ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution.

Protein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi.

Insecticidal Activities of Securidaca longepedunculata Fresen Extracts and Feeding Behavior of Schizaphis graminum Rondani: Electropenetrography Approach.

The aphid, Schizaphis graminum Rondani (Hemiptera: Aphididae), is one of the most destructive pests of wheat. It is responsible for significant economic losses in the agricultural sector, with an estimated 45% of wheat fields affected. Plant-based insecticides have seen a rapid increase in popularity in recent years due to their efficacy, cost-effectiveness, biodegradability, and lower toxicity compared to synthetic pesticides. The study aimed to evaluate the toxic potential of S. longipedunculata extracts against S. graminum and investigate the insect's feeding behavior on wheat. Initially macerated in methanol, the different extracts of S. longipedunculata organs were fractionated using n-hexane, chloroform, ethyl acetate, and butanol. The feeding behavior was analyzed by comparing the waveforms generated by the EPG with the control. After 72 h of treatment, the ethyl acetate fraction extracted from root had the highest toxicity against aphids, with mean 26 mortality of S. graminum at LC50 of 330 ppm; 25 mortality S. graminum at LC50 of 400 ppm for leaves; and mean 24.5 mortality S. graminum at LC50 of 540 ppm in stem bark. EPG analysis indicated that the extract fractions enhanced plant tissue resistance by significantly preventing aphid access to the phloem. The toxic effect of the botanical extracts significantly enhanced the chemical composition of the leaf medium, resulting in a drastic reduction in the number of tissue attacks by S. graminum. In summary, besides their toxicity to S. graminum, extracts of S. longipedunculata reinforce the plant's defense mechanisms, significantly reducing the S. graminum population. They also reinforce wheat's defense mechanisms. S. longipedunculata can, therefore, be used as a promising agent in the biological control of S. graminum.

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder.

Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.

Can Religiosity and Social Support Explain Effects of Trait Emotional Intelligence on Health-Related Quality of Life: A Cross-Cultural Study.

Religion and social support along with trait emotional intelligence (EI) help individuals to reduce stress caused by difficult situations. Their implications may vary across cultures in reference to predicting health-related quality of life (HRQoL). A convenience sample of N = 200 chronic heart failure (CHF) patients was recruited at cardiology centers in Germany (n = 100) and Pakistan (n = 100). Results indicated that trait-EI predicted better mental component of HRQoL in Pakistani and German CHF patients. Friends as social support appeared relevant for German patients only. Qualitative data indicate an internal locus of control in German as compared to Pakistani patients. Strengthening the beneficial role of social support in Pakistani patients is one example of how the current findings may inspire culture-specific treatment to empower patients dealing with the detrimental effects of CHF.

A cross-cultural comparison of the roles of emotional intelligence, metacognition, and negative coping for health-related quality of life in German versus Pakistani patients with chronic heart failure.

OBJECTIVES: Low emotional intelligence (EI) may predispose individuals to applying maladaptive coping strategies. This may maintain anxious worrying, which is highly prevalent in patients with chronic heart failure (CHF) and may affect mental (MCS) and physical component summaries (PCS) of health-related quality of life (HRQoL). DESIGN: The current study is a cross-sectional and cross-cultural survey. METHODS: N = 200 outpatients with CHF were recruited at cardiology institutes in Germany and Pakistan and assessed with self-report questionnaires. RESULTS: Path analysis (χ2 (4) = 7.59, p = .11, GFI = .99) revealed that the expected associations between low EI and lower SF-36 MCS and PCS of HRQoL were fully mediated by negative metacognition and maladaptive coping in the Pakistani sample (p's ≤ .05). The German sample applied different maladaptive coping strategies, which also led to lower MCS and PCS scores, but did not mediate a direct positive effect of EI on HRQoL. CONCLUSION: The current findings support culture-independent validity of the metacognitive model but also reveal major cultural differences regarding the application and effect of specific maladaptive coping strategies. This has important implications for caregivers in a cross-cultural context and highlights the need for culture-specific tailoring of psychosocial interventions. Statement of contribution What is already known on this subject? Worry, an integral component of generalized anxiety disorder (GAD) and highly comorbid in chronic heart failure (CHF) patients, contributes to anxiety and resulting stress as evident from metacognitive model of GAD. In addition, previous literature has also established the protective role of emotional intelligence (EI) against stress, thus maintaining quality of life. What does this study add? Cross-cultural (Pakistan vs. Germany) validation of the metacognitive model of GAD. Supportive evidence for the metacognitive model in patients with CHF. Mediation of maladaptive metacognitions and negative coping in the relationship of low trait EI and low health-related quality of life.