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Rationale: Thyroid-like low-grade nasopharyngeal papillary adenocarcinoma (TL-LGNPPA) is a rare neoplasm characterized by papillary epithelial proliferation and abnormal expression of thyroid transcription factor-1. To our knowledge, squamous differentiation in TL-LGNPPA is an unusual phenomenon, and only 1 case has been reported to date. The tumor occurs mainly on the roof of the nasopharynx. Herein, we report a case of TL-LGNPPA with squamous differentiation located on the posterior nasal septum. Patient concerns: A 45-year-old Chinese women presented to our hospital with a complaint of nasal obstruction for 10 years and the symptom has been getting worse for recent 3 years. Diagnoses: Microscopic examination of the tumor tissue revealed findings typical of TL-LGNPPA, and in addition to the typical components, squamous differentiation could be found in some areas within the fibrovascular cores of the papillary components. Immunohistochemically, the squamous cells were positive for P40 and P63 but negative for thyroid transcription factor-1. Interventions: The patient underwent complete resection of the tumor. Outcomes: Follow up results showed that the patient had no recurrence 41 months after removal of the tumor. Lessons: Our report anticipates that, although TL-LGNPPA with squamous differentiation in the posterior nasal septum is rare, this report will expand the existing knowledge associated with TL-LGNPPA.

Additional file 1. Details of the semi-supervised learning method and examples of the labeled samples.

Existing conversational systems are mostly agent-centric, which assumes the user utterances would closely follow the system ontology (for NLU or dialogue state tracking). However, in real-world scenarios, it is highly desirable that the users can speak freely in their own way. It is extremely hard, if not impossible, for the users to adapt to the unknown system ontology. In this work, we attempt to build a user-centric dialogue system. As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions. Learning such a mapping poses new challenges on reasoning over existing knowledge, ranging from factoid knowledge, commonsense knowledge to the users' own situations. To this end, we build a new dataset named NUANCED that focuses on such realistic settings for conversational recommendation. Collected via dialogue simulation and paraphrasing, NUANCED contains 5.1k dialogues, 26k turns of high-quality user responses. We conduct experiments, showing both the usefulness and challenges of our problem setting. We believe NUANCED can serve as a valuable resource to push existing research from the agent-centric system to the user-centric system. The code and data is publicly available at \url{https://github.com/facebookresearch/nuanced}., Comment: Findings of EMNLP 2021

The first phosphine-catalyzed [5+1] annulation of enones with N-sulfonylimines works efficiently to give tetrahydropyridines., Phosphine-catalyzed [5+1] annulation of δ-sulfonamido-substituted enones with N-sulfonylimines for the synthesis of 1,2,3,6-tetrahydropyridines is developed. The reaction proceeds smoothly under mild reaction conditions to give the annulation products in moderate to excellent yields. Mechanistic exploration of this new annulation shows that the δ-sulfonamido-substituted enone and the N-sulfonylimine serve as C5 and C1 synthons to furnish the annulation, respectively. Using chiral phosphine as the catalyst, an asymmetric variant of the model reaction gave the chiral product in up to 73% ee.

Deep shale gas reservoirs buried underground with depth being more than 3500 m are characterized by high in-situ stress, large horizontal stress difference, complex distribution of bedding and natural cracks, and strong rock plasticity. Thus, during hydraulic fracturing, these reservoirs often reveal difficult fracture extension, low fracture complexity, low stimulated reservoir volume (SRV), low conductivity and fast decline, which hinder greatly the economic and effective development of deep shale gas. In this paper, a specific and feasible technique of volume fracturing of deep shale gas horizontal wells is presented. In addition to planar perforation, multi-scale fracturing, full-scale fracture filling, and control over extension of high-angle natural fractures, some supporting techniques are proposed, including multi-stage alternate injection (of acid fluid, slick water and gel) and the mixed- and small-grained proppant to be injected with variable viscosity and displacement. These techniques help to increase the effective stimulated reservoir volume (ESRV) for deep gas production. Some of the techniques have been successfully used in the fracturing of deep shale gas horizontal wells in Yongchuan, Weiyuan and southern Jiaoshiba blocks in the Sichuan Basin. As a result, Wells YY1HF and WY1HF yielded initially 14.1 × 10 4 m 3 /d and 17.5 × 10 4 m 3 /d after fracturing. The volume fracturing of deep shale gas horizontal well is meaningful in achieving the productivity of 50 × 10 8 m 3 gas from the interval of 3500–4000 m in Phase II development of Fuling and also in commercial production of huge shale gas resources at a vertical depth of less than 6000 m.

Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and resource-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting. FURL divides model parameters into federated and private parameters. Private parameters, such as private user embeddings, are trained locally, but unlike federated parameters, they are not transferred to or averaged on the server. We show theoretically that this parameter split does not affect training for most model personalization approaches. Storing user embeddings locally not only preserves user privacy, but also improves memory locality of personalization compared to on-server training. We evaluate FURL on two datasets, demonstrating a significant improvement in model quality with 8% and 51% performance increases, and approximately the same level of performance as centralized training with only 0% and 4% reductions. Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.

Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is important. The data on each client is highly variable, so the benefit of training on different clients may differ dramatically. To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency. We propose a cheap, simple and intuitive sampling scheme which reduces the number of required training iterations by 20-70% while maintaining the same model accuracy, and which mimics well known resampling techniques under certain conditions.

Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especially when the set of available options itself can change frequently, can lead to sub-optimal user experiences. We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning. We demonstrate how to integrate this framework with run-time services to leverage online and interactive machine learning out-of-the-box. We also present results demonstrating the efficiencies that can be achieved using the Explore-Exploit framework.

Coal resources and water resources play an essential and strategic role in the development of China's social and economic development, being the priority for China's medium and long technological development. As the mining of the coal extraction is increasingly deep, the mine water inrush of high-pressure confined karst water becomes much more a problem. This paper carried out research on the hundred-year old Kailuan coal mine's karst groundwater system. With the help of advanced Visual Modflow software and numerical simulation method, the paper assessed the flow field of karst water area under large-scale exploitation. It also predicted the evolution ofgroundwaterflow field under different mining schemes of Kailuan Corp. The result shows that two cones of depression are formed in the karst flow field of Zhaogezhuang mining area and Tangshan mining area, and the water levels in two cone centers are −270 m and −31 m respectively, and the groundwater generally flows from the northeast to the southwest. Given some potential closed mines in the future, the mine discharge will decrease and the water level of Ordovician limestone will increase slightly. Conversely, given increase of coal yield, the mine drainage will increase, falling depression cone of Ordovician limestone flow field will enlarge. And in Tangshan's urban district, central water level of the depression cone will move slightly towards north due to pumping of a few mines in the north.

Sequence data are ubiquitous in diverse domains such as bioinformatics, computational neuroscience, and user behavior analysis. As a result, many critical applications require extracting knowledge from sequences in multi-level. For example, mining frequent patterns is the central goal of motif discovery in biological sequences, while in computational neuronal science, one essential task is to infer causal networks from neural event sequences (spike trains). Given the wide application of pattern and network mining tools for sequence data, they are facing new challenges posted by modern instruments. That is, as large scale and high resolution sequence data become available, we need new methods with better efficiency and higher accuracy.In this dissertation, we propose several approaches to improve existing pattern and network mining tools to meet new challenges in terms of efficiency and accuracy. The first problem is how to scale existing motif discovery algorithms. Our work on motif discovery focuses on the challenge of discovering motifs from a large scale of short sequences that none of existing motif finding algorithms can handle. We propose an anchor based clustering algorithm that could significantly improve the scalability of all the existing motif finding algorithms without losing accuracy at all. In particular, our algorithm could reduce the running time of a very popular motif finding algorithm, MEME, from weeks to a few minutes with even better accuracy.In another work, we study the problem of how to accurately infer a functional network from neural recordings (spike trains), which is an essential task in many real world applications such as diagnosing neurodegenerative diseases. We introduce a statistical tool that could be used to accurately identify inhibitory causal relations from spike trains. While most of existing works devote their efforts on characterizing the statistics of neural spike trains, we show that it is crucial to make predictions about the response of neurons to changes. More importantly, our results are validated by real biological experiments with a novel instrument, which makes this work the first of its kind. Furthermore, while most existing methods focus on learning functional networks from purely observational data, we propose an active learning framework that could intelligently generate and utilize interventional data. We demonstrate that by intelligently adopting interventional data using the active learning models we propose, the accuracy of the inferred functional network could be substantially improved with the same amount of training data.

Summary: The CRISPR/Cas9 system was recently developed as a powerful and flexible technology for targeted genome engineering, including genome editing (altering the genetic sequence) and gene regulation (without altering the genetic sequence). These applications require the design of single guide RNAs (sgRNAs) that are efficient and specific. However, this remains challenging, as it requires the consideration of many criteria. Several sgRNA design tools have been developed for gene editing, but currently there is no tool for the design of sgRNAs for gene regulation. With accumulating experimental data on the use of CRISPR/Cas9 for gene editing and regulation, we implement a comprehensive computational tool based on a set of sgRNA design rules summarized from these published reports. We report a genome-wide sgRNA design tool and provide an online website for predicting sgRNAs that are efficient and specific. We name the tool CRISPR-ERA, for clustered regularly interspaced short palindromic repeat-mediated editing, repression, and activation (ERA). Availability and implementation: http://CRISPR-ERA.stanford.edu. Contact: stanley.qi@stanford.edu or xwwang@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

As the most abundant, sustainable, and green energy source on the earth, solar energy has the potential to resolve environmental problems such as climate change and air pollution caused by fossil energy. Real-time solar irradiance mapping, which gives the real-time data on local solar energy distribution, would provide valuable information and lead to more efficient use of solar energy. State of California (CA) is abundant in solar energy. However, the data of real-time direct normal irradiance (DNI) in CA is still currently not available. This thesis focuses on developing-models that would use satellite data to map near real-time solar irradiance in CA and analyzing the potential economic and emission benefits of solar installation based on the modeled DNI data. The transmittance data was derived from GOES-WEST for the development of ON I model. The study showed that the major atmospheric factors influencing the solar irradiance are cloud coverage and aerosol concentration in non-clear and clear sky condition, respectively. This suggests that the transmittances of cloud and aerosol should be as accurate as possible when calculating DNI using satellite data. Next, the modeled DNI was compared with actual DNI data, and the results indicated a good agreement between them, implying that the modeled DNI data is a good estimate of the actual solar irradiance. Finally, the economic and emission benefits of solar use were analyzed based upon process-modeling and empirical regression approach. The results indicated that 0.2% coverage of solar PV in CA could reduce the C02, NOx and S02 emissions on average by 9.08%, 4.51 % and 2.73% during 2007 summertime, respectively. The cost savings are amount to 48.7%, 32.0% and 16.9% in summer under three levels of PV installation rate. In summary, we developed a model to map near real-time DNI spatial and temporal distribution based on satellite data. The outcomes from solar mapping are used to predict potential economic and emission benefits using empirical regression and process-based approach.