Photocatalytic dihydroxylation of light olefins to glycols by water.

Aliphatic diols such as ethylene and propylene glycol are the key products in the chemical industry for manufacturing polymers. The synthesis of these molecules usually implies sequential processes, including epoxidation of olefins using hydrogen peroxide or oxygen with subsequent hydrolysis to glycols. Direct hydroxylation of olefins by cheap and green oxidants is an economically attractive and environmentally friendly route for the synthesis of diols. Here, we report a photocatalytic reaction for the dihydroxylation of ethylene and propylene to their glycols at room temperature using water as the oxidant. The photocatalyst contains Pd clusters stabilized by sub-nanometric polyoxometalate with TiO2 as the host material. Under light irradiation, it results in production rates of ethylene glycol and propylene glycols of 146.8 mmol·gPd-1·h-1 and 28.6 mmol·gPd-1·h-1 with liquid-phase selectivities of 63.3 % and 80.0 %, respectively. Meanwhile, green hydrogen derived from water is produced as another valuable product. Combined spectroscopy investigation suggests that the reaction proceeds via π-bonded adsorption of olefins over Pd clusters with hydroxylation by hydroxyl radicals formed by photocatalytic dissociation of water.

Physiological and biochemical characteristics and microbial responses of Medicago sativa (Fabales: Fabaceae) varieties with different resistance to atrazine stress.

Atrazine, a commonly employed herbicide for corn production, can leave residues in soil, resulting in photosynthetic toxicity and impeding growth in subsequent alfalfa (Medicago sativa L.) crops within alfalfa-corn rotation systems. The molecular regulatory mechanisms by which atrazine affects alfalfa growth and development, particularly its impact on the microbial communities of the alfalfa rhizosphere, are not well understood. This study carried out field experiments to explore the influence of atrazine stress on the biomass, chlorophyll content, antioxidant system, and rhizosphere microbial communities of the atrazine-sensitive alfalfa variety WL-363 and the atrazine-resistant variety JN5010. The results revealed that atrazine significantly reduced WL-363 growth, decreasing plant height by 8.58 cm and root length by 5.42 cm (p < 0.05). Conversely, JN5010 showed minimal reductions, with decreases of 1.96 cm in height and 1.26 cm in root length. Chlorophyll content in WL-363 decreased by 35% under atrazine stress, while in JN5010, it was reduced by only 10%. Reactive oxygen species (ROS) accumulation increased by 60% in WL-363, compared to a 20% increase in JN5010 (p < 0.05 for both). Antioxidant enzyme activities, such as superoxide dismutase (SOD) and catalase (CAT), were significantly elevated in JN5010 (p < 0.05), suggesting a more robust defense mechanism. Although the predominant bacterial and fungal abundances in rhizosphere soils remained generally unchanged under atrazine stress, specific microbial groups exhibited variable responses. Notably, Promicromonospora abundance declined in WL-363 but increased in JN5010. FAPROTAX functional predictions indicated shifts in the abundance of microorganisms associated with pesticide degradation, resistance, and microbial structure reconstruction under atrazine stress, displaying different patterns between the two varieties. This study provides insights into how atrazine residues affect alfalfa rhizosphere microorganisms and identifies differential microbial responses to atrazine stress, offering valuable reference data for screening and identifying atrazine-degrading bacteria.

Predictive value of combining urinary N-acetyl-ß-D-glucosaminidase and serum homocysteine for contrast-induced nephropathy in patients after percutaneous coronary intervention.

Background: Contrast-induced nephropathy (CIN) can lead to serious complications following percutaneous coronary intervention (PCI). Urine N-Acetyl-ß-D-glucosaminidase (uNAG) and serum homocysteine (sHCY) are both potential predictors for CIN detection, but their combination has not been explored. We aimed to combine uNAG and sHCY as predictors for the early detection of CIN and for prognosis prediction in patients after PCI. Methods: A total of 232 consecutive patients who underwent PCI at a university hospital were recruited for this study. According to the European Society of Urology and Reproduction (ESUR) criterion, CIN is defined as an elevation of serum creatinine (sCr) by ≥25% or ≥0.5 mg/dl from baseline within 48 h. We assessed the use of individual biomarkers (uNAG and sHCY) measured around PCI and their combinations for CIN detection and prognosis prediction. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to evaluate the predictive efficiency of potential predictors. Results: In total, 54 (23.28%) patients developed CIN. Concentrations of uNAG and sHCY increased significantly in CIN subjects (p < 0.05) than non-CIN. CIN could be predicted by uNAG and sHCY but not by creatinine at an early stage. At pre-PCI, 0, 12, 24, and 48 h after PCI, the AUC-ROC value of uNAG in calculating total CIN was 0.594, 0.603, 0.685, 0.657, and 0.648, respectively. The AUC-ROC value of sHCY in calculating total CIN was 0.685, 0.726, 0.771, 0.755, and 0.821, respectively. The panel of uNAG plus sHCY detected CIN with significantly higher accuracy than either individual biomarker alone and earlier than sCr. For detecting total CIN, this panel yielded AUC-ROCs of 0.693, 0.754, 0.826, 0.796, and 0.844 at pre-PCI, 0, 12, 24, and 48 h after PCI, respectively, which were superior to those of the individual biomarkers. For predicting the incidence of major adverse cardiovascular events (MACE) within 30 days to 12 months, the AUC-ROC values for uNAG and sHCY measured before discharge were 0.637 and 0.826, respectively. The combined panel yielded an AUC-ROC of 0.832. The combined detection did not significantly enhance the predictive capability for MACE in patients with CIN. The CIN group and the non-CIN group showed no significant difference in the Coronary Heart Disease Intensive Care Unit (CCU) stay time, hospital stay time, demand for renal replacement therapy, CCU mortality rate, and in-hospital mortality rate. Conclusions: The uNAG and sHCY panel demonstrated better sensitivity and specificity for predicting the diagnosis and prognosis of CIN in patients after PCI, earlier than sCr. The combination of these biomarkers revealed a significantly superior discriminative performance for CIN detection and prognosis compared to using uNAG or sHCY alone.

Dynorphin modulates motivation through a pallido-amygdala cholinergic circuit.

The endogenous opioid peptide dynorphin and its receptor κ-opioid receptor (KOR) have been implicated in divergent behaviors, but the underlying mechanisms remain elusive. Here we show that dynorphin released from nucleus accumbens dynorphinergic neurons exerts powerful modulation over a ventral pallidum (VP) disinhibitory circuit, thereby controlling cholinergic transmission to the amygdala and motivational drive in mice. On one hand, dynorphin acts postsynaptically via KORs on local GABAergic neurons in the VP to promote disinhibition of cholinergic neurons, which release acetylcholine into the amygdala to invigorate reward-seeking behaviors. On the other hand, dynorphin also acts presynaptically via KORs on dynorphinergic terminals to limit its own release. Such autoinhibition keeps cholinergic neurons from prolonged activation and release of acetylcholine, and prevents perseverant reward seeking. Our study reveals how dynorphin exquisitely modulate motivation through cholinergic system, and provides an explanation for why these neuromodulators are involved in motivational disorders, including depression and addiction.

Unlocking opioid neuropeptide dynamics with genetically encoded biosensors.

Neuropeptides are ubiquitous in the nervous system. Research into neuropeptides has been limited by a lack of experimental tools that allow for the precise dissection of their complex and diverse dynamics in a circuit-specific manner. Opioid peptides modulate pain, reward and aversion and as such have high clinical relevance. To illuminate the spatiotemporal dynamics of endogenous opioid signaling in the brain, we developed a class of genetically encoded fluorescence sensors based on kappa, delta and mu opioid receptors: κLight, δLight and µLight, respectively. We characterized the pharmacological profiles of these sensors in mammalian cells and in dissociated neurons. We used κLight to identify electrical stimulation parameters that trigger endogenous opioid release and the spatiotemporal scale of dynorphin volume transmission in brain slices. Using in vivo fiber photometry in mice, we demonstrated the utility of these sensors in detecting optogenetically driven opioid release and observed differential opioid release dynamics in response to fearful and rewarding conditions.

Prefrontal cortical dynorphin peptidergic transmission constrains threat-driven behavioral and network states.

Prefrontal cortical (PFC) circuits provide top-down control of threat reactivity. This includes ventromedial PFC (vmPFC) circuitry, which plays a role in suppressing fear-related behavioral states. Dynorphin (Dyn) has been implicated in mediating negative affect and maladaptive behaviors induced by severe threats and is expressed in limbic circuits, including the vmPFC. However, there is a critical knowledge gap in our understanding of how vmPFC Dyn-expressing neurons and Dyn transmission detect threats and regulate expression of defensive behaviors. Here, we demonstrate that Dyn cells are broadly activated by threats and release Dyn locally in the vmPFC to limit passive defensive behaviors. We further demonstrate that vmPFC Dyn-mediated signaling promotes a switch of vmPFC networks to a fear-related state. In conclusion, we reveal a previously unknown role of vmPFC Dyn neurons and Dyn neuropeptidergic transmission in suppressing defensive behaviors in response to threats via state-driven changes in vmPFC networks.

Boosting Gas-Phase TiO2 Photocatalysis with Weak Electric Field Strengths of Volt/Centimeter.

Among semiconductor nanomaterials, titanium dioxide is at the forefront of heterogeneous photocatalysis, but its catalytic activity greatly suffers from the loss of photoexcited charge carriers through deleterious recombination processes. Here, we investigate the impact of an external electric field (EEF) applied to conventional P25 TiO2 nanopowder with or without Au nanoparticles (NPs) to circumvent this issue. The study of two redox reactions in the gas phase, water splitting and toluene degradation, reveals an enhancement of the photocatalytic activity with rather modest electric fields of a few volt/centimeters only. Such an improvement arises from the electric-field-induced quenching of the green emission in anatase, allowing the photoexcited charge carriers to be transferred to the adsorbed reactants instead of pointless radiative recombinations. Applying an EEF across a trap-rich metal oxide material, such as TiO2, which, when impregnated with Au NPs, leads, respectively, to 12- and 6-fold enhancements in the production of hydrogen and the oxidation of toluene for an electric field of 8 V/cm, without any electrolysis, is a simple and elegant strategy to meet higher photocatalytic efficiencies.

Opioidergic signaling contributes to food-mediated suppression of AgRP neurons.

Opioids are generally known to promote hedonic food consumption. Although much of the existing evidence is primarily based on studies of the mesolimbic pathway, endogenous opioids and their receptors are widely expressed in hypothalamic appetite circuits as well; however, their role in homeostatic feeding remains unclear. Using a fluorescent opioid sensor, deltaLight, here we report that mediobasal hypothalamic opioid levels increase by feeding, which directly and indirectly inhibits agouti-related protein (AgRP)-expressing neurons through the µ-opioid receptor (MOR). AgRP-specific MOR expression increases by energy surfeit and contributes to opioid-induced suppression of appetite. Conversely, its antagonists diminish suppression of AgRP neuron activity by food and satiety hormones. Mice with AgRP neuron-specific ablation of MOR expression have increased fat preference without increased motivation. These results suggest that post-ingestion release of endogenous opioids contributes to AgRP neuron inhibition to shape food choice through MOR signaling.

Prefrontal cortical dynorphin peptidergic transmission constrains threat-driven behavioral and network states.

Prefrontal cortical (PFC) circuits provide top-down control of threat reactivity. This includes ventromedial PFC (vmPFC) circuitry, which plays a role in suppressing fear-related behavioral states. Dynorphin (Dyn) has been implicated in mediating negative affect and mal-adaptive behaviors induced by severe threats and is expressed in limbic circuits, including the vmPFC. However, there is a critical knowledge gap in our understanding of how vmPFC Dyn-expressing neurons and Dyn transmission detect threats and regulate expression of defensive behaviors. Here, we demonstrate that Dyn cells are broadly activated by threats and release Dyn locally in the vmPFC to limit passive defensive behaviors. We further demonstrate that vmPFC Dyn-mediated signaling promotes a switch of vmPFC networks to a fear-related state. In conclusion, we reveal a previously unknown role of vmPFC Dyn neurons and Dyn neuropeptidergic transmission in suppressing defensive behaviors in response to threats via state-driven changes in vmPFC networks.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places.

Background: People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. Methods: In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. Results: The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. Conclusion: In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Use of symptom diary in primary students: association of nitrogen dioxide with prevalence of symptoms.

Air pollution is a global public health concern, and numerous studies have attempted to identify the health effects of air pollutants, including nitrogen dioxide (NO2). In China, there are few studies investigating the relationship between NO2 exposure and symptoms among children at an individual level. The aim of the study was to evaluate the acute effects of NO2 on prevalence of symptoms of primary students. An environmental and health questionnaire survey was administered to 4240 primary students in seven districts of Shanghai. Daily symptoms, as well as the daily air pollution and meteorological data from each community, were recorded during the corresponding period. A multivariable logistic regression model was utilized to analyze the relationship between the prevalence of symptoms and NO2 exposure in school-age children. A model with interaction items was adopted to estimate the interactive effects of NO2 and confounding factors on symptoms. The average NO2 level in central urban, industrial and rural areas were 62.07 ± 21.66, 54.86 ± 18.32 and 36.62 ± 21.23 µg m-3, respectively. Our findings demonstrate that the occurrence of symptoms was significantly affected by NO2 exposure in the short-term. The largest associations were observed for a 10 µg m-3 increase in 5-day moving average (lag04) NO2 concentration with prevalence of general symptoms (odds ratio [OR] = 1.15, 95% confidence interval [95% CI]: 1.07-1.22), throat symptoms (OR = 1.23, 95% CI: 1.13-1.35) and nasal symptoms (OR = 1.142, 95% CI: 1.02-1.27). Subgroup analysis showed that non-rural areas, boys, nearby environmental pollution source and history of present illness were all susceptible factors to the effects of NO2 exposure. Furthermore, there were interactive effects between NO2 exposure and area types on reported symptoms. NO2 can increase the risk of symptoms in primary students in the short-term, which could be significantly enhanced in central urban and industrial areas.

Psychedelics promote neuroplasticity through the activation of intracellular 5-HT2A receptors.

Decreased dendritic spine density in the cortex is a hallmark of several neuropsychiatric diseases, and the ability to promote cortical neuron growth has been hypothesized to underlie the rapid and sustained therapeutic effects of psychedelics. Activation of 5-hydroxytryptamine (serotonin) 2A receptors (5-HT2ARs) is essential for psychedelic-induced cortical plasticity, but it is currently unclear why some 5-HT2AR agonists promote neuroplasticity, whereas others do not. We used molecular and genetic tools to demonstrate that intracellular 5-HT2ARs mediate the plasticity-promoting properties of psychedelics; these results explain why serotonin does not engage similar plasticity mechanisms. This work emphasizes the role of location bias in 5-HT2AR signaling, identifies intracellular 5-HT2ARs as a therapeutic target, and raises the intriguing possibility that serotonin might not be the endogenous ligand for intracellular 5-HT2ARs in the cortex.

Direct Photocatalytic Synthesis of Acetic Acid from Methane and CO at Ambient Temperature Using Water as Oxidant.

Direct functionalization of methane selectively to value-added chemicals is still one of the main challenges in modern science. Acetic acid is an important industrial chemical produced nowadays by expensive and environmentally unfriendly carbonylation of methanol using homogeneous catalysts. Here, we report a new photocatalytic reaction route to synthesize acetic acid from CH4 and CO at room temperature using water as the sole external oxygen source. The optimized photocatalyst consists of a TiO2 support and ammonium phosphotungstic polyoxometalate (NPW) clusters anchored with isolated Pt single atoms (Pt1). It enables a stable synthesis of 5.7 mmol·L-1 acetic acid solution in 60 h with the selectivity over 90% and 66% to acetic acid on liquid-phase and carbon basis, respectively, with the production of 99 mol of acetic acid per mol of Pt. Combined isotopic and in situ spectroscopy investigation suggests that synthesis of acetic acid proceeds via a photocatalytic oxidative carbonylation of methane over the Pt1 sites, with the methane activation facilitated by water-derived hydroxyl radicals.

CO-tolerant RuNi/TiO2 catalyst for the storage and purification of crude hydrogen.

Hydrogen storage by means of catalytic hydrogenation of suitable organic substrates helps to elevate the volumetric density of hydrogen energy. In this regard, utilizing cheaper industrial crude hydrogen to fulfill the goal of hydrogen storage would show economic attraction. However, because CO impurities in crude hydrogen can easily deactivate metal active sites even in trace amounts such a process has not yet been realized. Here, we develop a robust RuNi/TiO2 catalyst that enables the efficient hydrogenation of toluene to methyl-cyclohexane under simulated crude hydrogen feeds with 1000-5000 ppm CO impurity at around 180 °C under atmospheric pressure. We show that the co-localization of Ru and Ni species during reduction facilitated the formation of tightly coupled metallic Ru-Ni clusters. During the catalytic hydrogenation process, due to the distinct bonding properties, Ru and Ni served as the active sites for CO methanation and toluene hydrogenation respectively. Our work provides fresh insight into the effective utilization and purification of crude hydrogen for the future hydrogen economy.

Fluorescence Imaging of Neural Activity, Neurochemical Dynamics, and Drug-Specific Receptor Conformation with Genetically Encoded Sensors.

Recent advances in fluorescence imaging permit large-scale recording of neural activity and dynamics of neurochemical release with unprecedented resolution in behaving animals. Calcium imaging with highly optimized genetically encoded indicators provides a mesoscopic view of neural activity from genetically defined populations at cellular and subcellular resolutions. Rigorously improved voltage sensors and microscopy allow for robust spike imaging of populational neurons in various brain regions. In addition, recent protein engineering efforts in the past few years have led to the development of sensors for neurotransmitters and neuromodulators. Here, we discuss the development and applications of these genetically encoded fluorescent indicators in reporting neural activity in response to various behaviors in different biological systems as well as in drug discovery. We also report a simple model to guide sensor selection and optimization.

Few-Atom Pt Ensembles Enable Efficient Catalytic Cyclohexane Dehydrogenation for Hydrogen Production.

Identification of catalytic active sites is pivotal in the design of highly effective heterogeneous metal catalysts, especially for structure-sensitive reactions. Downsizing the dimension of the metal species on the catalyst increases the dispersion, which is maximized when the metal exists as single atoms, namely, single-atom catalysts (SACs). SACs have been reported to be efficient for various catalytic reactions. We show here that the Pt SACs, although with the highest metal atom utilization efficiency, are totally inactive in the cyclohexane (C6H12) dehydrogenation reaction, an important reaction that could enable efficient hydrogen transportation. Instead, catalysts enriched with fully exposed few-atom Pt ensembles, with a Pt-Pt coordination number of around 2, achieve the optimal catalytic performance. The superior performance of a fully exposed few-atom ensemble catalyst is attributed to its high d-band center, multiple neighboring metal sites, and weak binding of the product.

Psychedelic-inspired drug discovery using an engineered biosensor.

Ligands can induce G protein-coupled receptors (GPCRs) to adopt a myriad of conformations, many of which play critical roles in determining the activation of specific signaling cascades associated with distinct functional and behavioral consequences. For example, the 5-hydroxytryptamine 2A receptor (5-HT2AR) is the target of classic hallucinogens, atypical antipsychotics, and psychoplastogens. However, currently available methods are inadequate for directly assessing 5-HT2AR conformation both in vitro and in vivo. Here, we developed psychLight, a genetically encoded fluorescent sensor based on the 5-HT2AR structure. PsychLight detects behaviorally relevant serotonin release and correctly predicts the hallucinogenic behavioral effects of structurally similar 5-HT2AR ligands. We further used psychLight to identify a non-hallucinogenic psychedelic analog, which produced rapid-onset and long-lasting antidepressant-like effects after a single administration. The advent of psychLight will enable in vivo detection of serotonin dynamics, early identification of designer drugs of abuse, and the development of 5-HT2AR-dependent non-hallucinogenic therapeutics.

Fully Exposed Cluster Catalyst (FECC): Toward Rich Surface Sites and Full Atom Utilization Efficiency.

Increasing attention has been paid to single-atom catalysts (SACs) in heterogeneous catalysis because of their unique electronic properties, maximized atomic utilization efficiency, and potential to serve as a bridge between the heterogeneous and homogeneous catalysis. However, SACs can have limited advantages or even constrained applications for the reactions that require designated metallic states with multiple atoms or surface sites with metal-metal bonds. As a cross-dimensional extension to the concept of SACs, fully exposed cluster catalysts (FECCs) offer diverse surface sites formed by an ensemble of metal atoms, for the adsorption and transformation of reactants/intermediates. More importantly, FECCs have the advantage of maximized atom utilization efficiency. Thus, FECCs provide a novel platform to design effective and efficient catalysts for certain chemical processes. This outlook summarizes recent advances and proposes prospective research directions in the design of catalysts and characterizations of FECCs, together with potential challenges.

Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.

An expanded palette of dopamine sensors for multiplex imaging in vivo.

Genetically encoded dopamine sensors based on green fluorescent protein (GFP) enable high-resolution imaging of dopamine dynamics in behaving animals. However, these GFP-based variants cannot be readily combined with commonly used optical sensors and actuators, due to spectral overlap. We therefore engineered red-shifted variants of dopamine sensors called RdLight1, based on mApple. RdLight1 can be combined with GFP-based sensors with minimal interference and shows high photostability, permitting prolonged continuous imaging. We demonstrate the utility of RdLight1 for receptor-specific pharmacological analysis in cell culture, simultaneous assessment of dopamine release and cell-type-specific neuronal activity and simultaneous subsecond monitoring of multiple neurotransmitters in freely behaving rats. Dual-color photometry revealed that dopamine release in the nucleus accumbens evoked by reward-predictive cues is accompanied by a rapid suppression of glutamate release. By enabling multiplexed imaging of dopamine with other circuit components in vivo, RdLight1 opens avenues for understanding many aspects of dopamine biology.