Provision of Spectacles Is Associated With Significant Improvement In Children's Self Reported Visual Function In Mexico

This study examines the self-reported benefit in visual function among Mexican children who have received glasses from Helen Keller International

Atypical chemokine receptor 4 shapes activated B cell fate

Activated B cells can initially differentiate into three functionally distinct fates-early plasmablasts (PBs), germinal center (GC) B cells, or early memory B cells-by mechanisms that remain poorly understood. Here, we identify atypical chemokine receptor 4 (ACKR4), a decoy receptor that binds and degrades CCR7 ligands CCL19/CCL21, as a regulator of early activated B cell differentiation. By restricting initial access to splenic interfollicular zones (IFZs), ACKR4 limits the early proliferation of activated B cells, reducing the numbers available for subsequent differentiation. Consequently, ACKR4 deficiency enhanced early PB and GC B cell responses in a CCL19/CCL21-dependent and B cell-intrinsic manner. Conversely, aberrant localization of ACKR4-deficient activated B cells to the IFZ was associated with their preferential commitment to the early PB linage. Our results reveal a regulatory mechanism of B cell trafficking via an atypical chemokine receptor that shapes activated B cell fate

Structure and Magnetism of Neutral and Anionic Palladium Clusters

The properties of neutral and anionic Pd_N clusters were investigated with spin-density-functional calculations. The ground state structures are three-dimensional for N>3 and they are magnetic with a spin-triplet for 2<=N<=7 and a spin nonet for N=13 neutral clusters. Structural- and spin-isomers were determined and an anomalous increase of the magnetic moment with temperature is predicted for a Pd_7 ensemble. Vertical electron detachment and ionization energies were calculated and the former agree well with measured values for anionic Pd_N clusters.Comment: 5 pages, 3 figures, fig. 2 in color, accepted to Phys. Rev. Lett. (2001

An Analysis of Resting-State Functional Transcranial Doppler Recordings from Middle Cerebral Arteries

Functional transcrannial Doppler (fTCD) is used for monitoring the hemodynamics characteristics of major cerebral arteries. Its resting-state characteristics are known only when considering the maximal velocity corresponding to the highest Doppler shift (so called the envelope signals). Significantly more information about the resting-state fTCD can be gained when considering the raw cerebral blood flow velocity (CBFV) recordings. In this paper, we considered simultaneously acquired envelope and raw CBFV signals. Specifically, we collected bilateral CBFV recordings from left and right middle cerebral arteries using 20 healthy subjects (10 females). The data collection lasted for 15 minutes. The subjects were asked to remain awake, stay silent, and try to remain thought-free during the data collection. Time, frequency and time-frequency features were extracted from both the raw and the envelope CBFV signals. The effects of age, sex and body-mass index were examined on the extracted features. The results showed that the raw CBFV signals had a higher frequency content, and its temporal structures were almost uncorrelated. The information-theoretic features showed that the raw recordings from left and right middle cerebral arteries had higher content of mutual information than the envelope signals. Age and body-mass index did not have statistically significant effects on the extracted features. Sex-based differences were observed in all three domains and for both, the envelope signals and the raw CBFV signals. These findings indicate that the raw CBFV signals provide valuable information about the cerebral blood flow which can be utilized in further validation of fTCD as a clinical tool. © 2013 Sejdić et al

Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are capable to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.Comment: 14 two-column pages, 5 figure