Platelet-activating factor contracts guinea pig esophageal muscularis mucosae by stimulating extracellular Ca2+ influx through diltiazem-insensitive Ca2+ channels.

Platelet-activating factor (PAF) is expected to increase esophageal motility. However, to the best of our knowledge, this has not been examined. Thus, we investigated the contractile effects of PAF on guinea pig (GP) esophageal muscularis mucosae (EMM) and the extracellular Ca2+ influx pathways responsible. PAF (10-9-10-6 M) contracted EMM in a concentration-dependent manner. PAF (10-6 M)-induced contractions were almost completely suppressed by apafant (a PAF receptor antagonist, 3 × 10-5 M). In EMM strips, PAF receptor and PAF-synthesizing/degrading enzyme mRNAs were detected. PAF (10-6 M)-induced contractions were abolished by extracellular Ca2+ removal but were not affected by diltiazem [a voltage-dependent Ca2+ channel (VDCC) inhibitor, 10-5 M]. PAF (10-6 M)-induced contractions in the presence of diltiazem were significantly suppressed by LOE-908 [a receptor-operated Ca2+ channel (ROCC) inhibitor, 3 × 10-5 M], SKF-96365 [an ROCC and store-operated Ca2+ channel (SOCC) inhibitor, 3 × 10-5 M], and LOE-908 plus SKF-96365. Among the tested ROCC/SOCC-related mRNAs, Trpc3, Trpc6, and Trpv4/Orai1, Orai3, and Stim2 were abundantly expressed in EMM strips. These results indicate that PAF potently induces GP EMM contractions that are dependent on extracellular Ca2+ influx through ROCCs/SOCCs, and VDCCs are unlikely to be involved.

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models.

Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of random-based modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model's score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation r2 scores of 0.8268 and 0.6875, respectively.

Cyclization-Carbonylation of 2-Alkynyl Primary Benzamides using p-Benzoquinone under Neutral Conditions.

A palladium-catalyzed cyclization-carbonylation of 2-alkynyl primary benzamides 1 afforded methyl 3-substituted 1-methoxyisoquinoline-4-carboxylates 6 in good to moderate yields. In the case of mesylate 1r, 12 was obtained directly via a cyclization-carbonylation-cyclization cascade. Compounds 6 were converted to isoquinolin-1(2H)-ones 8 in good yields under microwave irradiation. In the case of the mesylate 6q, tricyclic isoquinolinone 10 was obtained in good yield. The reactions of thiophene-2-carboxamide derivatives also proceeded well.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis.

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Deterioration of Cement-Based Materials in Low-Temperature Seawater.

Cementitious materials have potential for infrastructure development in low-temperature marine environments, including in seawater at high latitudes and in deep-sea environments (water depths of >1000 m). Although the marine deterioration of cementitious materials has been widely investigated, the influence of seawater temperature has not been elucidated. In this study, to determine the effects of low-temperature seawater on the durability of cementitious materials, cement paste specimens were immersed in a seawater tank at room temperature and 2 °C for 433 days. The specimen immersed in low-temperature seawater exhibited significant deterioration with a partially collapsed surface, whereas the specimen immersed in room-temperature seawater maintained its original shape. Following low-temperature immersion, Ca dissolution was more pronounced and dissolved portlandite, decalcified calcium (alumino)silicate hydrate (C-(A-)S-H), magnesium (alumino)silicate hydrate (M-(A-)S-H), and thaumasite were observed on the collapsed surface. Such significant deterioration can be attributed to the increased solubility of portlandite under low-temperature conditions, which could promote Ca dissolution and subsequently lead to C-(A-)S-H decalcification and the formation of M-(A-)S-H and thaumasite. These insights are expected to contribute to the successful construction and maintenance of cementitious structures in low-temperature seawater.

Toward the Golden Age of Materials Informatics: Perspective and Opportunities.

Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.

Water Purification by 2-Dimensional Dodecagonal Nitride and Graphenylene via First Principles Calculations.

2 dimensional (2D) dodecagonal boron nitride (D_BN) and graphenylene are being investigated to understand their potential applications in water purification. First principle calculations are performed to evaluate the water purification properties of D_BN and graphenylene. It is found that Na+ exothermically adsorbs on pores in D_BN, where the transition state energy via pores is calculated to be 0.03 eV. This indicates that Na+ can pass through D_BN pores more selectively than water molecules and other ions. In contrast, in the case of graphenylene, Na+ is repelled, and H2 O exothermically adsorbs on pores, where the transition state energy via pores is calculated to be 1.00 eV. Therefore, this demonstrates that D_BN exhibits an excellent potential for ion-sieving membranes, while graphenylene exhibits an excellent potential for reverse osmosis membranes. Consequently, this study provides valuable insights into the potential use of D_BN and graphenylene in water purification applications.

Catalysts informatics: paradigm shift towards data-driven catalyst design.

Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.

2D/1D MoS2 /TiO2 Heterostructure Photocatalyst with a Switchable CO2 Reduction Product.

Regulating the transfer pathway of charge carriers in heterostructure photocatalysts is of great importance for selective CO2 photoreduction. Herein, the charge transfer pathway and in turn the redox potential succeeded to regulate in 2D MoS2 /1D TiO2 heterostructure by varying the light wavelength range. Several in situ measurements and experiments confirm that charge transfer follows either an S-scheme mechanism under simulated solar irradiation or a heterojunction approach under visible light illumination, elucidating the switchable property of the MoS2 /TiO2 heterostructure. Replacing the simulated sunlight irradiation with the visible light illumination switches the photocatalytic CO2 reduction product from CO to CH4. 13 CO2 isotope labeling confirms that CO2 is the source of carbon for CH4 and CO products. The photoelectrochemical H2 generation further supports the switching property of MoS2 /TiO2 . Unlike previous studies, density functional theory calculations are used to investigate the band structure of Van der Waals MoS2 /TiO2 S scheme after contact, allowing to propose accurate charge transfer pathways, in which the theoretical results are well matched with the experimental results. This work opens the opportunity to develop photocatalysts with switchable charge transport and tunable redox potential for selective artificial photosynthesis.

Unveiling the reaction pathways of hydrocarbons via experiments, computations and data science.

Reaction networks of hydrocarbons are explored using first principles calculations, data science, and experiments. Transforming hydrocarbon data into networks reveals the prevalence of the formation and reaction of various molecules. Graph theory is implemented to extract knowledge from the reaction network. In particular, centralities analysis reveals that H+, CCC, CH3+, CC, and [CH2+]C have high degrees and are thus very likely to form or react with other molecules. Additionally, H+, CH3+, C2H5+, C8H15+, C8H17+, and C6H11+ are found to have high control throughout the network and lead towards a series of additional reactions. The constructed network is also validated in experiments while the shortest path analysis is implemented for further comparison between experiment and the network. Thus, combining network analysis with first principles calculations uncovers key points in the development of various hydrocarbons that can be used to improve catalyst design and targeted synthesis of desired hydrocarbons.

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation.

The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH4 to CH3 reaction are calculated. Several catalysts for the OCM reaction are designed based on key elements that are unveiled via data visualization and network analysis. Among the designed catalysts, several active catalysts such as CoAg/TiO2, Mg/BaO, and Ti/BaO are found to result in high C2 yield. Results illustrate that designing catalysts using high-throughput calculations is achievable in principle if appropriate trends and patterns within the data generated via high-throughput calculations are identified. Thus, high-throughput calculations in combination with catalyst informatics offer a potential alternative method for catalyst design.

Synthesis of the Proposed Structure of Mohangic Acid C.

The proposed structure of mohangic acid C (3) was stereoselectively synthesized via a catalytic asymmetric aldol reaction to install a p-aminoacetophenone moiety, Marshall propargylation furnishing two stereocenters, and Cu-mediated Stille-type coupling to construct the whole framework of 3.

Designing transformer oil immersion cooling servers for machine learning and first principle calculations.

A transfomer oil immersion cooling server is designed and constructed for machine learning applications and first principle calculations that are carried out for materials-related research. CPU, motherboard, random access memory, hard disk drive, solid state drive, graphic card, and the power supply unit are submerged into the transformer oil in order to cool the entire system. Benchmark tests reveal that overall performance is improved while performance times for multicore calculations are dramatically improved. Furthermore, calculation times for machine learning with large data sets and density functional theory calculations are shortened during single core calculations. Thus, a transformer oil immersion cooling server is proposed to be an alternative cooling system used for improving the performance of first principle calculations and machine learning.

Ca2+ imaging with two-photon microscopy to detect the disruption of brain function in mice administered neonicotinoid insecticides.

Neonicotinoid pesticides are a class of insecticides that reportedly have harmful effects on bees and dragonflies, causing a reduction in their numbers. Neonicotinoids act as neuroreceptor modulators, and some studies have reported their association with neurodevelopmental disorders. However, the precise effect of neonicotinoids on the central nervous system has not yet been identified. Herein, we conducted in vivo Ca2+ imaging using a two-photon microscope to detect the abnormal activity of neuronal circuits in the brain after neonicotinoid application. The oral administration of acetamiprid (ACE) (20 mg/kg body weight (BW) in mature mice with a quantity less than the no-observed-adverse-effect level (NOAEL) and a tenth or half of the median lethal dose (LD50) of nicotine (0.33 or 1.65 mg/kg BW, respectively), as a typical nicotinic acetylcholine receptor (nAChR) agonist, increased anxiety-like behavior associated with altered activities of the neuronal population in the somatosensory cortex. Furthermore, we detected ACE and its metabolites in the brain, 1 h after ACE administration. The results suggested that in vivo Ca2+ imaging using a two-photon microscope enabled the highly sensitive detection of neurotoxicant-mediated brain disturbance of nerves.

Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts.

Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C2 yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts.

Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts.

Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences.

Simultaneous quantification of imidacloprid and its metabolites in tissues of mice upon chronic low-dose administration of imidacloprid.

This study aimed to (i) develop a sensitive method for simultaneous detection and quantification of imidacloprid (IMI) and seven of its metabolites in tissue specimens, and to (ii) determine the biodistribution of the IMI compounds in tissues of C57BL/6J male mice; after exposure to 0.6 mg/kg bw/day of IMI (10% of no observable adverse effect level of IMI) through a powdered diet for 24 weeks. We successfully developed a method which was accurate (recoveries were ≥ 70% for most compounds), sensitive (LODs ≤ 0.47 ng/mL and LOQs ≤ 1.43 ng/mL were recorded for all detected compounds, R2 ≥ 0.99) and precise (RSDs ≤ 20%) for routine analysis of IMI and seven of its metabolites in blood and various tissue matrices. After bio-distributional analysis, IMI and five of its metabolites were detected in mice. Brain, testis, lung, kidney, inguinal white adipose tissue and gonadal white adipose tissue mainly accumulated IMI, blood and mesenteric white adipose tissue mainly accumulated IMI-olefin; liver mainly accumulated desnitro-IMI; pancreas predominately accumulated 4-hydroxy-IMI. The desnitro-dehydro-IMI and the desnitro-IMI metabolites recorded tissue-blood concentration ratios ≥ 1.0 for testis, brain, lung and kidney. The cumulative levels of the six detected IMI compounds (Σ6 IMI compounds) were found in the decreasing order: blood > testis > brain > kidney > lung > iWAT > gWAT > mWAT > liver > pancreas. Altogether, this study provided essential data needed for effective mechanistic elucidation of compound-specific adverse outcomes associated with chronic exposures to IMI in mammalian species.

Unveiling gas-phase oxidative coupling of methane via data analysis.

Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.

Aging-related changes in the sensitivity of behavioral effects of the neonicotinoid pesticide clothianidin in male mice.

Neonicotinoids, which act as agonists of the nicotinic acetylcholine receptors of insects, are widely used pesticides worldwide. Although epidemiological studies revealed that the detection amounts of neonicotinoids in urine are higher in the elderly population than other age-groups, there is no available information regarding the risks of neonicotinoids to older mammals. This study was aimed to investigate aging-related differences in the behavioral effects of the neonicotinoid pesticide clothianidin (CLO). We acutely administered a sub-NOAEL level (5 mg/kg) of CLO to adult (12-week-old) and aging (90-week-old) mice and conducted four behavioral tests focusing on the emotional behavior. In addition, we measured the concentrations of CLO and its metabolites in blood, brain and urine. There were age-related changes in most parameters in all behavioral tests, and CLO significantly decreased the locomotor activity in the open field test and elevated plus-maze test in the aging group, but not in the adult group. The concentrations of most CLO and its metabolites were significantly higher in the blood and brain and were slightly lower in the urine in the aging group compared to the adult group. These findings should contribute to our understanding of age-related differences in the adverse effects of neonicotinoids in mammals.