Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Machine learning for audio-visual kinship verification

Abstract Human faces implicitly indicate the family linkage, showing the perceived facial resemblance in people who are biologically related. Psychological studies found that humans have the ability to discriminate the parent-child pairs from unrelated pairs, just by observing facial images. Inspired by this finding, automatic facial kinship verification has emerged in the field of computer vision and pattern recognition, and many advanced computational models have been developed to assess the facial similarity between kinship pairs. Compared to human perception ability, automatic kinship verification methods can effectively and objectively capture subtle kin similarities such as shape and color. While many efforts have been devoted to improving the verification performance from human faces, multimodal exploration of kinship verification has not been properly addressed. This thesis proposes, for the first time, the combination of human faces and voices to verify kinship, which is referred to as audio-visual kinship verification, establishing the first comprehensive audio-visual kinship datasets, which consist of multiple videos of kin-related people speaking to the camera. Extensive experiments on these newly collected datasets are conducted, detailing the comparative performance of both audio and visual modalities and their combination using novel deep-learning fusion methods. The experimental results indicate the effectiveness of the proposed methods and that audio (voice) information is complementary and useful for the kinship verification problem.Tiivistelmä Ihmiskasvot osoittavat implisiittisesti perhesidonnaisuuden, mikä osoittaa biologisesti sukua olevien ihmisten koettua kasvojen samankaltaisuutta. Psykologiset tutkimukset havaitsivat, että ihmisillä on kyky erottaa vanhempi-lapsi-parit toisistaan riippumattomista pareista pelkästään kasvojen kuvien avulla. Tämän löydön innoittamana automaattinen kasvojen sukulaisuuden todentaminen on syntynyt tietokonenäön ja hahmontunnistuksen alalla, ja monia kehittyneitä laskennallisia malleja on kehitetty arvioimaan kasvojen samankaltaisuutta sukulaisparien välillä. Verrattuna ihmisen havainnointikykyyn automaattiset sukulaisuuden todentamismenetelmät voivat tehokkaasti ja objektiivisesti havaita hienovaraisia sukulaisyhteyksiä, kuten kasvojen muotoa ja ihonväriä. Vaikka monia ponnisteluja on tehty pyrkimyksenä parantaa ihmiskasvojen todentamista, sukulaisuuden todentamisen multimodaalista tutkimista ei ole käsitelty kunnolla. Tässä opinnäytetyössä ehdotetaan ensimmäistä kertaa ihmiskasvojen ja äänen yhdistämistä sukulaisuuden todentamiseksi tavalla, jota kutsutaan audiovisuaaliseksi sukulaisuustodentamiseksi. Näin luodaan ensimmäiset kattavat audiovisuaaliset sukulaisuustietojoukot, jotka koostuvat useista videoista, joissa esiintyy kameralle puhuvia sukulaisia. Näillä äskettäin kerätyillä tietojoukoilla tehdään laajoja kokeita, joissa kuvataan yksityiskohtaisesti sekä ääni että visuaalisten modaliteettien vertailevaa suorituskykyä ja niiden yhdistelmää käyttämällä uusia syvän oppimisen fuusiomenetelmiä. Kokeelliset tulokset osoittavat ehdotettujen menetelmien tehokkuuden ja sen, että ääni- (ääni)informaatio on täydentävää ja hyödyllistä sukulaisuuden todentamisongelmassa

Kinship verification using color features and extreme learning machine

Abstract Kinship verification from faces is a challenging task that is attracting an increasing attention in the recent years. The proposed methods so far are not robust enough to predict the kin between persons via facial appearance only. The initial studies using deep convolutional neural networks (CNN) have not shown their full potential as well, mainly due to limited training data. To mitigate this problem, we propose a new approach to kinship verification based on color features and extreme learning machines (ELM). While ELM aims to deal with small size training sets, color features are proven to provide significant enhancement over gray-scale counterparts. We evaluate our proposed method on three benchmark and publicly available kinship databases, namely KinFaceW-I, KinFaceW-II and TSKinFace. The obtained results compares favorably against some state-of-the-art methods including those based on deep learning

On the usefulness of color for kinship verification from face images

Abstract Automatic kinship verification from faces aims to determine whether two persons have a biological kin relation or not by comparing their facial attributes. This is a challenging research problem that has recently received lots of attention from the research community. However, most of the proposed methods have mainly focused on analyzing only the luminance (i.e. gray-scale) of the face images, hence discarding the chrominance (i.e. color) information which can be a useful additional cue for verifying kin relationships. This paper investigates for the first time the usefulness of color information in the verification of kinship relationships from facial images. For this purpose, we extract joint color-texture features to encode both the luminance and the chrominance information in the color images. The kinship verification performance using joint color-texture analysis is then compared against counterpart approaches using only gray-scale information. Extensive experiments using different color spaces and texture features are conducted on two benchmark databases. Our results indicate that classifying color images consistently shows superior performance in three different color spaces

Audio-visual kinship verification in the wild

Abstract Kinship verification is a challenging problem, where recognition systems are trained to establish a kin relation between two individuals based on facial images or videos. However, due to variations in capture conditions (background, pose, expression, illumination and occlusion), state-of-the-art systems currently provide a low level of accuracy. As in many visual recognition and affective computing applications, kinship verification may benefit from a combination of discriminant information extracted from both video and audio signals. In this paper, we investigate for the first time the fusion audio-visual information from both face and voice modalities to improve kinship verification accuracy. First, we propose a new multi-modal kinship dataset called TALking KINship (TALKIN), that is comprised of several pairs of video sequences with subjects talking. State-of-the-art conventional and deep learning models are assessed and compared for kinship verification using this dataset. Finally, we propose a deep Siamese network for multi-modal fusion of kinship relations. Experiments with the TALKIN dataset indicate that the proposed Siamese network provides a significantly higher level of accuracy over baseline uni-modal and multi-modal fusion techniques for kinship verification. Results also indicate that audio (vocal) information is complementary and useful for kinship verification problem

Facial kinship verification:a comprehensive review and outlook

Abstract The goal of Facial Kinship Verification (FKV) is to automatically determine whether two individuals have a kin relationship or not from their given facial images or videos. It is an emerging and challenging problem that has attracted increasing attention due to its practical applications. Over the past decade, significant progress has been achieved in this new field. Handcrafted features and deep learning techniques have been widely studied in FKV. The goal of this paper is to conduct a comprehensive review of the problem of FKV. We cover different aspects of the research, including problem definition, challenges, applications, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. In retrospect of what has been achieved so far, we identify gaps in current research and discuss potential future research directions