Editorial for the Special Issue "Machine Learning in Computer Vision and Image Sensing: Theory and Applications".

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [...].

Improved Video-Based Point Cloud Compression via Segmentation.

A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches are projected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity information and some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps maintain the points' proximity and retain more original points by exploiting the density and occlusion of the points. The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods regarding rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.

Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals.

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

Apigenin-Loaded PLGA-DMSA Nanoparticles: A Novel Strategy to Treat Melanoma Lung Metastasis.

The flavone apigenin (APG), alone as well as in combination with other chemotherapeutic agents, is known to exhibit potential anticancer effects in various tumors and inhibit growth and metastasis of melanoma. However, the potential of apigenin nanoparticles (APG-NPs) to prevent lung colonization of malignant melanoma has not been well investigated. APG-loaded PLGA-NPs were surface-functionalized with meso-2,3-dimercaptosuccinic acid (DMSA) for the treatment of melanoma lung metastasis. DMSA-conjugated APG-loaded NPs (DMSA-APG-NPs) administered by an oral route exhibited sustained APG release and showed considerable enhancement of plasma half-life, Cmax value, and bioavailability compared to APG-NPs both in plasma and the lungs. DMSA-conjugated APG-NPs showed comparably higher cellular internalization in B16F10 and A549 cell lines compared to that of plain NPs. Increased cytotoxicity was observed for DMSA-APG-NPs compared to APG-NPs in A549 cells. This difference between the two formulations was lower in B16F10 cells. Significant depolarization of mitochondrial transmembrane potential and an enhanced level of caspase activity were observed in B16F10 cells treated with DMSA-APG-NPs compared to APG-NPs as well. Western blot analysis of various proteins was performed to understand the mechanism of apoptosis as well as prevention of melanoma cell migration and invasion. DMSA conjugation substantially increased accumulation of DMSA-APG-NPs given by an intravenous route in the lungs compared to APG-NPs at 6 and 8 h. This was also corroborated by scintigraphic imaging studies with radiolabeled formulations administered by an intravenous route. Conjugation also allowed comparatively higher penetration as evident from an in vitro three-dimensional tumor spheroid model study. Finally, the potential therapeutic efficacy of the formulation was established in experimental B16F10 lung metastases, which suggested an improved bioavailability with enhanced antitumor and antimetastasis efficacy of DMSA-conjugated APG-NPs following oral administration.

A collection of parametric modal regression models for bounded data.

Modal regression is an alternative approach for investigating the relationship between the most likely response and covariates and can hence reveal important structure missed by usual regression methods. This paper provides a collection of parametric mode regression models for bounded response variable by considering some recently introduced probability distributions with bounded support along with the well-established Beta and Kumaraswamy distribution. The main properties of the distributions are highlighted and compared. An empirical comparison between the considered modal regression is demonstrated through the analysis of three data sets from health and social science. For reproducible research, the proposed models are freely available to users as an R package unitModalReg.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images.

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Controversy of Peptide Cyclization from Tripeptide.

The present investigation reports an attempt to synthesize naturally occurring α-cyclic tripeptide cyclo(Gly-l-Pro-l-Glu) 1, [cyclo(GPE)], previously isolated from the Ruegeria strain of bacteria with marine sponge Suberites domuncula. Three linear precursors, Boc-GPE(OBn)2, Boc-PE(OBn)G and Boc-E(OBn)GP, were synthesized using a solution phase peptide coupling protocol. Although cyclo(GPE) 1 was our original target, all precursors were dimerized and cyclized at 0 °C with high dilution to form corresponding α-cyclic hexapeptide, cyclo(GPE(OBn))27, which was then converted to cyclic hexapeptide cyclo(GPE)22. Cyclization at higher temperature induced racemization and gave cyclic tripeptide cyclo(GPDE(OBn)) 9. Structure characteristics of the newly synthesized cyclopeptides were determined using 1H-NMR, 13C-NMR and high-resolution mass spectrometry. The chemical shift values of carbonyls of 2 and 7 are larger than 170 ppm, indicating the formation of a cyclic hexapeptide.

C-C Bond Formation of Benzyl Alcohols and Alkynes Using a Catalytic Amount of KOt Bu: Unusual Regioselectivity through a Radical Mechanism.

We report a C-C bond-forming reaction between benzyl alcohols and alkynes in the presence of a catalytic amount of KOt Bu to form α-alkylated ketones in which the C=O group is located on the side derived from the alcohol. The reaction proceeds under thermal conditions (125 °C) and produces no waste, making the reaction highly atom efficient, environmentally benign, and sustainable. Based on our mechanistic investigations, we propose that the reaction proceeds through radical pathways.

Direct Conversion of Alcohols into Alkenes by Dehydrogenative Coupling with Hydrazine/Hydrazone Catalyzed by Manganese.

We have developed unprecedented methods for the direct transformation of primary alcohols to alkenes in the presence of hydrazine, and for the synthesis of mixed alkenes by the reaction of alcohols with hydrazones. The reactions are catalyzed by a manganese pincer complex and proceed in absence of added base or hydrogen acceptors, liberating dihydrogen, dinitrogen, and water as the only byproducts. The proposed mechanism, based on preparation of proposed intermediates and control experiments, suggests that the transformation occurs through metal-ligand cooperative N-H activation of a hydrazone intermediate.

Manganese Catalyzed α-Olefination of Nitriles by Primary Alcohols.

Catalytic α-olefination of nitriles using primary alcohols, via dehydrogenative coupling of alcohols with nitriles, is presented. The reaction is catalyzed by a pincer complex of an earth-abundant metal (manganese), in the absence of any additives, base, or hydrogen acceptor, liberating dihydrogen and water as the only byproducts.

Polymorphisms in ADH1B and ALDH2 genes associated with the increased risk of gastric cancer in West Bengal, India.

BACKGROUND: Gastric cancer (GC) is one of the most frequently diagnosed digestive tract cancers and carries a high risk of mortality. Acetaldehyde (AA), a carcinogenic intermediate of ethanol metabolism contributes to the risk of GC. The accumulation of AA largely depends on the activity of the major metabolic enzymes, alcohol dehydrogenase and aldehyde dehydrogenase encoded by the ADH (ADH1 gene cluster: ADH1A, ADH1B and ADH1C) and ALDH2 genes, respectively. This study aimed to evaluate the association between genetic variants in these genes and GC risk in West Bengal, India. METHODS: We enrolled 105 GC patients (cases), and their corresponding sex, age and ethnicity was matched to 108 normal individuals (controls). Genotyping for ADH1A (rs1230025), ADH1B (rs3811802, rs1229982, rs1229984, rs6413413, rs4147536, rs2066702 and rs17033), ADH1C (rs698) and ALDH2 (rs886205, rs968529, rs16941667 and rs671) was performed using DNA sequencing and RFLP. RESULTS: Genotype and allele frequency analysis of these SNPs revealed that G allele of rs17033 is a risk allele (A vs G: OR = 3.67, 95% CI = 1.54-8.75, p = 0.002) for GC. Significant association was also observed between rs671 and incidence of GC (p = 0.003). Moreover, smokers having the Lys allele of rs671 had a 7-fold increased risk of acquiring the disease (OR = 7.58, 95% CI = 1.34-42.78, p = 0.009). CONCLUSION: In conclusion, rs17033 of ADH1B and rs671 of ALDH2 SNPs were associated with GC risk and smoking habit may further modify the effect of rs671. Conversely, rs4147536 of ADH1B might have a protective role in our study population. Additional studies with a larger patient population are needed to confirm our results.

Massive isotopic effect in vacuum UV photodissociation of N2 and implications for meteorite data.

Nitrogen isotopic distributions in the solar system extend across an enormous range, from -400‰, in the solar wind and Jovian atmosphere, to about 5,000‰ in organic matter in carbonaceous chondrites. Distributions such as these require complex processing of nitrogen reservoirs and extraordinary isotope effects. While theoretical models invoke ion-neutral exchange reactions outside the protoplanetary disk and photochemical self-shielding on the disk surface to explain the variations, there are no experiments to substantiate these models. Experimental results of N2 photolysis at vacuum UV wavelengths in the presence of hydrogen are presented here, which show a wide range of enriched δ(15)N values from 648‰ to 13,412‰ in product NH3, depending upon photodissociation wavelength. The measured enrichment range in photodissociation of N2, plausibly explains the range of δ(15)N in extraterrestrial materials. This study suggests the importance of photochemical processing of the nitrogen reservoirs within the solar nebula.

Iron-Catalyzed Mild and Selective Hydrogenative Cross-Coupling of Nitriles and Amines To Form Secondary Aldimines.

The first example of a base-metal-catalyzed homogeneous hydrogenative coupling of nitriles and amines to selectively form secondary cross-imines is reported. The reaction is catalyzed under mild conditions by a well-defined (iPr-PNP)Fe(H)Br(CO) pincer pre-catalyst and catalytic tBuOK.

Manganese-Catalyzed N-Formylation of Amines by Methanol Liberating H2 : A Catalytic and Mechanistic Study.

The first example of a base metal (manganese) catalyzed acceptorless dehydrogenative coupling of methanol and amines to form formamides is reported herein. The novel pincer complex (iPr-PNH P)Mn(H)(CO)2 catalyzes the reaction under mild conditions in the absence of any additives, bases, or hydrogen acceptors. Mechanistic insight based on the observation of an intermediate and DFT calculations is also provided.

Effective Hamiltonians for correlated narrow energy band systems and magnetic insulators: Role of spin-orbit interactions in metal-insulator transitions and magnetic phase transitions.

Using a second-quantized many-electron Hamiltonian, we obtain (a) an effective Hamiltonian suitable for materials whose electronic properties are governed by a set of strongly correlated bands in a narrow energy range and (b) an effective spin-only Hamiltonian for magnetic materials. The present Hamiltonians faithfully include phonon and spin-related interactions as well as the external fields to study the electromagnetic response properties of complex materials and they, in appropriate limits, reduce to the model Hamiltonians due to Hubbard and Heisenberg. With the Hamiltonian for narrow-band strongly correlated materials, we show that the spin-orbit interaction provides a mechanism for metal-insulator transition, which is distinct from the Mott-Hubbard (driven by the electron correlation) and the Anderson mechanism (driven by the disorder). Next, with the spin-only Hamiltonian, we demonstrate the spin-orbit interaction to be a reason for the existence of antiferromagnetic phase in materials which are characterized by a positive isotropic spin-exchange energy. This is distinct from the Néel-VanVleck-Anderson paradigm which posits a negative spin-exchange for the existence of antiferromagnetism. We also find that the Néel temperature increases as the absolute value of the spin-orbit coupling increases.