Bringing class back in: class consciousness and solidarity among Chinese migrant workers in Italy and the UK

The growing literature on international migration has a tendency to emphasize homogenous elements such as shared ethnic background, social network and cultural similarities in shaping immigrants' identity. We argue that this underestimates the differences (and sometimes conflicts) of interests between ethnic employers and migrant workers and that class needs to be brought back into the studies of ethnic relationship. Based upon findings from a series of fieldwork in Veneto, Italy and East Midlands, UK, this article contends that class consciousness has co-existed, sometimes uneasily, alongside co-ethnic and cultural relationships among Chinese migrant workers and has played an important part in the making of new Chinese communities. By analysing the perspectives of Chinese migrant workers and their relationship with co-ethnic entrepreneurs, this article illustrates complex factors behind the formation, diffusion and development of class consciousness among Chinese migrant workers

A Journey into the City. Migrant Workers' Relation with the Urban Space and Struggle for Existence in Xu Zechen's Early Jingpiao Fiction

In contemporary China, rural-urban migrants constitute a new urban subject with entirely new identity-related issues. This study aims at demonstrating how literature can be a valid field in investigating such evolving subjectivities, through an analysis of Xu Zechen’s early novellas depicting migrants’ vicissitudes in Beijing. Combining a close reading of the texts and a review of the main social problems characterising rural-urban migration in China, this paper focuses on the representation of the identity crisis within the migrant self in Xu’s stories, taking into account the network of meanings employed by the writer to signify the objective and subjective tension between the city and the countryside

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The Nonperformativity of Antiracism

This paper explores institutional speech acts that declare organisations as being diverse and as committed to racial equality. It offers a simple thesis: such speech acts are non-performatives; they do not bring about the effects that they name. The paper draws on an analysis of policy documents, experience of writing equality documents, and interviews with diversity practitioners based in universities in the UK. By exploring different kinds of institutional speech acts (commitments, performances, descriptions), the paper concludes non-performatives can still do things; they may even do things that fail to bring into effect what they name. In other words, practitioners use such speech acts to expose the gap between what organisations say they do and what they do

The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection

Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and require domain knowledge. Thus, the problem is that the lack of professional ophthalmologists could result in the delay of cataract detection, where medical treatment is inevitable. Therefore, this study aimed to design a convolutional neural network (CNN) with digital camera images (CNNDCI) system to detect cataracts efficiently and effectively. The designed CNNDCI system can perform the cataract identification process accurately in a user-friendly manner using smartphones to collect digital images. In addition, the existing numerical results provided by the literature were used to demonstrate the performance of the proposed CNNDCI system for cataract detection. Numerical results revealed that the designed CNNDCI system could identify cataracts effectively with satisfying accuracy. Thus, this study concluded that the presented CNNDCI architecture is a feasible and promising alternative for cataract detection