Contrastive Self-supervised Learning for Neurodegenerative Disorder Classification.

Neurodegenerative diseases such as Alzheimer's disease (AD) or frontotemporal lobar degeneration (FTLD) involve specific loss of brain volume, detectable in vivo using T1-weighted MRI scans. Supervised machine learning approaches classifying neurodegenerative diseases require diagnostic-labels for each sample. However, it can be difficult to obtain expert labels for a large amount of data. Self-supervised learning (SSL) offers an alternative for training machine learning models without data-labels. We investigated if the SSL models can applied to distinguish between different neurodegenerative disorders in an interpretable manner. Our method comprises a feature extractor and a downstream classification head. A deep convolutional neural network trained in a contrastive self-supervised way serves as the feature extractor, learning latent representation, while the classifier head is a single-layer perceptron. We used N=2694 T1-weighted MRI scans from four data cohorts: two ADNI datasets, AIBL and FTLDNI, including cognitively normal controls (CN), cases with prodromal and clinical AD, as well as FTLD cases differentiated into its sub-types. Our results showed that the feature extractor trained in a self-supervised way provides generalizable and robust representations for the downstream classification. For AD vs. CN, our model achieves 82% balanced accuracy on the test subset and 80% on an independent holdout dataset. Similarly, the Behavioral variant of frontotemporal dementia (BV) vs. CN model attains an 88% balanced accuracy on the test subset. The average feature attribution heatmaps obtained by the Integrated Gradient method highlighted hallmark regions, i.e., temporal gray matter atrophy for AD, and insular atrophy for BV. In conclusion, our models perform comparably to state-of-the-art supervised deep learning approaches. This suggests that the SSL methodology can successfully make use of unannotated neuroimaging datasets as training data while remaining robust and interpretable.

Enhanced Four-Electron Selective Oxygen Reduction Reaction at Carbon-Nanotube-Supported Sulfonic-Acid-Functionalized Copper Phthalocyanine.

In the present work, the oxygen reduction reaction (ORR) is explored in an acidic medium with two different catalytic supports (multi-walled carbon nanotubes (MWCNTs) and nitrogen-doped multi-walled carbon nanotubes (NMWCNTs)) and two different catalysts (copper phthalocyanine (CuPc) and sulfonic acid functionalized CuPc (CuPc-SO3 - )). The composite, NMWCNTs-CuPc-SO3 - exhibits high ORR activity (assessed based on the onset potential (0.57 V vs. reversible hydrogen electrode) and Tafel slope) in comparison to the other composites. Rotating ring disc electrode (RRDE) studies demonstrate a highly selective four-electron ORR (less than 2.5 % H2 O2 formation) at the NMWCNTs-CuPc-SO3 - . The synergistic effect of the catalyst support (NMWCNTs) and sulfonic acid functionalization of the catalyst (in CuPc-SO3 - ) increase the efficiency and selectivity of the ORR at the NMWCNTs-CuPc-SO3 - . The catalyst activity of NMWCNTs-CuPc-SO3 - has been compared with many reported materials and found to be better than several catalysts. NMWCNTs-CuPc-SO3 - shows high tolerance for methanol and very small deviation in the onset potential (10 mV) between the linear sweep voltammetry responses recorded before and after 3000 cyclic voltammetry cycles, demonstrating exceptional durability. The high durability is attributed to the stabilization of CuPc-SO3 - by the additional coordination with nitrogen (Cu-Nx ) present on the surface of NMWCNTs.

Clinical Profile of Adult Bronchial Asthma Patients Presenting at a Tertiary Care Teaching Institute in Northern India.

BACKGROUND: In the previous four decades there have been remarkable changes and development in the approach toward the diagnosis and management of asthma. There are wide variations in the clinical profile of asthma patients in different parts of a vast country like India due to significant variations in the geography, culture, ethnicity, and socioeconomic profile of the Indian population. In the present study, we have aimed to study the clinical profile of adult asthmatic patients in a tertiary care teaching institute in Northern India. METHODS: In this observational cross-sectional study, a total of 966 asthma patients were included from August 2020 to July 2021 after following strict inclusion and exclusion criteria. After a thorough history and clinical examination, patients were subjected to relevant investigations including spirometry. RESULTS: Our study showed slight female preponderance (51.7%) over males among asthma patients. A maximum number of patients were of comparatively younger age groups and urban. The most common symptom at the time of presentation was breathlessness (94.5%) followed by cough in about 59.8%. Family history was present in about 9.3% of patients. A maximum number of patients presented in the months of November and December and rhinitis was the most commonly associated atopic condition. The majority (65.28%) of previously diagnosed patients had uncontrolled asthma at the time of their first presentation in our department. CONCLUSION: Results of our study endorse the poor awareness in society towards education and management of asthma. Females and comparatively younger patients are more commonly affected. Significant differences in our study from previous studies in different parts of India confirm that the pattern and clinical profile of asthma patients in one region cannot be extrapolated to other regions and the need for future studies in other regions of our country is also required.

Subacute invasive pulmonary aspergillosis in a nondiabetic immunocompetent female suffering from allergic broncho pulmonary aspergillosis.

A 55-year-old female presented with recent exacerbation of the chronic cough, dyspnea, and copious expectoration. The symptoms worsened during the winter months. In the past, she was misdiagnosed with pulmonary tuberculosis. A computed tomography scan revealed bronchiectasis changes, high attenuated mucus, and hypereosinophilia. The diagnosis of Allergic Bronchopulmonary Aspergillosis (ABPA) with subacute invasion was confirmed through bronchoscopy and fungal culture. Treatment with oral voriconazole significantly improved lung function and quality of life. This case highlights the importance of considering invasive pulmonary aspergillosis in patients with exacerbations of asthma and bronchiectasis. Early diagnosis and appropriate treatment are essential for improved outcomes in such cases.

Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter.

The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people's location. Global development, natural disasters, change in climatic conditions, agriculture crop growth, etc., are monitored using satellite observation. Hence, the satellite includes detailed information data, and it must be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data play an important role in this modern world; hence, the onboard-satellite data must secure through the proper selection of error detection and estimation schema. Lightweight deep learning algorithm based on Extended Kalman Filter (KFK) is proposed to detect and estimate onboard pointing error such as an error in attitude and orbit. The Extended Kalman Filter (EKF) is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne gravity. The information obtained through the observation of satellite data is compared with the accurate gravity model in detecting the error. The utilization of EKF reduces the dependence of the ground tracking system in satellite determination. The orbital altitude and orbital position are the most important challenges faced in the satellite determination system. The satellite model using the Extended Kalman Filter is an optimum method in estimating the orbital parameters. The errors in the linearization process are detected, and this can be overcome through the proper selection of linear expansion point with the EKF algorithmic model with the Jacobian matrix calculation. The results show that the EKF implementation helps in attaining better accuracy than other methodologies. Its contribution is enormous to many space missions, autonomous rendezvous and docking for manned and unmanned missions (e.g., ISS operations and beyond, in-orbit servicing, and in-orbit refueling), routine satellite OD operations, orbital debris removal systems, Space Situational Awareness (SSA) operations, and others.

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.

Recurrent Neural Networks for Feature Extraction from Dengue Fever.

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human's life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart's electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT-BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.

C2 -Symmetric Bissulfoximine-Based Metal Complexes: Synthesis, Characterization and Their Electrocatalytic Activity in Oxygen Reduction Reaction.

Sulfoximine-based metal complexes have gained much interest in recent years owing to their promising properties in synthetic organic chemistry. These sulfoximines have been recently introduced in the field of organometallics. However, the coordination properties of pincer sulfoximine ligands and their metal complexes have not been reported. Here, a C2 -symmetric bissulfoximine-based pincer ligand ((pyridine-2,6-diylbis(methylene))bis(azaneylylidene))bis(diphenyl-λ6 -sulfanone), namely S-P ligand and its metal [M(SP)2 ]2+ {M=Fe (SC-1), Co (SC-2), Ni (SC-3), Cu (SC-4)} complexes, were synthesized. The complexes were characterized by different spectroscopic techniques like UV-Vis, IR, NMR, ESI-MS, and single-crystal XRD. Complexes SC-1 and SC-3 exhibited octahedral geometry irrespective of the metal center. The complexes were systematically explored for electrocatalytic oxygen reduction reaction (ORR) by immobilizing them on a glassy carbon electrode using Nafion. These complexes showed selective two-electron catalytic reduction of O2 to H2 O2 in which complex SC-2 showed the highest selectivity (≈90%) and complex SC-4 showed the highest onset potential (0.75 V).

A Rare Case of Sternoclavicular Tuberculosis Diagnosed Using Microchip-Based Polymerase Chain Reaction in a Diabetic Female.

Osteoarticular tuberculosis (TB) accounts for almost 10% of all extrapulmonary TB cases. In the majority of cases, the spine, knee, hip, and large bones are involved; other sites like the sternoclavicular joint, elbow, wrist, and smaller joints are infrequently involved. Uncommon locations of extrapulmonary TB pose a challenge in diagnosis due to lack of clinical suspicion, non-availability of samples, and unavailability of suitable diagnostic modalities. Here we report a case of uncommon location of osteoarticular TB diagnosed through microchip-based real-time polymerase chain reaction (PCR).

Antiproliferative activity and electrochemical oxygen evolution by Ni(II) complexes of N'-(aroyl)-hydrazine carbodithioates.

The electrochemical water splitting by transition metal complexes is emerging very rapidly. The nickel complexes also play a very vital role in various biological activities. Here, three new ligands {H2mbhce = N'-(4-methyl-benzoyl), H2pchce = N'-(pyridine-carbonyl) and H2hbhce = N'-(2-hydroxy-benzoyl) hydrazine carbodithioic acid ethyl ester} and their corresponding Ni(II) complexes [Ni(Hmbhce)2(py)2] (1), [Ni(pchce)(o-phen)2]·CH3OH·H2O (2) and [Ni(hbhce)(o-phen)2]·1.75CHCl3·H2O (3) have been synthesized and fully characterized by various physicochemical and X-ray crystallography techniques. The photoluminescence study and thermal degradations were also examined. The treatment of K562 cells with the increasing concentrations of the nickel salts, ligands, and complexes 1, 2, and 3 showed dose-dependent cytotoxicity. The cytotoxic activity of ligands reveals that ligand H2mbhce is more potent in inhibiting the growth of tumor cells in comparison to other ligands H2pbhce and H2hbhce. Cytotoxicity assay results indicate that all complexes have remarkable cytotoxic potential in comparison to either nickel salts or the free ligands. Among these complexes, complex 1 has significantly better anti-tumor activity as compared to complexes 2 and 3. The electrochemical study of complexes 1, 2, and 3 for water oxidation reveals that all the complexes possess admirable electrocatalytic activity towards oxygen evolution reaction (OER) and have lower overpotential (328, 338, and 370 mV, respectively) than many previously reported complexes and RuO2 (390 mV). Among complexes 1, 2, and 3, complex-2 shows a better water oxidation response. Consequently, these complexes have great potential to be utilized in fuel cells. The more reliable electrochemical parameter TOF is also calculated for all three complexes.

Metal (Mn, Fe, Co, Ni, Cu, and Zn) Phthalocyanine-Immobilized Mesoporous Carbon Nitride Materials as Durable Electrode Modifiers for the Oxygen Reduction Reaction.

In the search for alternative sources to replace fossil fuels, carbon nitride materials can be used in a variety of ways. In the present work, porosity is introduced to the carbon nitride material using mesoporous silica material, MCM-41, as a hard template, and a mesoporous carbon nitride (MCN) material is synthesized. Further, the MCN is modified by immobilizing metal phthalocyanine (MPc, where M = Mn, Fe, Co, Ni, Cu, and Zn). The resulting MPc-incorporated MCN materials (MPc@MCN) were tested for the electrocatalytic oxygen reduction reaction (ORR) in acidic and basic media. Detailed studies reveal that the FePc@MCN and CoPc@MCN materials exhibit higher ORR activity than the other composites in 0.1 M KOH. FePc@MCN follows a direct four-electron oxygen reduction mechanism and shows ORR onset potential (vs RHE) at 0.93 V (in 0.1 M KOH), which is very close to the onset potential exhibited by the state-of-the-art material, Pt-C (1.0 V), and higher than several similar composites of MPc with carbon supports tested in similar environments. Besides, due to the inherent property of coordination through nitrogen present on the MCN, FePc@MCN shows excellent stability even after 3000 cyclic voltammetry (CV) cycles. FePc@MCN was found to have a better methanol tolerance in comparison to Pt-C in basic medium. CoPc@MCN shows a highly selective two-electron reduction reaction in both acidic and basic media at lower overpotential than many of the reported catalysts for the two-electron oxygen reduction. Therefore, these materials (FePc@MCN and CoPc@MCN) can be used as suitable alternatives to replace Pt and other expensive materials in ORR and related applications.

Designing Efficient Sinkhole Attack Detection Mechanism in Edge-Based IoT Deployment.

The sinkhole attack in an edge-based Internet of Things (IoT) environment (EIoT) can devastate and ruin the whole functioning of the communication. The sinkhole attacker nodes ( S H A s) have some properties (for example, they first attract the other normal nodes for the shortest path to the destination and when normal nodes initiate the process of sending their packets through that path (i.e., via S H A ), the attacker nodes start disrupting the traffic flow of the network). In the presence of S H A s, the destination (for example, sink node i.e., gateway/base station) does not receive the required information or it may receive partial or modified information. This results in reduction of the network performance and degradation in efficiency and reliability of the communication. In the presence of such an attack, the throughput decreases, end-to-end delay increases and packet delivery ratio decreases. Moreover, it may harm other network performance parameters. Hence, it becomes extremely essential to provide an effective and competent scheme to mitigate this attack in EIoT. In this paper, an intrusion detection scheme to protect EIoT environment against sinkhole attack is proposed, which is named as SAD-EIoT. In SAD-EIoT, the resource rich edge nodes (edge servers) perform the detection of different types of sinkhole attacker nodes with the help of exchanging messages. The practical demonstration of SAD-EIoT is also provided using the well known NS2 simulator to compute the various performance parameters. Additionally, the security analysis of SAD-EIoT is conducted to prove its resiliency against various types of S H A s. SAD-EIoT achieves around 95 . 83 % detection rate and 1 . 03 % false positive rate, which are considerably better than other related existing schemes. Apart from those, SAD-EIoT is proficient with respect to computation and communication costs. Eventually, SAD-EIoT will be a suitable match for those applications which can be used in critical and sensitive operations (for example, surveillance, security and monitoring systems).

Deep-rooted pigeon pea promotes the water relations and survival of shallow-rooted finger millet during drought-Despite strong competitive interactions at ambient water availability.

Bioirrigation has been defined as the transfer of hydraulically lifted water by a deep-rooted plant to a neighbouring shallow-rooted plant which cannot access deep soil moisture. In this study, we tested if facilitative effects of bioirrigation or the competition for water dominate the interaction of two intercropped plants-deep-rooted pigeon pea (PP) and shallow-rooted finger millet (FM) before and during a drought. Additionally, we tested how the presence of a common mycorrhizal network (CMN) affects the balance between facilitative (i.e. bioirrigation) and competitive interactions between two intercropping species. Our results show that PP can indeed promote the water relations of FM during a drought event. Specifically, stomatal conductance in FM controls dropped to low values of 27.1 to 33.6 mmol m-2s-1, while FM in intercropping treatments were able to maintain its stomatal conductance at 60 mmol m-2s-1. In addition, the presence of PP reduced the drought-induced foliar damage and mortality of FM. The observed facilitative effects of PP on FM were partially enhanced by the presence of a CMN. In contrast to the facilitative effects under drought, PP exerted strong competitive effects on FM before the onset of drought. This hindered growth and biomass production of FM when intercropped with PP, an effect that was even enhanced in the presence of a CMN. The results from our study thus indicate that in intercropping, deep-rooted plants may act as "bioirrigators" for shallow-rooted crops and that a CMN can promote these facilitative effects. However, the interspecific competition between the intercropped plants under conditions of abundant moisture supply can be strong and are enhanced by the presence of a CMN. In more general terms, our study shows that the extent by which the antagonistic effects of facilitation and competition are expressed in an intercropping system strongly depends on the availability of resources, which in the case of the present study was water and the presence of biotic interactions (i.e. the presence of a CMN).

Variation Among Orchardgrass (Dactylis glomerata) Germplasm for Choke Prevalence Caused by Epichloë typhina.

Orchardgrass, or cocksfoot (Dactylis glomerata L.), is a cool-season forage grass susceptible to the choke disease caused by Epichloë typhina. Choke has been reported in orchardgrass seed production fields across the temperate regions of the world, but fungicides have not been efficacious in reducing choke incidence or prevalence. To assess the potential for genetic resistance or tolerance of orchardgrass to choke, we evaluated the variation in orchardgrass cultivars and accessions for choke prevalence and characterized infected plants for endophyte secondary metabolite and mating type gene presence. Significant variation was detected across years and locations. Choke prevalence did not always increase with the age of the stand, nor did choke prevalence correlate with flowering time or swathing time of the entries. Both mating types of E. typhina were detected in approximately equal proportions, and no evidence for loline, ergot alkaloid, or indole-diterpene biosynthesis was found. Plants with multiple infected tillers often showed more than one mating type present in the plant, indicating multiple infection events rather than a single infection event that spread to multiple tillers. Both accessions and cultivars with significant choke, and no choke, were detected, which constitute sources of germplasm for further testing and breeding.

Correlation of Severity of Functional Gastrointestinal Disease Symptoms with that of Asthma and Chronic Obstructive Pulmonary Disease: A Multicenter Study.

INTRODUCTION: There is a growing clinical awareness about the influence of gut-lung axis on lung injury and coexisting manifestations of disease processes in both the intestine and lungs. Patients of chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and asthma very often present with coexistent gut symptoms. In the present study, we have tried to establish the correlation of severity of pulmonary pathology of COPD and asthma patients with functional gastrointestinal (GI) symptoms of the patients. MATERIALS AND METHODS: This is a prospective, questionnaire-based study comprising patients with asthma and COPD. After following strict inclusion and exclusion criteria, a total of 200 patients (100 patients of bronchial asthma and 100 patients of COPD) were included in the study. Functional GI symptom questionnaire [Annexure 1-Bowel Disease Questionnaire] is based on ROME III diagnostic criteria. On the basis of GOLD (Global Initiative for Obstructive Lung Disease) guidelines, COPD patients were divided into 4 categories (mild - GOLD 1, moderate - GOLD2, severe - GOLD3 and very severe - GOLD4). Asthma patients were divided into three categories (well controlled, partly controlled, uncontrolled) on the basis of GINA (Global Initiative for Asthma) guidelines. RESULTS: Highest percentage of patients with maximum GI symptoms was found in "GOLD-4" group among COPD patients and "uncontrolled" group among asthma patients. Highest percentage of patients with least GI symptoms was found in "GOLD-1" group among COPD patients and "well controlled" group among asthma patients. CONCLUSION: We can conclude from our study that the phenomenon of gut-lung axis not only exists but also the severity of symptoms of one system (gut) carries a high degree of concordance with severity of other (lung).

Prevalence of noise-induced hearing loss among woodworkers in Nepal: a pilot study.

BACKGROUND: The woodworking industry represents an important cause of occupational noise-induced hearing loss (NIHL), a significant yet underappreciated problem in many developing countries. OBJECTIVES: To describe the prevalence of occupational NIHL among woodworkers in Nepal and measure noise levels at workplaces. METHODS: We conducted a cross-sectional study with 124 woodworkers (88 carpenters, 36 sawyers), recruited through convenience sampling from 26 workplaces. Pure-tone audiometry between the frequencies 0·5 and 8 kHz ascertained participants' hearing status, and noise readings were taken at selected workplaces. RESULTS: In all, 31% of carpenters and 44% of sawyers met criteria for NIHL, with 7 and 17% meeting World Health Organization (WHO) criteria for hearing impairment (HI), respectively. Noise levels at various workplaces ranged from 71·2 to 93·9 dBA. CONCLUSIONS: Woodworkers in Nepal are at risk of occupational NIHL. As the industry develops, this problem will likely become more extensive, highlighting the need for workplace interventions and additional research.

Pediatric anterior visual pathway gliomas: trends in fluid and electrolyte dynamics and their management nuances.

PURPOSE: Trends in pre- and postoperative fluid, electrolyte and osmolarity changes, and incidence of diabetes insipidus (DI) were assessed in pediatric patients with anterior visual pathway gliomas (AVPGs). METHODS: Thirty-three patients with AVPGs (age < 16 years) were divided into two groups: (1) no hypothalamic involvement [NHI; n = 17 (51.5 %) including optic (5, 15.2 %); chiasmal (5, 15.2 %); and optico-chiasmal (7, 21.2 %)] and (2) hypothalamic involvement [HI; n = 16 (48.5 %) including chiasmal-hypothalamic (12, 36.4 %) and optico-chiasmal-hypothalamic (4, 12.1 %)]. Frontotemporal transylvian decompression/biopsy was undertaken in 32 patients, while one patient (with severe diencephalic syndrome) was treated conservatively. Their endocrinal and fluid/electrolyte balance, serum osmolarity, and DI status were noted. Chi-square test compared clinical/endocrinological parameters, and unpaired T test evaluated mean daily water/electrolyte changes (p value < 0.05: significant). RESULTS: Significant visual deterioration (perception of light (PL) positive (left: n = 4; right: n = 4) and PL negative (left: n = 5; right: n = 5) was encountered due to optic atrophy. Larger lesions (>3 cm), hydrocephalus [(NHI: n = 7, 41.18 %; HI: n = 12, 75 %), endocrinopathies (p = 0.047), Na(+)/K(+) derangements, and preoperative DI (n = 8, p = 0.004)] were present in the group HI. Increased postoperative urine output (almost double in those with hypothalamic involvement) and hypernatremia/hyperkalemia were seen in group HI until the sixth postoperative day (p < 0.05). Two patients with progressive hypernatremia without increased urine output showed dehydration on central venous pressure monitoring and improved with vasopressin administration. Five patients [NHI: n = 4 (23.5 %); HI: n = 1 (6.3 %)] had neurofibromatosis types I and 3 (NHI: n = 1, 5.9 %; HI: n = 2, 12.5 %) had a diencephalic syndrome. CONCLUSIONS: Hypothalamic infiltration significantly increases the incidence of DI and fluid and electrolyte disturbances. Strict vigilance over postoperative fluid balance is mandatory during the first postoperative week. Rapidly rising serial serum sodium values without increased urine output mandates immediate central venous pressure measurement to detect DI associated with dehydration.

Salicornia as a crop plant in temperate regions: selection of genetically characterized ecotypes and optimization of their cultivation conditions.

Rising sea levels and salinization of groundwater due to global climate change result in fast-dwindling sources of freshwater. Therefore, it is important to find alternatives to grow food crops and vegetables. Halophytes are naturally evolved salt-tolerant plants that are adapted to grow in environments that inhibit the growth of most glycophytic crop plants substantially. Members of the Salicornioideae are promising candidates for saline agriculture due to their high tolerance to salinity. Our aim was to develop genetically characterized lines of Salicornia and Sarcocornia for further breeding and to determine optimal cultivation conditions. To obtain a large and diverse genetic pool, seeds were collected from different countries and ecological conditions. The external transcribed spacer (ETS) sequence of 62 Salicornia and Sarcocornia accessions was analysed: ETS sequence data showed a clear distinction between the two genera and between different Salicornia taxa. However, in some cases the ETS was not sufficiently variable to resolve morphologically distinct species. For the determination of optimal cultivation conditions, experiments on germination, seedling establishment and growth to a harvestable size were performed using different accessions of Salicornia spp. Experiments revealed that the percentage germination was greatest at lower salinities and with temperatures of 20/10 °C (day/night). Salicornia spp. produced more harvestable biomass in hydroponic culture than in sand culture, but the nutrient concentration requires optimization as hydroponically grown plants showed symptoms of stress. Salicornia ramosissima produced more harvestable biomass than Salicornia dolichostachya in artificial sea water containing 257 mM NaCl. Based on preliminary tests on ease of cultivation, gain in biomass, morphology and taste, S. dolichostachya was investigated in more detail, and the optimal salinity for seedling establishment was found to be 100 mM. Harvesting of S. dolichostachya twice in a growing season was successful, but the interval between the harvests needs to be optimized to maximize biomass production.

A rare case of primary tuberculous osteomyelitis of skull vault.

In recent years, there has been a substantial increase in number of reports involving uncommon sites involving in tuberculosis. Rise in number of HIV positive patients has made the scenario worse. Calvarial tuberculosis has beenreported very rarely in world literature till now. We are reporting a case of primary tuberculous osteomyelitis of frontal bone in a 15-year-old female. With prompt as well as careful diagnostic workup and treatment, we were able to halt the disease progression and excellent response to treatment was observed in follow up.