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The Fengcheng Formation of the Mahu Sag is an unconventional reservoir that is of paramount importance for exploration and development of hydrocarbon resource. However, current research on natural fractures in the Fengcheng Formation remains limited, posing challenges for exploration of hydrocarbon resource in the region. This study is based on core observations, thin section identification, geochemical testing and the evolution of regional tectonic movements to investigate the characteristics and periods of formation of natural fractures to address this gap. According to the characteristics of natural fractures in the drilling core samples and microsections, the natural fractures in the Fengcheng Formation can be grouped into structural fractures and atectonic fractures. Structural fractures can be further divided into three subtypes: high-angle interlayer shear fractures, along-layer shear fractures, and tensile fractures. Additionally, non-tectonic fractures in this studied area are primarily bedding fractures, hydraulic fractures, and hydrocarbon-generating overpressure fractures. Vertically, fracture development is more prominent at the bottom of Feng #2 Formation and at the top of Feng #3 Formation. Results also indicate that natural fractures primarily formed during three distinct tectonic movement periods. The initial stage of fracture evolution pertains to the Late Permian period (243–266 Ma), filled with fibrous calcite, and exhibiting a uniform temperature of 70–100 °C. The second stage of fracture evolution occurred during the Late Indosinian to Early Yanshanian period (181–208 Ma), mostly filled or semi-filled with calcite, with a uniform temperature of 110–130 °C. The third stage of fracture development happened during the late Yanshanian to early Himalaya period (50–87 Ma), predominantly filled with calcite, and presenting a uniform temperature of 130–150 °C. Among the various types of structural fractures, the density of high-angle interlayer shear fractures demonstrates a positive correlation with daily gas production, indicating their vital role in promoting hydrocarbon resource production and transportation. Furthermore, microfractures generated by hydrocarbon-generating overpressure fractures exhibit small pore sizes and strong connectivity. These microfractures can create an effective permeability system by connecting previously isolated micropores in shale reservoirs, thus establishing interconnected pore spaces in the shale formation.

To realize the digital and intelligent management of the agricultural production process, reduce farmland pollution and alleviate the contradiction between supply and demand of agricultural water, this paper studies and designs a farmland environment collection and remote environmental monitoring system based on the network system. The system uses sensors to collect ecological information related to crop growth, such as high temperature, humidity, solar radiation, rainfall, soil temperature, and moisture. It then transmits the data to the data processing module through wireless transmission. At last, the STM 32 F 103 RCT 6 chips are used to process and analyze the collected data and visualize the collected real-time data and analysis results. As a result, according to the demand for water and fertilizer for crop growth, agricultural irrigation can be guided to effectively improve the efficiency of farmland management, which is of great significance for promoting social and economic development.

Background To explore the technique and the clinical results of total hip arthroplasty ( THA ) with transverse subtrochanteric osteotomy and monocortical locking plate for Crowe type Ⅳ developmental dysplasia of the hip ( DDH ). Methods From April 2015 to April 2019, 21 patients ( 23 hips ) with Crowe Ⅳ DDH were treated by the technique of THA with transverse subtrochanteric osteotomy and monocortical locking plate. Postoperative clinical and radiological results were evaluated. Results All of the patients were followed up for the average of 33 months. All patients had no osteotomy nonunion, infection, prosthesis loosening and vascular nerve injury. 1 patient experienced dislocation one week after the operation because of bad posture. Harris score changed from 35.6 points ( 25–51 ) before surgery to 88.1 points ( 76–96 ) after surgery. Conclusions THA with transverse subtrochanteric osteotomy and monocortical locking plate internal fixation for the patients with Crowe type Ⅳ DDH is an effective way.

In view of the fact that the current peanut test was still artificial, it was difficult to meet the actual demand of peanut test, this paper established a peanut high-throughput automatic test syst...

Learning to generate text with a given label is a challenging task because natural language sentences are highly variable and ambiguous. It renders difficulties in trade-off between sentence quality and label fidelity. In this paper, we present CARA to alleviate the issue, where two auxiliary classifiers work simultaneously to ensure that (1) the encoder learns disentangled features and (2) the generator produces label-related sentences. Two practical techniques are further proposed to improve the performance, including annealing the learning signal from the auxiliary classifier, and enhancing the encoder with pre-trained language models. To establish a comprehensive benchmark fostering future research, we consider a suite of four datasets, and systematically reproduce three representative methods. CARA shows consistent improvement over the previous methods on the task of label-conditional text generation, and achieves state-of-the-art on the task of attribute transfer.

Objective: The aim of this retrospective study was to investigate clinical effects of the treatment of intra-articular distal humeral fractures via the combined posterior olecranon osteotomy and anterior approach and the combined lateral Kaplan approach associated with the medial approach. Methods: Between July 2010 and September 2019, 28 cases of intercondylar fractures of the distal humerus (AO/OTA type C) were treated by open reduction and internal fixation (ORIF) via the posterior and anterior approach and combined lateral-medial approach, which were divided into the P-A (n = 13) and L-M (n = 15) groups, respectively. The outcomes in terms of function were assessed using the range of motion (ROM) of the elbow and forearm, humerotrochlear (HT) angle, Visual Analogue Scale (VAS) scores, Mayo Elbow Performance Score (MEPS), and efficacy grade evaluated using the scoring system of Orthopaedic Trauma Association (OTA) at the one-year follow-up. Results: The flexion-extension of elbows and pronation-supination of forearms were respectively 114.1° ± 12.4° and 157.3° ± 9.6° in the P-A group and 117.1° ± 14.5° and 161.3° ± 10.7° in the L-M group. No significant difference was observed between the two groups for the elbow and forearm ROM (p > 0.05). Furthermore, surgery duration, blood loss volume, HT angle, MEPS, VAS scores, OTA rating, and the morbidity of postoperative complications during the one-year follow-up did not differ between groups. Conclusion: The posterior olecranon osteotomy and anterior approach can be used efficiently for exposure of complex distal humeral injuries with similar clinical efficacy and complications in the treatment of intra-articular distal humeral fractures as compared to the lateral-medial approach.

The only food and drug administration (FDA)-approved drug currently available for the treatment of acute ischemic stroke is tissue plasminogen activator (tPA), yet the therapeutic benefits of this drug are partially outweighed by the increased risk of hemorrhagic transformation (HT). Analysis of the NIH trial has shown that cigarette smoking protected tPA-treated patients from HT, however, the underlying mechanism is not clear. Nicotinic acetylcholine receptors (nAChR) has shown anti-inflammatory effect and modulation nAChR could be a strategy to reduce ischemia/reperfusion-induced blood brain barrier (BBB) damage. Since melatonin could regulate the expression of α7nAchR and melatonin’s neuroprotective effect against ischemic injury is mediated via α7nAChR modulation, here, we aim to test the hypothesis that melatonin reduces ischemia and reperfusion (I/R)-induced BBB damage through modulation of α7nACh receptor (α7nAChR). Mice were subjected to 1.5 h ischemia and 24 h reperfusion and at the onset of reperfusion, mice received intraperitoneal administration (i.p.) of either drug or saline. Mice were randomly assigned into five groups: Saline; α7nAChR agonist PNU282987; Melatonin; Melatonin + Methyllycaconitine (MLA, α7nAChR antagonist) and MLA group. BBB permeability was assessed by detecting the extravasation of Evan’s blue and IgG. Our results showed that I/R significantly increased BBB permeability accompanied by occludin degradation, microglia activation, and high mobility group box 1 (HMGB1) release from the neuron. In addition, I/R significantly induced neuronal loss accompanied by the decrease of CREB regulated transcriptional coactivator 1 (CRTC1) and p-CREB expression. Melatonin treatment significantly inhibited the above changes through modulating α7nAChR. Taken together, these results demonstrate that melatonin provides a protective effect on ischemia/reperfusion-induced BBB damage, at least in part, depending on modulation of α7nAChR.

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines., Accepted to NAACL 2021 (Long Paper)

Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformation and fake news which cause confusion and chaos if not detected in a timely manner. Given the rapidly evolving nature of news events and the limited amount of annotated data, state-of-the-art systems on fake news detection face challenges for early detection. In this work, we exploit multiple weak signals from different sources from user engagements with contents (referred to as weak social supervision), and their complementary utilities to detect fake news. We jointly leverage limited amount of clean data along with weak signals from social engagements to train a fake news detector in a meta-learning framework which estimates the quality of different weak instances. Experiments on real-world datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.

Additional file 1: Figure S1. Age-related trabecular bone loss in C57BL/6 mice. Figure S2. Secretion of cytokines in peripheral serum of 6- and 18-month-old Balb/c mice. Figure S3. Age-related decline in Gal-1 levels in peripheral blood serum and bone marrow microenvironment in 18-month-old C57BL/6 mice and the correlation of trabecular bone volume fraction with Gal-1 levels in C57BL/6 mice. Figure S4. Comparison of Gal-1 protein expression of BMSC, HSC and MP in bone marrow between 6- and 18-month-old C57BL/6 mice. Figure S5. Uncropped blots of Fig. 4. Red lines indicate where they were cropped. Figure S6. Uncropped blots of Figure S4. Red lines indicate where they were cropped

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://github.com/zhangmozhi/mrs., ACL 2021

The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an under-studied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL., 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

Background Senile osteoporosis with age-related bone loss is diagnosed depending on radiographic changes of bone and bone mineral density (BMD) measurement. However, radiographic alterations are usually signs of medium-late stage osteoporosis. Therefore, biomarkers have been proposed as indicators of bone loss. In the current study, Galectin-1 (Gal-1) showed age-related decline in mice serum. The role of Gal-1 in osteoporosis has not been investigated so far. Hence, the current study illustrated the relationship of serum Gal-1 level with bone loss. Methods We employed 6- and 18-month-old mice to establish an animal model of age-related trabecular bone loss, whose bone density and microstructure were investigated by micro-CT. ELISA was used to measure the levels of Gal-1 in serum. The correlation analysis was performed to illustrate the relationship between serum Gal-1 levels and trabecular bone loss. In addition, immunohistochemistry was used to investigate the abundance of Gal-1 in bone marrow of mice. ELISA and western blot were performed to measure the secretion ability and protein expression of Gal-1 in bone marrow stromal cells (BMSC), hematopoietic stem cells (HSC) and myeloid progenitor (MP) respectively. Flow cytometry was used to measure BMSC number in bone marrow. Finally, male volunteers with age-related BMD decrease were recruited and the relationship between serum Gal-1 and BMD was analyzed. Results Gal-1 showed age-related decline in mice serum. Serum Gal-1 was positively associated with BV/TV of femur, tibia and L1 vertebrae in mice. BMSC secreted more Gal-1 compared with HSC and MP. BMSC number in bone marrow was significantly lower in aged mice compared with young mice. Significant attenuation of Gal-1 protein expression was observed in BMSC and HSC from aged mice compared with young mice. Further, we found a decline in serum Gal-1 levels in men with age-related BMD decrease. There was positive correlation between BMD and serum Gal-1 levels in these men. Conclusions Age-related trabecular bone loss is associated with a decline in serum Gal-1 level in mice and men. Our study suggested Gal-1 had great potential to be a biomarker for discovering BMSC senescence, diagnosing early osteoporosis and monitoring trabecular bone loss.

Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend appropriate actions. The problem has been mostly posed as a supervised learning problem where models of different complexities were proposed to classify an email message into a predefined taxonomy of intents or classes. The need for labeled data has always been one of the largest bottlenecks in training supervised models. This is especially the case for many real-world tasks, such as email intent classification, where large scale annotated examples are either hard to acquire or unavailable due to privacy or data access constraints. Email users often take actions in response to intents expressed in an email (e.g., setting up a meeting in response to an email with a scheduling request). Such actions can be inferred from user interaction logs. In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification that leverages both clean annotated data and noisy weak supervision along with a self-paced learning mechanism. Extensive experiments on three different intent detection tasks show that our approach can effectively leverage the weakly supervised data to improve intent detection in emails., 10 pages, 3 figures

The complex agricultural supply chains make it difficult to validate whether agricultural products meet the quality standards. In the supply chain of agricultural products, traceability system is an effective means to ensure the quality and safety of agricultural products. In this study, an easy-to-use simplified traceability system of agricultural products (STSAP) for small and medium-sized enterprises was constructed based on Android. The proposed STSAP provided a high level of traceability and had the characteristics of friendly interfaces, simple operation, and automatic generation of traceability files. STSAP consisted of six functional modules: account management, site management, planting materials management, agricultural activity management, processing management, and traceability files management. These modules recorded soil fertility, crop growth stages, fertilization, irrigation, and pesticide application. Thus, this study helps farmers improve agricultural production and helps consumers trace production processes and transaction records.

‘Phoenix man’ is an internet and mass media term popular in contemporary China. This chapter examines the rise and genealogy of this catchphrase in Chinese popular culture. The chapter presents textual analyses of several related television series and novels: Life (1982), New Age of Marriage (2006–2007), Double-Sided Adhesive Tape (2007) and In the Name of the People (2017). Through this analysis and a survey of the ‘phoenix man’ discourse on the internet, the chapter explores the urban–rural division, class inequality and gender issues embodied in the term.

Background As a common complication of diabetes, the incidence of diabetic peripheral neuropathy (DPN) is 60–70% worldwide. DPN is a major risk factor for diabetic foot, which may lead to foot ulceration and even amputation. The treatment of DPN remains challenging. Our preliminary study demonstrated that the external application of Tangbi Waixi (TW) decoction to the lower extremities relieved clinical symptoms and improved nerve conduction velocity in DPN patients. The aim of this study was to verify the efficacy of TW among DPN patients and evaluate the herb mixture’s safety using rigorous methodological designs. Methods/design This study is a multicenter, double-blind, randomized controlled trial. A total of 640 DPN patients will be recruited and randomized to receive a foot bath with either the TW decoction or control drug. Participants will be assessed at baseline and 12 and 24 weeks after treatment. The primary outcome was the change of the Toronto Clinical Scoring System (TCSS). Secondary outcomes were nerve conduction velocity, blood glucose, blood lipids, serum inflammatory cytokines, and the European Quality of Life Five-Dimension Scale (EQ-5D) and TCM symptom scores. Discussion This multicenter, prospective, randomized clinical trial will provide valuable data regarding the efficacy and safety of foot bath treatment with TW decoction. Positive results would provide a novel treatment regimen for DPN patients. Trial registration Chinese Clinical Trial Registry, ChiCTR-IOR-16009331. Registered on 8 October 2016. Electronic supplementary material The online version of this article (10.1186/s13063-018-2856-4) contains supplementary material, which is available to authorized users.

Spirit checklist. (DOCX 30 kb)

Software architects inhabit a complex, rapidly evolving technological landscape. An ever growing collection of competing architecturally significant technologies, ranging from distributed databases to middleware and cloud platforms, makes rigorously comparing alternatives and selecting appropriate solutions a daunting engineering task. To address this problem, we envisage an ecosystem of curated, automatically updated knowledge bases that enable straightforward and streamlined technical comparisons of related products. These knowledge bases would emulate engineering handbooks that are commonly found in other engineering disciplines. As a first step towards this vision, we have built a curated knowledge base for comparing distributed databases based on a semantically defined feature taxonomy. We report in this paper on the initial results of using supervised machine learning to assist with knowledge base curation. Our results show immense promise in recommending Web pages that are highly relevant to curators. We also describe the major obstacles, both practical and scientific, that our work has uncovered. These must be overcome by future research in order to make our vision of curated knowledge bases a reality.

The aim of this work was to explore the risk factors for distal radius fracture in postmenopausal women. A total of 611 postmenopausal women with distal radius fractures were included. In all, 173 patients with unstable distal radius fractures were included (unstable fracture group), while there were 438 patients with stable distal radius fractures (stable fracture group). The control group comprised 800 postmenopausal women with no fracture. A questionnaire survey was conducted. Compared with the control group, the 611 postmenopausal women with distal radius fractures had a higher body mass index (BMI). Advanced age and higher BMI were more common in the unstable fracture group than in the stable fracture group (P

Shift-invariant dictionary learning (SIDL) refers to the problem of discovering a set of latent basis vectors (the dictionary) that captures informative local patterns at different locations of the input sequences, and a sparse coding for each sequence as a linear combination of the latent basis elements. It differs from conventional dictionary learning and sparse coding where the latent basis has the same dimension as the input vectors, where the focus is on global patterns instead of shift-invariant local patterns. Unsupervised discovery of shift-invariant dictionary and the corresponding sparse coding has been an open challenge as the number of candidate local patterns is extremely large, and the number of possible linear combinations of such local patterns is even more so. In this paper we propose a new framework for unsupervised discovery of both the shift-invariant basis and the sparse coding of input data, with efficient algorithms for tractable optimization. Empirical evaluations on multiple time series data sets demonstrate the effectiveness and efficiency of the proposed method.