Triperyleno[3,3,3]propellane Triimides: Achieving a New Generation of Quasi-\u3cem\u3eD\u3c/em\u3e\u3csub\u3e3h\u3c/sub\u3e Symmetric Nanostructures in Organic Electronics

Rigid three-dimensional (3D) polycyclic aromatic hydrocarbons (PAHs), in particular 3D nanographenes, have garnered interest due to their potential use in semiconductor applications and as models to study through-bond and through-space electronic interactions. Herein we report the development of a novel 3D-symmetric rylene imide building block, triperyleno[3,3,3]propellane triimides (6), that possesses three perylene monoimide subunits fused on a propellane. This building block shows several promising characteristics, including high solubility, large π-surfaces, electron-accepting capabilities, and a variety of reactive sites. Further, the building block is compatible with different reactions to readily yield quasi-D3h symmetric nanostructures (9, 11, and 13) of varied chemistries. For the 3D nanostructures we observed red-shift absorption maxima and amplification of the absorption coefficients when compared to the individual subunits, indicating intramolecular electronic coupling among the subunits. In addition, the microplates of 9 exhibit comparable mobilities in different directions in the range of 10−3 cm2 V−1 s−1, despite the rather limited intermolecular overlap of the π-conjugated moieties. These findings demonstrate that these quasi-D3h symmetric rylene imides have potential as 3D nanostructures for a range of materials applications, including in organic electronic devices

MuseCoco: Generating Symbolic Music from Text

Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality

Reversible Zn metal anodes enabled by trace amounts of underpotential deposition initiators

Routine electrolyte additives are not effective enough for uniform zinc (Zn) deposition, because they are hard to proactively guide atomic-level Zn deposition. Here, based on underpotential deposition (UPD), we propose an "escort effect" of electrolyte additives for uniform Zn deposition at the atomic level. With nickel ion (Ni2+) additives, we found that metallic Ni deposits preferentially and triggers the UPD of Zn on Ni. This facilitates firm nucleation and uniform growth of Zn while suppressing side reactions. Besides, Ni dissolves back into the electrolyte after Zn stripping with no influence on interfacial charge transfer resistance. Consequently, the optimized cell operates for over 900 h at 1 mA cm-2 (more than 4 times longer than the blank one). Moreover, the universality of "escort effect" is identified by using Cr3+ and Co2+ additives. This work would inspire a wide range of atomic-level principles by controlling interfacial electrochemistry for various metal batteries

A Built-In Mechanism to Mitigate the Spread of Insect-Resistance and Herbicide-Tolerance Transgenes into Weedy Rice Populations

BACKGROUND: The major challenge of cultivating genetically modified (GM) rice (Oryza sativa) at the commercial scale is to prevent the spread of transgenes from GM cultivated rice to its coexisting weedy rice (O. sativa f. spontanea). The strategic development of GM rice with a built-in control mechanism can mitigate transgene spread in weedy rice populations. METHODOLOGY/PRINCIPAL FINDINGS: An RNAi cassette suppressing the expression of the bentazon detoxifying enzyme CYP81A6 was constructed into the T-DNA which contained two tightly linked transgenes expressing the Bt insecticidal protein Cry1Ab and the glyphosate tolerant 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), respectively. GM rice plants developed from this T-DNA were resistant to lepidopteran pests and tolerant to glyphosate, but sensitive to bentazon. The application of bentazon of 2000 mg/L at the rate of 40 mL/m(2), which is approximately the recommended dose for the field application to control common rice weeds, killed all F(2) plants containing the transgenes generated from the Crop-weed hybrids between a GM rice line (CGH-13) and two weedy rice strains (PI-63 and PI-1401). CONCLUSIONS/SIGNIFICANCE: Weedy rice plants containing transgenes from GM rice through gene flow can be selectively killed by the spray of bentazon when a non-GM rice variety is cultivated alternately in a few-year interval. The built-in control mechanism in combination of cropping management is likely to mitigate the spread of transgenes into weedy rice populations

Multistep Synthesis of a Radiolabeled Imaging Probe Using Integrated Microfluidics

Microreactor technology has shown potential for optimizing synthetic efficiency, particularly in preparing sensitive compounds. We achieved the synthesis of an [^(18)F]fluoride-radiolabeled molecular imaging probe, 2-deoxy-2-[18F]fluoro-d-glucose ([^(18)F]FDG), in an integrated microfluidic device. Five sequential processes—[^(18)F]fluoride concentration, water evaporation, radiofluorination, solvent exchange, and hydrolytic deprotection—proceeded with high radio-chemical yield and purity and with shorter synthesis time relative to conventional automated synthesis. Multiple doses of [^(18)F]FDG for positron emission tomography imaging studies in mice were prepared. These results, which constitute a proof of principle for automated multistep syntheses at the nanogram to microgram scale, could be generalized to a range of radiolabeled substrates

Predictive analytics on engineer manual 385 effectiveness of reducing number and severity of mishaps

The United States Army Corps of Engineering Manual 385 (EM 385) has become a vital part of construction operations on all Department of Defense (DOD) construction projects to create a safer work environment. With tremendous effort on developing and enforcing the EM 385, the question of whether the EM 385 provides any value for project safety is critical to the construction industry at large. This study looks for causation between the EM 385 and mishap reduction by isolating three dependent variables and a variety of explanatory variables. The data was compiled using both the OSHA Data Initiative (ODI) and the Federal Spending Database. A structural equation is developed to conduct multiple regression analysis assuming EM 385 will reduce the number of mishaps and the severity of mishaps. However, the result shows the effectiveness of EM 385 on reducing the number and severity of mishaps is not significant

Machine learning models for prediction of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus: a singled centered retrospective study

Abstract Objective This study aimed to develop and validate a machine learning algorithm-based model for predicting invasive Klebsiella pneumoniae liver abscess syndrome(IKPLAS) in diabetes mellitus and compare the performance of different models. Methods The clinical signs and data on the admission of 213 diabetic patients with Klebsiella pneumoniae liver abscesses were collected as variables. The optimal feature variables were screened out, and then Artificial Neural Network, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor, Decision Tree, and XGBoost models were established. Finally, the model's prediction performance was evaluated by the ROC curve, sensitivity (recall), specificity, accuracy, precision, F1-score, Average Precision, calibration curve, and DCA curve. Results Four features of hemoglobin, platelet, D-dimer, and SOFA score were screened by the recursive elimination method, and seven prediction models were established based on these variables. The AUC (0.969), F1-Score(0.737), Sensitivity(0.875) and AP(0.890) of the SVM model were the highest among the seven models. The KNN model showed the highest specificity (1.000). Except that the XGB and DT models over-estimates the occurrence of IKPLAS risk, the other models' calibration curves are a good fit with the actual observed results. Decision Curve Analysis showed that when the risk threshold was between 0.4 and 0.8, the net rate of intervention of the SVM model was significantly higher than that of other models. In the feature importance ranking, the SOFA score impacted the model significantly. Conclusion An effective prediction model of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus could be established by a machine learning algorithm, which had potential application value

Pterocarya rhoifolia Sieb. et Zucc.

原著和名: サハグルミ科名: クルミ科 = Juglandaceae採集地: 福島県 南会津郡 只見町 田子倉 (岩代 南会津郡 只見町 田子倉)採集日: 1987/5/21採集者: 萩庭丈壽整理番号: JH007271国立科学博物館整理番号: TNS-VS-95727