Miracle-dz/UniFAR-FTUs huggingface dataset:
ERBB2 and PTPN2 gene copy numbers as prognostic factors in HER2-positive metastatic breast cancer treated with trastuzumab

Trastuzumab has markedly improved the treatment and long-term prognosis of patients with HER2-positive breast cancer. A frequent clinical challenge in patients with relapsing and/or metastatic disease is de novo or acquired trastuzumab resistance, and to date no predictive biomarkers for palliative trastuzumab have been established. In the present study, the prognostic values of factors involved in the HER2-associated PI3K/Akt signalling pathway were explored. The first 46 consecutive patients treated at the Department of Oncology, Linköping University Hospital between 2000 and 2007 with trastuzumab for HER2-positive metastatic breast cancer were retrospectively included. The gene copy number variation and protein expression of several components of the PI3K/Akt pathway were assessed in the tumour tissue and biopsy samples using droplet digital polymerase chain reaction and immunohistochemistry. Patients with tumours displaying a high-grade ERBB2 (HER2) amplification level of ≥6 copies had a significantly improved overall survival hazard ratio [(HR)=0.4; 95%, confidence interval (CI): 0.2–0.9] and progression-free survival (HR=0.3; 95% CI: 0.1–0.7) compared with patients with tumours harbouring fewer ERBB2 copies. High-grade ERBB2 amplification was significantly associated with the development of central nervous system metastases during palliative treatment. Copy gain (≥3 copies) of the gene encoding the tyrosine phosphatase PTPN2 was associated with a shorter overall survival (HR=2.0; 95% CI: 1.0–4.0) and shorter progression-free survival (HR=2.1; 95% CI: 1.0–4.1). In conclusion, high ERBB2 amplification level is a potential positive prognostic factor in trastuzumab-treated HER2-positive metastatic breast cancer, whereas PTPN2 gain is a potential negative prognostic factor. Further studies are warranted on the role of PTPN2 in HER2 signalling.
Persistent organic pollutants in fish: biomonitoring and cocktail effect with implications for food safety

ABSTRACT The impact of anthropogenic wastes of persistent organic pollutants (POPs) on the marine environment has increased in the last decades. POPs include polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCs). To assess the levels of these POPs in the wild fish population, pelagic and benthopelagic predator fish species were selected as biomonitors. For detection and quantification of POPs in muscular tissues, a simple extraction through Accelerated-Solvent-Extraction (ASE) with an ‘in-line’ clean up purification approach was applied, followed by a GC–MS/MS analysis. Concentrations of sum DDT, sum HCH and endrin correlated with all PCB concentrations. Significant differences among fish species were found for all OCs and all PCBs except PCB 31 and 101. Blackspot seabream had the highest PCB concentrations; OCs were highest in tuna. Due to major concerns regarding fish population losses and the possible human chronic exposure to contaminated fish, studies addressing combined effects of multiple POPs (‘cocktail effect’) should be implemented. Our data motivate further experimental and observational studies in fish to define adequate baseline levels for cumulative human exposure and potential role of these contaminants for food safety.
Evaluating the influence of separated RGB channels with ensemble learning

Image recognition is of great significance currently, especially the one based on RGB images. Recently, as a substantial amount of RGB based CNNs showing great performance on image recognition problems, the initialization problem sees an increasingly importance as is highly associated with the variance as well as the convergence effect. Even though there exists several prevailing initialization methods, they didn’t consider the difference of contributions from different RGB channels on image recognition. Thus, in this paper, we put forward a proper approach to take the RGB contribution into account. Firstly, we conduct experiments to prove that RGB channels have respectively different contributions upon RGB based image recognition by the means that training one general CNN and three CNNs under datasets seperated from CIFAR-10 by RGB channel. Then, the ensemble of those CNNs shows how similar performance as the general one and also their respective accuracies are recorded as the influence proportion, namely their contribution ratio. As long as the influence proportion is obtained, according to that, we can initialize the weights of convolution filters in order to improve the effect of initialization.
Deep mapping gentrification in a large Canadian city using deep learning and Google Street View

Gentrification is multidimensional and complex, but there is general agreement that visible changes to neighbourhoods are a clear manifestation of the process. Recent advances in computer vision and deep learning provide a unique opportunity to support automated mapping or ‘deep mapping’ of perceptual environmental attributes. We present a Siamese convolutional neural network (SCNN) that automatically detects gentrification-like visual changes in temporal sequences of Google Street View (GSV) images. Our SCNN achieves 95.6% test accuracy and is subsequently applied to GSV sequences at 86110 individual properties over a 9-year period in Ottawa, Canada. We use Kernel Density Estimation (KDE) to produce maps that illustrate where the spatial concentration of visual property improvements was highest within the study area at different times from 2007–2016. We find strong concordance between the mapped SCNN results and the spatial distribution of building permits in the City of Ottawa from 2011 to 2016. Our mapped results confirm those urban areas that are known to be undergoing gentrification as well as revealing areas undergoing gentrification that were previously unknown. Our approach differs from previous works because we examine the atomic unit of gentrification, namely, the individual property, for visual property improvements over time and we rely on KDE to describe regions of high spatial intensity that are indicative of gentrification processes.
Silk Fibroin-Coated Nanoagents for Acidic Lysosome Targeting by a Functional Preservation Strategy in Cancer Chemotherapy

Background: Premature drug leakage and inefficient cellular uptake are stand out as considerable hurdles for low drug delivery efficiency in tumor chemotherapy. Thus, we established a novel drug delivery and transportation strategy mediated by biocompatible silk fibroin (SF)-coated nanoparticles to overcome these therapeutic hurdles. Methods: we first synthesised a TME-responsive biocompatible nanoplatform constructed of amorphous calcium carbonate (ACC) cores and SF shells for enhanced chemotherapy by concurrently inhibiting premature drug release, achieving lysosome-targeted explosion and locally sprayed DOX, and monitoring via PAI, which was verified both in vitro and in vivo. Results: The natural SF polymer first served as a “gatekeeper” to inhibit a drug from prematurely leaking into the circulation was demonstrated both in vitro and in vivo. Upon encountering TMEs and targeting to the acidic pH environments of lysosomes, the sensitive ACC nanoparticles were gradually degraded, eventually generating a large amount of Ca2+ and CO2, resulting in lysosomal collapse, thus preventing both the efflux of DOX from cancer cells and the protonation of DOX within the lysosome, releasing multiple hydrolytic enzyme to cytoplasm, exhibiting the optimal therapeutic dose and remarkable synergetic therapeutic performance. In particular, CO2 gas generated by the pH response of ACC nanocarriers demonstrated their imaging capability for PAI, providing the potential for quantifying and guiding drug release in targets. Conclusion: In this work, we constructed TME-responsive biocompatible NPs by coating DOX-preloaded ACC-DOX clusters with SF via a bioinspired mineralization method for efficient therapeutics. This functional lysosome-targeted preservation-strategy-based therapeutic system could provid novel insights into cancer chemotherapy.
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample $k$ elements without replacement. We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search. Even for exponentially large domains, the number of model evaluations grows only linear in $k$ and the maximum sampled sequence length. The algorithm creates a theoretical connection between sampling and (deterministic) beam search and can be used as a principled intermediate alternative. In a translation task, the proposed method compares favourably against alternatives to obtain diverse yet good quality translations. We show that sequences sampled without replacement can be used to construct low-variance estimators for expected sentence-level BLEU score and model entropy.
Duplication of ALK F1245 missense mutation due to acquired uniparental disomy associated with aggressive progression in a patient with relapsed neuroblastoma

Recent genome-wide analysis of neuroblastoma (NBL) revealed amplification and heterozygous mutation of anaplastic lymphoma kinase (ALK) are responsible for oncogenicity, frequently observed during relapses. A 3-year-old girl with relapsed high-risk NBL had a heterozygous ALK F1245L mutation at diagnosis, which became homozygous due to uniparental disomy (UPD) of the entire chromosome 2, confirmed by single nucleotide polymorphism array and variant allele frequency of this mutation. The ALK inhibitor, crizotinib, failed to control the tumor and the patient died of the disease. Further genomic analysis using targeted capture sequencing for 381 genes related to pediatric cancers identified more alterations acquired at relapse, such as TSC complex subunit 2 and protein tyrosine phosphatase receptor type D. In addition to these several acquired mutations, this extremely rare duplication of ALK mutation might explain the aggressive clinical course after relapse, because acquired UPD, resulting in the duplication of an oncogenic mutation, has been reported for various neoplasms. Although a clinical benefit of ALK inhibitors in patients with NBL has not been confirmed yet, a treatment based on the ALK mutation status will be promising in future using more potent next-generation ALK inhibitors.
And sympathy is what we need my friend—Polite requests improve negotiation results

The wording negotiators use shapes the emotions of their counterparts. These emotions, in turn, influence their counterparts’ economic decisions. Building on this rationale, we examined how the language used during negotiation affects discount rate and willingness to engage in future deals. In three studies, participants assumed the role of retailers. Alleged counterparts (actually a computerized program) asked for a discount under three conditions: request, want, and demand. Results show that less extreme language (request/want) resulted in better outcomes than demanding a discount. Moreover, while the language used by the customer had an effect on experienced emotions, the positive emotions (sympathy and empathy) participants felt toward the customer mediated the relationship between the linguistic cue and the negotiation outcome. Our results inform both psycholinguistic research and negotiation research by demonstrating the causal role of linguistic cues in activating concept-knowledge relevant to different emotional experiences, and point to the down-the-line impact on shaping negotiation preferences.
Translational Assessment of Drug‐Induced Proximal Tubule Injury Using a Kidney Microphysiological System

Drug‐induced kidney injury, a major cause of acute kidney injury, results in progressive kidney disease and is linked to increased mortality in hospitalized patients. Primary injury sites of drug‐induced kidney injury are proximal tubules. Clinically, kidney injury molecule‐1, an established tubule‐specific biomarker, is monitored to assess the presence and progression of injury. The ability to accurately predict drug‐related nephrotoxicity preclinically would reduce patient burden and drug attrition rates, yet state‐of‐the‐art in vitro and animal models fail to do so. In this study, we demonstrate the use of kidney injury molecule‐1 measurement in the kidney microphysiological system as a preclinical model for drug toxicity assessment. To show clinical relevance, we use quantitative systems pharmacology computational models for in vitro–in vivo translation of the experimental results and to identify favorable dosing regimens for one of the tested drugs.
Safety Analysis of Dorsal Root Ganglion Stimulation in the Treatment of Chronic Pain

Stimulation of the dorsal root ganglion (DRG) in the treatment of chronic, intractable pain has shown excellent clinical results in multiple published studies, including a large prospective, randomized, controlled trial. Both safety and efficacy have been demonstrated utilizing this therapeutic approach for many chronic complaints. Continued assessment of neuromodulation therapies, such as DRG stimulation, are not only an important aspect of vigilant care, but are also necessary for the evaluation for safety.
Whey protein concentrate limits venous thrombosis in rats.

To study the influence of whey protein concentrate (WPC-80) on the development of thrombosis, rats were supplemented with two doses of WPC-80 (0.3 or 0.5 g/kg) for 7, 14 or 21 days. Then, one hour venous thrombosis model was performed in a half of the animals. Coagulation parameters, platelet count, and thrombus weight were assessed. Thrombus weight was decreased in rats obtaining WPC-80 and that was significant only for 14 and 21-day-supplementation. There were slight differences between groups in coagulation parameters and platelet count but without evident direction. Further research is needed to clarify the observed effects.
Semi-supervised image classification via attention mechanism and generative adversarial network

Image classification plays a vital role in the field of computer vision. Many existing image classification methods with high accuracy are based on supervised learning, which requires a great number of labeled images. However, the labeling of images requires a lot of human and material resources. In this paper, we focus on semi-supervised image classification, which can build a classifier using a few labeled images and plenty of unlabeled images. We propose an attention-based generative adversarial network (GAN) for semi-supervised image classification, which can capture global dependencies and adaptively extract important information. Furthermore, we apply spectral normalization, which can stabilize the training of attention-based GAN. The experimental results obtained with the CIFAR-10 dataset show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.
A spatiotemporal convolutional network approach for convection storm nowcasting

The purpose of convective storms nowcasting is to predict whether a region will experience strong convective weather in the next 30 minutes. Unlike traditional prediction, we study the problem of convective storm nowcasting from the perspective of machine learning. First, this paper divides the research area into a number of position-fixed small cells, then transforms the nowcasting problem into a question: Is there a radar echo > 35 dBZ in a cell within 30 minutes? Secondly, the problem of nowcasting is formulated as a kind of spatiotemporal classification learning. From this point of view, this paper introduces a sliding oversampling method to mitigate the class imbalance issue of convective storm nowcasting. A spatiotemporal convolutional network nowcasting method is proposed which is in less computational cost and easier to train than recurrent neural network. In experiments, this spatiotemporal convolution network can better captures temporal and spatial correlations, compared with previous studies, therefore results in better predictive performance. Although no sophisticated tracking algorithms are used, storm movement trends and storm growth can be predicted with reasonable skill.
An adaptive fast Inter mode decision algorithm for AVS2

The second generation Audio Video Standard (AVS2) adopts the flexible partitioning structure, which recursively divides the coding tree unit (CTU). The prediction models (PM) in inter prediction include PSKIP, P2Nx2N, P2NxN, PNx2N, PHOR_UP, PHOR_DOWN, PVER_LEFT and PVER_RIGHT. Each PM needs to perform integer motion estimation (IME) and fractional motion estimation (FME) in the motion estimation (ME) process. These methods adopted by AVS2 contribute significant coding efficiency while generating great encoding complexity. In order to reduce the encoding complexity, an adaptive inter mode decision fast algorithm is proposed in this paper from two aspects. Firstly, we statistically analyze the PMs in both spatially and temporally adjacent positions and establish a mode complexity (MC) measure metric. Through experiments we know that the complexity of inter mode decision decreases with the reduction of MC. Therefore, MC can be utilized to guide skipping some PMs with low occurrence probability. Secondly, a fast algorithm based on the parent prediction unit (PU) is proposed. FME in child PUs is skipped when the parent PU’s MV from IME is equaled to FME. Experimental results show that, compared to the AVS2 reference software RD17.0, the proposed fast algorithm reduces total encoding time by an average of 21.2% while the performance loss is negligible.
Clinical Evaluation of Low-Molecular-Weight Hyaluronic Acid-Based Treatment on Onset of Acute Side Effects in Women Receiving Adjuvant Radiotherapy after Cervical Surgery: A Randomized Clinical Trial

Background: Radiotherapy treatment for cervical cancer (CC) often induces side effects, including inflammation, dryness, dyspareunia. Considering its key role in the healing process, hyaluronic acid (HA) could be useful for the completion of radiotherapy. Objectives: The aim of this work was to evaluate the ability of HA to reduce the onset of side effects due to radiotherapy. Materials and Methods: In total, 180 women undergoing radiotherapy were randomized into two arms: controls and those treated with vaginal suppositories containing low-molecular-weight HA from day 1 of radiotherapy. The study lasted 5 weeks and was characterized by three visits: at baseline (T0), 15 days later (T1), and at the end of the radiotherapy cycle (T2). The onset of side effects, pain, safety, efficacy, acceptability of treatment, and compliance to the therapy were evaluated. Results: Patients in the control arm reported the onset and worsening of all symptoms with a moderate or severe grade at T2, whereas in the treatment arm almost 90% of patients reported the absence of symptoms or a mild grade. All patients in the treatment arm referred a lower intensity of pain on a visual analog scale compared with the control arm at T2 (6.85 ± 0.94 vs. 1.88 ± 1.02). Conclusions: HA was able to help vaginal mucosa healing during radiotherapy in patients with CC.
Expanding the repertoire of glucocorticoid receptor target genes by engineering genomic response elements

This study shows that addition of a single transcription factor binding site can be sufficient to convert a gene into a glucocorticoid receptor target. The glucocorticoid receptor (GR), a hormone-activated transcription factor, binds to a myriad of genomic binding sites yet seems to regulate a much smaller number of genes. Genome-wide analysis of GR binding and gene regulation has shown that the likelihood of GR-dependent regulation increases with decreased distance of its binding to the transcriptional start site of a gene. To test if we can adopt this knowledge to expand the repertoire of GR target genes, we used CRISPR/Cas-mediated homology-directed repair to add a single GR-binding site directly upstream of the transcriptional start site of each of four genes. To our surprise, we found that the addition of a single GR-binding site can be enough to convert a gene into a GR target. The gain of GR-dependent regulation was observed for two of four genes analyzed and coincided with acquired GR binding at the introduced binding site. However, the gene-specific gain of GR-dependent regulation could not be explained by obvious differences in chromatin accessibility between converted genes and their non-converted counterparts. Furthermore, by introducing GR-binding sequences with different nucleotide compositions, we show that activation can be facilitated by distinct sequences without obvious differences in activity between the GR-binding sequence variants we tested. The approach to use genome engineering to build genomic response elements facilitates the generation of cell lines with tailored repertoires of GR-responsive genes and a framework to test and refine our understanding of the cis-regulatory logic of gene regulation by testing if engineered response elements behave as predicted.
Perceptual spatial-temporal video compressive sensing network

Deep neural networks have been applied to video compressive sensing (VCS) task recently. The existing DNN-based VCS methods compress and reconstruct the scene video only in space or time dimensions, which ignores the spatial-temporal correlation of the video. And they generally utilize pixel-wise loss as the loss function, which causes the results to be over-smoothed. In this paper, we propose a perceptual spatial-temporal VCS network. The spatial-temporal VCS network, which compresses and recovers the video in both space and time dimensions, can preserve the spatial-temporal correlation of the video. Besides, we refine the perceptual loss by selecting specific feature-wise loss terms and adding a pixel-wise loss term. The refined perceptual loss can guide the spatial-temporal network to retain more textures and structures. Experimental results show the proposed method can achieve better visual effect with less recovery time than the state-of-the-art.