Increased complement activation is a distinctive feature of severe SARS-CoV-2 infection

Complement activation has been implicated in the pathogenesis of severe SARS-CoV-2 infection. However, it remains to be determined whether increased complement activation is a broad indicator of critical illness (and thus, no different in COVID-19). It is also unclear which pathways are contributing to complement activation in COVID-19, and if complement activation is associated with certain features of severe SARS-CoV-2 infection, such as endothelial injury and hypercoagulability. To address these questions, we investigated complement activation in the plasma from patients with COVID-19 prospectively enrolled at two tertiary care centers: Washington University School of Medicine (n=134) and Yale School of Medicine (n=49). We compared our patients to two non-COVID cohorts: (a) patients hospitalized with influenza (n=54), and (b) patients admitted to the intensive care unit (ICU) with acute respiratory failure requiring invasive mechanical ventilation (IMV, n=22). We demonstrate that circulating markers of complement activation are elevated in patients with COVID-19 compared to those with influenza and to patients with non-COVID-19 respiratory failure. Further, the results facilitate distinguishing those who are at higher risk of worse outcomes such as requiring ICU admission, or IMV. Moreover, the results indicate enhanced activation of the alternative complement pathway is most prevalent in patients with severe COVID-19 and is associated with markers of endothelial injury (i.e., angiopoietin-2) as well as hypercoagulability (i.e., thrombomodulin and von Willebrand factor). Our findings identify complement activation to be a distinctive feature of COVID-19, and provide specific targets that may be utilized for risk prognostication, drug discovery and personalized clinical trials

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Sustainable weed management

Weed management is an essential element of successful crop production. In recent times, an exponential rise in human population and drastic changes in climate and production techniques have intensified the crop production systems and increased the weed infestations. The evolution of herbicide resistance in a large number of weed species across the world has further aggravated the situation. These circumstances require sustainable weed management tools that can be used effectively to achieve decent crop yields without affecting the environment and ecosystem services negatively. Some of the conventional weed control methods, including the use of preventive measures, tillage and mechanical control, crop competition, soil coverage, crop rotations and crop diversification, are still effective and eco-friendly. The ecological phenomenon of allelopathy could also be explored in different ways for sustainable weed management. Recent advances in the fields of renewable energy, remote sensing, modelling, automation and robotics have opened new windows for more physical weed control methods such as thermal weed control, precision weed control and harvest weed seed control. These methods are quite expensive and do not suit all geographical conditions, but these are going through rapid evolution and modifications that will make them more affordable, precise and pragmatic in the near future. Although the ecologically-based, cultural and physical weed control methods have great potential for sustainable weed management, herbicides cannot be completely left out. In fact, herbicides could be an effective tool in an integrated weed management kit. However, the true integrated weed management strategies must have a balance between chemical and non-chemical options that can be used judiciously in order to achieve a reasonable weed control. Sustainable weed management is an achievable goal provided that efforts are made to diversify the management

Real Word Spelling Error Detection and Correction for Urdu Language

Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40&#x0025; error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67&#x0025;

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies

Trackable CEMB-Klean Cotton Transgenic Technology: Afforadable Climate Neutral Agri-biotech Industrialization for Developing Countries

Background: Transgenic technology reflects the incorporation of novel useful traits in crop plants like cotton for economic benefits by overcoming the problems including insects' pests and weeds in special. The present study is the success story of the continuous effort of CEMB team started back in the 1990s. Methods: This study includes characterization of a large number of Bacillus thuringiensis (Bt) strains taken from local soil and subjected to direct transformation of isolated BT genes into local cotton cultivars. Protocols for transformation into cotton plants were optimized and validated by the development of double gene codon optimized (Cry1Ac and Cry2A) transgenic cotton varieties. Results: The resulting GMOs in the form of CEMB-33, CA-12, CEMB-66 have been approved by Punjab Seed Council in 2013 and 2016 respectively. Double Bt and weedicide resistant cotton harboring CEMB-Modified and codon optimized cp4EPSPS (GTGene). These varieties can tolerate glyphosate spray @ 1900ml per acre without the appearance of necrotic spots/shedding and complete removal of all surrounding weeds in the cotton field is a significant advance to boost cotton production without spending much on insecticides and herbicides. Conclusion: In the current report, two unique sets of primers which amplify 1.1 Kb for CEMB-double Bt genes and 660 bp product for CEMB-Modified cp4EPSPS (GTGene) were tested. CEMB cotton variety CKC-01 is specially designed as low cost and easy to use by local farmer's technology has the potential to revolutionize the cotton growing culture of the country.Higher Education Commission (HEC) of PakistanOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]