ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.

Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning-driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF-VIM) yielded the highest five-fold cross-validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting-edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms.

BACKGROUND: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic stresses. Therefore, it is crucial to identify TFs associated with abiotic stress response for breeding of abiotic stress tolerant crop cultivars. METHODS: Based on a machine learning framework, a computational model was envisaged to predict TFs associated with abiotic stress response in plants. To numerically encode TF sequences, four distinct sequence derived features were generated. The prediction was performed using ten shallow learning and four deep learning algorithms. For prediction using more pertinent and informative features, feature selection techniques were also employed. RESULTS: Using the features chosen by the light-gradient boosting machine-variable importance measure (LGBM-VIM), the LGBM achieved the highest cross-validation performance metrics (accuracy: 86.81%, auROC: 92.98%, and auPRC: 94.03%). Further evaluation of the proposed model (LGBM prediction method + LGBM-VIM selected features) was also done using an independent test dataset, where the accuracy, auROC and auPRC were observed 81.98%, 90.65% and 91.30%, respectively. CONCLUSIONS: To facilitate the adoption of the proposed strategy by users, the approach was implemented as a prediction server called ASPTF, accessible at https://iasri-sg.icar.gov.in/asptf/. The developed approach and the corresponding web application are anticipated to supplement experimental methods in the identification of transcription factors (TFs) responsive to abiotic stress in plants.

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms.

DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.

Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).

In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and ß-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.

RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features.

RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.

ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants.

Abiotic stresses have become a major challenge in recent years due to their pervasive nature and shocking impacts on plant growth, development, and quality. MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of specific abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational model for prediction of miRNAs associated with four specific abiotic stresses such as cold, drought, heat and salt. The pseudo K-tuple nucleotide compositional features of Kmer size 1 to 5 were used to represent miRNAs in numeric form. Feature selection strategy was employed to select important features. With the selected feature sets, support vector machine (SVM) achieved the highest cross-validation accuracy in all four abiotic stress conditions. The highest cross-validated prediction accuracies in terms of area under precision-recall curve were found to be 90.15, 90.09, 87.71, and 89.25% for cold, drought, heat and salt respectively. Overall prediction accuracies for the independent dataset were respectively observed 84.57, 80.62, 80.38 and 82.78%, for the abiotic stresses. The SVM was also seen to outperform different deep learning models for prediction of abiotic stress-responsive miRNAs. To implement our method with ease, an online prediction server "ASmiR" has been established at https://iasri-sg.icar.gov.in/asmir/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for identification of specific abiotic stress-responsive miRNAs in plants.

ASLncR: a novel computational tool for prediction of abiotic stress-responsive long non-coding RNAs in plants.

Abiotic stresses are detrimental to plant growth and development and have a major negative impact on crop yields. A growing body of evidence indicates that a large number of long non-coding RNAs (lncRNAs) are key to many abiotic stress responses. Thus, identifying abiotic stress-responsive lncRNAs is essential in crop breeding programs in order to develop crop cultivars resistant to abiotic stresses. In this study, we have developed the first machine learning-based computational model for predicting abiotic stress-responsive lncRNAs. The lncRNA sequences which were responsive and non-responsive to abiotic stresses served as the two classes of the dataset for binary classification using the machine learning algorithms. The training dataset was created using 263 stress-responsive and 263 non-stress-responsive sequences, whereas the independent test set consists of 101 sequences from both classes. As the machine learning model can adopt only the numeric data, the Kmer features ranging from sizes 1 to 6 were utilized to represent lncRNAs in numeric form. To select important features, four different feature selection strategies were utilized. Among the seven learning algorithms, the support vector machine (SVM) achieved the highest cross-validation accuracy with the selected feature sets. The observed 5-fold cross-validation accuracy, AU-ROC, and AU-PRC were found to be 68.84, 72.78, and 75.86%, respectively. Furthermore, the robustness of the developed model (SVM with the selected feature) was evaluated using an independent test dataset, where the overall accuracy, AU-ROC, and AU-PRC were found to be 76.23, 87.71, and 88.49%, respectively. The developed computational approach was also implemented in an online prediction tool ASLncR accessible at https://iasri-sg.icar.gov.in/aslncr/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for the identification of abiotic stress-responsive lncRNAs in plants.

PlDBPred: a novel computational model for discovery of DNA binding proteins in plants.

DNA-binding proteins (DBPs) play crucial roles in numerous cellular processes including nucleotide recognition, transcriptional control and the regulation of gene expression. Majority of the existing computational techniques for identifying DBPs are mainly applicable to human and mouse datasets. Even though some models have been tested on Arabidopsis, they produce poor accuracy when applied to other plant species. Therefore, it is imperative to develop an effective computational model for predicting plant DBPs. In this study, we developed a comprehensive computational model for plant specific DBPs identification. Five shallow learning and six deep learning models were initially used for prediction, where shallow learning methods outperformed deep learning algorithms. In particular, support vector machine achieved highest repeated 5-fold cross-validation accuracy of 94.0% area under receiver operating characteristic curve (AUC-ROC) and 93.5% area under precision recall curve (AUC-PR). With an independent dataset, the developed approach secured 93.8% AUC-ROC and 94.6% AUC-PR. While compared with the state-of-art existing tools by using an independent dataset, the proposed model achieved much higher accuracy. Overall results suggest that the developed computational model is more efficient and reliable as compared to the existing models for the prediction of DBPs in plants. For the convenience of the majority of experimental scientists, the developed prediction server PlDBPred is publicly accessible at https://iasri-sg.icar.gov.in/pldbpred/.The source code is also provided at https://iasri-sg.icar.gov.in/pldbpred/source_code.php for prediction using a large-size dataset.

ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants.

One of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress-responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K-mer compositional features were used as input. With ACC, K-mer, and ACC + K-mer compositional features, the overall accuracy of ∼60-77, ∼75-86, and ∼61-78% were respectively obtained using the SVM algorithm with fivefold cross-validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross-validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri-sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins.

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India.

BACKGROUND: Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. METHODS: In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. RESULTS: An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. CONCLUSIONS: Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features.

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server "ASRmiRNA" has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.

Comparison of Oral Triclofos and Oral Midazolam as Premedication in Children undergoing Elective Surgery.

BACKGROUND: Children who have experienced previous hospital admission, operation, procedures, and needle pricks are more reactive to subsequent anesthetic procedures. Many sedative agents have been used for the purpose of premedication, but few of them can be given orally, thus avoiding the pricks. Midazolam, being one such choices, can be given orally, intranasally, and parenterally but has unpredictable response. Triclofos, available as sweet syrup, is a phosphorylated derivative of chloral hydrate, has been proven to be effective within 30 min in doses of 25-75 mg/kg. Hence, this study compares triclofos hydrochloride with midazolam oral to know the efficacy of both the drugs as premedication. AIM: This study aims to assess sedation score, level of anxiety/resistance, and behavior of the child in the preoperative period. SETTINGS AND DESIGN: After parental and institutional approval, a total of 70 children were studied based on computer-generated randomization and divided into groups M and T of 35 each. MATERIALS AND METHODS: Group M patients received oral midazolam 0.5 mg/kg. Group T patients received commercially available triclofos syrup containing 100 mg/ml of drug in dose of 75 mg/kg. The response of children to taste of premedication was noted, whether completely ingested or not. In case of vomiting, the child was excluded from further study. STATISTICAL ANALYSIS: Numerical variables were analyzed using Student's paired t-test and other variables using Mann-Whitney U-test, Fisher exact test, and Friedman ANOVA. RESULTS: Sedation score at 5 min interval from 0 to 30 min showed P = 0.54, 0.71, 0.65, 0.92, 0.29, 0.42, and 0.15; none were statistically significant. Anxiety score during parental separation, intravenous cannulation, and mask application were also similar in both the groups. CONCLUSION: From data obtained, it can be concluded that parenteral formulation of either midazolam or triclofos can be safely used as premedicant in children.

Anesthetic issues and Difficult Airway Management in a Case of Grisel's Syndrome.

Grisel's syndrome is a nontraumatic subluxation of atlanto-axial joint which is associated with inflammatory conditions of head and neck and occurs primarily in children. Anesthetic management is such cases constitute a multitude of challenges, especially related to the airway management. We presented here a case of 16-year-old male child weighing 23 kg, came to our hospital for the treatment of torticollis who was previously treated with intravenous antibiotics for rhinopharyngitis and diagnosed as a case of Grisel's syndrome. The child was operated on for unilateral resection of sternocleidomastoid muscle under general anesthesia. This case report pertains to the successful airway and anesthetic management in the background of difficult airway.

Comparisons of single-injection thoracic paravertebral block with ropivacaine and bupivacaine in breast cancer surgery: A prospective, randomized, double-blinded study.

BACKGROUND: Regional anesthesia using paravertebral block has been suggested as an ideal adjunct to general anesthesia for modified radical mastectomy. Paravertebral block is an effective management of peri-operative pain for Modified radical mastectomy, however, there are no established guidelines regarding what is the most suitable strategy when varying drugs and dosages between different groups. AIM: To evaluate the effectiveness of paravertebral block comparing the most frequently employed drugs in this procedure (bupivacaine vs ropivacaine). STUDY DESIGN: Prospective randomized double blind study. METHODS: A total 70 ASA I and II adult female patients undergoing Modified radical mastectomy under paravertebral block followed by general anesthesia were randomly divided into two groups. The first group was administered 0.375% Ropivacaine in a dose 0.25 ml /kg in paravertebral block. The second group was administered bupivacaine 0.375% in dose 0.25 ml /kg in paravertebral block. Standard induction technique followed. Heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), were recorded pre block, post block 5 min, post block 10 min, at skin incision, post skin incision initially at 5 interval for first 15 min till one hour, and every 30 min till end of surgery. Post-operative visual analogue score for pain was recorded at 1 hr, 6 hr and 24 hr. STATISTICAL ANALYSIS: Chi-square test (Fisher's exact test) for qualitative variables. Independent sample t-test for quantitative data. RESULTS: Ropivacaine and Bupivacaine had no difference in intraoperative analgesia as shown by intraoperative hemodynamic parameters. Bupivacaine got better post-operative VAS scores (P < 0.05) in mean and after first, 6 h and 24 h.

Intermolecular Tl···H-C anagostic interactions in luminescent pyridyl functionalized thallium(I) dithiocarbamates.

Crystal structures of novel pyridyl functionalised [Tl(L)]∞ (L = (N-benzyl-N-methylpyridyl) dithiocarbamate(L1) 1, bis(N-methylpyridyl) dithiocarbamate(L2) 2, (N-methyl(1,4-benzodioxane-6-yl)-N-methylpyridyl)dithiocarbamate(L3) 3, (N-ferrocenyl-N-methylpyridyl) dithiocarbamate(L4) 4) complexes revealed rare intermolecular C-H···Tl anagostic and C-S···Tl interactions forming a six-membered chelate ring about the metal center, which have been assessed by DFT calculations. The strong thallophilic bonding is responsible for the strong luminescent characteristics of the complexes in the solid phase.

Photosensitizing activity of ferrocenyl bearing Ni(II) and Cu(II) dithiocarbamates in dye sensitized TiO2 solar cells.

Biferrocene bearing planar metal dithiocarbamates, namely, [M(FcCH2dtc)2] (dtc = furan-2-ylmethyldithiocarbamate, M = Cu(II) 1, Ni(II) 4; dtc = benzo[d][1,3]dioxol-5-ylmethyl dithiocarbamate, M = Cu(II) 2, Ni(II) 5; dtc = pyridin-2-ylmethyldithiocarbamate, M = Cu(II) 3, Ni(II) 6; Fc = ferrocenyl; Fe(η(5)-C5H5)(η(5)-C5H4-)), have been synthesized and characterized by microanalysis, magnetic susceptibility and cyclic voltammetry. Structures of 1, 2 and 4 have been obtained by single crystal X-ray diffraction. These complexes with pyridyl, piperonyl and furfuryl as heteroaromatic groups in the dithiocarbamate ligands have been exploited as sensitizers in dye sensitized TiO2 solar cells for converting sunlight into electrical energy. Light-to-electrical energy conversion efficiencies achieved using these sensitizers are considerably greater than those obtained with analogous compounds previously reported by us. The overall conversion efficiency (η) is found to be dependent upon the nature of the heteroaromatic conjugated linkers and increases in the order η (ferrocenylfurfuryl) > η (ferrocenylpiperonyl) > η (ferrocenylpyridyl) all values being lower than that obtained in the reference Ru dye N719 under similar experimental conditions. The conversion efficiencies also vary with the metal being higher for Ni (4, 5 and 6) than for Cu complexes (1, 2 and 3). The X-ray structural analyses reveal the existence of rare M···H-C intermolecular anagostic interactions involving the metal atom in chain motifs in 1 and 4, which are retained in solution as evidenced by (1)H NMR spectroscopy.

Bell palsy in a neonate with rapid response to oral corticosteroids: a case report.

Idiopathic facial nerve palsy, also known as Bell palsy is rare in the neonatal age group. Other more common causes such as birth trauma; infections, especially otitis media; and congenital malformations need to be excluded. We present here a 4-week-old neonate with Bell palsy who responded rapidly to oral corticosteroids. Such an early presentation of idiopathic facial nerve palsy and use of corticosteroids in neonates is scarcely reported in the literature.

Sudden endotracheal tube block in a patient of Achalasia Cardia.

Endotracheal tube block due to various mechanical causes such as mucous, blood clot, denture, and ampoules have been reported. A patient of achalasia cardia with chronic passive aspiration pneumonitis developed mucoid mass in the respiratory passage which dislodged during the surgical procedure. The episode occurred almost an hour after induction of anesthesia and the dislodged mucoid mass blocked the lumen of endotracheal tube, leading to hypoxia and impending cardiac arrest. However, the patient was salvaged by replacing the tube.

Strong positive growth responses to salinity by Ceriops tagal, a commonly occurring mangrove of the Gujarat coast of India.

BACKGROUND AND AIMS: Mangroves of Western Gujarat (India) are subject to die-back. Salinity intolerance is one possible cause, especially in young plants. We therefore quantified the extent to which young plants of one widely occurring mangrove species (Ceriops tagal) tolerate high salt in terms of establishment, growth, water status, proline content and mineral accumulation. METHODOLOGY: In a greenhouse study, juvenile plants were established from mature propagules over 40 days in soil containing added NaCl, raising soil water salinity to 0.2, 2.5, 5.1, 7.7, 10.3, 12.6, 15.4, 17.9, 20.5 and 23.0 ppt (w/v). Growth and physiological characteristics were monitored over the subsequent 6 months. PRINCIPAL RESULTS: Despite a negative relationship between the percentage of young plant establishment and salt concentration (50 % loss at 22.3 ppt), the remaining plants proved highly tolerant. Growth, in dry weight, was significantly promoted by low salinity, which is optimal at 12.6 ppt. Water content, leaf expansion and dry matter accumulation in tissues followed a similar optimum curve with leaf area being doubled at 12.6 ppt NaCl. Salinity >12.6 and <23 ppt inhibited plant growth, but never to below control levels. Root:shoot dry weight ratios were slightly reduced by salinity (maximum 19 %), but the water potential of roots, leaves and stems became more negative as salinity increases while proline increases in all tissues. The concentration of Na increased, whereas concentrations of K, Ca, N and P decreased and that of Mg remained stable. CONCLUSIONS: Ceriops tagal has a remarkably high degree of salinity tolerance, and shows an optimal growth when soil water salinity is 12.6 ppt. Salinity tolerance is linked to an adaptive regulation of hydration and ionic content. The cause of localized die-back along the coastal region of Gujarat is thus unlikely to be a primary outcome of salinity stress although amendments with Ca and K, and perhaps proline, may help protect against extreme salinity.