The role of Volatile Anesthetics in Cardioprotection: a systematic review.

This review evaluates the mechanism of volatile anesthetics as cardioprotective agents in both clinical and laboratory research and furthermore assesses possible cardiac side effects upon usage. Cardiac as well as non-cardiac surgery may evoke perioperative adverse events including: ischemia, diverse arrhythmias and reperfusion injury. As volatile anesthetics have cardiovascular effects that can lead to hypotension, clinicians may choose to administer alternative anesthetics to patients with coronary artery disease, particularly if the patient has severe preoperative ischemia or cardiovascular instability. Increasing preclinical evidence demonstrated that administration of inhaled anesthetics - before and during surgery - reduces the degree of ischemia and reperfusion injury to the heart. Recently, this preclinical data has been implemented clinically, and beneficial effects have been found in some studies of patients undergoing coronary artery bypass graft surgery. Administration of volatile anesthetic gases was protective for patients undergoing cardiac surgery through manipulation of the potassium ATP (KATP) channel, mitochondrial permeability transition pore (mPTP), reactive oxygen species (ROS) production, as well as through cytoprotective Akt and extracellular-signal kinases (ERK) pathways. However, as not all studies have demonstrated improved outcomes, the risks for undesirable hemodynamic effects must be weighed against the possible benefits of using volatile anesthetics as a means to provide cardiac protection in patients with coronary artery disease who are undergoing surgery

Elliptic flow in the Gaussian model of eccentricity fluctuations

We discuss a specific model of elliptic flow fluctuations due to Gaussian fluctuations in the initial spatial $x$ and $y$ eccentricity components \left\{\mean{(\sigma_y^2-\sigma_x^2)/(\sigma_x^2+\sigma_y^2)}, \mean{2\sigma_{xy}/(\sigma_x^2+\sigma_y^2)} \right\}. We find that in this model \vfour, elliptic flow determined from 4-particle cumulants, exactly equals the average flow value in the reaction plane coordinate system, \mean{v_{RP}}, the relation which, in an approximate form, was found earlier by Bhalerao and Ollitrault in a more general analysis, but under the same assumption that $v_2$ is proportional to the initial system eccentricity. We further show that in the Gaussian model all higher order cumulants are equal to \vfour. Analysis of the distribution in the magnitude of the flow vector, the $Q-$distribution, reveals that it is totally defined by two parameters, \vtwo, the flow from 2-particle cumulants, and \vfour, thus providing equivalent information compared to the method of cumulants. The flow obtained from the $Q-$distribution is again \vfour=\mean{v_{RP}}.Comment: Very minor changes, as submitted to Phys. Lett.

A Numerical Model for Heat Transfer and Moisture Evaporation Processes in Hot-Press Drying—An Integral Approach

A numerical model, which was based on the energy principle that the rate of water evaporation from the interface (or wet line) at a given time during hot-press drying was controlled by the rate of heat energy reaching the interface at that time, has been developed. The model treated the drying as a process in which the retreat of the interface and free water flow to the interface occur simultaneously. After all parameters were determined according to the available literature and experiments, the numerical model worked well in predicting the drying curves from process and material variables. The model, which has a sound theoretical base but is numerically simple, has a good potential to be expanded for general high temperature drying and to be adopted in a production line to presort the lumber for good drying practice

Pilot scale study of chlorination-induced transport property changes of a seawater reverse osmosis membrane

A pilot-scale study was performed to assess variations of reverse osmosis (RO) membrane water permeance (A) and salt retention (Robs) induced by chlorination and to compare them with those observed at the lab-scale. A chlorination protocol was adapted to expose only the surface active layer (an aromatic polyamide)of a composite RO membrane to consecutive free chlorine doses ranging from 40 to 4000 ppm h, at pH 6.9. Along the long-term filtration of seawater, performed with a 4" spiral wound RO module, we monitored the variations of A, the decrease of Robs and the rate of increase of A with time, and found themquantitatively similar to those reported in previous studies performed at the lab-scale under accelerated exposure conditions. The elemental analysis of the feed and permeate streams revealed that the rejection of divalent ions remained constant (ca. 100%), irrespective of the free chlorine dose reached, whereas the rejection of monovalent ions of the seawater (mainly sodium, chloride and bromide ions) decreased as the exposure dose increased. Overall, transposing the characterization procedure to the pilot-scale further supports that chlorination of PA, under pH conditions usually found in desalination plants (6.9 to 8.0), is controlled by the concentration of HOCl, as observed from elemental analysis of the surface by XPS

Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing

Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7%–15% speed improvement

Binning sequences using very sparse labels within a metagenome

<p>Abstract</p> <p>Background</p> <p>In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, <it>k</it>-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity.</p> <p>Results</p> <p>The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds.</p> <p>The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, <it>k</it>-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all <it>k</it>-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the ≥ 10 reads datasets and comparable in the ≥ 8 kb benchmark tests.</p> <p>Conclusion</p> <p>In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.</p

Multi-dimensional data refining strategy for effective fine-tuning LLMs

Data is a cornerstone for fine-tuning large language models, yet acquiring suitable data remains challenging. Challenges encompassed data scarcity, linguistic diversity, and domain-specific content. This paper presents lessons learned while crawling and refining data tailored for fine-tuning Vietnamese language models. Crafting such a dataset, while accounting for linguistic intricacies and striking a balance between inclusivity and accuracy, demands meticulous planning. Our paper presents a multidimensional strategy including leveraging existing datasets in the English language and developing customized data-crawling scripts with the assistance of generative AI tools. A fine-tuned LLM model for the Vietnamese language, which was produced using resultant datasets, demonstrated good performance while generating Vietnamese news articles from prompts. The study offers practical solutions and guidance for future fine-tuning models in languages like Vietnamese