“Quo Vadis Diagnosis”: Application of Informatics in Early Detection of Pneumothorax

A pneumothorax is a condition that occurs in the lung region when air enters the pleural space—the area between the lung and chest wall—causing the lung to collapse and making it difficult to breathe. This can happen spontaneously or as a result of an injury. The symptoms of a pneumothorax may include chest pain, shortness of breath, and rapid breathing. Although chest X-rays are commonly used to detect a pneumothorax, locating the affected area visually in X-ray images can be time-consuming and prone to errors. Existing computer technology for detecting this disease from X-rays is limited by three major issues, including class disparity, which causes overfitting, difficulty in detecting dark portions of the images, and vanishing gradient. To address these issues, we propose an ensemble deep learning model called PneumoNet, which uses synthetic images from data augmentation to address the class disparity issue and a segmentation system to identify dark areas. Finally, the issue of the vanishing gradient, which becomes very small during back propagation, can be addressed by hyperparameter optimization techniques that prevent the model from slowly converging and poorly performing. Our model achieved an accuracy of 98.41% on the Society for Imaging Informatics in Medicine pneumothorax dataset, outperforming other deep learning models and reducing the computation complexities in detecting the disease.

Optimal energy management system for the design of off grid hybrid renewable energy systems: A hybrid technique

This manuscript proposes an efficient hybrid strategy to obtain the optimal solution of operational cost reduction, size reduction of hybrid renewable energy sources and optimal power flow control for off-grid system. Here, off-grid is incorporated with photovoltaic array, wind turbine, Diesel generator, and battery energy storage system. The hybrid method is joint execution of Giza Pyramids Construction (GPC) and Billiards-inspired optimization algorithm (BOA) hence it is named GPC-BOA technique. The major purpose of proposed method is minimizing the operational cost as well as size of hybrid renewable energy sources and improves the power flow of system. In this energy management system of off-grid provides cost reduction which includes the generation, replacement, operating and maintenance, cost of fuel consumption, cost of exchanged power with grid, and the penalty for emissions. Here, the GPC method is employed for forecasting the load requirement of system. The BOA technique optimizes the off-grid system through the deliberation of forecasted load requirement. At last, the proposed approach is performed on MATLAB platform and the performance is assessed using existing techniques.

Optimally detecting and classifying the transmission line fault in power system using hybrid technique

In this paper, a hybrid system is proposed to predict and classifies the power system transmission line faults. The proposed technique is the consolidation of both the truncated singular value decomposition (TSVD) and Human urbanization algorithm (HUA) based Recurrent Perceptron Neural Network (RPNN), and hence it is named as TSVD-HUARPNN technique. TSVD is matrix decomposition, this technique qualify the outcome it as fast or not. In the proposed work, the qualification of the results from the TSVD is improved by a lemma theorem; it is a proven proposition which is used to obtain a larger and optimal result. For that reason, it is also known as a "helping theorem" or an "auxiliary theorem". Here, it has two modules for power system fault analysis: (i) fault detection, (ii) fault classification. The first process of the proposed system is the generation of the dataset of normal and abnormal conditions of transmission line parameters of power system using TSVD. The extracted dataset is assessed by HUA-based RPNN system to classify the fault analysis that occurs in transmission system. The TSVD-HUARPNN system is used to predict and classify the fault present in the transmission line. The proposed TSVD-HUARPNN system ensures the system with less complexity for the detection and classification of the fault, therefore the accuracy of the system is increased. By then, the proposed model is activated in MATLAB/Simulink, its performance is evaluated with the existing models. The performance with noise at 20 dB of the proposed technique is 99.77%.

Diagnosis of COVID-19 patients by adapting hyper parametertuned deep belief network using hosted cuckoo optimization algorithm

COVID-19 is an infection caused by recently discovered corona virus. The symptoms of COVID-19 are fever, cough and dumpiness of breathing. A quick and accurate identification is essential for an efficient fight against COVID-19. A machine learning technique is initiated for categorizing the chest x-ray images into two cases: COVID-19 positive case or negative case. In this manuscript, the categorization of COVID-19 can be determined by hyper parameter tuned deep belief network using hosted cuckoo optimization algorithm. At first, the input chest x-ray images are pre-processed for removing noises. In this manuscript, the deep belief network method is enhanced by hosted cuckoo optimization approach for getting optimum hyper tuning parameters. By this, exact categorization of COVID-19 is attained effectively. The proposed methodology is stimulated at MATLAB. The proposed approach attains 28.3% and 23.5% higher accuracy for Normal and 32.3% and 31.5% higher accuracy for COVID-19, 19.3% and 28.5% higher precision for Normal and 45.3% and 28.5% higher precision for COVID-19, 20.3% and 21.5% higher F-score for Normal and 40.3% and 21.5% higher F-score for COVID-19. The proposed methodology is analyzed using two existing methodologies, as Convolutional Neural Network with Social Mimic Optimization (CNN-SMO) and Support Vector Machine classifier using Bayesian Optimization algorithm (SVM-BOA).

Regiospecific formal [3 + 2] annulation of tert-propargyl alcohols with acyclic 1,3-diketones via the cycloisomerization of homoallenyl ketones

A one-pot, regiospecific synthesis of dihydrofurans bearing a quaternary centre is developed from the formal [3 + 2] annulation of tert-propargyl alcohols and 1,3-diketones through the S_N2^I mechanism to form homoallenyl ketone and a subsequent cycloisomerization reaction.

Carbon: Nitrogen (C:N) ratio level variation influences microbial community of the system and growth as well as immunity of shrimp (Litopenaeus vannamei) in biofloc based culture system

Biofloc technology (BFT) is a novel modern aquaculture farming technique used to reduce toxic nitrogen concentration, act as in situ food source and eradicate pollutants using carbon and therefore to control C:N ratio in an aquaculture system. In this study, effect of different C:N ratios of a biofloc based system on water quality such as the level of Total ammonia nitrogen (TAN) nitrite-nitrogen (NO₂^--N) and nitrate nitrogen (NO₃^--N) were explored. Further, the growth and immunity status of shrimp L. vannamei under the influence of different C:N ratios were evaluated. Two of the C:N ratios (15 and 20) could significantly (P < 0.05) reduce TAN, NO₂-N and NO₃-N levels (0.456 ± 0.01, 0.145 ± 0.09, and 0.102 ± 0.02 ppm) compared to control (1.45 ± 0.1, 0.749 ± 0.14 and 0.675 ± 0.16 ppm). Large variations in the frequency distribution of operational taxonomic units (OTUs) for the bacterial community in water with different C:N ration (BFT) and control were observed. Vibrios often considered as opportunistic pathogens, where the most dominant bacterial flora of water in control (79%) and C:N5 (37%) group. In C:N10, Thauera (62%) was most represented genus. Similarly, Attheyaceae (56%), followed by Peridiniaceae (30%) were the most dominant groups in C:N15 treatment. The diversity of bacterial flora was more spread in C:N20 treatments with Psychrobacter (26%), Proteobacteria (25%) and Peridiniaceae (20%) as the major groups. The trend of Vibrio dominance decreased with the increase in C:N ratios and thus confirming the dominance of heterotrophic bacteria in high C:N ratio groups. Upon challenge with pathogens, shrimps from C:N10, C:N15 and C:N20 groups showed significantly higher survival (P < 0.05) compared to the C:N5 and control group. Similarly, better growth rate was also observed in BFT tanks compared to control both during the culture and at harvest. Comparatively higher expression of four immune-related genes (ras-related nuclear gene (RAN), serine proteinase gene (SP), prophenoloxidase activating enzyme (PPAE), and crustin were observed in different C:N ratio ponds than control and these were in increasing trend with the C:N ratio. Gene expression analysis showed that the transcripts of those immune genes were significantly increased among all C:N treatments than that of control. Overall, these findings demonstrated that with optimum C:N ratio, BFT can be used to optimize the bacterial community composition for both optimal water quality and optimal shrimp health. This study thus indicates the possibility of obtaining better performance of L. vannamei culture with proper adjustment of C:N ratio in a biofloc based system.

Positron Annihilation Studies on Chemically Synthesized FeCo Alloy

Equiatomic flower-like FeCo magnetic nanoparticles are synthesized through a modified one-pot polyol technique. The as-prepared samples are annealed at 700 and 800 °C under reducing atmosphere. The saturation magnetization and coercivity of the flower-like FeCo are found to be 198 (1) emu/g and 243 (10) Oe respectively. The magnetic properties of FeCo approach the bulk behavior with annealing. Positron lifetime studies on the chemically synthesized equiatomic FeCo magnetic nanoparticles with flower-like morphology are reported and compared with Fe, Co and FeCo annealed at various temperatures. The FeCo is characterized by different lifetime components corresponding to positron annihilation events in vacancies and various open volume defects due to their unique morphology. The studies suggest defects arising out of cluster vacancies and interpetal gap that reduce on annealing. The average pore size obtained from positron annihilation studies closely matches with the interpetal distance obtained from the electron microscopic analysis for the flower-like FeCo.

Correction to "Highly Regioselective Synthesis of Oxindolyl-Pyrroles and Quinolines via a One-Pot, Sequential Meyer-Schuster Rearrangement, Anti-Michael Addition/C(sp3)-H Functionalization, and Azacyclization"

[This corrects the article DOI: 10.1021/acsomega.8b00147.].

Hybrid Analysis Technique to detect Advanced Persistent Threats

Advanced persistent threats (APT) are major threats in the field of system and network security. They are extremely stealthy and use advanced evasion techniques like packing and behaviour obfuscation to hide their malicious behaviour and evade the detection methods. Existing behavior-based detection technique fails to detect the APTs due to its high persistence mechanism and sophisticated code nature. Hence, a novel hybrid analysis technique using Behavior based Sandboxing approach is proposed. The proposed technique consists of four phases namely, Static, Dynamic, Memory and System state analysis. Initially, static analysis is performed on the sample which involves packer detection and signature verification. If the sample is found stealthy and remains undetected, then it is executed inside a sandbox environment to analyze its behavior. Further, memory analysis is performed to extract memory artefacts of the current system state. Finally, system state analysis is performed by correlating clean system state and infected system state to determine whether the system is compromised

Highly Regioselective Synthesis of Oxindolyl-Pyrroles and Quinolines via a One-Pot, Sequential Meyer-Schuster Rearrangement, Anti-Michael Addition/C(sp3)-H Functionalization, and Azacyclization

A one-pot, sequential Meyer-Schuster (MS) rearrangement of oxindole-derived propargyl alcohols to the corresponding α,β-unsaturated enones and their anti-Michael addition, followed by intramolecular azacyclization is described in a highly regioselective manner using Ca(OTf)₂ as the promoter. Further, we described the one-pot MS rearrangement, followed by C_(sp³)-H functionalization of 2-methyl azaarenes at α-carbon of these doubly activated alkenes. Control experiments and computational calculations were performed to propose the reaction mechanism.

The Biological Potentialsof Indian Traditional Medicine, Curcumin for Treating Human Diseases

Curcumin is an active compound extracted from the rhizomes of Curcuma longa L. (Turmeric). It possesses a di phenolic structure and there are three important fractions of curcumin namely curcumin, mono-desmethoxycurcumin and bis-desmethoxycurcumin. Curcumin is having high medicinal value. It is widely in Ayurveda, Siddha and Unani medicine formulations to treat various diseases. It is effective against microbial pathogens like bacteria, virus and fungi. It is highly potent and kills cancerous cells. Curcumin exhibits antioxidant activity and reduces lipoperoxidation. Cardioprotective nature of curcumin is also reported. It can protect the brain from neurodegenerative disorders caused by oxidative stress. It is safe to use in humans and animals. In addition to its medicinal value, it is also having numerous commercial applications like dyeing, cooking purposes and antiseptics etc. The cultivation of turmeric by means of inorganic inputs makes it contaminated with heavy metals is reported. Improving the biological use of curcumin provides healthy humankind and increases the economic status of a country.

Estimation of serum lactate dehydrogenase in smokeless tobacco consumers

STATEMENT OF PROBLEM

Salivary and serum lactate dehydrogenase (LDH) levels have been correlated with potentially malignant lesions. Salivary LDH levels require special testing and can be expensive. The need for a simple and cost-effective analysis tool is essential to detect the oral malignant lesions to benefit rural populations.

AIM

The aim of this study is to estimate the serum LDH in patients with oral lesions due to the consumption of smokeless tobacco.

MATERIALS AND METHODS

Thirty-five patients with lesions were selected for this study while twenty patients served as a control. Levels of serum LDH were assessed. Age- and sex-matched controls (n = 10) with no smokeless consumption habit and with the habit but without lesion (n = 10) also had their blood drawn to assess the basal level of LDH. The results were then analyzed through the two-tailed t-test and Chi-square analysis using the SPSS statistics software.

RESULTS

The mean LDH value of patients with habit and lesion is 446.8 U/L; the mean LDH value for patients with habit but without the presence of a lesion is 421.2 U/L, and the mean LDH value for patients without a habit or lesion is 269.4 U/L. The patients having the habit of using smokeless tobacco had higher LDH values compared to the other two groups, and it is found to be statistically significant (p < 0.05).

CONCLUSION

Serum LDH values increased in patients with the habit of chewing smokeless tobacco.