The solvent-free mechanochemical reaction has aroused increasing interest among scientists. Mechanical ball-milling can implement reactions under mild conditions, shorten the reaction time, and ...improve the reaction efficiency. Particularly, the most attractive characteristic of mechanochemistry is that it can alter the reaction pathway. However, few such examples have been reported so far. In this paper, we report the reaction of aldoximes with NaCl and Oxone under ball-milling conditions to afford
-acyloxyimidoyl chlorides, which are different from those of the liquid-phase counterpart.
Solvent-free mechanical milling is a new, environmentally friendly and cost-effective technology that is now widely used in the field of organic synthesis. The mechanochemical solvent-free synthesis ...of furoxans from aldoximes was achieved through dimerization of the in situ generated nitrile oxides in the presence of sodium chloride, Oxone and a base. A variety of furoxans was obtained with up to a 92% yield. The present protocol has the advantages of high reaction efficiency and mild reaction conditions.
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in ...experiments on translating radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest computed tomography lung cancer screening scans and 76 brain magnetic resonance imaging metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.27 in the five-point system with 0.08 places of information missing and 0.07 places of misinformation. In terms of the suggestions provided by ChatGPT, they are generally relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.
Flipover outperforms dropout in deep learning Liang, Yuxuan; Niu, Chuang; Yan, Pingkun ...
Visual computing for industry, biomedicine and art,
02/2024, Volume:
7, Issue:
1
Journal Article
Peer reviewed
Open access
Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their ...connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.
6,6-Phenyl-C
-butyric acid methyl ester (PCBM), a star molecule in the fullerene field, has found wide applications in materials science. Herein, electrosynthesis of buckyballs with fused-ring ...systems has been achieved through radical α-C-H functionalization of the side-chain ester for both PCBM and its analogue, 6,6-phenyl-C
-propionic acid methyl ester (PCPM), in the presence of a trace amount of oxygen. Two classes of buckyballs with fused bi- and tricyclic carbocycles have been electrochemically synthesized. Furthermore, an unknown type of a bisfulleroid with two tethered 6,6-open orifices can also be efficiently generated from PCPM. All three types of products have been confirmed by single-crystal X-ray crystallography. A representative intramolecularly annulated isomer of PCBM has been applied as an additive to inverted planar perovskite solar cells and boosted a significant enhancement of power conversion efficiency from 15.83% to 17.67%.
Endohedral clusterfullerenes exhibit unique chemical properties due to intramolecular electron transfer of the encaged metal cluster to the outer fullerene cages. We report the synthesis of two ...Sc3N@D3h‐C78 monoadducts 2 a and 2 b through the 1,3‐dipolar reaction of Sc3N@D3h‐C78 with carbonyl ylide bearing anomalous cis‐conformation regioselectivity. The molecular structures of these monoadducts are unambiguously confirmed by single‐crystal X‐ray crystallography, revealing that both 2 a and 2 b have cis‐conformations with the furan moiety grafted via 6,6‐closed addition patterns. Under the same conditions, the control reaction of C60 with carbonyl ylide affords two monoadducts 3 a and 3 b, which exhibit cis‐ and trans‐conformations, respectively, with 6,6‐closed addition patterns. According to theoretical calculations, the exclusive formation of the cis‐only Sc3N@D3h‐C78 monoadducts is a consequence of conjunct effects of thermodynamic stability of adducts, the reactivity of the addition site, and the cis‐dipole intermediate from trans 1.
Sc3N@D3h‐C78 was reacted with carbonyl ylide to form two isomeric monoadducts 2 a and 2 b bearing anomalous cis‐conformation regioselectivity and a 6,6‐closed addition pattern. Whereas isomeric monoadducts 3 a and 3 b with cis‐ and trans‐conformations were produced simultaneously for C60 under the same conditions. Adduct stability, reactivity of the addition site, and the nature of intermediates play a role in the different regioselectivity.
Extracting objects of interest from remote sensing imagery is an essential part in various practical applications. The objects that people pay attention to in the remote sensing scene mainly include ...buildings, roads, vehicles, etc. In this article, extracting the aforementioned objects are collectively referred to as the target extraction task. Arising from object scale variation, appearance similarity between adjacent patches, diversity of imaging orientation, and complexity of background, it is difficult to extract complete objects from cluttered backgrounds. Deep neural network has made great achievement in dense prediction for target extraction. However, most of the previous works are still faced with a formidable challenge in discriminative context feature representation to extract targets of various categories and correctly classify pixels around the boundary. In this article, we propose a target extraction neural network, named discriminative context-aware network, to focus on discriminative high-level context features and preserve spatial information. First, a discriminative context-aware feature module is designed to generate the feature maps in the top layer, which not only captures the rich image context information but also aggregates the contrasted local information at multiple scales. Second, a refine decoder module is adopted to preserve spatial information from low-level layers and enhance the feature representation, leading to precise segmentation results. We conducted extensive experiments on building and road extraction benchmarks, including WHU building dataset and Massachusetts road dataset, together with a self-constructed dataset for vehicle extraction in SAR images. Our method achieves state-of-the-art results with fewer parameters and faster inference.
The three‐ and four‐component reactions of 60fullerene with triethylamine/diethylamine and aldehydes affording 2‐arylvinyl‐substituted fulleropyrrolidines in high stereoselectivity have been ...achieved. Depending on the reaction conditions, cis‐ and trans‐fulleropyrrolidines can be stereoselectively synthesized. The thermodynamically stable cis‐fulleropyrrolidines can be obtained by rearrangement of the kinetically formed trans‐fulleropyrrolidines. A plausible reaction mechanism is proposed to elucidate the formation of fulleropyrrolidines.
The three‐ and four‐component reactions of 60fullerene with triethylamine/diethylamine and aldehydes affording cis‐ or trans‐isomers of 2‐arylvinyl‐substituted fulleropyrrolidines in high stereoselectivity have been achieved.
A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny ...perturbations, (2) small features missed in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. Here, we propose an analytic compressed iterative deep (ACID) framework to address this challenge. ACID synergizes a deep network trained on big data, kernel awareness from compressed sensing (CS)-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the ACID reconstruction is accurate, is stable, and sheds light on the converging mechanism of the ACID iteration under a bounded relative error norm assumption. ACID not only stabilizes an unstable deep reconstruction network but also is resilient against adversarial attacks to the whole ACID workflow, being superior to classic sparsity-regularized reconstruction and eliminating the three kinds of instabilities.
•Deep reconstruction solution to the instabilities identified by a recent PNAS paper•Analytic compressed iterative deep (ACID) method is for hybrid reconstruction•ACID framework combines benefits from deep learning and compressed sensing•ACID is accurate and stable, superior to sparsity-regularized reconstruction alone
Tomographic image reconstruction with deep learning has been a rapidly emerging field since 2016. Recently, a PNAS paper revealed that several well-known deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI), and, in contrast, compressed sensing (CS)-inspired reconstruction methods are stable because of their theoretically proven property known as “kernel awareness.” Therefore, for deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is critically important to stabilize deep reconstruction networks. Here, we propose an analytic compressed iterative deep (ACID) framework to synergize deep learning and compressed sensing through iterative refinement. We anticipate that this integrative model-based data-driven approach will promote the development and translation of deep tomographic image reconstruction networks.
We propose an analytic compressed iterative deep (ACID) framework for accurate yet stable deep reconstruction. ACID synergizes a deep reconstruction network trained on big data, kernel awareness from compressed sensing-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. We anticipate that this integrative model-based data-driven approach will promote the development and translation of deep tomographic image reconstruction networks.
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