Indocyanine green plasma clearance rate and 99mTc-galactosyl human serum albumin single-photon emission computed tomography evaluated preoperative remnant liver

BACKGROUND Preoperative evaluation of future remnant liver reserves is important for safe hepatectomy.If the remnant is small,preoperative portal vein embolization(PVE)is useful.Liver volume analysis has been the primary method of preoperative evaluation,although functional examination may be more accurate.We have used the functional evaluation liver using the indocyanine green plasma clearance rate(KICG)and 99mTc-galactosyl human serum albumin single-photon emission computed tomography(99mTc-GSA SPECT)for safe hepatectomy.AIM To analyze the safety of our institution’s system for evaluating the remnant liver reserve.METHODS We retrospectively reviewed the records of 23 patients who underwent preoperative PVE.Two types of remnant liver KICG were defined as follows:Anatomical volume remnant KICG(a-rem-KICG),determined as the remnant liver anatomical volume rate×KICG;and functional volume remnant KICG(frem-KICG),determined as the remnant liver functional volume rate based on 99mTc-GSA SPECT×KICG.If either of the remnant liver KICGs were>0.05,a hepatectomy was performed.Perioperative factors were analyzed.We defined the marginal group as patients with a-rem-KICG of<0.05 and a f-rem-KICG of>0.05 and compared the postoperative outcomes between the marginal and not marginal(both a-rem-KICG and f-rem-KICG>0.05)groups.RESULTS All 23 patients underwent planned hepatectomies.Right hepatectomy,right trisectionectomy and left trisectionectomy were in 16,6 and 1 cases,respectively.The mean of blood loss and operative time were 576 mL and 474 min,respectively.The increased amount of frem-KICG was significantly larger than that of a-rem-KICG after PVE(0.034 vs 0.012,P=0.0273).The not marginal and marginal groups had 17(73.9%)and 6(26.1%)patients,respectively.The complications of Clavian-Dindo classification grade II or higher and post-hepatectomy liver failure were observed in six(26.1%)and one(grade A,4.3%)patient,respectively.The 90-d mortality was zero.The marginal group had no significant difference in postoperative outcomes(prothrombin time/international normalised ratio,total bilirubin,complication,post-hepatectomy liver failure,hospital stay,90-d,and mortality)compared with the not-marginal group.CONCLUSION Functional evaluation of the remnant liver enabled safe hepatectomy and may extend the indication for hepatectomy after PVE treatment.

数据主义理论溯源及其技术人文未来

数据主义代表着大数据时代的一种社会思潮,是人类意识形态上的数据化变革。数据主义的生成逻辑是文化、技术以及社会因素共同作用的结果。其中,自古至今根植于人类内心对神秘未知力量的崇拜本性是数据主义生成的文化基石;大数据技术在信息维度上建构的“数据世界”是数据主义生成的技术动因;后真相社会导致盲目崇拜、物性依赖的双重遮蔽环境是数据主义生成的肥沃土壤。而数据主义的思想实质包含了从本体到实践的思想溯源。在本体论层面,数据主义是“万物皆数”思想的现代化发展;在认识论层面,数据主义是一种经验主义思想的回归;在价值观层面,数据主义者奉行“数据解决一切”的工具理性至上思想;在意识形态领域,数据主义是一种饱含欺骗性意识形态的技治主义幻想;在思想实践上,数据主义是计算主义思想的具体实践路径。数据主义的出现向人类主体性地位发起了挑战,为挽救失落的人文,数据主义应走向“技术人文主义”。技术人文主义下的一切“人-技”共生关系都是人类自我存在的新维度,一切数据自由终将是人类自由意志的体现。

数字人文的本体论反思——围绕离散主义的陷阱

数字人文的本体论反思始终是数字人文发展中较为薄弱的领域,而理解数字人文概念本身又是深入理解数字人文及其批判的首要环节。首先,应充分意识到数字人文所依赖的计算机科学及其相关计算科学的数理基础,这一点在莫莱蒂的世界文学猜想理论和远读中已显露端倪。数字人文所依赖的计算机技术拥有离散数学的数理基础,由此数字人文研究需警惕离散主义的陷阱。而与此相关,对计算技术应用于人文学科的过度信任又可能造成强计算主义的误区。最后,数字人文又面临解释自身方法论逻辑连通性的任务。而这三个方面的难题又正由离散型的认识论所衔接。

认知的形式计算本质及其精神实践研究

认知作为人的精神世界的核心要素,关注与心灵相关的事实性难题,与人类现实问题紧密相关。究其本质,主体心智现象的认知能力及其活动方式可以被理解为符号信息的形式化计算和表征,如此一来就为人脑的思维活动和计算机的符号操作架起了一座桥梁,为人的精神世界的诠释和重建提供了有效的分析框架和事实依据。

人工智能时代教育的“算法”风险及其规避

人工智能与生物科学技术的结合似乎将要颠覆人类传统意义上的教育价值,教育只有在算法逻辑的操作下才能发挥其应有的功能。因此,人工智能时代的人被称为"可算度的人",教育也被称为"可算度的教育"。当各种现象与问题逐渐成为一种算法的时候,人工智能所带来的教育风险应该引起人们的深度思考,这种风险主要体现在以下三个方面:人工智能时代教育的人文价值蜕化;数据"算法"的确定性与教育的不确定性之间存在着矛盾;智能技术滋生新的政治与伦理议题,人类可能陷入被边缘化的危险,等等。因此,未来的教育学应该遵循人的主体性价值取向,复兴教育的文化传统,在这个意义上打破"算法"的确定性操控,转而走向一种弱式的教育立场,以此引导人们对新的政治与道德议题展开闲暇之思。

理性 物性 新人性:“后人类研究”的科学理路与人文省思

从理论界当下对于"后人类主义"广泛而持久的讨论中,浮现出三个亟需回答的问题:何为后人类?我们应当提倡何种版本的后人类?面对后人类境况提出的深刻挑战,文艺工作者与人文学者应当反思些什么,又可以期待什么?以上三题彼此关联,在逻辑上逐次递进。对此,文章尝试给出一种可能的回答:后人类既是一个受到技术和时代因素驱动的历史现象,亦是一种新型的存在之思;具身化版本的后人类可引领我们克服认知计算主义所带来的离身化迷思,是一种更为积极正向的理论方案;打破人为架设在科学与人文间的藩篱,溯源科学理路,返归人文省思,建构一种沟通文理的"第三种文化",是后人类语境对于文艺创作和人文研究提出的全新命题,亦是我们的恒久期待。上述回答共同指向了一种积极主动的介入性方案,它回溯了后人类思潮的科学源头,讲求科学精神与经验实证,主张以一种更为平和理性的方式看待我们的后人类未来,进而构建人机共生的理想图景,开拓人机协作的文艺新边疆。

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

Crowd intelligence in AI 2.0 era

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

Large-scale Data Collection and Analysis via a Gamified Intelligent Crowdsourcing Platform

In this contribution, we present iHEARu-PLAY, an online, multi-player platform for crowdsourced database collection and labelling, including the voice analysis application (VoiLA), a free web-based speech classification tool designed to educate iHEARu-PLAY users about state-of-the-art speech analysis paradigms. Via this associated speech analysis web interface, in addition, VoiLA encourages users to take an active role in improving the service by providing labelled speech data. The platform allows users to record and upload voice samples directly from their browser, which are then analysed in a state-of-the-art classification pipeline. A set of pre-trained models targeting a range of speaker states and traits such as gender, valence, arousal, dominance, and 24 different discrete emotions is employed. The analysis results are visualised in a way that they are easily interpretable by laymen, giving users unique insights into how their voice sounds. We assess the effectiveness of iHEARu-PLAY and its integrated VoiLA feature via a series of user evaluations which indicate that it is fun and easy to use, and that it provides accurate and informative results.

Refining Linked Data with Games with a Purpose

With the rise of linked data and knowledge graphs,the need becomes compelling to find suitable solutions to increase the coverage and correctness of data sets,to add missing knowledge and to identify and remove errors.Several approaches-mostly relying on machine learning and natural language processing techniques-have been proposed to address this refinement goal;they usually need a partial gold standard,i.e.,some“ground truth”to train automatic models.Gold standards are manually constructed,either by involving domain experts or by adopting crowdsourcing and human computation solutions.In this paper,we present an open source software framework to build Games with a Purpose for linked data refinement,i.e.,Web applications to crowdsource partial ground truth,by motivating user participation through fun incentive.We detail the impact of this new resource by explaining the specific data linking“purposes”supported by the framework(creation,ranking and validation of links)and by defining the respective crowdsourcing tasks to achieve those goals.We also introduce our approach for incremental truth inference over the contributions provided by players of Games with a Purpose(also abbreviated as GWAP):we motivate the need for such a method with the specificity of GWAP vs.traditional crowdsourcing;we explain and formalize the proposed process,explain its positive consequences and illustrate the results of an experimental comparison with state-of-the-art approaches.To show this resource’s versatility,we describe a set of diverse applications that we built on top of it;to demonstrate its reusability and extensibility potential,we provide references to detailed documentation,including an entire tutorial which in a few hours guides new adopters to customize and adapt the framework to a new use case.

Sensorless Sensing with WiFi

Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world＇s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.