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Enlargement of the cerebrospinal fluid (CSF)-filled brain ventricles (cerebral ventriculomegaly), the cardinal feature of congenital hydrocephalus (CH), is increasingly recognized among patients with autism spectrum disorders (ASD). KATNAL2, a member of Katanin family microtubule-severing ATPases, is a known ASD risk gene, but its roles in human brain development remain unclear. Here, we show that nonsense truncation of Katnal2 (Katnal2Δ17) in mice results in classic ciliopathy phenotypes, including impaired spermatogenesis and cerebral ventriculomegaly. In both humans and mice, KATNAL2 is highly expressed in ciliated radial glia of the fetal ventricular-subventricular zone as well as in their postnatal ependymal and neuronal progeny. The ventriculomegaly observed in Katnal2Δ17 mice is associated with disrupted primary cilia and ependymal planar cell polarity that results in impaired cilia-generated CSF flow. Further, prefrontal pyramidal neurons in ventriculomegalic Katnal2Δ17 mice exhibit decreased excitatory drive and reduced high-frequency firing. Consistent with these findings in mice, we identified rare, damaging heterozygous germline variants in KATNAL2 in five unrelated patients with neurosurgically treated CH and comorbid ASD or other neurodevelopmental disorders. Mice engineered with the orthologous ASD-associated KATNAL2 F244L missense variant recapitulated the ventriculomegaly found in human patients. Together, these data suggest KATNAL2 pathogenic variants alter intraventricular CSF homeostasis and parenchymal neuronal connectivity by disrupting microtubule dynamics in fetal radial glia and their postnatal ependymal and neuronal descendants. The results identify a molecular mechanism underlying the development of ventriculomegaly in a genetic subset of patients with ASD and may explain persistence of neurodevelopmental phenotypes in some patients with CH despite neurosurgical CSF shunting.
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Cílios , Hidrocefalia , Microtúbulos , Animais , Feminino , Humanos , Masculino , Camundongos , ATPases Associadas a Diversas Atividades Celulares/genética , ATPases Associadas a Diversas Atividades Celulares/metabolismo , Transtorno do Espectro Autista/genética , Transtorno do Espectro Autista/patologia , Transtorno do Espectro Autista/metabolismo , Cílios/metabolismo , Cílios/patologia , Epêndima/metabolismo , Epêndima/patologia , Hidrocefalia/genética , Hidrocefalia/patologia , Hidrocefalia/metabolismo , Katanina/metabolismo , Katanina/genética , Microtúbulos/metabolismo , Neurônios/metabolismo , Células Piramidais/metabolismo , Células Piramidais/patologiaRESUMO
Hydrogen sulfide (H2S) is an endogenously produced gasotransmitter involved in many physiological processes that are integral to proper cellular functioning. Due to its profound anti-inflammatory and antioxidant properties, H2S plays important roles in preventing inflammatory skin disorders and improving wound healing. Transdermal H2S delivery is a therapeutically viable option for the management of such disorders. However, current small-molecule H2S donors are not optimally suited for transdermal delivery and typically generate electrophilic byproducts that may lead to undesired toxicity. Here, we demonstrate that H2S release from metal-organic frameworks (MOFs) bearing coordinatively unsaturated metal centers is a promising alternative for controlled transdermal delivery of H2S. Gas sorption measurements and powder X-ray diffraction (PXRD) studies of 11 MOFs support that the Mg-based framework Mg2(dobdc) (dobdc4- = 2,5-dioxidobenzene-1,4-dicarboxylate) is uniquely well-suited for transdermal H2S delivery due to its strong yet reversible binding of H2S, high capacity (14.7 mmol/g at 1 bar and 25 °C), and lack of toxicity. In addition, Rietveld refinement of synchrotron PXRD data from H2S-dosed Mg2(dobdc) supports that the high H2S capacity of this framework arises due to the presence of three distinct binding sites. Last, we demonstrate that transdermal delivery of H2S from Mg2(dobdc) is sustained over a 24 h period through porcine skin. Not only is this significantly longer than sodium sulfide but this represents the first example of controlled transdermal delivery of pure H2S gas. Overall, H2S-loaded Mg2(dobdc) is an easily accessible, solid-state source of H2S, enabling safe storage and transdermal delivery of this therapeutically relevant gas.
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Administração Cutânea , Sulfeto de Hidrogênio , Estruturas Metalorgânicas , Sulfeto de Hidrogênio/química , Sulfeto de Hidrogênio/administração & dosagem , Estruturas Metalorgânicas/química , Animais , Suínos , Pele/metabolismoRESUMO
Despite decades of research, the dominant charge generation mechanism in organic bulk heterojunction (BHJ) devices is not completely understood. While the local dielectric environments of the photoexcited molecules are important for exciton dissociation, conventional characterizations cannot separately measure the polarizability of electron-donor and electron-acceptor, respectively, in their blends, making it difficult to decipher the spectrally different charge generation efficiencies in organic BHJ devices. Here, by spectrally resolved electroabsorption spectroscopy, we report extraction of the excited state polarizability for individual donors and acceptors in a series of organic blend films. Regardless of the donor and acceptor, we discovered that larger exciton polarizability is linked to larger π-π coherence length and faster charge transfer across the heterojunction, which fundamentally explains the origin of the higher charge generation efficiency near 100% in the BHJ photodiodes. We also show that the molecular packing of the donor and acceptor influence each other, resulting in a synergetic enhancement in the exciton polarizability.
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Diamine-appended Mg2(dobpdc) (dobpdc4- = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) metal-organic frameworks are promising candidates for carbon capture that exhibit exceptional selectivities and high capacities for CO2. To date, CO2 uptake in these materials has been shown to occur predominantly via a chemisorption mechanism involving CO2 insertion at the amine-appended metal sites, a mechanism that limits the capacity of the material to â¼1 equiv of CO2 per diamine. Herein, we report a new framework, pip2-Mg2(dobpdc) (pip2 = 1-(2-aminoethyl)piperidine), that exhibits two-step CO2 uptake and achieves an unusually high CO2 capacity approaching 1.5 CO2 per diamine at saturation. Analysis of variable-pressure CO2 uptake in the material using solid-state nuclear magnetic resonance (NMR) spectroscopy and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) reveals that pip2-Mg2(dobpdc) captures CO2 via an unprecedented mechanism involving the initial insertion of CO2 to form ammonium carbamate chains at half of the sites in the material, followed by tandem cooperative chemisorption and physisorption. Powder X-ray diffraction analysis, supported by van der Waals-corrected density functional theory, reveals that physisorbed CO2 occupies a pocket formed by adjacent ammonium carbamate chains and the linker. Based on breakthrough and extended cycling experiments, pip2-Mg2(dobpdc) exhibits exceptional performance for CO2 capture under conditions relevant to the separation of CO2 from landfill gas. More broadly, these results highlight new opportunities for the fundamental design of diamine-Mg2(dobpdc) materials with even higher capacities than those predicted based on CO2 chemisorption alone.
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PURPOSE: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive functions are often not included. This paper introduces a novel automatic segmentation tool specifically designed for the unique challenges posed by pediatric patients undergoing brain RT, as well as its seamless integration into the existing clinical workflow. METHODS AND MATERIALS: Images of 47 pediatric brain cancer patients aged 1 to 20 years old and 33 two-year-old healthy infants were used to train a vision transformer, UNesT, for the segmentation of five brain OARs. The trained model was then incorporated to clinical workflow via DICOM connections between a treatment planning system (TPS) and a server hosting the trained model such that scans are sent from TPS to the server, automatically segmented, and sent back to TPS for treatment planning. RESULTS: The proposed automatic segmentation framework achieved a median dice similarity coefficient of 0.928 (frontal white matter), 0.908 (corpus callosum), 0.933 (hippocampi), 0.819 (temporal lobes), and 0.960 (brainstem) with a mean ± SD run time of 1.8 ± 0.67 s over 20 test cases. CONCLUSIONS: The pediatric brain segmentation tool showed promising performance on five OARs linked to neurocognitive functions and can easily be extended for additional structures. The proposed integration to the clinic enables easy access to the tool from clinical platforms and minimizes disruption to existing workflow while maximizing its benefits.
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Neoplasias Encefálicas , Aprendizado Profundo , Humanos , Criança , Lactente , Pré-Escolar , Adolescente , Adulto Jovem , Adulto , Fluxo de Trabalho , Processamento de Imagem Assistida por Computador/métodos , Órgãos em Risco , Planejamento da Radioterapia Assistida por Computador/métodos , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/radioterapia , Encéfalo/diagnóstico por imagemRESUMO
Miniaturized silicon thermal probes for plant's sap flow measurement, or micro sap flow sensors, have advantages in minimum invasiveness, low power consumption, and fast responses. Practical applications in sap flow measurement has been demonstrated with the single-probe silicon micro sensors. However, the sensors could not detect flow directions and require estimating zero sap flow output that leads to significant source of uncertainty. Furthermore, silicon-needles would break easily during the insertion into plants. We present the first three-element micro thermal sap flow sensor packaged on a durable printed circuit board needle that can measure bidirectional flows with improved dynamics and precision. The performance of the newly designed calorimetric flow sensor was confirmed through precision calibration and field test on tomato stems. A calibration curve for a tomato stem was obtained with a sensitivity of 0.299 K/(µL mm-2 s-1) under the maximum temperature increase of 4.61 K. Results from the field test for one month revealed a correlation between the measured sap flux density and related conditions such as solar radiation, vapor pressure deficit, sunshade and irrigation. The developed sensor will contribute to practical long-term sap flow monitoring for small and delicate plants with minimal physical invasion.
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The obligate intracellular bacterium Chlamydia alternates between two functional forms during its developmental cycle: elementary body (EB) and reticulate body (RB). However, the molecular mechanisms governing the transitions between these forms are unknown. Here, we present evidence cyclic di-AMP (c-di-AMP) is a key factor in triggering the transition from RB to EB (i.e., secondary differentiation) in the chlamydial developmental cycle. We made strains producing different levels of c-di-AMP, which we linked to changes in secondary differentiation status. Increases in c-di-AMP resulted in an earlier increase in transcription of EB-associated genes, and this was further manifested in earlier production of EBs. In contrast, when c-di-AMP levels were decreased, secondary differentiation was delayed. Based on these data, we conclude there is a threshold level of c-di-AMP needed to trigger secondary differentiation in Chlamydia . This is the first study to identify a physiological function for c-di-AMP production in Chlamydia as well as a mechanism by which secondary differentiation is initiated in these pathogens. Importance: The second messenger molecule, cyclic di-AMP, shows diverse functions in bacteria. This molecule is usually detected in Gram-positive bacteria and is related to the osmotic stress response, DNA replication, and sporulation. Chlamydia trachomatis , a Gram-negative bacterium, encodes genes related to cyclic di-AMP synthesis. Cyclic di-AMP has been detected in C. trachomatis , where it has been shown to trigger a STING-dependent immune response in host cells. However, its physiological function in C. trachomatis is unknown. In this study, we identify a function for cyclic di-AMP in triggering gene expression linked to secondary differentiation in chlamydial developmental cycle. Our findings are important in understanding the molecular mechanism of the chlamydial developmental cycle and contribute to providing new therapeutic strategies for chlamydial infectious diseases.
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Purpose: Segmentation of the prostate and surrounding organs at risk from computed tomography is required for radiation therapy treatment planning. We propose an automatic two-step deep learning-based segmentation pipeline that consists of an initial multi-organ segmentation network for organ localization followed by organ-specific fine segmentation. Approach: Initial segmentation of all target organs is performed using a hybrid convolutional-transformer model, axial cross-attention UNet. The output from this model allows for region of interest computation and is used to crop tightly around individual organs for organ-specific fine segmentation. Information from this network is also propagated to the fine segmentation stage through an image enhancement module, highlighting regions of interest in the original image that might be difficult to segment. Organ-specific fine segmentation is performed on these cropped and enhanced images to produce the final output segmentation. Results: We apply the proposed approach to segment the prostate, bladder, rectum, seminal vesicles, and femoral heads from male pelvic computed tomography (CT). When tested on a held-out test set of 30 images, our two-step pipeline outperformed other deep learning-based multi-organ segmentation algorithms, achieving average dice similarity coefficient (DSC) of 0.836±0.071 (prostate), 0.947±0.038 (bladder), 0.828±0.057 (rectum), 0.724±0.101 (seminal vesicles), and 0.933±0.020 (femoral heads). Conclusions: Our results demonstrate that a two-step segmentation pipeline with initial multi-organ segmentation and additional fine segmentation can delineate male pelvic CT organs well. The utility of this additional layer of fine segmentation is most noticeable in challenging cases, as our two-step pipeline produces noticeably more accurate and less erroneous results compared to other state-of-the-art methods on such images.
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BACKGROUND: Sarcopenic obesity, defined as the coexistence of low muscle mass and high adiposity, is associated with cardiovascular disease (CVD) and mortality. However, to what extent sarcopenia contributes to these risks independently or in conjunction with other cardiovascular risk factors remains unclear. This study aimed to investigate the association of low muscle mass, central obesity (COB), metabolic abnormalities, and their combinations with CVD and mortality risk. METHODS: This cross-sectional analysis used data from the National Health and Nutrition Examination Survey 1999-2006 and 2011-2018. Participants aged >20 years and with reported whole-body dual X-ray absorptiometry data were included. Participants were divided into eight groups based on low muscle mass, metabolic abnormalities, and COB status. RESULTS: The mean age of participants was 55 years, and 50.4% of participants were male. Low muscle mass was observed in 2472 (14.6%) out of 16 839 participants. Among the eight groups, the metabolically unhealthy COB group with low muscle mass had the highest hazard ratio (HR) for all-cause mortality (HR, 2.00; 95% CI, 1.56-2.56; P < 0.001), whereas the metabolically healthy COB group with low muscle mass had the highest HR for CVD mortality (HR, 3.18; 95% CI, 1.53-6.65; P = 0.001). The mediation analysis showed that low muscle mass directly increased the risk of both all-cause mortality (HR, 1.56; 95% CI, 1.35-1.79; P < 0.001) and CVD mortality (HR, 1.80; 95% CI, 1.40-2.31; P < 0.001). Additionally, subgroup analysis revealed that low muscle mass significantly increased the risk of all-cause and CVD mortality in participants without a prior CVD history and those with diabetes mellitus. CONCLUSIONS: Low muscle mass is an independent risk factor for all-cause and CVD mortality, especially in individuals with metabolic abnormalities and COB.
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Doenças Cardiovasculares , Sarcopenia , Adulto , Masculino , Humanos , Pessoa de Meia-Idade , Feminino , Doenças Cardiovasculares/etiologia , Estudos Transversais , Inquéritos Nutricionais , Índice de Massa Corporal , Obesidade/complicações , Obesidade/epidemiologia , Sarcopenia/complicações , Músculos/metabolismoRESUMO
Image-guided interventional oncology procedures can greatly enhance the outcome of cancer treatment. As an enhancing procedure, oncology smart material delivery can increase cancer therapy's quality, effectiveness, and safety. However, the effectiveness of enhancing procedures highly depends on the accuracy of smart material placement procedures. Inaccurate placement of smart materials can lead to adverse side effects and health hazards. Image guidance can considerably improve the safety and robustness of smart material delivery. In this study, we developed a novel generative deep-learning platform that highly prioritizes clinical practicality and provides the most informative intra-operative feedback for image-guided smart material delivery. XIOSIS generates a patient-specific 3D volumetric computed tomography (CT) from three intraoperative radiographs (X-ray images) acquired by a mobile C-arm during the operation. As the first of its kind, XIOSIS (i) synthesizes the CT from small field-of-view radiographs;(ii) reconstructs the intra-operative spacer distribution; (iii) is robust; and (iv) is equipped with a novel soft-contrast cost function. To demonstrate the effectiveness of XIOSIS in providing intra-operative image guidance, we applied XIOSIS to the duodenal hydrogel spacer placement procedure. We evaluated XIOSIS performance in an image-guided virtual spacer placement and actual spacer placement in two cadaver specimens. XIOSIS showed a clinically acceptable performance, reconstructed the 3D intra-operative hydrogel spacer distribution with an average structural similarity of 0.88 and Dice coefficient of 0.63 and with less than 1 cm difference in spacer location relative to the spinal cord.
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BACKGROUND AND OBJECTIVE: Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios. METHODS: Axial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis. KEY FINDINGS AND LIMITATIONS: The AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists. CONCLUSIONS AND CLINICAL IMPLICATIONS: Our AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs. PATIENT SUMMARY: We developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.
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BACKGROUND: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains. PURPOSE: To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans. METHODS: We extended our previous RL approach to enable VMAT MPO of a 3D beam model for a clinical linac through a policy network. It accepts an input state describing the current control point and predicts continuous machine parameters for the next control point, which are used to update the input state, repeating until plan termination. RL training was conducted to minimize a dose-based cost function for prescription of 60 Gy in 20 fractions using CT scans and contours from 136 retrospective localized prostate cancer patients, 20 of which had existing plans used to initialize training. Data augmentation was employed to mitigate over-fitting, and parameter exploration was achieved using Gaussian perturbations. Following training, RL VMAT was applied to an independent cohort of 15 patients, and the resultant dosimetry was compared to clinical plans. We also combined the RL approach with our clinical treatment planning system (TPS) to automate final plan refinement, and creating the potential for manual review and edits as required for clinical use. RESULTS: RL training was conducted for 5000 iterations, producing 40 000 plans during exploration. Mean ± SD execution time to produce deliverable VMAT plans in the test cohort was 3.3 ± 0.5 s which were automatically refined in the TPS taking an additional 77.4 ± 5.8 s. When normalized to provide equivalent target coverage, the RL+TPS plans provided a similar mean ± SD overall maximum dose of 63.2 ± 0.6 Gy and a lower mean rectum dose of 17.4 ± 7.4 compared to 63.9 ± 1.5 Gy (p = 0.061) and 21.0 ± 6.0 (p = 0.024) for the clinical plans. CONCLUSIONS: An approach for VMAT MPO using RL for a clinical linac model was developed and applied to automatically generate deliverable plans for localized prostate cancer patients, and when combined with the clinical TPS shows potential to rapidly generate high-quality plans. The RL VMAT approach shows promise to discover advanced linac control policies through trial-and-error, and algorithm limitations and future directions are identified and discussed.
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Aprendizado Profundo , Neoplasias da Próstata , Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade Modulada , Masculino , Humanos , Neoplasias da Próstata/radioterapia , Neoplasias da Próstata/diagnóstico por imagem , Planejamento da Radioterapia Assistida por Computador/métodos , Dosagem Radioterapêutica , Aprendizado de MáquinaRESUMO
PURPOSE: To evaluate the relationship between delivered radiation (RT) and post-RT inversion-recovery ultrashort-echo-time (IR-UTE) MRI signal-intensity (SI) in gynecologic cancer patients treated with high-dose-rate (HDR) brachytherapy (BT). METHODS: Seven patients underwent whole-pelvis RT (WPRT) followed by BT to the high-risk clinical target volume (HR-CTV). MR images were acquired at three time-points; pre-RT, post-WPRT/pre-BT, and 3-6 months post-BT. Diffuse-fibrosis (FDiffuse) was imaged with a non-contrast dual-echo IR (inversion time [TI] = 60 ms) UTE research application, with image-subtraction of the later echo, only retaining the ultrashort-echo SI. Dense-fibrosis (FDense) imaging utilized single-echo Late-Gadolinium-Enhanced IR-UTE, acquired â¼ 15 min post-Gadavist injection. Resulting FDiffuse and FDense SI were normalized to the corresponding gluteal-muscle SI. Images were deformably registered between time-points based on normal tissue anatomy. The remnant tumor at both time-points was segmented using multi-parametric MRI. Contours corresponding to the 50%, 100%, 150%, and 200% isodose lines (IDLs) of the prescription BT-dose were created. Mean FDiffuse and FDense SI within (i) each IDL contour and (ii) the remnant tumor were calculated. Post-BT FDiffuse and FDense SI were correlated with prescribed BT-dose. To determine the relationship between BT-dose and IR-UTE SI, the differences in the post-BT FDense across IDLs was determined using paired t-tests with Bonferroni correction. RESULTS: FDense was higher in regions of higher dose for 6/7 patients, with mean ± SD values of 357 ± 103% and 331 ± 97% (p = .03) in the 100% and 50% IDL, respectively. FDense was higher in regions of higher dose in the responsive regions with mean ± SD values of 380 ± 122% and 356 ± 135% (p = .03) in the 150% and 50% IDL, respectively. Within the segmented remnant tumor, an increase in prescribed dose correlated with an increase in FDense post-BT (n = 5, r = .89, p = .04). Post-BT FDiffuse inversely correlated (n = 7, r = -.83, p = .02) with prescribed BT-dose within the 100% IDL. CONCLUSIONS: Results suggest that FDense SI 3-6 months post-BT is a sensitive measure of tissue response to heterogeneous BT radiation-dose. Future studies will validate whether FDiffuse and FDense are accurate biomarkers of fibrotic radiation response.
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Braquiterapia , Neoplasias dos Genitais Femininos , Imageamento por Ressonância Magnética , Dosagem Radioterapêutica , Humanos , Feminino , Braquiterapia/métodos , Imageamento por Ressonância Magnética/métodos , Neoplasias dos Genitais Femininos/radioterapia , Neoplasias dos Genitais Femininos/diagnóstico por imagem , Pessoa de Meia-Idade , Idoso , Planejamento da Radioterapia Assistida por Computador/métodosRESUMO
With the increasing demand for ammonia applications, there is a significant focus on improving NH3 detection performance at room temperature. In this study, we introduce a groundbreaking NH3 gas sensor based on Cu(I)-based coordination polymers, featuring semiconducting, single stranded 1D-helical nanowires constructed from Cu-Cl and N-methylthiourea (MTCP). The MTCP demonstrates an exceptional response to NH3 gas (>900% at 100 ppm) and superior selectivity at room temperature compared to current materials. The interaction mechanism between NH3 and the MTCP sensor is elucidated through a combination of empirical results and computational calculations, leveraging a crystal-determined structure. This reveals the formation of NH3-Cu and NH3-H3C complexes, indicative of a thermodynamically favorable reaction. Additionally, Ag-doped MTCP exhibits higher selectivity and a response over two times greater than the original MTCP, establishing it as a prominent NH3 detection system at room temperature.
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Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model, designed to explicitly address temporal heterogeneity. The BMT model embeds the burstiness and memory coefficient into the self-attention module, enhancing the learning process with insights derived from the bursty patterns. Furthermore, we employed a novel loss function designed to optimize the burstiness and memory coefficient values, as well as their corresponding discretized one-hot vectors, both individually and jointly. Numerical experiments conducted on diverse synthetic and real-world datasets demonstrated the outstanding performance of the BMT model in terms of accurately predicting event times and intensity functions compared to existing models and control groups. In particular, the BMT model exhibits remarkable performance for temporally heterogeneous data, such as those with power-law inter-event time distributions. Our findings suggest that the incorporation of burst-related parameters assists the Transformer in comprehending heterogeneous event sequences, leading to an enhanced predictive performance.
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Metal-organic frameworks (MOFs) are porous, crystalline materials constructed from organic linkers and inorganic nodes with potential utility in gas separations, drug delivery, sensing, and catalysis. Small variations in MOF synthesis conditions can lead to a range of accessible frameworks with divergent chemical or photophysical properties. New methods to controllably access phases with tailored properties would broaden the scope of MOFs that can be reliably prepared for specific applications. Herein, we demonstrate that simply increasing the reaction concentration during the solvothermal synthesis of M2(dobdc) (M = Mg, Mn, Ni; dobdc4- = 2,5-dioxido-1,4-benzenedicarboxylate) MOFs unexpectedly leads to trapping of a new framework termed CORN-MOF-1 (CORN = Cornell University) instead. In-depth spectroscopic, crystallographic, and computational studies support that CORN-MOF-1 has a similar structure to M2(dobdc) but with partially protonated linkers and charge-balancing or coordinated formate groups in the pores. The resultant variation in linker spacings causes CORN-MOF-1 (Mg) to be strongly photoluminescent in the solid state, whereas H4dobdc and Mg2(dobdc) are weakly emissive due to excimer formation. In-depth photophysical studies suggest that CORN-MOF-1 (Mg) is the first MOF based on the H2dobdc2- linker that likely does not emit via an excited state intramolecular proton transfer (ESIPT) pathway. In addition, CORN-MOF-1 variants can be converted into high-quality samples of the thermodynamic M2(dobdc) phases by heating in N,N-dimethylformamide (DMF). Overall, our findings support that high-concentration synthesis provides a straightforward method to identify new MOFs with properties distinct from known materials and to produce highly porous samples of MOFs, paving the way for the discovery and gram-scale synthesis of framework materials.