Efficacy of Laccases Obtained from the White Rot Basidiomycete \u3cem\u3eSchizophyllum commune\u3c/em\u3e-NI 07 in Enhancing the \u3cem\u3ein Vitro\u3c/em\u3e Digestibility of Crop Residues for Ruminants

Crop residues like maize stover, finger millet straw, wheat straw, rice straw, etc. generally constitute the main dietary component for animals. The rumen microbial utilization of these crop residues is hindered by the presence of lignin, which limits its overall digestion process and can significantly influence animal performance, because it is resistant to most of the enzymatic hydrolysis by microorganisms. In nature lignin is degraded by lignolytic enzymes of white rot fungi (WRF). These residues can thus be converted into high quality feed by means of fungal delignification improving their nutritive value. Fungal ligninolysis breaks down the cellulose-hemicellulose matrix liberating degradable components utilizable by rumen microflora. Earlier we reported on the enhancement in digestibility of ragi straw with lignolytic enzyme extracts. Laccase is one amongst these lignolytic enzymes holding immense potential in biodelignification of crop residues (Sridhar et al., 2014). However, its low level in the native state limits its practical use in the degradation of lingo cellulosics for ruminants necessitating the need for enhancing production. In the current work we report the efficacy of laccases isolated from Schizophyllum commune, in enhancing in vitro digestibility of some commonly used crop residues for ruminants

Fostering Youth-Led Innovations to Accelerate Progress on the United Nations Sustainable Development Goals: A Guide for Policy Makers at COP28

In today’s world, to address the most pressing global challenges, education must equip all learners with the values, skills, and knowledge that nurture cooperation, resilience, respect for diversity, gender justice, and human rights. This concept is called Global Citizenship Education which is a target of the Sustainable Development Goal 4 – Quality Education. I commend the Mission 4.7 initiative facilitated by Columbia University’s Center for Sustainable Development, UNESCO, UN SDSN and the Ban Ki-moon Centre for Global Citizens, for playing a pivotal role in addressing SDG Target 4.7 and on the release of the “Fostering Youth-led Innovations to Accelerate Progress on the United Nations Sustainable Development Goals: A Guide for Policymakers at COP28. The report recommends that policymakers create supportive environments for youth innovators by establishing or opening innovation hubs, incubators, and accelerators for young individuals. A key element is the renewed emphasis on integrating global citizenship and systems thinking into school curricula to foster sustainable development. Global Citizenship Education and youth empowerment is essential for a better future, I hope that this report contributes to shaping the agenda on SDG Target 4.7 at COP28 and beyond. H.E. Ban Ki-moon8th Secretary-General, United Nations Co-chair, Mission 4.7Co-chair, Ban Ki-moon Centre for Global Citizen

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Assessment of Cognitive Aging Using an SSVEP-Based Brain–Computer Interface System

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain−computer interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes steady-state visually evoked potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital Electroencephalographic (EEG) responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20−40, and band power declined throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20−40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age-related cognitive decline

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In nature lignin degrading enzymes of white rot fungi (WRF) degrade lignin, amongst which laccase plays an important role. Our objective was to screen for laccase secreting indigenous WRF. Culture-dependant and molecular methods were used in combination for identification and characterization. NI-07, the most potent laccase producing isolate was identified as Schizophyllum commune, strain (MTCC- 11893). It had pinkish grey split gills, radiating from the attachment point and cylindrical to ellipsoidal smooth white spores. The generative hyphae were thin-walled, having septa and clamp connections, skeletal hyphae were swollen centrally and broad and binding hyphae were comparatively thick-walled and branched. The ITS/ 5.8S rRNA gene sequence as per phylogenetic tree results showed best matching with Agaricaceae sp. 647 (Sequence ID: gb|JQ312209.1|) as per cladogram while the phylogram showed significant variation and is deposited with GenBank (Bank It 1679236 Schizophyllum KF911323). Production of lignolytic enzymes by S.commune was investigated in liquid medium under conditions of vegetative growth as well as solid state fermentation in comparison to the reference culture Trametes versicolor . Laccase was the sole enzyme detected in S. commune with highest activities of 737.78±42.1UmL-1 being obtained in submerged fermentation on day 7 and 338.62±42.5U mL-1 in SSF on day 5 and was considerably higher in comparison to T.versicolor. A preliminary investigation conducted to assess the efficacy of this WRF in delignification confirmed its competence in enhancing digestibility of straw by 10- 14%.Department of Science and Technology (DST), Govt. of Indi

A Deep Neural Network for Working Memory Load Prediction from EEG Ensemble Empirical Mode Decomposition

Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of WM dysfunction is essential to predict the onset of MCI and AD. Artificial Intelligence (AI)-based algorithms are increasingly used to identify biomarkers for detecting subtle changes in loaded WM. This paper presents an approach using electroencephalograms (EEG), time-frequency signal processing, and a Deep Neural Network (DNN) to predict WM load in normal and MCI-diagnosed subjects. EEG signals were recorded using an EEG cap during working memory tasks, including block tapping and N-back visuospatial interfaces. The data were bandpass-filtered, and independent components analysis was used to select the best electrode channels. The Ensemble Empirical Mode Decomposition (EEMD) algorithm was then applied to the EEG signals to obtain the time-frequency Intrinsic Mode Functions (IMFs). The EEMD and DNN methods perform better than traditional machine learning methods as well as Convolutional Neural Networks (CNN) for the prediction of WM load. Prediction accuracies were consistently higher for both normal and MCI subjects, averaging 97.62%. The average Kappa score for normal subjects was 94.98% and 92.49% for subjects with MCI. Subjects with MCI showed higher values for beta and alpha oscillations in the frontal region than normal subjects. The average power spectral density of the IMFs showed that the IMFs (p = 0.0469 for normal subjects and p = 0.0145 for subjects with MCI) are robust and reliable features for WM load prediction

Morphological and phylogenetic identification of a hyper laccase producing strain of <em>Schizophyllum commune</em> NI-07 exhibiting delignification potential

302-315In nature lignin degrading enzymes of white rot fungi (WRF) degrade lignin, amongst which laccase play an important role. Our objective was to isolate hyper laccase secreting indigenous WRF. Culture-dependant and molecular methods were used in combination for identification and characterization. The isolate NI-07, the most potent laccase producing isolate was identified as Schizophyllum commune, strain (MTCC- 11893). It had pinkish grey split gills, radiating from the attachment point and cylindrical to ellipsoidal smooth white spores. The generative hyphae were thin-walled, having septae and clamp connections, skeletal hyphae were swollen centrally and broad and binding hyphae were comparatively thick-walled and branched. The ITS/5.8S rRNA gene sequence as per phylogenetic tree results showed best matching with Agaricaceae sp. 647 (Sequence ID: gb|JQ312209.1|) as per cladogram while the phylogram showed significant variation and is deposited with GenBank (Bank It 1679236 Schizophyllum KF911323). Production of lignolytic enzymes by S. commune was investigated in liquid medium under conditions of vegetative growth as well as solid state fermentation in comparison to the reference culture Trametes versicolor. Laccase was the sole enzyme detected in S. commune with highest activities of 737.78 ± 42.1 U mL-1 being obtained in submerged fermentation on day 7 and 338.62 ± 42.5 U mL-1 in SSF on day 5 and was considerably higher in comparison to T. versicolor. A preliminary investigation conducted to assess the efficacy of this WRF in delignification confirmed its competence in enhancing digestibility of straw by 10-14%

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Not AvailableA high demand for fungal laccases on account of innumerable biotechnological applications necessitates enhanced production. Three novel isolates of white rot fungi (WRF) designated NI-07, NI-09 and NI-04 were subjected to submerged cultivation in liquid basal medium for laccase production. Of a total of 23 factors evaluated, 22 factors were selected for GLM using Proc GLM procedure of SAS (Version 9.3). The first optimization step identified the significant factors for laccase production and were used to construct RSM using PROC RSREG of SAS along with Lack Fit to maximize laccase enzyme activity. The statistical software, Design-Expert (version 9.0.1.0) was used to analyze the data and generate Response Surface Graphs (3D) for statistical optimal condition given by SAS (RSM) to build an optimized response using Response Surface Methodology (RSM). The initial laccase activity of 737U/mL increased to 7833U/mL in isolate NI-07, from 700 U/mL to7480 U/mL in NI-09 and from 1132 U/mL to 11141 U/mL in NI-04. When compared to the conventional method, a 10 fold increase in laccase activity could be obtained in the three isolates of WRF (10.62 for NI-07, 10.68 for NI-09 and9.84 for NI-04) after statistical optimization employing RSM. Validation experiments proved that experimentally determined production values were in close agreement with statistically predicted ones, confirming the reliability of the model. The results of this study serve as reference for optimization of medium composition for enhancing laccase production in WRF in submerged fermentation. Through statistical optimization maximum yields of laccase could be achieved at a minimum production cost.Not Availabl

Improved Image Search and Retrieval based on Dominant Colors

Growth and development of multimedia technology has led to an exponential increase in visual information. Where traditional keyword based information retrieval techniques just do not meet the users demand, CBIR hopes to prevail. CBIR refers to the retrieval of images from a database using information derived from the images themselves rather than solely from accompanying text indices. In this paper we describe an approach of content based retrieval of images from a database, based on dominant colors in the foreground and background of the image. These dominant colors along with histogram and some statistical features form a substantial set to determine the overall similarity between the images. This technique is tested on Simplicity test database and promising results are observed