeled helminth eggs.CONCLUSIONS: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.
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| 7. |
- Jönsson, Christina, et al.
(författare)
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Microplastics shedding from textiles-developing analytical method for measurement of shed material representing release during domestic washing
- 2018
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Ingår i: Sustainability. - : MDPI AG. - 2071-1050. ; 10:7
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Tidskriftsartikel (refereegranskat)abstract
- The topic of shedding of micro-sized polymeric particles, so called microplastics, from textiles has been covered by an increasing number of studies over the past years. However, the methods with which the shedding of microplastics from textiles has been measured so far has shown a large variation. Consequently, the results regarding the amount of shed particles also vary, from 120 to 728,289 particles from similar garments in recent studies. This article presents research enabling for identification of whether the shedding of microplastics from different types of fabric was dependent on construction parameters. As none of the methods in the existing literature could be used for evaluating shedding of microplastics from textiles, a method was developed for this purpose. The resulting final method is described in this paper as well as the work with minimizing the error sources and consequently the standard deviation of the results through selection of material samples, equipment and procedure for sample preparation, washing, filtering the washing water and analyzing the shed microplastics. Comparing the environmental load of different garments, or identifying improvement possibilities in garment construction are two examples of how the method can be utilized.
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| 8. |
- Lundin, Johan, et al.
(författare)
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Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting
- 2024
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Ingår i: PLoS Neglected Tropical Diseases. - : Public Library of Science (PLoS). - 1935-2727 .- 1935-2735. ; 18:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries.Methodology/Principal findings: Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples.Conclusions/Significance: Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs.
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