To better understand the cellular origin of breast cancer, we developed a mouse model that recapitulates expression of the ETV6-NTRK3 (EN) fusion oncoprotein, the product of the t(12;15)(p13;q25) translocation characteristic of human secretory breast carcinoma. Activation of EN expression in mammary tissues by Wap-Cre leads to fully penetrant, multifocal malignant breast cancer with short latency. We provide genetic evidence that, in nulliparous Wap-Cre;EN females, committed alveolar bipotent or CD61(+) luminal progenitors are targets of tumorigenesis. Furthermore, EN transforms these otherwise transient progenitors through activation of the AP1 complex. Given the increasing relevance of chromosomal translocations in epithelial cancers, such mice serve as a paradigm for the study of their genetic pathogenesis and cellular origins, and generation of preclinical models.

Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks.In this paper, we study different types of pre-trained transformer based models such as autoregressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. On three classification benchmarks, pre-trained Seq2Seq model outperforms other models. Further, we explore how different pretrained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.

A cyanobacterial bloom in the lower part of the Nakdong River was investigated during the dry summer of 1994. High levels of phytoplankton biomass, mainly Microcystis aeruginosa, in the surface waters (chl. α 193 ± 436 µg L-1, mean ± s.d.; >105 cells mL-1 , n = 15) were maintained for three months from mid July to mid October. After the last major rainfall in mid June, water temperature increased sharply within three weeks (18 June, 24°C; 9 July, 33°C). The highest cell density (5 × 106 cells mL-1) and highest concentration of chl. α (>500 µg L-1) in the surface water were recorded in the early phase of the bloom (21–26 July) as the drought persisted. Concentrations of dissolved inorganic nitrogen (DIN) and total phosphorus (TP) during the bloom were high (DIN 2.5 ± 0.9 mg L-1 ; TP 155 ± 98 mg L-1 ; n = 23). pH was low (~7) until the initial stage but was high (pH >9) as the bloom formed. Elevated water temperature (>30°C) along with low discharge and high irradiance were major factors contributing to the Microcystis spp. bloom in this river–reservoir system.

Linguistic resources such as part-ofspeech (POS) tags have been extensively used in statistical machine translation (SMT) frameworks and have yielded better performances. However, usage of such linguistic annotations in neural machine translation (NMT) systems has been left under-explored.In this work, we show that multi-task learning is a successful and a easy approach to introduce an additional knowledge into an end-to-end neural attentional model. By jointly training several natural language processing (NLP) tasks in one system, we are able to leverage common information and improve the performance of the individual task.We analyze the impact of three design decisions in multi-task learning: the tasks used in training, the training schedule, and the degree of parameter sharing across the tasks, which is defined by the network architecture. The experiments are conducted for an German to English translation task. As additional linguistic resources, we exploit POS information and named-entities (NE). Experiments show that the translation quality can be improved by up to 1.5 BLEU points under the low-resource condition. The performance of the POS tagger is also improved using the multi-task learning scheme.

The Salicornia europaea L. (SE) plant is a halophyte that has been widely consumed as a seasoned vegetable, and it has been recently reported to counteract chronic diseases related to oxidative and inflammatory stress. In this study, we performed an initial phytochemical analysis with in vitro biochemical tests and chromatographic profiling of desalted and enzyme-digested SE ethanol extract (SE-EE). Subsequently, we evaluated the anti-neuroinflammatory and ameliorative potential of SE-EE in LPS-inflicted BV-2 microglial cells and scopolamine-induced amnesic C57/BL6N mice, respectively. SE-EE possess considerable polyphenols and flavonoids that are supposedly responsible to improve its bio-efficacy. SE-EE dose-dependently attenuated LPS-induced inflammation in BV-2 cells, significantly repressed behavioural/cognitive impairment, dose-dependently regulated the cholinergic function, suppressed oxidative stress markers, regulated inflammatory cytokines/associated proteins expression and effectively ameliorated p-CREB/BDNF levels, neurogenesis (DCX stain), neuron proliferation (Ki67 stain) in scopolamine-administered mice. Thus, SE-EE extract shows promising multifactorial disease modifying activities and can be further developed as an effective functional food, drug candidate, or supplemental therapy to treat neuroinflammatory mediated disorders.