Manufacturing Orgasm: Visuality, Aurality, and Female Sexual Pleasure in Tsai Ming-liang’s The Wayward Cloud

publication-status: Publishedtypes: Article© 2011 by IntellectIn the study of both sex and the city, sound tends to be an aspect that does not receive as much attention as visuality. By examining the sound of sex in Tsai Ming-liang’s 2005 film, The Wayward Cloud, this article will argue that the aural is privileged over the visual and explore its implications for female subjectivity, sexual intimacy and gender politics. It suggests that the film challenges us to think whether it might be possible to forge what Mary Ann Doane calls ‘a political erotics of the voice’, but in a wayward manner that deploys comatose bodies that have no voice, that fragments the unity of voice and body and that privileges the representation of the sonic over the visual in a cinematic tradition that generally dictates otherwise

Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma.

BACKGROUND: Diffuse large B-cell lymphomas (DLBCLs) are phenotypically and genetically heterogeneous. Gene-expression profiling has identified subgroups of DLBCL (activated B-cell-like [ABC], germinal-center B-cell-like [GCB], and unclassified) according to cell of origin that are associated with a differential response to chemotherapy and targeted agents. We sought to extend these findings by identifying genetic subtypes of DLBCL based on shared genomic abnormalities and to uncover therapeutic vulnerabilities based on tumor genetics. METHODS: We studied 574 DLBCL biopsy samples using exome and transcriptome sequencing, array-based DNA copy-number analysis, and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on the co-occurrence of genetic alterations. RESULTS: We identified four prominent genetic subtypes in DLBCL, termed MCD (based on the co-occurrence of MYD88L265P and CD79B mutations), BN2 (based on BCL6 fusions and NOTCH2 mutations), N1 (based on NOTCH1 mutations), and EZB (based on EZH2 mutations and BCL2 translocations). Genetic aberrations in multiple genes distinguished each genetic subtype from other DLBCLs. These subtypes differed phenotypically, as judged by differences in gene-expression signatures and responses to immunochemotherapy, with favorable survival in the BN2 and EZB subtypes and inferior outcomes in the MCD and N1 subtypes. Analysis of genetic pathways suggested that MCD and BN2 DLBCLs rely on "chronic active" B-cell receptor signaling that is amenable to therapeutic inhibition. CONCLUSIONS: We uncovered genetic subtypes of DLBCL with distinct genotypic, epigenetic, and clinical characteristics, providing a potential nosology for precision-medicine strategies in DLBCL. (Funded by the Intramural Research Program of the National Institutes of Health and others.).This research was supported by the Intramural Research Program of the NIH, Center for Cancer Research, National Cancer Institute and by a National Cancer Institute Strategic Partnering to Evaluate Cancer Signatures (SPECS II) grant (5U01CA157581-05). R.S. was supported by the Dr Mildred Scheel Stiftung für Krebsforschung (Deutsche Krebshilfe). D.J.H. was a Kay Kendall Leukaemia Fund Intermediate research fellow. M.K. was supported by the National Institutes of Health Oxford-Cambridge Scholars Program and the Washington University in St. Louis Medical Scientist Training Progra

Surface-Related and Internal Multiple Elimination Using Deep Learning

Multiple elimination has always been a key, challenge, and hotspot in the field of hydrocarbon exploration. However, each multiple elimination method comes with one or more limitations at present. The efficiency and success of each approach strongly depend on their corresponding prior assumptions, in particular for seismic data acquired from complex geological regions. The multiple elimination approach using deep learning encodes the input seismic data to multiple levels of abstraction and decodes those levels to reconstruct the primaries without multiples. In this study, we employ a classic convolution neural network (CNN) with a U-shaped architecture which uses extremely few seismic data for end-to-end training, strongly increasing the neural network speed. Then, we apply the trained network to predict all seismic data, which solves the problem of difficult elimination of global multiples, avoids the regularization of seismic data, and reduces massive amounts of calculation in traditional methods. Several synthetic and field experiments are conducted to validate the advantages of the trained network model. The results indicate that the model has the powerful generalization ability and high calculation efficiency for removing surface-related multiples and internal multiples effectively

Surface-Related and Internal Multiple Elimination Using Deep Learning

Multiple elimination has always been a key, challenge, and hotspot in the field of hydrocarbon exploration. However, each multiple elimination method comes with one or more limitations at present. The efficiency and success of each approach strongly depend on their corresponding prior assumptions, in particular for seismic data acquired from complex geological regions. The multiple elimination approach using deep learning encodes the input seismic data to multiple levels of abstraction and decodes those levels to reconstruct the primaries without multiples. In this study, we employ a classic convolution neural network (CNN) with a U-shaped architecture which uses extremely few seismic data for end-to-end training, strongly increasing the neural network speed. Then, we apply the trained network to predict all seismic data, which solves the problem of difficult elimination of global multiples, avoids the regularization of seismic data, and reduces massive amounts of calculation in traditional methods. Several synthetic and field experiments are conducted to validate the advantages of the trained network model. The results indicate that the model has the powerful generalization ability and high calculation efficiency for removing surface-related multiples and internal multiples effectively

DNA barcoding of <i>Actinidia</i> (Actinidiaceae) using internal transcribed spacer, <i>matK</i>, <i>rbcL</i> and <i>trnH</i>-<i>psbA</i>, and its taxonomic implication

<p><i>Actinidia</i> is taxonomically difficult and economically important. Four traditional barcoding markers, internal transcribed spacer (ITS), <i>rbcL</i>, <i>matK</i> and <i>trnH</i>-<i>psbA</i>, were used to identify the 29 <i>Actinidia</i> species sampled. High-quality sequences could be obtained easily for <i>rbcL</i>, <i>matK</i> and <i>trnH</i>-<i>psbA</i>, and <i>matK</i> performed best at resolving species among these three markers. ITS had a moderate sequencing success of 72% and the species resolution proportion was 60.7%. Sequencing success rate for <i>matK </i>+ <i>rbcL</i> was 97.4% and it discriminated 48.3% of the species analysed. The barcode <i>trnH</i>-<i>psbA</i> could only identify <i>Actinidia eriantha. MatK </i>+ <i>rbcL</i> and ITS are useful markers to barcode <i>Actinidia</i>; the utility of ITS in barcoding needs further investigation using high-throughput sequencing technology. Phylogenetic analyses based on ITS, <i>matK</i>, <i>matK </i>+ <i>rbcL</i> and <i>matK </i>+ <i>rbcL </i>+ <i>trnH</i>-<i>psbA</i> indicated Sect. <i>Leiocarpae</i> to be paraphyletic, while Sect. <i>Maculatae</i> and Sect. <i>Strigosae</i> together with Sect. <i>Stellatae</i> formed a monophyletic group. We recommended the subdivision of <i>Actinidia</i> into two groups: one consisting of Sect. <i>Leiocarpae</i> (ovaries glabrous, fruits spotless), and the other comprising sections <i>Maculatae</i>, <i>Strigosae</i> and <i>Stellatae</i> (ovaries hairy, fruits spotted). This study supported the separation of <i>Actinidia chinensis</i> var. <i>chinensis</i> and var. <i>deliciosa</i> at the infraspecific level, and the separation of <i>Actinidia tetramera</i> and <i>Actinidia kolomikta</i> at the specific level. The treatment of <i>Actinidia maloides</i> as a synonym of <i>A</i>. <i>kolomikta</i> and <i>Actinidia cinerascen</i>s as a variety of <i>Actinidia fulvicoma</i> was also warranted.</p

Cumulative Risk Assessment of Soil-Crop Potentially Toxic Elements Accumulation under Two Distinct Pollution Systems

High geological background and human activities are the two major pollution sources for soil potentially toxic elements (PTEs) accumulation around the world. Mining is the prime human activity that poses a serious threat to the farmland&rsquo;s ecosystem safety. This study assesses the farmland safety in the typical high geological background area and the superimposed area of high background-mining activity in eastern Yunnan in China by systematic analysis of the accumulation and risk characteristics of seven PTEs such as arsenic (As), mercury (Hg), copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), and chromium (Cr). Furthermore, we used Cd as the characteristic element to establish a relationship model between crop PTEs accumulation and the physical and chemical characteristics of the soil. We find that in the farmland soil from the superimposed area, the accumulation point over-standard rate of seven PTEs is higher than in the typical high geological background area. The accumulation of Pb, Cd, Cu, and Zn is related to frequent man-made mining activities. The bioavailability relationship model, using Cd as the soil-crop characteristic element, reveals that only in the crops (cereals, vegetables) of the high geological background area; the Cd bio-concentration factor significantly correlate with the physical and chemical properties of the soil. This suggests that the PTEs contaminated farmland in high geological background areas can be concomitantly restored during usage by adjusting the soil&rsquo;s physical and chemical properties, while in the superimposed area, the farmland area needs prior restoration by removing man-made mining activities

Cumulative Risk Assessment of Soil-Crop Potentially Toxic Elements Accumulation under Two Distinct Pollution Systems

High geological background and human activities are the two major pollution sources for soil potentially toxic elements (PTEs) accumulation around the world. Mining is the prime human activity that poses a serious threat to the farmland’s ecosystem safety. This study assesses the farmland safety in the typical high geological background area and the superimposed area of high background-mining activity in eastern Yunnan in China by systematic analysis of the accumulation and risk characteristics of seven PTEs such as arsenic (As), mercury (Hg), copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), and chromium (Cr). Furthermore, we used Cd as the characteristic element to establish a relationship model between crop PTEs accumulation and the physical and chemical characteristics of the soil. We find that in the farmland soil from the superimposed area, the accumulation point over-standard rate of seven PTEs is higher than in the typical high geological background area. The accumulation of Pb, Cd, Cu, and Zn is related to frequent man-made mining activities. The bioavailability relationship model, using Cd as the soil-crop characteristic element, reveals that only in the crops (cereals, vegetables) of the high geological background area; the Cd bio-concentration factor significantly correlate with the physical and chemical properties of the soil. This suggests that the PTEs contaminated farmland in high geological background areas can be concomitantly restored during usage by adjusting the soil’s physical and chemical properties, while in the superimposed area, the farmland area needs prior restoration by removing man-made mining activities