Laparoscopy-guided transverse abdominis plane block versus port site infiltration for post-operative pain relief after laparoscopic cholecystectomy.

INTRODUCTION: Post-operative analgesia is an important component of patient satisfaction and early discharge from the hospital. A variety of modalities have been tested and are still evolving. The present study is one such evaluation of a novel technique of laparoscopy-guided transverse abdominis plane (LTAP) block for post-operative analgesia in laparoscopic cholecystectomy. PATIENTS AND METHODS: A prospective randomised control trial was conducted to verify the effectiveness of LTAP block over port site local anaesthesia infiltration (PSLAI) for post-operative analgesia in patients undergoing laparoscopic cholecystectomy. This study was done over a period of 18 months in a tertiary hospital. A total of 84 patients were recruited and were divided equally into two groups (LTAP and PSLAI). RESULTS: There was no statistical difference between the two groups with respect to gender distribution, comorbidities, number of gallstones, duration of symptoms and surgery time. The rescue dose of diclofenac requirement was less for the LTAP group in comparison to the PSLAI group. No difference was observed in the pain score at 1st h. However, LTAP proved efficient later in the post-operative course (6 h, 24 h and at discharge). There was significant a difference in the pain scores between the two groups, favouring the LTAP group. Hospital stay in the LTAP group was less in comparison to the PSLAI group. CONCLUSION: LTAP block is an effective method of post-operative analgesia. It impacts analgesia to the whole anterior abdominal wall for a prolonged period when compared to the PSLAI without adding any extra procedural time.

Exploring the viability of anti-microbial, superhydrophobic jute bags as an approach to sustainable food packaging system.

Jute in food packaging offers several advantages, including cost-effectiveness, biodegradability, renewability, and low environmental impact. Nevertheless, its hydrophilic characteristic makes it susceptible to airborne humidity and precipitation moisture. We combated this by chemically treating jute to make it water-resistant. The coated jute (WCA = âˆ¼162°) exhibits high mechanical endurance against exposure to air (>1 month), ultrasonic washing (6 h), brush scrubbing (>50 cycles), and mutual abrasion (>150 cycles), along with good thermal stability. During a 2-month experiment involving seed storage in an RH of 85%, wheat grains stored in the coated bag showed 8.08% less moisture content than that stored in control. Furthermore, the preserved grains in the control jute exhibited altered colour, texture, and fungal development. Additionally, compared to the control, the coated jute delivers >50% bacterial growth reduction in 48 h. The proposed jute offers a sustainable packaging solution that promotes eco-friendly practices and reduces plastic waste.

Premorbid Blood Pressure Control Modifies Risk of DWI Lesions With Acute Blood Pressure Reduction in Intracerebral Hemorrhage.

BACKGROUND: Hypoperfusion due to blood pressure (BP) reduction is a potential mechanism of cerebral ischemia after intracerebral hemorrhage. However, prior evaluations of the relationship between BP reduction and ischemia have been conflicting. Untreated chronic hypertension is common in intracerebral hemorrhage and alters cerebral autoregulation. We hypothesized that the risk of diffusion-weighted imaging (DWI) hyperintensities from acute BP reduction is modified by premorbid BP control. METHODS: Individuals enrolled in the ERICH study (Ethnic/Racial Variations of Intracerebral Hemorrhage) from 2010 to 2015 were categorized as untreated, treated, or nonhypertensive based on preintracerebral hemorrhage diagnosis and antihypertensive medication use. The percent reduction of systolic BP (SBP) was calculated between presentation and 24 hours from admission. The primary outcome was the presence of DWI lesions. Using logistic regression, we tested the association between chronic hypertension status, SBP reduction, and their interaction with DWI lesion presence. RESULTS: From 3000 participants, 877 with available magnetic resonance imaging met inclusion (mean age, 60.5±13.3 years; 42.5% women). DWI lesions were detected in 25.9%. Untreated, treated, and no hypertension accounted for 32.6%, 47.9%, and 19.5% of cases, respectively. SBP reduction was not directly associated with DWI lesions; however, an interaction effect was observed between SBP reduction and chronic hypertension status (P=0.036). Nonhypertensive subjects demonstrated a linear risk of DWI lesion presence with greater SBP reduction, whereas untreated hypertension demonstrated a stable risk across a wide range of SBP reduction (P=0.023). CONCLUSIONS: Premorbid BP control, especially untreated hypertension, may influence the relationship between DWI lesions and acute BP reduction after intracerebral hemorrhage. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01202864.

Identifying the Origin of Thermal Modulation of Exchange Bias in MnPS3/Fe3GeTe2 van der Waals Heterostructures.

The exchange bias phenomenon, inherent in exchange-coupled ferromagnetic and antiferromagnetic systems, has intrigued researchers for decades. Van der Waals materials, with their layered structures, offer an ideal platform for exploring exchange bias. However, effectively manipulating exchange bias in van der Waals heterostructures remains challenging. This study investigates the origin of exchange bias in MnPS3/Fe3GeTe2 van der Waals heterostructures, demonstrating a method to modulate nearly 1000% variation in magnitude through simple thermal cycling. Despite the compensated interfacial spin configuration of MnPS3, a substantial 170 mT exchange bias is observed at 5 K, one of the largest observed in van der Waals heterostructures. This significant exchange bias is linked to anomalous weak ferromagnetic ordering in MnPS3 below 40 K. The tunability of exchange bias during thermal cycling is attributed to the amorphization and changes in the van der Waals gap during field cooling. The findings highlight a robust and adjustable exchange bias in van der Waals heterostructures, presenting a straightforward method to enhance other interface-related spintronic phenomena for practical applications. Detailed interface analysis reveals atom migration between layers, forming amorphous regions on either side of the van der Waals gap, emphasizing the importance of precise interface characterization in these heterostructures.

Generative Adversarial Network-Based Augmentation with Noval 2-step Authentication for Anti-coronavirus Peptide Prediction.

The virus poses a longstanding and enduring danger to various forms of life. Despite the ongoing endeavors to combat viral diseases, there exists a necessity to explore and develop novel therapeutic options. Antiviral peptides are bioactive molecules with a favorable toxicity profile, making them promising alternatives for viral infection treatment. Therefore, this article employed a generative adversarial network for antiviral peptide augmentation and a novel two-step authentication process for augmented synthetic peptides to enhance antiviral activity prediction. Additionally, five widely utilized deep learning models were employed for classification purposes. Initially, a GAN was used to augment the antiviral peptide. In a two-step authentication process, the NCBI-BLAST was utilized to identify the antiviral activity resemblance between the synthetic and real peptide. Subsequently, the hydrophobicity, hydrophilicity, hydroxylic nature, positive charge, and negative charge of synthetic and authentic antiviral peptides were compared before their utilization. Later, to examine the impact of authenticated peptide augmentation in the prediction of antiviral peptides, a comparison is conducted with the outcomes of non-peptide augmented prediction. The study demonstrates that the 1-D convolution neural network with augmented peptide exhibits superior performance compared to other employed classifiers and state-of-the-art models. The network attains a mean classification accuracy of 95.41%, an AUC value of 0.95, and an MCC value of 0.90 on the benchmark antiviral and anti-corona peptides dataset. Thus, the performance of the proposed model indicates its efficacy in predicting the antiviral activity of peptides.

A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets.

Traumatic brain injury (TBI) heterogeneity remains a critical barrier to translating therapies. Identifying final common pathways/molecular signatures that integrate this heterogeneity informs biomarker and therapeutic-target development. We present the first large-scale murine single-cell atlas of the transcriptomic response to TBI (334,376 cells) across clinically relevant models, sex, brain region, and time as a foundational step in molecularly deconstructing TBI heterogeneity. Results were unique to cell populations, injury models, sex, brain regions, and time, highlighting the importance of cell-level resolution. We identify cell-specific targets and previously unrecognized roles for microglial and ependymal subtypes. Ependymal-4 was a hub of neuroinflammatory signaling. A distinct microglial lineage shared features with disease-associated microglia at 24 h, with persistent gene-expression changes in microglia-4 even 6 months after contusional TBI, contrasting all other cell types that mostly returned to naive levels. Regional and sexual dimorphism were noted. CEREBRI, our searchable atlas (https://shiny.crc.pitt.edu/cerebri/), identifies previously unrecognized cell subtypes/molecular targets and is a leverageable platform for future efforts in TBI and other diseases with overlapping pathophysiology.

Anemia From Inflammation After Intracerebral Hemorrhage and Relationships With Outcome.

BACKGROUND: Baseline anemia is associated with poor intracerebral hemorrhage (ICH) outcomes. However, underlying drivers for anemia and whether anemia development after ICH impacts clinical outcomes are unknown. We hypothesized that inflammation drives anemia development after ICH and assessed their relationship to outcomes. METHODS AND RESULTS: Patients with serial hemoglobin and iron biomarker concentrations from the HIDEF (High-Dose Deferoxamine in Intracerebral Hemorrhage) trial were analyzed. Adjusted linear mixed models assessed laboratory changes over time. Of 42 patients, significant decrements in hemoglobin occurred with anemia increasing from 19% to 45% by day 5. Anemia of inflammation iron biomarker criteria was met in 88%. A separate cohort of 521 patients with ICH with more granular serial hemoglobin and long-term neurological outcome data was also investigated. Separate regression models assessed whether (1) systemic inflammatory response syndrome (SIRS) scores related to hemoglobin changes over time and (2) hemoglobin changes related to poor 90-day outcome. In this cohort, anemia prevalence increased from 30% to 71% within 2 days of admission yet persisted beyond this time. Elevated systemic inflammatory response syndrome was associated with greater hemoglobin decrements over time (adjusted parameter estimate: -0.27 [95% CI, -0.37 to -0.17]) and greater hemoglobin decrements were associated with poor outcomes (adjusted odds ratio per 1 g/dL increase, 0.76 [95% CI, 0.62-0.93]) independent to inflammation and ICH severity. CONCLUSIONS: We identified novel findings that acute anemia development after ICH is common, rapid, and related to inflammation. Because anemia development is associated with poor outcomes, further work is required to clarify if anemia, or its underlying drivers, are modifiable treatment targets that can improve ICH outcomes. REGISTRATION: https://www.clinicaltrials.gov Unique identifier: NCT01662895.

Discerning the Role of DNA Sequence, Shape, and Flexibility in Recognition by Drosophila Transcription Factors.

The precise spatial and temporal orchestration of gene expression is crucial for the ontogeny of an organism and is mainly governed by transcription factors (TFs). The mechanism of recognition of cognate sites amid millions of base pairs in the genome by TFs is still incompletely understood. In this study, we focus on DNA sequence composition, shape, and flexibility preferences of 28 quintessential TFs from Drosophila melanogaster that are critical to development and body patterning mechanisms. Our study finds that TFs exhibit distinct predilections for DNA shape, flexibility, and sequence compositions in the proximity of transcription factor binding sites (TFBSs). Notably, certain zinc finger proteins prefer GC-rich areas with less negative propeller twist, while homeodomains mainly seek AT-rich regions with a more negative propeller twist at their sites. Intriguingly, while numerous cofactors share similar binding site preferences and bind closer to each other in the genome, some cofactors that have different preferences bind farther apart. These findings shed light on TF DNA recognition and provide novel insights into possible cofactor binding and transcriptional regulation mechanisms.

Probabilistic microkinetic modeling: Species balance equations for a catalyst surface containing multiple short-range order parameters to capture spatial correlations.

Adsorbed molecules on a catalyst almost always arrange themselves in a manner that is far from perfectly random, which gives rise to spatial correlations. These correlations are a result of the interactions between the adsorbed species (adspecies) as well as elementary processes such as diffusion and reaction events that shape the adspecies arrangements. Despite their importance, spatial correlations are usually ignored while writing species balance equations for the modeling of heterogeneous catalytic systems. Recently, we have introduced a probabilistic microkinetic modeling (p-MKM) framework that aims at incorporating spatial correlations in the form of a short-ranged order (SRO) parameter into species balance equations. Here, we extend the approach to catalytic systems of higher complexity, namely, longer interactions and multiple species. This is made possible by including multiple pair probabilities in the p-MKM model for the first time. The interplay between different SRO parameters is probed. An important consideration is how many pair probabilities should be included to capture the underlying complexity with sufficient accuracy.

In-situ oil-spill remediation by an electrodeposited superhydrophobic copper mesh.

The present study aims to combat the problem of oil in water pollution via its separation using a superhydrophobic copper mesh. An ecofriendly superhydrophobic copper mesh with a water contact angle of 166 ± 2° is developed by a facile two-step process (electrodeposition followed by coating). The coated mesh with mechanical robustness, chemical endurance and thermal stability is a promising choice for real-world conditions. Additionally, its resistance to corrosion in harsh chemical environments ensures its long-term durability. With a separation efficiency of 99.9 %, the coated mesh serves as an efficient medium for oil-water separation. It can be used as a reusable filtering medium with high separation efficiency in alkali and neutral environments. Besides, it is also competent for continuous oil-water separation and collection of oil from wastewater. Thus, the above study clearly manifests that the coated mesh holds tremendous potential for large-scale oil spill cleanup.

Navigating the signaling landscape of Ralstonia solanacearum: a study of bacterial two-component systems.

Ralstonia solanacearum, the bacterium that causes bacterial wilt, is a destructive phytopathogen that can infect over 450 different plant species. Several agriculturally significant crop plants, including eggplant, tomato, pepper, potato, and ginger, are highly susceptible to this plant disease, which has a global impact on crop quality and yield. There is currently no known preventive method that works well for bacterial wilt. Bacteria use two-component systems (TCSs) to sense their environment constantly and react appropriately. This is achieved by an extracellular sensor kinase (SK) capable of sensing a suitable signal and a cytoplasmic response regulator (RR) which gives a downstream response. Moreover, our investigation revealed that R. solanacearum GMI1000 possesses a substantial count of TCSs, specifically comprising 36 RRs and 27 SKs. While TCSs are known targets for various human pathogenic bacteria, such as Salmonella, the role of TCSs in R. solanacearum remains largely unexplored in this context. Notably, numerous inhibitors targeting TCSs have been identified, including GHL (Gyrase, Hsp, and MutL) compounds, Walk inhibitors, and anti-TCS medications like Radicicol. Consequently, the investigation into the involvement of TCSs in virulence and pathogenesis has gained traction; however, further research is imperative to ascertain whether TCSs could potentially supplant conventional anti-wilt therapies. This review delves into the prospective utilization of TCSs as an alternative anti-wilt therapy, focusing on the lethal phytopathogen R. solanacearum.

Adipose tissue macrophage-derived microRNA-210-3p disrupts systemic insulin sensitivity by silencing GLUT4 in obesity.

Management of chronic obesity-associated metabolic disorders is a key challenge for biomedical researchers. During chronic obesity, visceral adipose tissue (VAT) undergoes substantial transformation characterized by a unique lipid-rich hypoxic AT microenvironment which plays a crucial role in VAT dysfunction, leading to insulin resistance (IR) and type 2 diabetes. Here, we demonstrate that obese AT microenvironment triggers the release of miR-210-3p microRNA-loaded extracellular vesicles from adipose tissue macrophages, which disseminate miR-210-3p to neighboring adipocytes, skeletal muscle cells, and hepatocytes through paracrine and endocrine actions, thereby influencing insulin sensitivity. Moreover, EVs collected from Dicer-silenced miR-210-3p-overexpressed bone marrow-derived macrophages induce glucose intolerance and IR in lean mice. Mechanistically, miR-210-3p interacts with the 3'-UTR of GLUT4 mRNA and silences its expression, compromising cellular glucose uptake and insulin sensitivity. Therapeutic inhibition of miR-210-3p in VAT notably rescues high-fat diet-fed mice from obesity-induced systemic glucose intolerance. Thus, targeting adipose tissue macrophage-specific miR-210-3p during obesity could be a promising strategy for managing IR and type 2 diabetes.

An Observational Dosimetric Study in Definitively Treated Primary Head and Neck Cancers: To Assess the Effect of Weight Loss and Change in Lateral Neck Dimensions on the Difference Between Dose Planned and Received by the Parotid(s) and Correlation with Adaptive Radiation Therapy.

Purpose: The head and neck cancer (HNC) literature is rife with reports of differences in planned versus actual radiation doses to the parotid gland (PG) due to changes in anatomy during the course of radiation therapy. We prospectively studied and quantified changes in planned and delivered doses due to weight loss and changes in lateral neck dimensions. Methods and Materials: Sixty patients were enrolled in this prospective non-randomized observational study. The inclusion criterion was having a newly diagnosed, histologically proven squamous cell carcinoma of HNC. Weight loss (WL) and change in lateral neck dimensions (LND) were assessed weekly, and new hybrid plans were generated using interval replanning CT scans. Dose variations were monitored and extrapolated for replanning CT scans and correlated with WL and change in LND. Results: The Pearson correlation coefficients for WL and difference in Dmean of ipsilateral and contralateral PG was 0.3292 (P = .0124) and 0.4232 (P = .0010), respectively. There was significantly higher change in the Dmean of bilateral PG (Ipsilateral(I) > contralateral(C)) in patients who experienced WL of >5%. Change in LND correlated with difference in Dmean of ipsilateral PG at 0.4829 (P = .0001) and difference in D50 at 0.4146 (P < .0013). Contralateral PG correlated with difference in Dmean at 0.5952 (P < .0001). The difference in Dmean for ipsilateral PG was 1.8535 Gy for those showing reduction in LND of >1 cm compared with 0.8596 Gy (P = .0091) for those who had ≤1 cm reduction in LND. Conclusions: Either WL of >5% or reduction in LND of >1 cm can be used as an external parameter to help select patients who might benefit most from replanning and adaptive radiation therapy.

Harnessing Van der Waals CrPS4 and Surface Oxides for Nonmonotonic Preset Field Induced Exchange Bias in Fe3GeTe2.

Two-dimensional van der Waals (vdW) heterostructures are an attractive platform for studying exchange bias due to their defect-free and atomically flat interfaces. Chromium thiophosphate (CrPS4), an antiferromagnetic material, possesses uncompensated magnetic spins in a single layer, rendering it a promising candidate for exploring exchange bias phenomena. Recent findings have highlighted that naturally oxidized vdW ferromagnetic Fe3GeTe2 exhibits exchange bias, attributed to the antiferromagnetic coupling of its ultrathin surface oxide layer (O-FGT) with the underlying unoxidized Fe3GeTe2. Anomalous Hall measurements are employed to scrutinize the exchange bias within the CrPS4/(O-FGT)/Fe3GeTe2 heterostructure. This analysis takes into account the contributions from both the perfectly uncompensated interfacial CrPS4 layer and the interfacial oxide layer. Intriguingly, a distinct and nonmonotonic exchange bias trend is observed as a function of temperature below 140 K. The occurrence of exchange bias induced by a "preset field" implies that the prevailing phase in the polycrystalline surface oxide is ferrimagnetic Fe3O4. Moreover, the exchange bias induced by the ferrimagnetic Fe3O4 is significantly modulated by the presence of the van der Waals antiferromagnetic CrPS4 layer, forming a heterostructure, along with additional iron oxide phases within the oxide layer. These findings underscore the intricate and complex nature of exchange bias in van der Waals heterostructures, highlighting their potential for tailored manipulation and control.

CDBProm: the Comprehensive Directory of Bacterial Promoters.

The decreasing cost of whole genome sequencing has produced high volumes of genomic information that require annotation. The experimental identification of promoter sequences, pivotal for regulating gene expression, is a laborious and cost-prohibitive task. To expedite this, we introduce the Comprehensive Directory of Bacterial Promoters (CDBProm), a directory of in-silico predicted bacterial promoter sequences. We first identified that an Extreme Gradient Boosting (XGBoost) algorithm would distinguish promoters from random downstream regions with an accuracy of 87%. To capture distinctive promoter signals, we generated a second XGBoost classifier trained on the instances misclassified in our first classifier. The predictor of CDBProm is then fed with over 55 million upstream regions from more than 6000 bacterial genomes. Upon finding potential promoter sequences in upstream regions, each promoter is mapped to the genomic data of the organism, linking the predicted promoter with its coding DNA sequence, and identifying the function of the gene regulated by the promoter. The collection of bacterial promoters available in CDBProm enables the quantitative analysis of a plethora of bacterial promoters. Our collection with over 24 million promoters is publicly available at https://aw.iimas.unam.mx/cdbprom/.

Unravelling the genomic secrets of bacterial fish pathogens: a roadmap to aquaculture sustainability.

In the field of aquaculture, bacterial pathogens pose significant challenges to fish health and production. Advancements in genomic technologies have revolutionized our understanding of bacterial fish pathogens and their interactions with their host species. This review explores the application of genomic approaches in the identification, classification, and characterization of bacterial fish pathogens. Through an extensive analysis of the literature, we have compiled valuable data on 79 bacterial fish pathogens spanning 13 different phyla, encompassing their whole genome sequences. By leveraging high-throughput sequencing techniques, researchers have gained valuable insights into the genomic makeup of these pathogens, enabling a deeper understanding of their virulence factors and mechanisms of host interaction. Furthermore, genomic approaches have facilitated the discovery of potential vaccine and drug targets, opening up new avenues for the development of effective interventions against fish pathogens. Additionally, the utilization of genomics in fish disease resistance and control in aquaculture has shown promising results, enabling the identification of genetic markers associated with disease resistance traits. This review highlights the significant contributions of genomics to the field of fish pathogen research and underscores its potential for improving disease management strategies and enhancing the sustainability of aquaculture practices.

Reverse vaccinology and immunoinformatics approach to design a chimeric epitope vaccine against Orientia tsutsugamushi.

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi and it is reportedly associated with up to 20 % of hospitalized cases of febrile illnesses. The major challenge of vaccine development is the lack of identified antigens that can induce both heterotypic and homotypic immunity including the production of antibodies, cytotoxic T lymphocyte, and helper T lymphocytes. We employed a comprehensive immunoinformatic prediction algorithm to identify immunogenic epitopes of the 56-kDa type-specific cell membrane surface antigen and surface cell antigen A of O. tsutsugamushi to select potential candidates for developing vaccines and diagnostic assays. We identified 35 linear and 29 continuous immunogenic B-cell epitopes and 51 and 27 strong-binding T-cell epitopes of major histocompatibility complex class I and class II molecules, respectively, in the conserved and variable regions of the 56-kDa type-specific surface antigen. The predicted B- and T-cell epitopes were used to develop immunogenic multi-epitope candidate vaccines and showed to elicit a broad-range of immune protection. A stable interactions between the multi-epitope vaccines and the host fibronectin protein were observed using docking and simulation methods. Molecular dynamics simulation studies demonstrated that the multi-epitope vaccine constructs and fibronectin docked models were stable during simulation time. Furthermore, the multi-epitope vaccine exhibited properties such as antigenicity, non-allergenicity and ability to induce interferon gamma production and had strong associations with their respective human leukocyte antigen alleles of world-wide population coverage. A correlation of immune simulations and the in-silico predicted immunogenic potential of multi-epitope vaccines implicate for further investigations to accelerate designing of epitope-based vaccine candidates and chimeric antigens for development of serological diagnostic assays for scrub typhus.

Mechanisms of Electrical Switching of Ultrathin CoO/Pt Bilayers.

We study current-induced switching of the Néel vector in CoO/Pt bilayers to understand the underlying antiferromagnetic switching mechanism. Surprisingly, we find that for ultrathin CoO/Pt bilayers electrical pulses along the same path can lead to an increase or decrease of the spin Hall magnetoresistance signal, depending on the current density of the pulse. By comparing these results to XMLD-PEEM imaging of the antiferromagnetic domain structure before and after the application of current pulses, we reveal the details of the reorientation of the Néel vector in ultrathin CoO(4 nm). This allows us to understand how opposite resistance changes can result from a thermomagnetoelastic switching mechanism. Importantly, our spatially resolved imaging shows that regions where the current pulses are applied and regions further away exhibit different switched spin structures, which can be explained by a spin-orbit torque-based switching mechanism that can dominate in very thin films.

In-situ assessment of the performance of oil-water separation by superhydrophobic coated cotton under extreme conditions.

The present study aims to address the issue of oil in water pollution by application of a superhydrophobic cotton fabric. The superhydrophobic cotton fabric with a water contact angle of 158 ± 2°, is developed by a solution immersion technique using zirconium dioxide (ZrO2) nanoparticles and hexadecyltrimethoxysilane. The synthesis parameters such as concentration, curing temperature, and immersion time were optimized using Box-Behnken design method. With mechanical durability, chemical resilience and thermal stability, the coated fabric can separate different oil-water mixtures with an efficiency of 99.9 %. The coated fabric can also be reused for 50 separation cycles in acidic and neutral medium. Besides, droplet dynamic behavior of oil-water mixture has also been studied to ascertain the effect of mixture impact velocities on separation performance. Additionally, coated fabric possesses self-cleaning feature, which makes it viable for muddy oil-water separation. Prepared coated fabric holds tremendous potential for industrial use and oil-water separation in extreme conditions.

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI.

Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.