A hot-emitter transistor based on stimulated emission of heated carriers.

Hot-carrier transistors are a class of devices that leverage the excess kinetic energy of carriers. Unlike regular transistors, which rely on steady-state carrier transport, hot-carrier transistors modulate carriers to high-energy states, resulting in enhanced device speed and functionality. These characteristics are essential for applications that demand rapid switching and high-frequency operations, such as advanced telecommunications and cutting-edge computing technologies1-5. However, the traditional mechanisms of hot-carrier generation are either carrier injection6-11 or acceleration12,13, which limit device performance in terms of power consumption and negative differential resistance14-17. Mixed-dimensional devices, which combine bulk and low-dimensional materials, can offer different mechanisms for hot-carrier generation by leveraging the diverse potential barriers formed by energy-band combinations18-21. Here we report a hot-emitter transistor based on double mixed-dimensional graphene/germanium Schottky junctions that uses stimulated emission of heated carriers to achieve a subthreshold swing lower than 1 millivolt per decade beyond the Boltzmann limit and a negative differential resistance with a peak-to-valley current ratio greater than 100 at room temperature. Multi-valued logic with a high inverter gain and reconfigurable logic states are further demonstrated. This work reports a multifunctional hot-emitter transistor with significant potential for low-power and negative-differential-resistance applications, marking a promising advancement for the post-Moore era.

A C9orf72-CARM1 axis regulates lipid metabolism under glucose starvation-induced nutrient stress.

Cells undergo metabolic adaptation during environmental changes by using evolutionarily conserved stress response programs. This metabolic homeostasis is exquisitely regulated, and its imbalance could underlie human pathological conditions. We report here that C9orf72, which is linked to the most common forms of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), is a key regulator of lipid metabolism under stress. Loss of C9orf72 leads to an overactivation of starvation-induced lipid metabolism that is mediated by dysregulated autophagic digestion of lipids and increased de novo fatty acid synthesis. C9orf72 acts by promoting the lysosomal degradation of coactivator-associated arginine methyltransferase 1 (CARM1), which in turn regulates autophagy-lysosomal functions and lipid metabolism. In ALS/FTD patient-derived neurons or tissues, a reduction in C9orf72 function is associated with dysregulation in the levels of CARM1, fatty acids, and NADPH oxidase NOX2. These results reveal a C9orf72-CARM1 axis in the control of stress-induced lipid metabolism and implicates epigenetic dysregulation in relevant human diseases.

Bloodmeals fuel dengue virus replication in the female mosquito Aedes aegypti.

Vector competence defines the ability of a vector to acquire, host, and transmit a pathogen. Understanding the molecular determinants of the mosquitos' competence to host dengue virus (DENV) holds promise to prevent its transmission. To this end, we employed RNA-seq to profile mRNA transcripts of the female Aedes aegypti mosquitos feeding on naïve vs viremic mouse. While most transcripts (12,634) did not change their abundances, 360 transcripts showed decreases. Biological pathway analysis revealed representatives of the decreased transcripts involved in the wnt signaling pathway and hippo signaling pathway. One thousand three hundred fourteen transcripts showed increases in abundance and participate in 21 biological pathways including amino acid metabolism, carbon metabolism, fatty acid metabolism, and oxidative phosphorylation. Inhibition of oxidative phosphorylation with antimycin A reduced oxidative phosphorylation activity and ATP concentration associated with reduced DENV replication in the Aedes aegypti cells. Antimycin A did not affect the amounts of the non-structural proteins 3 and 5, two major components of the replication complex. Ribavirin, an agent that reduces GTP concentration, recapitulated the effects of reduced ATP concentration on DENV replication. Knocking down one of the oxidative phosphorylation components, ATP synthase subunit ß, reduced DENV replication in the mosquitos. In summary, our results suggest that DENV enhances metabolic pathways in the female Aedes aegypti mosquitos to supply nutrients and energy for virus replication. ATP synthase subunit ß knockdown might be exploited to reduce the mosquitos' competence to host and transmit DENV. IMPORTANCE: Through evolution, the mosquito-borne viruses have adapted to the blood-feeding behaviors of their opportunist hosts to fulfill a complete lifecycle in humans and mosquitos. Disruption in the mosquitos' ability to host these viruses offers strategies to prevent diseases caused by them. With the advent of genomic tools, we discovered that dengue virus (DENV) benefited from the female mosquitos' bloodmeals for metabolic and energetic supplies for replication. Chemical or genetic disruption in these supplies reduced DENV replication in the female mosquitos. Our discovery can be exploited to produce genetically modified mosquitos, in which DENV infection leads to disruption in the supplies and thereby reduces replication and transmission. Our discovery might be extrapolated to prevent mosquito-borne virus transmission and the diseases they cause.

Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing.

The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning.

Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based on morphology. However, accurately classifying such patterns can be challenging. The study objective was to develop a deep learning pipeline (Silva3-AI) that automatically analyzes whole slide image-based histopathologic images and identifies Silva patterns with high accuracy. Initially, a total of 202 patients with EACs and histopathologic slides were obtained from Qilu Hospital of Shandong University for developing and internally testing the Silva3-AI model. Subsequently, an additional 161 patients and slides were collected from seven other medical centers for independent testing. The Silva3-AI model was developed using a vision transformer and recurrent neural network architecture, utilizing multi-magnification patches, and its performance was evaluated based on a class-specific area under the receiver-operating characteristic curve. Silva3-AI achieved a class-specific area under the receiver-operating characteristic curve of 0.947 for Silva A, 0.908 for Silva B, and 0.947 for Silva C on the independent test set. Notably, the performance of Silva3-AI was consistent with that of professional pathologists with 10 years' diagnostic experience. Furthermore, the visualization of prediction heatmaps facilitated the identification of tumor microenvironment heterogeneity, which is known to contribute to variations in Silva patterns.

Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks.

Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task Interaction Graph Convolutional Network (MTIGCN) for anti-cancer drug response prediction. MTIGCN first utilizes an graph convolutional network-based model to produce embeddings for both cell lines and drugs. After that, the model employs multi-task learning to predict anti-cancer drug response, which involves training the model on three different tasks simultaneously: the main task of the drug sensitive or resistant classification task and the two auxiliary tasks of regression prediction and similarity network reconstruction. By sharing parameters and optimizing the losses of different tasks simultaneously, MTIGCN enhances the feature representation and reduces overfitting. The results of the experiments on two in vitro datasets demonstrated that MTIGCN outperformed seven state-of-the-art baseline methods. Moreover, the well-trained model on the in vitro dataset GDSC exhibited good performance when applied to predict drug responses in in vivo datasets PDX and TCGA. The case study confirmed the model's ability to discover unknown drug responses in cell lines.

Enhanced Plasmonic Trapping and Fluorescent Emission of Nitrogen-Vacancy Nanodiamonds Using a High-Efficiency Nanofocusing Device.

Fluorescent nanodiamonds (FNDs) with nitrogen-vacancy centers are pivotal for advancing quantum photonics and imaging through deterministic quantum state manipulation. However, deterministic integration of quantum emitters into photonic devices remains a challenge due to the need for high coupling efficiency and Purcell enhancement. We report a deterministic FND-integrated nanofocusing device achieved by assembling FNDs at a plasmonic waveguide tip through plasmonic-enhanced optical trapping. This technique not only increases the emission rate by 58.6 times compared to isolated FNDs but also preferentially directs radiation into the waveguide at a rate 5.3 times higher than that into free space, achieving an exceptional figure-of-merit of ∼3000 for efficient energy transfer. Our findings represent a significant step toward deterministic integration in quantum imaging and communication, opening new avenues for quantum technology advancements.

SiX2 (X = S, Se) Nanowire Gate-All-Around MOSFETs for Sub-5 nm Applications.

The gate-all-around (GAA) field-effect transistor (FET) holds great potential to support next-generation integrated circuits. Nanowires such as carbon nanotubes (CNTs) are one important category of channel materials in GAA FETs. Based on first-principles investigations, we propose that SiX2 (X = S, Se) nanowires are promising channel materials that can significantly elevate the performance of GAA FETs. The sub-5 nm SiX2 (X = S, Se) nanowire GAA FETs exhibit excellent ballistic transport properties that meet the requirements of the 2013 International Technology Roadmap for Semiconductors (ITRS). Compared to CNTs, they are also advantageous or at least comparable in terms of gate controllability, device dimensions, etc. Importantly, SiSe2 GAA FETs show superb gate controllability due to the ultralow minimum subthreshold swing (SSmin) that breaks "Boltzmann's tyranny". Moreover, the energy-delay product (EDP) of SiX2 GAA FETs is significantly lower than that of the CNT FETs. These features make SiX2 nanowires ideal channel material in the sub-5 nm GAA FET devices.

Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer.

Despite the use of machine learning tools, it is challenging to properly model cause-specific deaths in colorectal cancer (CRC) patients and choose appropriate treatments. Here, we propose an interesting feature selection framework, namely union with recursive feature elimination (U-RFE), to select the union feature sets that are crucial in CRC progression-specific mortality using The Cancer Genome Atlas (TCGA) dataset. Based on the union feature sets, we compared the performance of 5 classification algorithms, including logistic regression (LR), support vector machines (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and Stacking, to identify the best model for classifying 4-category deaths. In the first stage of U-RFE, LR, SVM, and RF were used as base estimators to obtain subsets containing the same number of features but not exactly the same specific features. Union analysis of the subsets was then performed to determine the final union feature set, effectively combining the advantages of different algorithms. We found that the U-RFE framework could improve various models' performance. Stacking outperformed LR, SVM, RF, and XGBoost in most scenarios. When the target feature number of the RFE was set to 50 and the union feature set contained 298 deterministic features, the Stacking model achieved F1_weighted, Recall_weighted, Precision_weighted, Accuracy, and Matthews correlation coefficient of 0.851, 0.864, 0.854, 0.864, and 0.717, respectively. The performance of the minority categories was also significantly improved. Therefore, this recursive feature elimination-based approach of feature selection improves performances of classifying CRC deaths using clinical and omics data or those using other data with high feature redundancy and imbalance.

IL-6 from cerebrospinal fluid causes widespread pain via STAT3-mediated astrocytosis in chronic constriction injury of the infraorbital nerve.

BACKGROUND: The spinal inflammatory signal often spreads to distant segments, accompanied by widespread pain symptom under neuropathological conditions. Multiple cytokines are released into the cerebrospinal fluid (CSF), potentially inducing the activation of an inflammatory cascade at remote segments through CSF flow. However, the detailed alteration of CSF in neuropathic pain and its specific role in widespread pain remain obscure. METHODS: A chronic constriction injury of the infraorbital nerve (CCI-ION) model was constructed, and pain-related behavior was observed on the 7th, 14th, 21st, and 28th days post surgery, in both vibrissa pads and hind paws. CSF from CCI-ION rats was transplanted to naïve rats through intracisternal injection, and thermal and mechanical allodynia were measured in hind paws. The alteration of inflammatory cytokines in CCI-ION's CSF was detected using an antibody array and bioinformatic analysis. Pharmacological intervention targeting the changed cytokine in the CSF and downstream signaling was performed to evaluate its role in widespread pain. RESULTS: CCI-ION induced local pain in vibrissa pads together with widespread pain in hind paws. CCI-ION's CSF transplantation, compared with sham CSF, contributed to vibrissa pad pain and hind paw pain in recipient rats. Among the measured cytokines, interleukin-6 (IL-6) and leptin were increased in CCI-ION's CSF, while interleukin-13 (IL-13) was significantly reduced. Furthermore, the concentration of CSF IL-6 was correlated with nerve injury extent, which gated the occurrence of widespread pain. Both astrocytes and microglia were increased in remote segments of the CCI-ION model, while the inhibition of astrocytes in remote segments, but not microglia, significantly alleviated widespread pain. Mechanically, astroglial signal transducer and activator of transcription 3 (STAT3) in remote segments were activated by CSF IL-6, the inhibition of which significantly mitigated widespread pain in CCI-ION. CONCLUSION: IL-6 was induced in the CSF of the CCI-ION model, triggering widespread pain via activating astrocyte STAT3 signal in remote segments. Therapies targeting IL-6/STAT3 signaling might serve as a promising strategy for the widespread pain symptom under neuropathological conditions.

High-Strength and High-Toughness Supramolecular Materials for Self-Healing Triboelectric Nanogenerator.

The development of self-healing materials provides a new opportunity and challenge for advancing triboelectric nanogenerators (TENGs). However, the low strength and low toughness of self-healing triboelectric materials often result in the deformation or breakage of TENG under high mechanical loads, thereby limiting their potential applications. Herein, a new strategy for fabricating self-healing triboelectric materials is reported, which introduces cross-linking networks with hydrogen bonds and metal coordination bonds. The desired high performance can be achieved by simply adjusting the molar ratio of the metal to the ligand. When the molar ratio is 1:2, the tensile strength, toughness, and elongation at break of the material reached 13.7 MPa, 76.9 MJ m-3, and 1321%, respectively. Furthermore, its self-healing efficiency can reach 74% at 70 °C in 6 h. Working in contact-separation mode, the electrical output can reach 164 V, 18.2 µA, 57.5 nC, with a maximum power density of 2.54 W m-2. Notably, even if it is sheared, the electrical output performances of TENG can be completely recovered to the original state. In addition, the developed TENG exhibits excellent output stability over 10 000 contact separation cycles. This study presents a promising approach for the development of stretchable smart generators.

Trends and insights in dengue virus research globally: a bibliometric analysis (1995-2023).

BACKGROUND: Dengue virus (DENV) is the most widespread arbovirus. The World Health Organization (WHO) declared dengue one of the top 10 global health threats in 2019. However, it has been underrepresented in bibliometric analyses. This study employs bibliometric analysis to identify research hotspots and trends, offering a comprehensive overview of the current research dynamics in this field. RESULTS: We present a report spanning from 1995 to 2023 that provides a unique longitudinal analysis of Dengue virus (DENV) research, revealing significant trends and shifts not extensively covered in previous literature. A total of 10,767 DENV-related documents were considered, with a notable increase in publications, peaking at 747 articles in 2021. Plos Neglected Tropical Diseases has become the leading journal in Dengue virus research, publishing 791 articles in this field-the highest number recorded. Our bibliometric analysis provides a comprehensive mapping of DENV research across multiple dimensions, including vector ecology, virology, and emerging therapies. The study delineates a complex network of immune response genes, including IFNA1, DDX58, IFNB1, STAT1, IRF3, and NFKB1, highlighting significant trends and emerging themes, particularly the impacts of climate change and new outbreaks on disease transmission. Our findings detail the progress and current status of key vaccine candidates, including the licensed Dengvaxia, newer vaccines such as Qdenga and TV003, and updated clinical trials. The study underscores significant advancements in antiviral therapies and vector control strategies for dengue, highlighting innovative drug candidates such as AT-752 and JNJ-1802, and the potential of drug repurposing with agents like Ribavirin, Remdesivir, and Lopinavir. Additionally, it discusses biological control methods, including the introduction of Wolbachia-infected mosquitoes and gene-editing technologies. CONCLUSION: This bibliometric study underscores the critical role of interdisciplinary collaboration in advancing DENV research, identifying key trends and areas needing further exploration, including host-virus dynamics, the development and application of antiviral drugs and vaccines, and the use of artificial intelligence. It advocates for strengthened partnerships across various disciplines to effectively tackle the challenges posed by DENV.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Pathogenicity and transcriptomic resolution in dengue virus serotype 1 infected AGB6 mouse model.

Dengue viruses are the causative agents of dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, which are mainly transmitted by Aedes aegypti and Aedes albopictus mosquitoes, and cost billions of dollars annually in patient treatment and mosquito control. Progress in understanding DENV pathogenesis and developing effective treatments has been hampered by the lack of a suitable small pathological animal model. Until now, the candidate vaccine, antibody, and drug for DENV have not been effectively evaluated. Here, we analyzed the pathogenicity of DENV-1 in type Ⅰ and type Ⅱ interferon receptor-deficient mice (AGB6) by intraperitoneal inoculation. Infected mice showed such neurological symptoms as opisthotonus, hunching, ataxia, and paralysis of one or both hind limbs. Viremia can be detected 3 days after infection. It was found that 6.98 × 103 PFU or higher dose induce 100% mortality. To determine the cause of lethality in mice, heart, liver, spleen, lung, kidney, intestinal, and brain tissues were collected from AGB6 mice (at an attack dose of 6.98 × 103 PFU) for RNA quantification, and it was found that the viral load in brain tissues peaked at moribund states (14 dpi) and that the viral loads in the other tissues and organs decreased over time. Significant histopathologic changes were observed in brain tissue (hippocampal region and cerebral cortex). Hematological analysis showed hemorrhage and hemoconcentration in infected mice. DENV-1 can be isolated from the brain tissue of infected mice. Subsequently, brain tissue transcriptome sequencing was performed to assess host response characteristics in infected AGB6 mice. Transcriptional patterns in brain tissue suggest that aberrant expression of pro-inflammatory cytokines induces antiviral responses and tissue damage. Screening of hub genes and their characterization by qPCR and ELISA, it was hypothesized that IL-6 and IFN-γ might be the key factors in dengue virus-induced inflammatory response. Therefore, this study provides an opportunity to decipher certain aspects of dengue pathogenesis further and provides a new platform for drug, antibody, and vaccine testing.

Computer-Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions.

Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain.

MARK2 phosphorylates eIF2α in response to proteotoxic stress.

The regulation of protein synthesis is essential for maintaining cellular homeostasis, especially during stress responses, and its dysregulation could underlie the development of human diseases. The critical step during translation regulation is the phosphorylation of eukaryotic initiation factor 2 alpha (eIF2α). Here we report the identification of a direct kinase of eIF2α, microtubule affinity-regulating kinase 2 (MARK2), which phosphorylates eIF2α in response to proteotoxic stress. The activity of MARK2 was confirmed in the cells lacking the 4 previously known eIF2α kinases. MARK2 itself was found to be a substrate of protein kinase C delta (PKCδ), which serves as a sensor for protein misfolding stress through a dynamic interaction with heat shock protein 90 (HSP90). Both MARK2 and PKCδ are activated via phosphorylation in proteotoxicity-associated neurodegenerative mouse models and in human patients with amyotrophic lateral sclerosis (ALS). These results reveal a PKCδ-MARK2-eIF2α cascade that may play a critical role in cellular proteotoxic stress responses and human diseases.

Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis.

BACKGROUND & AIMS: aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance. METHODS: A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125). RESULTS: The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). CONCLUSIONS: The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.

Copper-catalyzed tandem cyclization reaction of ethynylbenzoxazinones and thiols: facile construction of 2-thiomethylene indoles.

The first successful copper-catalyzed decarboxylative cyclization reaction of ethynylbenzoxazinones and thiols has been developed. A rarely studied α-addition process to a copper-allenylidene intermediate promoted this reaction. Using this protocol, a range of 2-thiomethylene indole compounds have been obtained. This methodology offers significant advantages including mild reaction conditions, cheap catalysts, good yields and broad substrate compatibility.

Trimethylamine from Subtropical Forests Rival Total Farmland Emissions in China.

Many types of living plants release gaseous trimethylamine (TMA), making it a potentially important contributor to new particle formation (NPF) in remote areas. However, a panoramic view of the importance of forest biogenic TMA at the regional scale is lacking. Here, we pioneered nationwide mobile measurements of TMA across a transect of contiguous farmland in eastern China and a transect of subtropical forests in southern China. In contrast to the farmland route, TMA concentrations measured during the subtropical forest route correlated significantly with isoprene, suggesting potential TMA emissions from leaves. Our high time-resolved concentrations obtained from a weak photo-oxidizing atmosphere reflected freshly emitted TMA, indicating the highest emission intensity from irrigated dryland (set as the baseline of 10), followed by paddy field (7.1), subtropical evergreen forests (5.9), and subtropical broadleaf and mixed forests (4.3). Extrapolating their proportions roughly to China, subtropical forests alone, which constitute half of the total forest area, account for nearly 70% of the TMA emissions from the nation's total farmland. Our estimates, despite the uncertainties, take the first step toward large-scale assessment of forest biogenic amines, highlighting the need for observational and modeling studies to consider this hitherto overlooked source of TMA.