Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular predictors of prevention of recurrence in HCC with sorafenib as adjuvant treatment and prognostic factors in the phase 3 STORM trial

Objective Sorafenib is the standard systemic therapy for advanced hepatocellular carcinoma (HCC). Survival benefits of resection/local ablation for early HCC are compromised by 70% 5-year recurrence rates. The phase 3 STORM trial comparing sorafenib with placebo as adjuvant treatment did not achieve its primary endpoint of improving recurrence-free survival (RFS). The biomarker companion study BIOSTORM aims to define (A) predictors of recurrence prevention with sorafenib and (B) prognostic factors with B level of evidence. Design Tumour tissue from 188 patients randomised to receive sorafenib (83) or placebo (105) in the STORM trial was collected. Analyses included gene expression profiling, targeted exome sequencing (19 known oncodrivers), immunohistochemistry (pERK, pVEGFR2, Ki67), fluorescence in situ hybridisation (VEGFA) and immunome. A gene signature capturing improved RFS in sorafenib-treated patients was generated. All 70 RFS events were recurrences, thus time to recurrence equalled RFS. Predictive and prognostic value was assessed using Cox regression models and interaction test. Results BIOSTORM recapitulates clinicopathological characteristics of STORM. None of the biomarkers tested (related to angiogenesis and proliferation) or previously proposed gene signatures, or mutations predicted sorafenib benefit or recurrence. A newly generated 146-gene signature identifying 30% of patients captured benefit to sorafenib in terms of RFS (p of interaction=0.04). These sorafenib RFS responders were significantly enriched in CD4+ T, B and cytolytic natural killer cells, and lacked activated adaptive immune components. Hepatocytic pERK (HR=2.41; p=0.012) and microvascular invasion (HR=2.09; p=0.017) were independent prognostic factors. Conclusion In BIOSTORM, only hepatocytic pERK and microvascular invasion predicted poor RFS. No mutation, gene amplification or previously proposed gene signatures predicted sorafenib benefit. A newly generated multigene signature associated with improved RFS on sorafenib warrants further validation. Trial registration number NCT00692770.
Calibration of a Photometer for Quantification of Ethanol in Gasoline: Absorbance Determination

This paper presents a study about the multivariate calibration of a LED near-infrared photometer applied to the quantification of ethanol in gasoline. Based on the analysis of works of other authors, we implemented seven different methods for determination of absorbances from the raw optical data, which were used for calibration of the prototype. The methods either use different experimental conditions for the background reference measurement: with empty cuvettes, with cuvettes containing a reference sample, or without cuvettes; or are independent of a reference measurement. The results obtained by each method were compared in terms of the root-mean-square errors, coefficients of determination, and residual prediction deviations (RPDs). We discussed characteristics of each method and concepts regarding the modeling of the instrumentation system, spectral preprocessing, and calibration using multiple linear regression, which are applicable both to other photometers and to other types of substances. Experimental results showed that the accuracy of the equipment in quantifying the analyte is determined not only by the accuracy of the light measurement system, but also by the processing of the optical data.
CONtrast Conformed Electrical Properties Tomography (CONCEPT) Based on Multi- Channel Transmission and Alternating Direction Method of Multipliers

In magnetic resonance-based electrical properties tomography (EPT), circularly polarized magnetic field <inline-formula> <tex-math notation="LaTeX">${B}_{\textsf {1}}$ </tex-math></inline-formula> from a transmit radiofrequency (RF) coil is measured and utilized to infer the electrical conductivity and permittivity of biological tissues. Compared with a quadrature RF coil, a multi-channel transmit coil provides a plurality of unique transmit <inline-formula> <tex-math notation="LaTeX">${B}_{\textsf {1}}$ </tex-math></inline-formula> patterns that help to alleviate the under-determinedness of EPT reconstruction problem, and it also allows to circumvent the “transceive phase assumption” that fails at ultra-high-field MRI. Here, a new approach, contrast conformed electrical properties tomography or CONCEPT, is proposed based on the multi-channel transmission that retrieves electrical properties (EPs) by solving a linear partial differential equation with discriminated <inline-formula> <tex-math notation="LaTeX">${L}_{\textsf {1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${L}_{\textsf {2}}$ </tex-math></inline-formula> norm regularization informed by intermediate EP gradient. The theory of CONCEPT and a fast reconstruction algorithm based on the alternating direction method of multipliers are described and evaluated using numerical simulations, phantom experiment, and analysis of in vivo human brain data at 7 T MRI. Compared with the multi-channel gradient-based EPT (gEPT) method, this new technology does not require receive-<inline-formula> <tex-math notation="LaTeX">${B}_{\textsf {1}}$ </tex-math></inline-formula> sensitivity profiles and does not rely on symmetry assumption regarding RF coil design and imaged target. Moreover, it is not dependent on external prior information, such as integration seed point or anatomical MRI, which can be sources of bias in reconstructed EP values. By deriving EPs from transmit <inline-formula> <tex-math notation="LaTeX">${B}_{\textsf {1}}$ </tex-math></inline-formula> profiles only, CONCEPT can be used with RF coils that include receive-only arrays with large channel count which can, in turn, offer substantial gains in signal-to-noise ratio. It also holds potentials to image unsymmetrical body organs and diseased brain. CONCEPT provides solutions for the practical problems during the implementation of gEPT, thus representing a more generalized framework in the context of multi-channel RF transmission.
End-to-End Representation Learning for Chemical-Chemical Interaction Prediction

Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years. However, in most cases, deep learning has been used only for classification even though it has feature extraction capabilities. Thus, in this paper, we propose an end-to-end representation learning method for CCI, named DeepCCI, which includes feature extraction and a learning-based approach. Our proposed architecture is based on the Siamese network. Hidden representations are extracted from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure using weight-shared convolutional neural networks. Subsequently, L1 element-wise distances between the two extracted hidden representations are measured. The performance of DeepCCI is compared with those of 12 fingerprint-method combinations. The proposed DeepCCI shows the best performance in most of the evaluation metrics used. In addition, DeepCCI was experimentally validated to guarantee the commutative property. The automatically extracted features can alleviate the efforts required for manual feature engineering and improve prediction performance.
An Experimental Approach to Characterizing the Channel Local Temperature Induced by Self-Heating Effect in FinFET

In this paper, we have developed a methodology of a lateral profiling technique of the channel local temperature in 14 nm FinFET, incurred by the self-heating effect (SHE). As SHE happens, the thermal source generated near the drain will dissipate toward the source side. Since the interaction between RTN trap and channel carriers is very sensitive to the temperature, the channel local temperature can be extracted through this interaction process between random-telegraph-noise (RTN) trap and carriers, and the position of the channel local temperature can be obtained from the RTN trap position. The results show that the highest temperature happens at the drain edge during SHE and pFinFET exhibits a much higher temperature than that of nFinFET. Furthermore, the distribution of channel local temperature can be described by the Fourier’s law of thermal conduction. Averaged channel temperature can be used to extract the thermal resistance, $R_{\mathrm{ th}}$ , which increases rapidly as the channel length is scaled down to 20 nm, further degrading the SHE, in terms of a significant short channel effect. We also found that the incremental channel resistance is proportional to the incremental channel local temperature, whose slope indicates the degree of SHE, and the slope of pFinFET is larger than that of nFinFET. Finally, SHE will cause 10% and 14% degradation of $I_{\mathrm{ d}} V_{\mathrm{ ds}}$ for n- and pFinFET respectively. This can be reasonably explained by the decay of saturation velocity in high temperature. The results obtained based on this methodology will help us on the understanding of the SHE impact on a nano-scaled FinFET device.
Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I–III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.
Sentence Weighting for Neural Machine Translation Domain Adaptation

In this paper, we propose a new sentence weighting method for the domain adaptation of neural machine translation. We introduce a domain similarity metric to evaluate the relevance between a sentence and an available entire domain dataset. The similarity of each sentence to the target domain is calculated with various methods. The computed similarity is then integrated into the training objective to weight sentences. The adaptation results on both IWSLT Chinese-English TED task and a task with only synthetic training parallel data show that our sentence weighting method is able to achieve an significant improvement over strong baselines.
Getting answers from semantic repositories: a keywords-based approach

The Web of Data proposes to publish and connect data by applying the semantic web technologies for the representation of knowledge and data and the definition of queries. The success of the Web of Data requires that both humans and machines are able to extract information from such semantic repositories. For this purpose, query interfaces for humans must make the interaction with the repository as transparent as possible. In this work, we present a generic method for querying semantic repositories based on processing and recognizing the keywords input by the users as entities of the ontology used in the description of the data. A SPARQL query is automatically derived from the query graph extracted from the list of keywords. We also describe the application of the method to three different semantic repositories from different domains.
The Federalist Papers revisited: A collaborative attribution scheme

This paper presents and evaluates a collaborative attribution strategy based on six authorship attribution schemes representing the two main paradigms used in authorship studies. Based on very frequent words as features, the classical paradigm (or similarity-based methods) proposes to compute an intertextual distance between the disputed text and the different author profiles (concatenation of their writings). As a second paradigm, we can apply different machine learning schemes such as the naive Bayes, and the support vector machines (SVM). As an evaluation corpus, we have used The Federalist Papers, a well-known collection in authorship attribution. During our evaluation, we have tried to follow the recommendations and the best practices known to assess the various attribution schemes. The evaluation shows that, in the two paradigms, we can find effective attribution schemes. But when combining these individual results using a vote aggregation method, the final collaborative decision is always correct and robust. Moreover, to indicate the degree of belief attached to the combined attribution, we can consider the percentage of votes obtained by each possible assignment. When analyzing the output given by the individual attribution schemes, we also found that the provided information is difficult to interpret, at least, for the end-user.
A Structural Optimisation Method for a Soft Pneumatic Actuator

This study aims to investigate the effects of various design parameters on the actuation performance of a pneumatic network actuator (PNA), optimise its structure using the finite element method (FEM), and subsequently quantify the performance of the resulting actuator topology experimentally. The effects of the structural parameters, including the operation pressure, the wall thickness and the gap between the chambers, bottom layer thickness, and the geometry of the channel cross section, on the deformation and bending angle of the actuator were evaluated to optimise the performance of the pneumatic actuator. A Global Analysis of Variance (ANOVA) was performed to investigate how the variables affect the mechanical output of the actuator and, thus, the significance of variables affecting the deformation (and bending angle) of the pneumatic actuator was identified. After the parameter optimisation, a pneumatic channel with a 4.5 mm bottom layer thickness, 1.5 mm wall thickness, and 1.5 mm gap between sequential chambers is recommended to perform optimised bending motion for the pneumatic network actuator. The optimised FE model results were verified experimentally. This design optimisation method based on the FEM and ANOVA analysis can be extended to the topology optimisation of other soft actuators.
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation

Generative dialog models usually adopt beam search as the inference method to generate responses. However, small-width beam search only focuses on the limited current optima. This deficiency named as myopic bias ultimately suppresses the diversity and probability of generated responses. Although increasing the beam width mitigates the myopic bias, it also proportionally slows down the inference efficiency. To alleviate the myopic bias in small-width beam search, this paper proposes a Prospective-Performance Network (PPN) to predict the future reward of the given partially-generated response, and the future reward is defined by the expectation of the partial response appearing in the top-ranked responses given by a larger-width beam search. Enhanced by PPN, the decoder can promote the results with great potential during the beam search phase. The experimental results on both Chinese and English corpora show that our method is promising to increase the quality and diversity of generated responses, with inference efficiency well maintained.
Molecular Identification of Endophytic Fungi from Banana Leaves (Musa spp.)

Endophytic fungi are part of microbial community found in various types of plant tissues including the leave, and display a range of symbiotic interactions with the plant host. In this study, endophytic fungi isolated from banana leaves were identified using ITS (Internal Transcribed Spacer region) sequences of which 10 genera comprising 17 species were molecularly identified. Endophytic fungal species identified were Nigrospora oryzae, Nigrospora sphaerica, Colletotrichum gloeosporioides, Colletotrichum siamense, Fusarium equiseti, Fusarium chlamydosporum, Phoma sorghina, Pestalotiopsis oxyanthi, Pestalotiopsis theae, Pestalotiopsis eugeniae, Penicillium steckii, Penicillium purpurogenum, Bipolaris papendorfii, Bipolaris sp. , Lasidiodiplodia theobromae, Cochliobolus intermedius dan Aspergillus niger. The present study showed that several endophytic fungal genera/species are common plant pathogen and there is a possibility that these endophytes can become pathogenic. Some of the fungal endophyte might be mutualist or saprophyte.
Effects of Oat Bran on Nutrient Digestibility, Intestinal Microbiota, and Inflammatory Responses in the Hindgut of Growing Pigs

Oat bran has drawn great attention within human research for its potential role in improving gut health. However, research regarding the impact of oat bran on nutrient utilization and intestinal functions in pigs is limited. The purpose of this study was to investigate the effects of oat bran on nutrient digestibility, intestinal microbiota, and inflammatory responses in the hindgut of growing pigs. Twenty-six growing pigs were fed either a basal diet (CON) or a basal diet supplemented with 10% oat bran (OB) within a 28 day feeding trial. Results showed that digestibility of dietary gross energy, dry matter, organic matter, and crude protein were lower in the OB group compared to the CON group on day 14, but no differences were observed between the two groups on day 28. In the colon, the relative abundance of operational taxonomic units (OTUs) associated with Prevotella, Butyricicoccus, and Catenibacterium were higher, while those associated with Coprococcus and Desulfovibrio were lower in the OB group compared to the CON group. Oat bran decreased mRNA expression of caecal interleukin-8 (IL-8), as well as colonic IL-8, nuclear factor-κB (NF-κB), and tumor necrosis factor-α (TNF-α) of the pigs. In summary, oat bran treatment for 28 day did not affect dietary nutrient digestibility, but promoted the growth of cellulolytic bacteria and ameliorated inflammatory reactions in the hindgut of growing pigs.
Top-Down Tree Structured Text Generation

Text generation is a fundamental building block in natural language processing tasks. Existing sequential models performs autoregression directly over the text sequence and have difficulty generating long sentences of complex structures. This paper advocates a simple approach that treats sentence generation as a tree-generation task. By explicitly modelling syntactic structures in a constituent syntactic tree and performing top-down, breadth-first tree generation, our model fixes dependencies appropriately and performs implicit global planning. This is in contrast to transition-based depth-first generation process, which has difficulty dealing with incomplete texts when parsing and also does not incorporate future contexts in planning. Our preliminary results on two generation tasks and one parsing task demonstrate that this is an effective strategy.
Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German

In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon all hope”. This is similar to how negation words like “not” can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.
LanguageNet: Learning to Find Sense Relevant Example Sentences

In this paper, we present a system, LanguageNet, which can help second language learners to search for different meanings and usages of a word. We disambiguate word senses based on the pairs of an English word and its corresponding Chinese translations in a parallel corpus, UM-Corpus. The process involved performing word alignment, learning vector space representations of words and training a classifier to distinguish words into groups of senses. LanguageNet directly shows the definition of a sense, bilingual synonyms and sense relevant examples.
FtsW activity and lipid II synthesis are required for recruitment of MurJ to midcell during cell division in Escherichia coli

Peptidoglycan (PG) is the unique cell shape‐determining component of the bacterial envelope, and is a key target for antibiotics. PG synthesis requires the transmembrane movement of the precursor lipid II, and MurJ has been shown to provide this activity in Escherichia coli. However, how MurJ functions in vivo has not been reported. Here we show that MurJ localizes both in the lateral membrane and at midcell, and is recruited to midcell simultaneously with late‐localizing divisome proteins and proteins MraY and MurG. MurJ septal localization is dependent on the presence of a complete and active divisome, lipid II synthesis and PBP3/FtsW activities. Inactivation of MurJ, either directly by mutation or through binding with MTSES, did not affect the midcell localization of MurJ. Our study visualizes MurJ localization in vivo and reveals a possible mechanism of MurJ recruitment during cell division.