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Quantitative assessment of the brain’s structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the structural connectome from diffusion MRI data involves a series of complex and ill-posed computations. For the perinatal period, this analysis is further challenged by the rapid brain development and difficulties of imaging subjects at this stage. These factors, along with high inter-subject variability, have made it difficult to chart the normative development of the structural connectome. Hence, there is a lack of baseline trends in connectivity metrics that can be used as reliable references for assessing normal and abnormal brain development at this critical stage. In this paper we propose a computational framework, based on spatio-temporal atlases, for determining such baselines. We apply the framework on data from 169 subjects between 33 and 45 postmenstrual weeks. We show that this framework can unveil clear and strong trends in the development of structural connectivity in the perinatal stage. Some of our interesting findings include that connection weighting based on neurite density produces more consistent trends and that the trends in global efficiency, local efficiency, and characteristic path length are more consistent than in other metrics.

Stroboscopic visual training has carried out a series of tests in many aspects, such as attention, visual sensitivity, predictive time ability, visual motor response speed, visual memory and so on. However, the improvement boundary of stroboscopic vision training is not clear yet.Therefore, this paper provides a systematic review of the relevant literature on the impact of visual cognitive function after stroboscopic visual training. The purpose of this paper is to further clarify the mechanism of stroboscopic visual training, and to provide a theoretical basis for its application in sports training.

Additional file 1: Table S1. Network topological analysis of 34 core therapeutic targets in PPI network. Table S2. TSMT improves TOP 10 GO categories of core targets of atherosclerosis. Table S3. TSMT alleviates TOP 20 KEGG pathway of core targets of atherosclerosis. Table S4. Crucial anti-AS ingredients were selected as candidate active compounds in TSMT. Table S5. Methodological investigation results of the content determination method of TSMT. Table S6. Similarity evaluation results of HPLC fingerprint of nine batches of S1-S9 in TSMT.

Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adversarial samples, adversarial training methods are often slow. In this paper, we study the problem of learning a robust dataset such that any classifier naturally trained on the dataset is adversarially robust. Such a dataset benefits the downstream tasks as natural training is much faster than adversarial training, and demonstrates that the desired property of robustness is transferable between models and data. In this work, we propose a principled, tri-level optimization to formulate the robust dataset learning problem. We show that, under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset. Extensive experiments on MNIST, CIFAR10, and TinyImageNet demostrate the effectiveness of our algorithm with different network initializations and architectures.

Additional file 2: Figure S1. a protein sequence alignment of TgANT using MEGA7 from Toxoplasma gondii, Saccharomyces cerevisiae, Plasmodium falciparum, Cyclospora cayetanensis, Arabidopsis thaliana, Cardiosporidium cionae, Homo sapiens and other species. b Prediction results of TMHMM Server v.2.0 showed that TgANT has four transmembrane domains.

Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner. It leverages an emotion lexicon and uses contrastive learning and deep clustering. We further integrate the style representation as a conditioned embedding in a multi-style Transformer TTS. Comparing with multi-style TTS by predicting style tags trained on the same dataset but with human annotations, our method achieves improved results according to subjective evaluations on both in-domain and out-of-domain test sets in audiobook speech. Moreover, with implicit context-aware style representation, the emotion transition of synthesized audio in a long paragraph appears more natural. The audio samples are available on the demo web., Comment: Accepted by Interspeech 2022

Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of speech recognition, machine translation and speech synthesis. To ensure the translated speech to be well aligned with the corresponding video, the length/duration of the translated speech should be as close as possible to that of the original speech, which requires strict length control. Previous works usually control the number of words or characters generated by the machine translation model to be similar to the source sentence, without considering the isochronicity of speech as the speech duration of words/characters in different languages varies. In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech. Specifically, we control the speech length of generated sentence by guiding the prediction of each word with the duration information, including the speech duration of itself as well as how much duration is left for the remaining words. We design experiments on four language directions (German -> English, Spanish -> English, Chinese English), and the results show that the proposed method achieves better length control ability on the generated speech than baseline methods. To make up the lack of real-world datasets, we also construct a real-world test set collected from films to provide comprehensive evaluations on the video dubbing task., AAAI 2023 camera version

Using a text description as prompt to guide the generation of text or images (e.g., GPT-3 or DALLE-2) has drawn wide attention recently. Beyond text and image generation, in this work, we explore the possibility of utilizing text descriptions to guide speech synthesis. Thus, we develop a text-to-speech (TTS) system (dubbed as PromptTTS) that takes a prompt with both style and content descriptions as input to synthesize the corresponding speech. Specifically, PromptTTS consists of a style encoder and a content encoder to extract the corresponding representations from the prompt, and a speech decoder to synthesize speech according to the extracted style and content representations. Compared with previous works in controllable TTS that require users to have acoustic knowledge to understand style factors such as prosody and pitch, PromptTTS is more user-friendly since text descriptions are a more natural way to express speech style (e.g., ''A lady whispers to her friend slowly''). Given that there is no TTS dataset with prompts, to benchmark the task of PromptTTS, we construct and release a dataset containing prompts with style and content information and the corresponding speech. Experiments show that PromptTTS can generate speech with precise style control and high speech quality. Audio samples and our dataset are publicly available., Comment: Submitted to ICASSP 2023

Figure S1. Nitrogen absorption isotherms of GNR@mSiO2. Fig S2 (a) UV-Vis curves of DOX at different concentrations. (b) Calibration curve used for estimation of DOX. Figure S3. FTIR spectra of GNR@mSiO2 and GNR@mSiO2-DOX/PFP@PDA. Figure S4. Microscopic images of microbubbles at different concentrations. Figure S5. Release profiles from GNR@mSiO2-DOX/PFP at different pH values with or without NIR. Figure S6. Cell cytotoxicity of normal cells (BALB/3T3) without NIR or with NIR (power density of 2 W/cm2) for 5 min. Figure S7. (a) H&E staining images of tumor sections after different treatments. (b) H&E-stained major organ tissue slices after different treatments. (Scale bar = 150 μm)

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/., Comment: 13 pages, 5 figures

Additional file 1. Additional figures and tables.

Background Different morphological structures of hairs having properties like defense and camouflage help animals survive in the wild environment. Horse is one of the rare kinds of animals with complex hair phenotypes in one individual; however, knowledge of horse hair follicle is limited in literature and their molecular basis remains unclear. Therefore, the investigation of horse hair follicle morphogenesis and pigmentogenesis attracts considerable interest. Result Histological studies revealed the morphology and pigment synthesis of hair follicles are different in between four different parts (mane, dorsal part, tail, and fetlock) of the bay Mongolian horse. Hair follicle size, density, and cycle are strongly associated with the activity of alkaline phosphatase (ALP). We observed a great difference in gene expression between the mane, tail, and fetlock, which had a greater different gene expression pattern compared with the dorsal part through transcriptomics. The development of the hair follicle in all four parts was related to angiogenesis, stem cells, Wnt, and IGF signaling pathways. Pigmentogenesis-related pathways were involved in their hair follicle pigment synthesis. Conclusions Hair follicle morphology and the activity of ALP differ among four body parts in bay Mongolian horse. Hair follicles of the different body parts of the are not synchronized in their cycle stages. GO terms show a regional specificity pattern between different skin parts of the bay Mongolian horse. These results provide an insight into the understanding of the biological mechanism of the hair follicle in other mammals.