Fused in sarcoma (FUS) inhibits milk production efficiency in mammals.

Efficient milk production in mammals confers evolutionary advantages by facilitating the transmission of energy from mother to offspring. However, the regulatory mechanism responsible for the gradual establishment of milk production efficiency in mammals, from marsupials to eutherians, remains elusive. Here, we find that mammary gland of the marsupial sugar glider contained milk components during adolescence, and that mammary gland development is less dynamically cyclic compared to that in placental mammals. Furthermore, fused in sarcoma (FUS) is found to be partially responsible for this establishment of low efficiency. In mouse model, FUS inhibit mammary epithelial cell differentiation through the cyclin-dependent kinase inhibitor p57Kip2, leading to lactation failure and pup starvation. Clinically, FUS levels are negatively correlated with milk production in lactating women. Overall, our results shed light on FUS as a negative regulator of milk production, providing a potential mechanism for the establishment of milk production from marsupial to eutherian mammals.

Fast high quality computational ghost imaging based on saliency variable sampling detection.

Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate 0.76 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields.

A Portable Integrated Electrochemical Sensing System for On-Site Nitrite Detection in Food.

Ensuring an appropriate nitrite level in food is essential to keep the body healthy. However, it still remains a huge challenge to offer a portable and low-cost on-site food nitrite analysis without any expensive equipment. Herein, a portable integrated electrochemical sensing system (IESS) is developed to achieve rapid on-site nitrite detection in food, which is composed of a low-cost disposable microfluidic electrochemical patch for few-shot nitrite detection, and a reusable smartphone-assisted electronic device based on self-designed circuit board for signal processing and wireless transmission. The electrochemical patch based on MXene-Ti3C2Tx/multiwalled carbon nanotubes-cyanocobalamin (MXene/MWCNTs-VB12)-modified working electrode achieves high sensitivity of 10.533 µA mm-1 and low nitrite detection limit of 4.22 µm owing to strong electron transfer ability of hybrid MXene/MWCNTs conductive matrix and high nitrite selectivity of VB12 bionic enzyme-based ion-selective layer. Moreover, the portable IESS can rapidly collect pending testing samples through a microfluidic electrochemical patch within 1.0 s to conduct immediate nitrite analysis, and then wirelessly transmit data from a signal-processing electronic device to a smartphone via Bluetooth module. Consequently, this proposed portable IESS demonstrates rapid on-site nitrite analysis and wireless data transmission within one palm-sized electronic device, which would pave a new avenue in food safety and personal bespoke therapy.

Effortful and effortless training of executive functions improve brain multiple demand system activities differently: an activation likelihood estimation meta-analysis of functional neuroimaging studies.

Both effortful and effortless training have been shown to be effective in enhancing individuals' executive functions. Effortful training improves domain-specific EFs, while effortless training improves domain-general EFs. Furthermore, effortful training has significantly higher training effects on EFs than effortless training. The neural mechanism underlying these different effects remained unclear. The present study conducted meta-analysis on neuroimaging studies to explore the changes of brain activations induced by effortful and effortless training. The results showed that effortful training induced greater activation in superior frontal gyrus, while effortless training induced greater activation in middle frontal gyrus, precuneus and cuneus. The brain regions of MD system enhanced by effortful training were more associated with core cognitive functions underlying EFs, while those enhanced by effortless training were more correlated with language functions. In addition, the significant clusters induced by effortful training had more overlaps with the MD system than effortless training. These results provided us with possibility to discuss the different behavioral results brought by effortful and effortless training.

Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network.

Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.

Double filter iterative ghost imaging for high quality edge and image acquisition.

Improving imaging quality and reducing time consumption are the key problems that need to be solved in the practical application of ghost imaging. Hence, we demonstrate a double filter iterative ghost imaging method, which adopts the joint iteration of projected Landweber iterative regularization and double filtering based on block matching three dimensional filtering and guided filtering to achieve high-quality image reconstruction under low measurement and low iteration times. This method combines the advantages of ill-posed problem solution of projected Landweber iterative regularization with double filtering joint iterative de-noising and edge preservation. The numerical simulation results show that our method outperforms the comparison method by 4 to 6 dB in terms of peak signal-to-noise ratio for complex binary target 'rice' and grayscale target 'aircraft' after 1500 measurements. The comparison results of experiments and numerical simulations using similar aircraft targets show that this method is superior to the comparison method, especially in terms of richer and more accurate edge detection results. This method can simultaneously obtain high quality reconstructed image and edge feature information under low measurement and iteration times, which is of great value for the practical application fields of imaging and edge detection at the same time, such as intelligent driving, remote sensing and other fields.

Low sampling high quality image reconstruction and segmentation based on array network ghost imaging.

High-quality imaging under low sampling time is an important step in the practical application of computational ghost imaging (CGI). At present, the combination of CGI and deep learning has achieved ideal results. However, as far as we know, most researchers focus on one single pixel CGI based on deep learning, and the combination of array detection CGI and deep learning with higher imaging performance has not been mentioned. In this work, we propose a novel multi-task CGI detection method based on deep learning and array detector, which can directly extract target features from one-dimensional bucket detection signals at low sampling times, especially output high-quality reconstruction and image-free segmentation results at the same time. And this method can realize fast light field modulation of modulation devices such as digital micromirror device to improve the imaging efficiency by binarizing the trained floating-point spatial light field and fine-tuning the network. Meanwhile, the problem of partial information loss in the reconstructed image due to the detection unit gap in the array detector has also been solved. Simulation and experimental results show that our method can simultaneously obtain high-quality reconstructed and segmented images at sampling rate of 0.78 %. Even when the signal-to-noise ratio of the bucket signal is 15 dB, the details of the output image are still clear. This method helps to improve the applicability of CGI and can be applied to resource-constrained multi-task detection scenarios such as real-time detection, semantic segmentation, and object recognition.

The alterations in granule, shell, blocklets, and molecular structure of pea starch induced by ultrasound.

Understanding the alterations to starch multi-scale structure induced by ultrasound treatment can help in determining the effective application of ultrasound in functional-starch preparation. This study aimed to comprehensively characterize and understand the morphological, shell, lamellae, and molecular structures of pea starch granules treated by ultrasound under different temperatures. Scanning electron microscopy and X-ray diffraction analyses showed that UT (ultrasound treatment) did not change C-type of crystalline, but caused a pitted surface and endowed a looser structure and higher enzyme susceptibility as the temperature increased above 35 °C for pea starch granules. Fourier transform infrared spectroscopy and small-angle X-ray scattering analyses revealed that UT reduced the short-range ordering and increased the thickness of semi-crystalline and amorphous lamellae by inducing starch chain depolymerization, which was manifested by molecule weight and chain length distribution analysis. The sample ultrasound-treated at 45 °C had the higher proportion of B2 chains compared with the other ultrasound-treated samples because the higher ultrasonic temperature altered the disruption sites of starch chains.

A Deep Neural Network-Based Co-Coding Method to Predict Drug-Protein Interactions by Analyzing the Feature Consistency Between Drugs and Proteins.

Exploring drug-protein interactions (DPIs) through computational methods can effectively reduce the workload and the cost of DPI identification. Previous works try to predict DPIs by integrating and analyzing the unique features of drugs and proteins. They cannot adequately analyze the consistency between the drug features and the protein features due to their different semantics. However, the consistency of their features, such as the correlation originating from their sharing diseases, may reveal some potential DPIs. Here we propose a deep neural network-based co-coding method (DNNCC for short) to predict novel DPIs. DNNCC projects the original features of drugs and proteins to a common embedding space through a co-coding strategy. In this way, the embedding features of drugs and proteins have the same semantics. Therefore, the prediction module can discover the unknown DPIs by exploring the feature consistency between drugs and proteins. The experimental results indicate that the performance of DNNCC is significantly superior to five state-of-the-art DPI prediction methods under several evaluation metrics. The superiority of integrating and analyzing the common features of drugs and proteins is proved by the ablation experiments. The novel DPIs predicted by DNNCC verify that DNNCC is a powerful prior tool that can effectively discover potential DPIs.

CPMCP: a database of Chinese patent medicine and compound prescription.

Although several traditional Chinese medicine (TCM)-related databases have emerged, they focus on researching single medicinal materials, which is far from sufficient for clinical research and application. In comparison, compound prescriptions are more informative and meaningful in TCM, for they embody the information on the compatibility of TCM besides the relatively isolated information about single medicinal materials. The compatibility information is essential in TCM because it conveys not only what components are involved to treat special diseases but also how to combine these single medical materials. We established a database of Chinese patent medicine and compound prescription (CPMCP). It demonstrates the prescription information of Chinese patent medicines (CPMs) and ancient Chinese medicine prescriptions (CMPs). CPMCP reports their comprehensive and standardized information such as the components, indications and contraindications. It is worth mentioning that we organized relevant experts and spent lots of time manually mapping the functions of compound prescriptions in ancient Chinese to the standardized TCM symptom vocabularies, obtaining a total of 71 414 associations between compound prescriptions and TCM symptoms. In this way, CPMCP established the associations between TCM and modern medicine (MM) according to the associations between TCM symptoms and MM symptoms. In addition, to further exhibit the compatibility mechanism of compound prescriptions, CPMCP summarizes a set of common drug combination principles by analyzing the existing prescriptions. We believe that CPMCP can promote the modernization of TCM and make greater contributions to MM. Database URL http://cpmcp.top.

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system.

The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay (S-G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R2, root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.

Effects and Moderators of Computer-Based Training on Children's Executive Functions: A Systematic Review and Meta-Analysis.

Computer-based training has attracted increasing attention from researchers in recent years. Several studies have found that computer-based training resulted in improved executive functions (EFs) in adults. However, it remains controversial whether children can benefit from computer-based training and what moderator could influence the training effects. The focus of the present meta-analysis was to examine the effects of computer-based training on EFs in children: working memory, cognitive flexibility, and inhibitory control. A thorough search of published work yielded a sample of 36 studies with 216 effect sizes. The results indicated that computer-based training showed moderate training effects on improving EFs in children (g = 0.35, k = 36, p < 0.001), while training effects of working memory were significantly higher. Furthermore, we found near-transfer effects were marginally significantly higher than far-transfer effects. The standard training method was significantly more effective than training with game elements. In computer-based training, typically developing children had significantly better training effects than atypically developing children. Some additional factors, such as the number of training sessions and age, also modulated the training effects. In conclusion, the present study investigated the effects and moderators of computer-based training for children's EFs. The results provided evidence that computer-based training (especially standard training) may serve as an efficient way to improve EFs in children (especially typically developing individuals). We also discussed some directions for future computer-based training studies.

User-defined microstructures array fabricated by DMD based multistep lithography with dose modulation.

A flexible and efficient strategy, digital micromirror devices (DMD) based multistep lithography (DMSL), is proposed to fabricate arrays of user-defined microstructures. Through the combination of dose modulation, flexible pattern generation of DMD, and high-resolution step movement of piezoelectrical stage (PZS), this method enables prototyping a board range of 2D lattices with periodic/nonperiodic spatial distribution and arbitrary shapes and the critical feature size is down to 600 nm. We further explore the use of DMSL to fabricate microlens array by combining with the thermal reflowing process. The square shape and hexagonal shape microlens with customized distribution are realized and characterized. The results indicate that the proposed DMSL can be a significant role in the microfabrication techniques for manufacturing functional microstructures array.