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A thiolactone-containing maleimide Tla-Mal is synthesized and used for the copolymerization with N- iso -propylacrylamide (NIPAAm) via RAFT polymerization to prepare thermo-responsive diblock copolymer PNIPAAm-b-P(Tla-Mal). Thiolactone groups on the side chains can be opened by primary amine, releasing thiol groups which can be used for further thiol-Michael addition reaction. Double postmodification of PNIPAAm-b-P(Tla-Mal) is revealed using mPEG 11 -NH 2 as nucleophile for the thiolactone ring-opening reaction and light-responsive o -nitrobenzyl acrylate for the subsequent thiol-Michael addition reaction. Because of the introduction of light-responsive o -nitrobenzyl ester group, the double modified polymer shows light- and thermo-responsiveness. The responsiveness of PNIPAAm-b-P(Tla-Mal) and its double modified polymer PNIPAAm-b-P(Tla-Mal) DM is demonstrated. It provides an easy strategy for the design of tailor-made stimuli-responsive materials using thiolactone-containing maleimide.

Digital polymers with precisely ordered units acting as the coded 0- or 1-bit, are introduced as a promising option for molecular data storage. However, the pursuit of better performance in terms of high storage capacity and useful functions never stops. Herein, we propose a concept of an information-coded 2D digital dendrimer. The divergent growth via thiol-maleimide Michael coupling allows precise arrangements of the 0- and 1-bits in the uniform dendrimers. A protocol for calculating the storage capacity of non-linear binary digital dendrimer is established based on data matrix barcode, generated by the tandem mass spectrometry decoding and encryption. Furthermore, the generated data matrix barcode can be read by a common hand-held device to cater the applications such as item identification, traceability and anticouterfeiting purpose. This work demonstrates the high data storage capacity of a uniform dendrimer and uncovers good opportunities for the digital polymers. Digital polymers with precisely ordered units for next-generation data storage media are continually investigated for higher storage capacity. Here the authors show the synthesis of information-coded 2D digital dendrimers and the generated data matrix barcode can be read by a common hand-held device.

The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods.

Purpose. To review the recent neuroimaging studies on cognitive-behavioral therapy (CBT) for pain management, with the aim of exploring possible mechanisms of CBT. Recent Findings. Current studies can be divided into four categories, mixed pain, fibromyalgia, migraine, and experimental pain, based on the type of disease included, with the same or different changes of brain regions after CBT intervention. According to structural and functional MRI analyses, changes of brain gray matter volume, activation and deactivation of brain regions, and intrinsic connectivity between brain regions were observed after CBT sessions. The brain regions involved mainly included some areas related to cognitive and emotional regulation. After comparison, the DLPFC, OFC, VLPFC, PCC and amygdala were found to be recurrent in multiple studies and may be key regions for CBT intervention in pain management. In the treatment of mixed chronic pain, CBT may decrease the gray matter volume of DLPFC, reduce ICN connection of OFC within the DAN network, and increase fALFF of the PCC. For FM intervention, CBT may activate the bilateral OFC and VLPFC, while in migraine, only the right OFC, VLPFC, and DLPFC were found to be more activated after CBT. In addition, the differential action of the left and right amygdala has also been shown in the latest study of migraine. In heat-evoked pain, CBT may increase the deactivation of the PCC, the connectivity between the DMN and right VLPFC, while diminishing the deactivation of VLPFC. Summary. After CBT, the brain showed stronger top-down pain control, cognitive reassessment, and altered perception of stimulus signals (chronic pain and repeated acute pain). The DLPFC, OFC, VLPFC, PCC, and amygdala may be the key brain regions in CBT intervention of pain.

Objectives: In this study, we examine the allocation of oral cholera vaccines (OCVs) across 25 African countries between 2013 and 2019. Methods: We constructed a dataset combining cholera outbreaks and requests, decisions, and deliveries of OCVs from the Global Task Force on Cholera Control, alongside additional covariates. Using machine learning algorithms, we assess the relative importance of socio-demographic, governance, and weather variables in predicting cholera outbreaks. We constructed and used an “index of cholera risk” as an instrumental variable to predict the likelihood of suspected cases and estimate the impact of cholera outbreaks on OCV allocation. Results: The majority of OCVs (77.4%) were allocated reactively. Governments took an average of 299.6 days to request doses, international agencies took 10.4 days to decide, and it took 84 days for vaccines to be delivered. Countries experiencing a cholera outbreak were 31.7 and 36.5 percentage points more likely to request and receive a vaccine delivery in the same month as the outbreak, respectively. We confirmed that the probability of obtaining vaccines through a reactive mechanism was 48.4 percentage points higher compared to preventive allocation. When exploring the heterogeneity of impacts, OCVs were more likely to be requested, allocated, and delivered in countries with strong institutions and those not facing crisis situations. OCVs were also more likely to be allocated in the central parts of the continent. Conclusions: While OCV allocation is responsive to cholera outbreaks, addressing delays, particularly in high-risk countries, could improve their distribution and mitigate the impact of cholera outbreaks. This study highlights the need for targeted strategies to ensure vaccine access in fragile and conflict-affected settings, where institutional capacity is weaker.

We performed a retrospective study describing the characteristics of myeloid sarcoma (MS) and evaluated the outcome of hematopoietic stem cell transplantation (HSCT) in patients with MS. There were 27 patients with de novo isolated MS, 34 with de novo leukemic MS and 13 with secondary leukemic MS in our study. Sixty-three patients received induction chemotherapy. Following induction therapy, 35 patients underwent HSCT, including 10 autogenous HSCT (auto-HSCT) and 25 allogeneic HSCT (allo-HSCT) cases. Compared with intensive chemotherapy only as consolidation treatment, HSCT (auto-/allo-HSCT) significantly improved the overall survival (OS) of MS patients (p < 0.05), while allo-HSCT also improved progression-free survival (PFS, p = 0.032). According to multivariate analysis, poorer prognosis in terms of OS was observed in older patients (p = 0.024, HR = 1.030, 95% CI 1.004–1.057), while HSCT (auto/allo-HSCT) had a favorable impact on OS for patients with MS (auto-HSCT, p = 0.044, HR = 0.201, 95% CI 0.042–0.959; allo-HSCT, p = 0.038, HR = 0.341, 95% CI 0.124–0.943). Extramedullary disease without complete remission (CR) after induction therapy was the sole variable independent of high OS and PFS (p = 0.049, HR = 2.243, 95% CI: 1.005–5.005; p = 0.017, HR = 2.535, 95% CI 1.180–5.448, respectively). The data indicate that HSCT is an effective treatment for patients with MS who have achieved CR of extramedullary disease after induction therapy.

Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery. Multi-scale learning still struggles with imbalanced information and greedy characteristics. Here the authors present MUSE, an Expectation-Maximization-based multi-scale framework, improving predictions across molecular interactions and atomic interfaces.

Single-cell sequencing provides transcriptomic profiling at single-cell resolution, uncovering cellular heterogeneity with unprecedented precision. Yet, current single cell data analysis suffers from the inherent data noises, batch effects, and sparsity, highlighting the requirement of a unified model to represent cellular states. To circumvent this problem, many recent efforts focus on training single-cell foundation models based on large datasets. However, current human foundation models are still limited by the sizes of training data and model parameters. Here, we have collected a diverse dataset of 100 million human cells, on which we train a single-cell foundation model (CellFM) containing 800 million parameters. To balance efficiency and performance, the model is trained through a modified RetNet framework on the MindSpore. Extensive experiments have shown that CellFM outperforms existing models in cell annotation, perturbation prediction, gene function prediction, and gene-gene relationship capturing. Single-cell sequencing reveals cellular heterogeneity but is challenged by technical noise and batch effects. Here, authors present CellFM, an 800-million-parameter foundation model trained on 100 million human cells through the MindSpore framework, which outperforms existing models in downstream tasks.

Although HIV testing is helpful for early detection and treatment of HIV, its utilization rate is low among college students in China. Understanding the acceptance and associated factors of HIV testing is the key to improve the detection rate. The purpose of the systematic review was to examine the acceptance and associated factors of HIV testing (including HIV self-testing and HIV counseling and testing services) among college students in China. This systematic review was reported following PRISMA guidelines 2020. Electronic sources such as PubMed, Embase, Web of Science, CNKI, CBM, Wanfang Database and VIP Database were searched for relevant studies published before September 2022. The tool by Agency for Healthcare Research and Quality (AHRQ) was used to assess quality for cross-sectional studies. The random-effects and fixed-effect model were employed to estimate the pooled proportions and associated factor of HIV testing acceptance. The Cochrane's Q statistic and I2 test were used to examine heterogeneity. All the quantitative meta analyses were conducted using STATA version 12 software. A total of 21 eligible studies with 100, 821 participants were included in the systematic review. The pooled acceptance rate of HIV testing was 68% (95% CI = 60, 76), and varies between regions in China. Male, heterosexual and urban college students had higher HIV testing acceptance. Gender, medical specialty, sexual education, sexual behavior, HIV/AIDS knowledge, perception HIV risk, and previous HIV testing were the factors associated with HIV testing acceptance. The review revealed that most of the college students intend to accept HIV detection, and the proportion of acceptance influenced by different factors. Therefore, the government and universities should implement targeted measures, improve HIV testing services, and promote HIV testing behavior. PROSPERO CRD42022367976.

Biogenic compounds are important materials for drug discovery and chemical biology. In this work, we report a quasi-biogenic molecule generator (QBMG) to compose virtual quasi-biogenic compound libraries by means of gated recurrent unit recurrent neural networks. The library includes stereo-chemical properties, which are crucial features of natural products. QMBG can reproduce the property distribution of the underlying training set, while being able to generate realistic, novel molecules outside of the training set. Furthermore, these compounds are associated with known bioactivities. A focused compound library based on a given chemotype/scaffold can also be generated by this approach combining transfer learning technology. This approach can be used to generate virtual compound libraries for pharmaceutical lead identification and optimization.