Chemical Patterns Research Articles

The formation and conversion of characteristic aroma profiles and key harmful substances in different high-temperature processing of hairtail (Trichiurus Haumela)

The balance regulation between characteristic aroma and hazards in high-temperature processed fish is a hot spot. This study was aimed to explore the interactive relationship between the nutritional value, microstructures, aroma, and harmful substances of hairtail under different frying methods including traditional frying (TF), air frying (AF), and vacuum frying (VF) via chemical pattern recognition. The results indicated that VF-prepared hairtail could form a crunchy mouthfeel and retain the highest content of protein (645.53 mg/g) and the lowest content of fat (242.03 mg/g). Vacuum frying reduced lipid oxidation in hairtail, resulting in the POV reaching 0.02 mg/g, significantly lower than that of TF (0.05 mg/g) and AF (0.21 mg/g), and TBARS reached 0.83 mg/g, significantly lower than that of AF (1.96 mg/g) (P < 0.05), respectively. Notable variations were observedin the aroma profileof hairtail preparedfrom different frying methods. Vacuum frying of hairtail resulted in higher levels of pyrazines and alcohols, whereas traditional frying and air frying were associated with the formation of aldehydes and ketones, respectively. Air frying was not a healthy way to cook hairtail which produced the highest concentration of harmful substances (up to 190.63 ng/g), significantly higher than VF (5.72 ng/g) and TF (52.78 ng/g) (P < 0.05), especially norharman (122.57 ng/g), significantly higher than VF (4.50 ng/g) and TF (32.63 ng/g) (P < 0.05). Norharman and acrylamide were the key harmful substances in hairtail treated with traditional frying. The vacuum frying method was an excellent alternative for deep-fried hairtail as a snack food with fewer harmful substances and a fine aroma, providing a theoretic guidance for preparing healthy hairtail food with high nutrition and superior sensory attraction.

A comprehensive model for viscoplastic flows in channels with a patterned wall: longitudinal, transverse and oblique flows

We develop a comprehensive model for the creeping Poiseuille Bingham flow in channels equipped with a patterned wall, i.e. decorated with grooves or stripes that may represent a superhydrophobic (SH) or a chemically patterned (CP) surface, respectively, with longitudinal, transverse and oblique groove (stripe) orientations with respect to the applied pressure gradient. We rely on the Navier slip law to model the boundary condition on the slippery grooves. We develop semi-analytical, explicit-form and complementary computational fluid dynamics models, with solutions that have reasonable agreement. In contrast to its Newtonian analogue, a distinct solution for the oblique configuration, with an a priori unknown transform matrix, must be developed due to the viscoplastic nonlinear rheology. Our focus is to systematically analyse the effects of the Bingham number ( $B$ ), slip number ( $b$ ), groove periodicity length ( $\ell$ ), slip area fraction ( $\varphi$ ) and groove orientation angle ( $\theta$ ), on the slip velocities, effective slip length ( $\chi$ ), slip angle difference ( $\theta -s$ ), mixing index ( $I_M$ ), flow anisotropy and flow regimes. In particular, we demonstrate that, as $B$ increases, the maximum values of the shear component of $\chi$ , $\theta -s$ and $I_M$ occur progressively at smaller values of $\theta$ , compared with their Newtonian counterparts.

The intermediate neutron capture process

Context. The intermediate neutron capture process (i-process) can develop during proton ingestion events (PIE), potentially during the early stages of low-mass low-metallicity asymptotic giant branch (AGB) stars. Aims. We examine the impact of overshoot mixing on the triggering and development of i-process nucleosynthesis in AGB stars of various initial masses and metallicities. Methods. We computed AGB stellar models, with initial masses of 1, 2, 3, and 4 M⊙ and metallicities in the −2.5 ≤ [Fe/H] ≤ 0 range, using the stellar evolution code STAREVOL with a network of 1160 nuclei coupled to the transport equations. We considered different overshooting profiles below and above the thermal pulses, and below the convective envelope. Results. The occurrence of PIEs is found to be primarily governed by the amount of overshooting at the top of pulse (ftop) and to increase with rising ftop. For ftop = 0, 0.02, 0.04, and 0.1, we find that 0%, 6%, 24%, and 86% of our 21 AGB models with −2 &lt; [Fe/H] &lt; 0 experience a PIE, respectively. Variations of the overshooting parameters during a PIE leads to a scatter on abundances of 0.5 − 1 dex on elements, with 36 &lt; Z &lt; 56; however, this barely impacts the production of elements with 56 &lt; Z &lt; 80, which therefore appear to be a reliable prediction of our models. Actinides are only produced if the overshooting at the top of pulse is small enough. We also find that PIEs leave a 13C-pocket at the bottom of the pulse that can give rise to an additional radiative s-process nucleosynthesis. In the case of the 2 M⊙ models with [Fe/H] = −1 and −0.5, it produces a noticeable mixed i + s chemical signature at the surface. Finally, the chemical abundance patterns of 22 observed r/s-stars candidates (18 dwarfs or giants and 4 post-AGB) with −2 &lt; [Fe/H] &lt; −1 are found to be in reasonable agreement with our AGB model predictions. The binary status of the dwarfs/giants being unclear, we suggest that these stars have acquired their chemical pattern either from the mass transfer of a now-extinct AGB companion or from an early generation AGB star that polluted the natal cloud. Conclusions. The occurrence of PIEs and the development of i-process nucleosynthesis in AGB stars remains sensitive to the overshooting parametrization. A high (yet realistic) ftop value triggers PIEs at (almost) all metallicities. The existence of r/s-stars at [Fe/H] ≃ −1 is in favour of an i-process operating in AGB stars up to this metallicity. Stricter constraints from multi-dimensional hydrodynamical models on overshoot coefficients could deliver new insights into the contribution of AGB stars to heavy elements in the Universe.

2D unified atmosphere and wind simulations of O-type stars

Context. Massive and luminous O-type star (O star) atmospheres with winds have been studied primarily using one-dimensional (1D), spherically symmetric, and stationary models. However, observations and theory have suggested that O star atmospheres are highly structured, turbulent, and time-dependent. As such, when making comparisons to observations, present-day 1D modeling tools require the introduction of ad hoc quantities such as photospheric macro- and microturbulence, wind clumping, and other relevant properties. Aims. We present a series of multi-dimensional, time-dependent, radiation-hydrodynamical (RHD) simulations for O stars that encapsulate the deeper sub-surface envelope (down to T ~ 450 kK), as well as the supersonic line-driven wind outflow in one unified approach. Our overarching aim is to develop a framework that is free from the ad-hoc prescriptions that plague present-day 1D models. Here, we start with an analysis of a small set of such multi-dimensional simulations and then compare them to atmospheric structures predicted by their 1D counterparts. Methods. We performed time-dependent, two-dimensional (2D) simulations of O star atmospheres with winds using a flux-limiting RHD finite volume modelling technique. Opacities are computed using a hybrid approach combining tabulated Rosseland means with calculations (based on the Sobolev approximation) of the enhanced line opacities expected for supersonic flows. The initial conditions and comparison models were derived using similar procedures as those applied in standard 1D stationary model atmosphere with wind codes. Results. Structure starts appearing in our simulations just below the iron-opacity peak at ~200 kK. Local pockets of gas with radiative accelerations that exceed gravity then shoot up from these deep layers into the upper atmosphere, where they interact with the line-driven wind outflow initiated around or beyond the variable photosphere. This complex interplay creates large turbulent velocities in the photospheric layers of our simulations, on the order of ~30–100km s−1, with higher values for models with higher luminosity-to-mass ratios. This offers a generally good agreement with observations of large photospheric ‘macroturbulence’ in O stars. When compared to 1D models, the average structures in the 2D simulations display less envelope expansion and no sharp density-inversions, along with density and temperature profiles that are significantly less steep around the photosphere, and a strong anti-correlation between velocity and density in the supersonic wind. Although the wind initiation region is complex and highly variable in our simulations, our average mass-loss rates agree well with stationary wind models computed by means of full co-moving frame radiative transfer solutions. Conclusions. The different atmospheric structures found in 2D and 1D simulations are likely to affect the spectroscopic determination of fundamental stellar and wind parameters for O stars as well as the empirical derivation of their chemical abundance patterns. To qualitatively match the different density and temperature profiles seen in our multi-dimensional and 1D models, we need to add a modest amount of convective energy transport in the deep sub-surface layers and a large turbulent pressure around the photosphere to the 1D models.

Uncovering the metabolite complexity and variability of cultivated hemp (Cannabis sativa L.): A first phytochemical diversity mapping in Greece

The high value of fiber-type Cannabis sativa L. (hemp) due to its phytochemicals has yet to be fully recognized and leveraged. Besides cannabidiol (CBD), which is the most prevalent non-psychoactive cannabinoid, hemp contains numerous other cannabinoids with unexplored bioactivities, in addition to various compound classes. Previous works have aimed to correlate chemical profiles of C. sativa inflorescences with important parameters, mostly based on experiments under controlled conditions. However, mapping studies that explore the phytochemical diversity of hemp in a more realistic context are crucial to guide decisions at multiple levels, especially in areas where hemp cultivation was recently re-authorized, including Mediterranean countries. In this work, a powerful strategy was followed to map the phytochemical diversity of cultivated hemp in Greece, being the first study of its kind for this environment. A panel of 98 inflorescence samples, covering two harvesting years, eleven geographical regions and seven commonly used EU varieties, were studied using a combination of targeted and untargeted approaches. Quantitative results based on UPLC-PDA revealed relatively constant CBD/THC (total) ratios, while profiling by LC-HRMS effectively probed the phytochemical variability of samples, and led to the annotation of 88 metabolites, including a multitude of minor cannabinoids. Multivariate analysis substantiated a strong effect of harvesting year in sample discrimination and related biomarkers were revealed, belonging to fatty acids and flavonoids. The effect of geographical region and, especially, variety on chemical variation patterns was more intricate to interpret. The results of this work are envisioned to enhance our understanding of the real-world phytochemical complexity of C. sativa (hemp), with a view to maximized utilization of hemp for the promotion of human well-being.

A Perspective on the Milky Way Bulge Bar as Seen from the Neutron-capture Elements Cerium and Neodymium with APOGEE

This study probes the chemical abundances of the neutron-capture elements cerium and neodymium in the inner Milky Way from an analysis of a sample of ∼2000 stars in the Galactic bulge bar spatially contained within ∣X Gal∣ < 5 kpc, ∣Y Gal∣ < 3.5 kpc, and ∣Z Gal∣ < 1 kpc, and spanning metallicities between −2.0 ≲ [Fe/H] ≲ +0.5. We classify the sample stars into low- or high-[Mg/Fe] populations and find that, in general, values of [Ce/Fe] and [Nd/Fe] increase as the metallicity decreases for the low- and high-[Mg/Fe] populations. Ce abundances show a more complex variation across the metallicity range of our bulge-bar sample when compared to Nd, with the r-process dominating the production of neutron-capture elements in the high-[Mg/Fe] population ([Ce/Nd] < 0.0). We find a spatial chemical dependence of Ce and Nd abundances for our sample of bulge-bar stars, with low- and high-[Mg/Fe] populations displaying a distinct abundance distribution. In the region close to the center of the MW, the low-[Mg/Fe] population is dominated by stars with low [Ce/Fe], [Ce/Mg], [Nd/Mg], [Nd/Fe], and [Ce/Nd] ratios. The low [Ce/Nd] ratio indicates a significant contribution in this central region from r-process yields for the low-[Mg/Fe] population. The chemical pattern of the most metal-poor stars in our sample suggests an early chemical enrichment of the bulge dominated by yields from core-collapse supernovae and r-process astrophysical sites, such as magnetorotational supernovae.

Transition of chemical production pattern motivated by CO2 utilization: Multi-dimensional evaluation and future projections

Carbon dioxide utilization (CDU) for chemical production is beneficial for achieving carbon neutrality and constructing an artificial carbon cycle. However, the industrialization of CDU technology is facing the concerns about environmental impacts, energy consumption and economic effects. To better illustrate the comprehensive performances of CDU technology and optimize chemical production, this study conducts a multi-dimensional evaluation on CDU and traditional pathways following a scenario-based optimization, which covers 23 pathways for 7 identified bulk chemicals. The results reveal that ethanol is the most suitable chemical to be transitioned preferentially and produced with CDU technology based on China’s current technology level. The development of electro-catalytic conversion may benefit CDU pathways especially for oxirane. The results of scenario analysis reveal that the competitivity and priority of various pathways may change significantly with the deployment of low-carbon strategies. Then the results of conditional optimization reveal that the transition of chemical production pattern motivated by CDU may significantly impact on resource consumption and technology proportion, which brings about an increase of 680 % in H2 consumption. The transition may also decouple the relationship between carbon emissions and economic growth, which drives the production emissions to drop from 193 Mt/year to −27 Mt/year and carbon intensity to drop from 80 t/104 USD to −8 t/104 USD.

A Walk on the Retrograde Side (WRS) project

Relics of ancient accretion events experienced by the Milky Way are predominantly located within the stellar halo of our Galaxy. However, debris from different objects display overlapping distributions in dynamical spaces, making it extremely challenging to properly disentangle their contribution to the build-up of the Galaxy. To shed light on this chaotic context, we initiated a program aimed at the homogeneous chemical tagging of the local halo of the Milky Way, focusing on the component in retrograde motion, since this is expected to host a large fraction of stars accreted from past mergers. The A Walk on the Retrograde Side (WRS) project targets retrograde halo stars in the solar neighborhood with accurate 6D phase space information available, measuring the precise chemical abundance of several chemical elements from high-resolution spectroscopy. In this first paper, we present the project and the analysis of high-resolution spectra obtained with UVES at VLT and at LBT for 186 stars. We obtained accurate radial velocity and chemical abundances for several elements for all the target stars. In particular, we focus on the chemical composition of a specific subset of substructures that have been dynamically identified in the literature. Our study reveals that two among the more recently discovered structures in the retrograde halo, namely, Antaeus/L-RL64 and ED-3, have identical chemical patterns and similar integrals of motion, suggesting a common origin. In turn, the abundance patterns of this unified system differ from that of Gaia-Enceladus, confirming that it is an independent structure. Finally, Sequoia exhibits a different chemistry with respect to that of Gaia-Enceladus at [Fe/H] &lt; −1.5 dex, showcasing an excess of stars with lower Mg and Ca in the common metallicity range.

Identification of Vernonia patula Merr. and Its Similar Varieties Based on a Combination of HPLC Fingerprinting and Chemical Pattern Recognition.

Vernonia patula Merr. (VP) is a traditional medicine used by the Zhuang and Yao people, known for its therapeutic properties in treating anemopyretic cold and other diseases. Distinguishing VP from similar varieties such as Praxelis clematidea (PC), Ageratum conyzoides L. (AC) and Ageratum houstonianum Mill (AH) was challenging due to their similar traits and plant morphology. The HPLC fingerprints of 40 batches of VP and three similar varieties were established. SPSS 20.0 and SIMCA-P 13.0 were used to statistically analyze the chromatographic peak areas of 37 components. The results showed that the similarity of the HPLC fingerprints for each of the four varieties was >0.9, while the similarity between the control chromatogram of VP and its similar varieties was <0.678. Cluster analysis and partial least squares discriminant analysis provided consistent results, indicating that all four varieties could be individually clustered together. Through further analysis, we found isochlorogenic acid A and isochlorogenic acid C were present only in the original VP, while preconene II was present in the three similar varieties of VP. These three components are expected to be identification points for accurately distinguishing VP from PC, AC and AH.

A comprehensive evaluation of lipid profiles and nutritional quality in different animal source muscle tissues

Lipid composition is crucial for assessing the nutrition and flavor of animal-derived raw materials, a key concern for consumers. Based on high-resolution mass spectrometry and chemical pattern recognition, the intermuscular lipids fingerprint of seven types of livestock, poultry, and fish muscle were analyzed. In total, 704 lipids were detected, containing fourteen lipid molecules, with significant content differences among samples, notably triglycerides, phosphatidylcholines, phosphatidylethanolamines, diglycerides, and fatty acids. Livestock and poultry muscle had a low content of lipids with an unsaturation degree above 4 (around 10% and 25%). These unsaturated lipid molecules in fish muscle ranged from approximately 60%–75%. Rainbow trout had higher contents of triglycerides and phosphatidylcholines with chain lengths exceeding 13. Mutton had more of these lipids within the 13–18 chain length, exceeding 0.3 g/(g·protein), which may be crucial for their unique texture and flavor. The results will help the deep processing and nutritional assessment of muscle foods.

Application of graphite carbon black assisted-laser desorption ionization-mass spectrometry for soy sauce product discrimination.

In a previous study, we developed a novel analytical method to directly and simultaneously detect taste- and odor-active compounds using graphite carbon black (GCB)-assisted laser desorption ionization mass spectrometry (LDI-MS). In this study, we aimed to evaluate food quality using a variety of soy sauces using the method to discriminate each product. Graphite carbon black-laser desorption ionization-mass spectrometry allowed the provision of hundreds of MS peaks derived from soy sauces in both positive and negative modes without any tedious sample pretreatments. Principal component analysis using the obtained MS peaks clearly distinguished three soy sauce products based on the manufacturing countries (Japan, China, and India). Moreover, this method identified distinct MS peaks for discrimination, which significantly correlated with their quantitative amounts in the products. Thus, GCB-LDI-MS analysis was established as a simple and rapid technique for food analysis, illustrating the chemical patterns of food products.

Abstract 2088: Targeting transcriptional factor IWS1 as a potential treatment of advanced dedifferentiated liposarcoma

Abstract Introduction. Surgery remains the only curative treatment for patients with liposarcoma (LPS). The translation of novel therapies has been hampered, and there has been no significant improvement in prognosis for 30 years. Our recent findings demonstrate that IWS1, a transcription elongation and alternative splicing factor, is elevated in dedifferentiated LPS (ddLPS) patient tumor samples and is associated with worse survival and a shorter time to relapse in ddLPS. In this study, we present preliminary results on a novel treatment for advanced ddLPS: an IWS1 inhibitor, serving as a way to discover a spliceosome as a novel therapeutic target. Methods. Utilizing computer-aided, structure-based virtual screening approaches, we identified a class of compounds that exhibited inhibitory potential. A subset of FDA-approved drugs was evaluated by molecular docking calculations, and compounds with the highest calculated theoretical potency were selected for further analysis. To assess the ability of specific compounds to disrupt IWS1/Spt6 binding, Co-immunoprecipitation (Co-IP) experiments on ddLPS cell line Lipo863 were conducted. Transwell invasion and migration assays were performed to evaluate the effect of the drug candidate on the metastatic potential of cells. Additionally, to evaluate the impact of IWS1 inhibition on spheroid formation, a matrigel extracellular matrix scaffold was employed. Results. Through virtual screening, we identified a class of compounds characterized by a specific chemical pattern featuring a tricyclic fused-ring structure. This class of compounds includes the well-known FDA-approved drug Ketotifen, an antihistamine and mast cell stabilizer. Co-immunoprecipitation of IWS1/Spt6 complex after 48-hour treatment with Ketotifen showed a decrease of over 50% of the isolated complex. Migration and invasion of the ddLPS cell line, Lipo863, were reduced in a dose-dependent manner by 18-44% and 17-52%, respectively, when treated with Ketotifen, without affecting cell viability. In a 3D model, a 14-day treatment of Ketotifen significantly reduced spheroid formation, confirming its impact on tumor growth and cell proliferation. Conclusion. Ketotifen effectively disrupted IWS1/Spt6 complex in vitro. Importantly, it did not affect cell viability while reducing the migratory and invasive potential of ddLPS. The dose-dependent effect of Ketotifen suggests that there may be an optimal in vivo concentration to prevent cells from invading nearby structures and/or metastasizing. We are currently testing the ability of these compounds in preclinical mouse models of ddLPS. Citation Format: Marina Goryunova, Yiran He, Nipin Sp, Elizaveta K. Titerina, Sayumi Tahara, Carson Karakis, Alessandro La Ferlita, Michael Sorkin, Alex Kim, Hua Zhu, Christopher M. Hadad, Raphael E. Pollock, Joal D. Beane. Targeting transcriptional factor IWS1 as a potential treatment of advanced dedifferentiated liposarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2088.

Connection number-based molecular descriptors of skin cancer drugs

Connection number-based molecular descriptors are one type of quantitative measure that provides insight into the connectivity of atoms and bonds within a molecule. The investigation of skin cancer treatments based on connection number-based molecular descriptors requires quantifying the structural features of these pharmaceuticals based on their atom and bond connections. By employing connection number-based descriptors, it is possible to understand the chemical structure and connectivity patterns of these drugs. In this article, we calculated a few connection number-based molecular descriptors for skin cancer drugs, particularly Cobimetinib, Picato, Vismodegib, Odomzo, Imiquimod, Vemurafenib, Daurismo, Trametinib, Fluorouracil, Dacarbazine, Dabrafenib, Encorafenib, and Binimeinib. To summarise, the practical implementation of the research findings presented in the paper allows Dermathera Pharmaceuticals to effectively address obstacles in the process of discovering and developing drugs for skin cancer. The organization enhances its workflow by including molecular descriptors based on connection numbers. This allows for improved chemical selection, accurate prediction of therapy results, and the development of more efficient and safer treatments for patients with skin cancer. This application highlights the concrete advantages of the research in practical pharmaceutical engineering environments.

Morphological, chemical and growth patterns characterization in shells of Phorcus species along the northeastern coasts of Tunisia

This work is a characterization of the morphometry, the relative growth and the metal contamination degree of the shells of two species belonging to Phorcus genus along the northeastern rocky coasts of Tunisia. The gastropods were sampled from nine locations during the winter 2017. Our findings suggested a spatial morphometric variability probably linked to the extent of the continental shelf, the coasts’ geomorphology, the dynamics of water masses, the predation and the competition effects. Furthermore, the shell investigation allowed the study of the growth pattern and indicated that the development in shell size was faster than the increase in weight. Moreover, the use of Phorcus shells as bioindicators of long-term metal contamination seems to be of growing interest, due to their abundance, high longevity, easy sampling and ecobiological features, both in a scientific and ecosystem management perspective that aims to the establishment of conservation measures targeting marine coastal environments.

Chemical modification patterns for microRNA therapeutic mimics: a structure-activity relationship (SAR) case-study on miR-200c.

microRNA (miRNA) mimics are an emerging class of oligonucleotide therapeutics, with a few compounds already in clinical stages. Synthetic miRNAs are able to restore downregulated levels of intrinsic miRNAs, allowing for parallel regulation of multiple genes involved in a particular disease. In this work, we examined the influence of chemical modifications patterns in miR-200c mimics, assessing the regulation of a selection of target messenger RNAs (mRNA) and, subsequently, of the whole transcriptome in A549 cells. We have probed 37 mimics and provided an initial set of instructions for designing miRNA mimics with potency and selectivity similar to an unmodified miRNA duplex. Additionally, we have examined the stability of selected mimics in serum. Finally, the selected two modification patterns were translated to two other miRNAs, miR-34a and miR-155. To differing degrees, these designs acted on targetmRNAs in a similar manner to the unmodified mimic. Here, for the first time, we describe a structured overview of 'miRNA mimics modification templates' that are chemically stabilised and optimised for use in an in vitro set up and highlight the need of further sequence specific optimization when mimics are to be used beyond in vitro tool experiments.

Lifetimes and timescales of tropospheric ozone

The lifetime of tropospheric O3 is difficult to quantify because we model O3 as a secondary pollutant, without direct emissions. For other reactive greenhouse gases like CH4 and N2O, we readily model lifetimes and timescales that include chemical feedbacks based on direct emissions. Here, we devise a set of artificial experiments with a chemistry-transport model where O3 is directly emitted into the atmosphere at a quantified rate. We create 3 primary emission patterns for O3, mimicking secondary production by surface industrial pollution, that by aviation, and primary injection through stratosphere–troposphere exchange (STE). The perturbation lifetimes for these O3 sources includes chemical feedbacks and varies from 6 to 27 days depending on source location and season. Previous studies derived lifetimes around 24 days estimated from the mean odd-oxygen loss frequency. The timescales for decay of excess O3 varies from 10 to 20 days in northern hemisphere summer to 30 to 40 days in northern hemisphere winter. For each season, we identify a single O3 chemical mode applying to all experiments. Understanding how O3 sources accumulate (the lifetime) and disperse (decay timescale) provides some insight into how changes in pollution emissions, climate, and stratospheric O3 depletion over this century will alter tropospheric O3. This work incidentally found 2 distinct mistakes in how we diagnose tropospheric O3, but not how we model it. First, the chemical pattern of an O3 perturbation or decay mode does not resemble our traditional view of the odd-oxygen family of species that includes NO2. Instead, a positive O3 perturbation is accompanied by a decrease in NO2. Second, heretofore we diagnosed the importance of STE flux to tropospheric O3 with a synthetic “tagged” tracer O3S, which had full stratospheric chemistry and linear tropospheric loss based on odd-oxygen loss rates. These O3S studies predicted that about 40% of tropospheric O3 was of stratospheric origin, but our lifetime and decay experiments show clearly that STE fluxes add about 8% to tropospheric O3, providing further evidence that tagged tracers do not work when the tracer is a major species with chemical feedbacks on its loss rates, as shown previously for CH4.

Chemical Instability-Induced Wettability Patterns on Superhydrophobic Surfaces.

Chemical instability of liquid-repellent surfaces is one of the nontrivial hurdles that hinders their real-world applications. Although much effort has been made to prepare chemically durable liquid-repellent surfaces, little attention has been paid to exploit the instability for versatile use. Herein, we propose to create hydrophilic patterns on a superhydrophobic surface by taking advantage of its chemical instability induced by acid solution treatment. A superhydrophobic Cu(OH)2 nanoneedle-covered Cu plate that shows poor stability towards HCl solution (1.0 M) is taken as an example. The results show that 2.5 min of HCl solution exposure leads to the etching of Cu(OH)2 nanoneedles and the partial removal of the self-assembled fluoroalkyl silane molecular layer, resulting in the wettability transition from superhydrophobocity to hydrophilicity, and the water contact angle decreases from ~160° to ~30°. Hydrophilic dimples with different diameters are then created on the superhydrophobic surfaces by depositing HCl droplets with different volumes. Afterwards, the hydrophilic dimple-patterned superhydrophobic surfaces are used for water droplet manipulations, including controlled transfer, merging, and nanoliter droplet deposition. The results thereby verify the feasibility of creating wettability patterns on superhydrophobic surfaces by using their chemical instability towards corrosive solutions, which broadens the fabrication methods and applications of functional liquid-repellent surfaces.

Predicting the formation of NADES using a transformer-based model

The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.

Transfer learning graph representations of molecules for pKa, 13C-NMR, and solubility

We explore transfer learning models from a pre-trained graph convolutional neural network representation of molecules, obtained from SchNet, to predict 13C-NMR, pKa, and log S solubility. SchNet learns a graph representation of a molecule by associating each atom with an “embedding vector” and interacts the atom-embeddings with each other by leveraging graph convolutional filters on their interatomic distances. We pre-trained SchNet on molecular energy and demonstrate that the pre-trained atomistic embeddings can then be used as a transferable representation for a wide array of properties. On the one hand, for atomic properties such as micro-pK1 and 13C-NMR, we investigate two models, one linear and one neural net, that input pre-trained atom-embeddings of a particular atom (e.g. carbon) and predict a local property (e.g., 13C-NMR). On the other hand, for molecular properties such as solubility, a size-extensive graph model is built using the embeddings of all atoms in the molecule as input. For all cases, qualitatively correct predictions are made with relatively little training data (&lt;1000 training points), showcasing the ease with which pre-trained embeddings pick up on important chemical patterns. The proposed models successfully capture well-understood trends of pK1 and solubility. This study advances our understanding of current neural net graph representations and their capacity for transfer learning applications in chemistry.

Cell Membrane Perforation: Patterns, Mechanisms and Functions.

Cell membrane is crucial for the cellular activities, and any disruption to it may affect the cells. It is demonstrated that cell membrane perforation is associated with some biological processes like programmed cell death (PCD) and infection of pathogens. Specific developments make it a promising technique to perforate the cell membrane controllably and precisely. The pores on the cell membrane provide direct pathways for the entry and exit of substances, and can also cause cell death, which means reasonable utilization of cell membrane perforation is able to assist intracellular delivery, eliminate diseased or cancerous cells, and bring about other benefits. This review classifies the patterns of cell membrane perforation based on the mechanisms into 1) physical patterns, 2) biological patterns, and 3) chemical patterns, introduces the characterization methods and then summarizes the functions according to the characteristics of reversible and irreversible pores, with the aim of providing a comprehensive summary of the knowledge related to cell membrane perforation and enlightening broad applications in biomedical science.