A resource-saving collective approach to biomedical semantic role labeling

BACKGROUND: Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. RESULTS: We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. CONCLUSIONS: This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future

The Effect of Financial Resources on Fertility: Evidence from Administrative Data on Lottery Winners

This paper utilizes wealth shocks from winning lottery prizes to examine the effect of financial resources on fertility. Using administrative data on Taiwanese lottery winners and a difference-in-differences design, we compare the trend in fertility between households receiving lottery prizes of more than 1 million NT$(33,000 US$) with those winning less than 10,000 NT$(330 US$). The results show that the receipt of a big lottery prize significantly increases fertility, and effects are driven by households with less financial resources. Moreover, big lottery wins mainly trigger childless households to have children and induce people to get married earlier

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interference-aware deployment (IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed algorithm can alleviate the problem of overlapping coverage between adjacent UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel interference between UAV-BSs will improve user satisfaction and ensure that most GUs can achieve the minimum data rate requirement. Simulation results show that our proposed IAD outperforms comparative methods by more than 10% in user satisfaction in high-density environments.Comment: 5 pages, 3 figures, to appear in IEEE Wireless Communications Letter

A pharmacogenetic study of perampanel: association between rare variants of glutamate receptor genes and outcomes

Introduction: The selection of antiseizure medication usually requires a trial-and-error process. Our goal is to investigate whether genetic markers can predict the outcome of perampanel (PER) use in patients with epilepsy.Method: The studied participants were selected from our previous epilepsy genetics studies where whole exome sequencing was available. We reviewed the medical records of epilepsy patients older than 20 years old treated with PER. The outcome of PER treatment included the response to PER, the occurrence of any adverse drug reaction (ADR), the presence of behavior ADR, and the ability to adhere to PER for more than 1 year. We investigated the association between the rare variants of the glutamate receptor genes and the outcomes of PER use.Result: A total of 83 patients were collected. The gene group burden analysis showed that enriched genetic variants of the glutamate receptor gene group were statistically significantly associated with the occurrence of ADR, while the glutamate ionotropic receptor delta type subunit had a nominal association with the occurrence of ADR. The gene collapse analysis found that GRID1 had a nominal association with the occurrence of ADR and GRIN3A had a nominal association with the occurrence of behavior ADR. However, these nominal associations did not remain statistically significant once adjusted for multiple testing.Discussion: We found that enriched rare genetic variants of the glutamate receptor genes were associated with the occurrence of ADR in patients taking PER. In the future, combining the results of various pharmacogenetic studies may lead to the development of prediction tools for the outcome of antiseizure medications

p-Cu2O-shell/n-TiO2-nanowire-core heterostucture photodiodes

This study reports the deposition of cuprous oxide [Cu2O] onto titanium dioxide [TiO2] nanowires [NWs] prepared on TiO2/glass templates. The average length and average diameter of these thermally oxidized and evaporated TiO2 NWs are 0.1 to 0.4 μm and 30 to 100 nm, respectively. The deposited Cu2O fills gaps between the TiO2 NWs with good step coverage to form nanoshells surrounding the TiO2 cores. The p-Cu2O/n-TiO2 NW heterostructure exhibits a rectifying behavior with a sharp turn-on at approximately 0.9 V. Furthermore, the fabricated p-Cu2O-shell/n-TiO2-nanowire-core photodiodes exhibit reasonably large photocurrent-to-dark-current contrast ratios and fast responses

High ERCC1 expression predicts cisplatin-based chemotherapy resistance and poor outcome in unresectable squamous cell carcinoma of head and neck in a betel-chewing area

<p>Abstract</p> <p>Background</p> <p>This study was to evaluate the effect of excision repair cross-complementation group 1(ERCC1) expression on response to cisplatin-based induction chemotherapy (IC) followed by concurrent chemoradiation (CCRT) in locally advanced unresectable head and neck squamous cell carcinoma (HNSCC) patients.</p> <p>Methods</p> <p>Fifty-seven patients with locally advanced unresectable HNSCC who received cisplatin-based IC followed by CCRT from January 1, 2006 through January 1, 2008. Eligibility criteria included presence of biopsy-proven HNSCC without a prior history of chemotherapy or radiotherapy. Immunohistochemistry was used to assess ERCC1 expression in pretreatment biopsy specimens from paraffin blocks. Clinical parameters, including smoking, alcohol consumption and betel nuts chewing, were obtained from the medical records.</p> <p>Results</p> <p>The 12-month progression-free survival (PFS) and 2-year overall survival (OS) rates of fifty-seven patients were 61.1% and 61.0%, respectively. Among these patients, thirty-one patients had low ERCC1 expression and forty-one patients responded to IC followed by CCRT. Univariate analyses showed that patients with low expression of ERCC1 had a significantly higher 12-month PFS rates (73.3% vs. 42.3%, p < 0.001) and 2-year OS (74.2 vs. 44.4%, p = 0.023) rates. Multivariate analysis showed that for patients who did not chew betel nuts and had low expression of ERCC1 were independent predictors for prolonged survival.</p> <p>Conclusions</p> <p>Our study suggest that a high expression of ERCC1 predict a poor response and survival to cisplatin-based IC followed by CCRT in patients with locally advanced unresectable HNSCC in betel nut chewing area.</p

Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To learn fine-grained visual understanding, we decouple spatial-temporal modeling and propose a hybrid pipeline, Decoupled Spatial-Temporal Encoders, integrating an image- and a video-language encoder. The former encodes spatial semantics from larger but sparsely sampled frames independently of time, while the latter models temporal dynamics at lower spatial but higher temporal resolution. To help the video-language model learn temporal relations for video QA, we propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Extensive experiments demonstrate that our model outperforms previous work pre-trained on orders of magnitude larger datasets.Comment: BMVC 2022. Code is available at https://github.com/shinying/des

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

BACKGROUND: Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing. RESULTS: To develop our ML-based Bio-NER system, we employ conditional random fields, which have performed effectively in several well-known tasks, as our underlying ML model. Adding selected conjunction features, applying numerical normalization, and employing pattern-based post-processing improve the F-scores by 1.67%, 1.04%, and 0.57%, respectively. The combined increase of 3.28% yields a total score of 72.98%, which is better than the baseline system that only uses singleton features. CONCLUSION: We demonstrate the benefits of using the sequential forward search algorithm to select effective conjunction feature groups. In addition, we show that numerical normalization can effectively reduce the number of redundant and unseen features. Furthermore, the Smith-Waterman local alignment algorithm can help ML-based Bio-NER deal with difficult cases that need longer context windows