Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Purpose: We evaluated the performance of the newly proposed radiomics of multiparametric MRI (RMM), developed and validated based on a multicenter dataset adopting a radiomic strategy, for pretreatment prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Experimental Design: A total of 586 potentially eligible patients were retrospectively enrolled from four hospitals (primary cohort and external validation cohort 1-3). Quantitative imaging features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging before NAC for each patient. With features selected using a coarse to fine feature selection strategy, four radiomic signatures were constructed based on each of the three MRI sequences and their combination. RMM was developed based on the best radiomic signature incorporating with independent clinicopathologic risk factors. The performance of RMM was assessed with respect to its discrimination and clinical usefulness, and compared with that of clinical information-based prediction model. Results: Radiomic signature combining multiparametric MRI achieved an AUC of 0.79 (the highest among the four radiomic signatures). The signature further achieved good performances in hormone receptor-positive and HER2negative group and triple-negative group. RMM yielded an AUC of 0.86, which was significantly higher than that of clinical model in two of the three external validation cohorts. Conclusions: The study suggested a possibility that RMM provided a potential tool to develop a model for predicting pCR to NAC in breast cancer.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

No abstract

Great strides have recently been made in quantitative measurements of tear film thickness and thinning, mathematical modeling thereof and linking these to sensory perception. This paper summarizes recent progress in these areas and reports on new results. The complete blink cycle is used as a framework that attempts to unify the results that are currently available. Understanding of tear film dynamics is aided by combining information from different imaging methods, including fluorescence, retroillumination and a new high-speed stroboscopic imaging system developed for studying the tear film during the blink cycle. During the downstroke of the blink, lipid is compressed as a thick layer just under the upper lid which is often released as a narrow thick band of lipid at the beginning of the upstroke. “Rippling” of the tear film/air interface due to motion of the tear film over the corneal surface, somewhat like the flow of water in a shallow stream over a rocky streambed, was observed during lid motion and treated theoretically here. New mathematical predictions of tear film osmolarity over the exposed ocular surface and in tear breakup are presented; the latter is closely linked to new in vivo observations. Models include the effects of evaporation, osmotic flow through the cornea and conjunctiva, quenching of fluorescence, tangential flow of aqueous tears and diffusion of tear solutes and fluorescein. These and other combinations of experiment and theory increase our understanding of the fluid dynamics of the tear film and its potential impact on the ocular surface.

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.

Background and aims Perfluorinated compounds (PFCs) are of particular environmental concern. The migration of PFCs from soil to plants is a likely pathway for PFCs to enter the human food chain. This study aimed to investigate the uptake mechanisms of perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) by maize (Zea mays L. cv. TY2).Methods Hydroponic greenhouse experiments were performed. Results The kinetics of PFOS and PFOA uptake fitted Mechaelis-Menten equation well, suggesting their carrier-mediated influx processes. Uptake of PFOS was insensitive to metabolic inhibitors (NaN 3 and Na 3 VO 4 ). In contrast, treated with NaN 3 and Na 3 VO 4 reduced the uptake of PFOA by 83 and 43 % respectively. PFOS uptake was decreased by 31 % and 25 % when plants were treated with aquaporin inhibitors, AgNO 3 and glycerol, respectively, while aquaporin inhibitors had no effect on PFOA uptake. Anion channel blockers, 4, 4′-diisothiocyanostibene-2,2′-disolfonate (DID) and 5-nitro 2-(3-phenylpropylamine) benzoic acid (NPPB) inhibited the uptake of PFOS by 33 % and 30 %, respectively. Anion channel blocker anthracene-9-carboxylic acid (9-AC) decreased the uptake of PFOA by 28 %. No competitive uptake was found between PFOS and PFOA. Conclusions Uptake of PFOS and PFOA by maize may have different mechanisms.

Neurofibrillary tangles of abnormally hyperphosphorylated tau is a hallmark of Alzheimer’s disease (AD) and related tauopathies. Tau is truncated at multiple sites by various proteases in AD brain. While many studies have reported the effect of truncation on the aggregation of tau, these studies mostly employed highly artificial conditions, using heparin sulfate or arachidonic acid to induce aggregation. Here, we report for the first time the pathological activities of various truncations of tau, including site-specific phosphorylation, self-aggregation, binding to hyperphosphorylated and oligomeric tau isolated from AD brain tissue (AD O-tau), and aggregation seeded by AD O-tau. We found that deletion of the first 150 or 230 amino acids (a.a.) enhanced tau’s site-specific phosphorylation, self-aggregation, and its binding to AD O-tau, and aggregation seeded by AD O-tau, but deletion of the first 50 a.a. did not produce a significant effect. Deletion of the last 50 a.a. was found to modulate tau’s site-specific phosphorylation, promote its self-aggregation, and cause it to be captured by and aggregation seeded by AD O-tau, whereas deletion of the last 20 a.a. had no such effects. Among the truncated taus, Tau151-391 showed the highest pathological activities. AD O-tau induced aggregation of Tau151-391 in vitro and in cultured cells. These findings suggest that the first 150 a.a and the last 50 a.a. protect tau from pathological characteristics and that their deletions facilitate pathological activities. Thus, inhibition of tau truncation may represent a potential therapeutic approach to suppress tau pathology in AD and related tauopathies.