Accurate prediction of B-form/A-form DNA conformation propensity from primary sequence: A machine learning and free energy handshake

Factors to predict recurrence after epidural blood patch in patients with spontaneous intracranial hypotension

OBJECTIVES

This study aimed to identify predictors for the recurrence of spontaneous intracranial hypotension (SIH) after epidural blood patch (EBP).

BACKGROUND

Epidural blood patch is the main treatment option for SIH; however, the characteristics of patients who experience relapse after successful EBP treatment for SIH remain understudied.

METHODS

In this exploratory, retrospective, case-control study, we included 19 patients with SIH recurrence after EBP and 36 age- and sex-matched patients without recurrence from a single tertiary medical institution. We analyzed clinical characteristics, neuroimaging findings, and volume changes in intracranial structures after EBP treatment. Machine learning methods were utilized to predict the recurrence of SIH after EBP treatment.

RESULTS

There were no significant differences in clinical features between the recurrence and no-recurrence groups. Among brain magnetic resonance imaging signs, diffuse pachymeningeal enhancement and cerebral venous dilatation were more prominent in the recurrence group than no-recurrence group after EBP (14/19 [73%] vs. eight of 36 [22%] patients, p = 0.001; 11/19 [57%] vs. seven of 36 [19%] patients, p = 0.010, respectively). The midbrain-pons angle decreased in the recurrence group compared to the no-recurrence group after EBP, at a mean (standard deviation [SD]) of -12.0 [16.7] vs. +1.8[18.3]° (p = 0.048). In volumetric analysis, volume changes after EBP were smaller in the recurrence group than in the no-recurrence group in intracranial cerebrospinal fluid (mean [SD] -11.6 [15.3] vs. +4.8 [17.1] mL, p = 0.001) and ventricles (mean [SD] +1.0 [2.0] vs. +2.0 [2.5] mL, p = 0.003). Notably, the random forest classifier indicated that the model constructed with brain volumetry was more accurate in discriminating SIH recurrence (area under the curve = 0.80 vs. 0.52).

CONCLUSION

Our study suggests that volumetric analysis of intracranial structures may aid in predicting recurrence after EBP treatment in patients with SIH.

Accurate Prediction of Antifreeze Protein from Sequences through Natural Language Text Processing and Interpretable Machine Learning Approaches

Antifreeze proteins (AFPs) bind to growing iceplanes owing to their structural complementarity nature, thereby inhibiting the ice-crystal growth by thermal hysteresis. Classification of AFPs from sequence is a difficult task due to their low sequence similarity, and therefore, the usual sequence similarity algorithms, like Blast and PSI-Blast, are not efficient. Here, a method combining n-gram feature vectors and machine learning models to accelerate the identification of potential AFPs from sequences is proposed. All these n-gram features are extracted from the K-mer counting method. The comparative analysis reveals that, among different machine learning models, Xgboost outperforms others in predicting AFPs from sequence when penta-mers are used as a feature vector. When tested on an independent dataset, our method performed better compared to other existing ones with sensitivity of 97.50%, recall of 98.30%, and f1 score of 99.10%. Further, we used the SHAP method, which provides important insight into the functional activity of AFPs.

Advancements in technology and innovation for sustainable agriculture: Understanding and mitigating greenhouse gas emissions from agricultural soils

In recent decades, Technology and Innovation (TI) have shown tremendous potential for improving agricultural productivity and environmental sustainability. However, the adoption and implementation of TI in the agricultural sector and its impact on the environment remain limited. To gain deeper insights into the significance of TI in enhancing agricultural productivity while maintaining environmental balance, this study investigates 21 agriculture-dependent Asian countries. Two machine learning techniques, LASSO (Least Absolute Shrinkage and Selection Operator) and Elastic-Net, are employed to analyze the data, which is categorized into three regional groups: ASEAN (Association of Southeast Asian Nations), SAARC (South Asian Association for Regional Cooperation), and GCC (Gulf Cooperation Council). The findings of this study highlight the heterogeneous nature of technology adoption and its environmental implications across the three country groups. ASEAN countries emerge as proactive adopters of relevant technologies, effectively enhancing agricultural production while simultaneously upholding environmental quality. Conversely, SAARC countries exhibit weaker technology adoption, leading to significant fluctuations in environmental quality, which in turn impact agricultural productivity. Notably, agricultural emissions of N2O (nitrous oxide) and CO2 (carbon dioxide) in SAARC countries show a positive association with agricultural production, while CH4 (methane) emissions have an adverse effect. In contrast, the study reveals a lack of evidence regarding technological adoption in agriculture among GCC countries. Surprisingly, higher agricultural productivity in these countries is correlated with increased N2O emissions. Moreover, the results indicate that deforestation and expansion of cropland contribute to increased agricultural production; however, this expansion is accompanied by higher emissions related to agricultural activities. This research represents a pioneering empirical analysis of the impact of TI and environmental emission gases on agricultural productivity in the three aforementioned country groups. It underscores the imperative of embracing relevant technologies to enhance agricultural output while concurrently ensuring environmental sustainability. The findings of this study provide valuable insights for policymakers and stakeholders in formulating strategies to promote sustainable agriculture and technological advancement in the context of diverse regional dynamics.

Non-canonical DNA structures in the human ribosomal DNA

Non-canonical structures (NCS) refer to the various forms of DNA that differ from the B-conformation described by Watson and Crick. It has been found that these structures are usual components of the genome, actively participating in its essential functions. The present review is focused on the nine kinds of NCS appearing or likely to appear in human ribosomal DNA (rDNA): supercoiling structures, R-loops, G-quadruplexes, i-motifs, DNA triplexes, cruciform structures, DNA bubbles, and A and Z DNA conformations. We discuss the conditions of their generation, including their sequence specificity, distribution within the locus, dynamics, and beneficial and detrimental role in the cell.

The molecular grammar of protein disorder guiding genome-binding locations

Intrinsically disordered regions (IDRs) direct transcription factors (TFs) towards selected genomic occurrences of their binding motif, as exemplified by budding yeast's Msn2. However, the sequence basis of IDR-directed TF binding selectivity remains unknown. To reveal this sequence grammar, we analyze the genomic localizations of >100 designed IDR mutants, each carrying up to 122 mutations within this 567-AA region. Our data points at multivalent interactions, carried by hydrophobic-mostly aliphatic-residues dispersed within a disordered environment and independent of linear sequence motifs, as the key determinants of Msn2 genomic localization. The implications of our results for the mechanistic basis of IDR-based TF binding preferences are discussed.

Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal

It has been proposed that being overweight may provide an advantage with respect to mortality in older people, although this has not been investigated fully. Therefore, to confirm that and elucidate the underlying mechanism, we investigated mortality in older people using explainable artificial intelligence (AI) with the gradient-boosting algorithm XGboost. Baseline body mass indexes (BMIs) of 5699 people (79.3 ± 3.9 years) were evaluated to determine the relationship with all-cause mortality over eight years. In the unadjusted model, the first negative (protective) BMI range for mortality was 25.9−28.4 kg/m2. However, in the adjusted cross-validation model, this range was 22.7−23.6 kg/m2; the second and third negative BMI ranges were then 25.8−28.2 and 24.6−25.8 kg/m2, respectively. Conversely, the first advancing BMI range was 12.8−18.7 kg/m2, which did not vary across conditions with high feature importance. Actual and predicted mortality rates in participants aged <90 years showed a negative-linear or L-shaped relationship with BMI, whereas predicted mortality rates in men aged ≥90 years showed a blunt U-shaped relationship. In conclusion, AI predicted that being overweight may not be an optimal condition with regard to all-cause mortality in older adults. Instead, it may be that a high normal weight is optimal, though this may vary according to the age and sex.

Developing Community Resources for Nucleic Acid Structures

In this review, we describe the creation of the Nucleic Acid Database (NDB) at Rutgers University and how it became a testbed for the current infrastructure of the RCSB Protein Data Bank. We describe some of the special features of the NDB and how it has been used to enable research. Plans for the next phase as the Nucleic Acid Knowledgebase (NAKB) are summarized.