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Person re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under pre-defined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value. [ABSTRACT FROM AUTHOR]

Increasing evidence points toward an essential role for complement activation in the pathogenesis of diabetic kidney disease (DKD). However, the precise molecular mechanisms remain unclear, and the pathway predominantly contributing to complement activation in DKD is of particular interest. In this study, the glomerular proteome, especially the profiles of the complement proteins, was analyzed in kidney biopsies from 40 DKD patients and 10 normal controls using laser microdissection-assisted liquid chromatography-tandem mass spectrometry (LMD-LC-MS/MS). The glomerular abundances of three proteins related to classical pathway (CP) (C1q, C1r, C1s), five proteins related to alternative pathway (AP) (CFB, CFH, CFHR1, CFHR3, CFHR5), one common protein related to CP and lectin pathway (LP) (C4), and six proteins related to terminal complement pathway (C3, C5, C6, C7, C8, C9) were significantly increased in DKD. Notably, none of the proteins unique to the lectin complement pathway, including mannose-binding lectin (MBL) and its associated proteins, were detected in DKD glomeruli. Furthermore, the glomerular complement proteins of CP and AP were positively correlated with glomerular pathological grades and proteinuria, and negatively correlated with eGFR in DKD patients. Our results highlight a critical role for complement activation of the CP and AP, rather than the LP, in DKD progression. [ABSTRACT FROM AUTHOR]

The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents' activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra‐similarities, inter‐differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned. [ABSTRACT FROM AUTHOR]

Unsupervised person re-identification (Re-ID) has garnered significant attention because of its data-friendly nature, as it does not require labeled data. Existing approaches primarily address this challenge by employing feature-clustering techniques to generate pseudo-labels. In addition, camera-proxy-based methods have emerged because of their impressive ability to cluster sample identities. However, these methods often blur the distinctions between individuals within inter-camera views, which is crucial for effective person re-ID. To address this issue, this study introduces an inter-camera-identity-difference-based contrastive learning framework for unsupervised person Re-ID. The proposed framework comprises two key components: (1) a different sample cross-view close-range penalty module and (2) the same sample cross-view long-range constraint module. The former aims at penalizing excessive similarity among different subjects across inter-camera views, whereas the latter mitigates the challenge of excessive dissimilarity among the same subject across camera views. To validate the performance of our method, we conducted extensive experiments on three existing person Re-ID datasets (Market-1501, MSMT17, and PersonX). The results demonstrate the effectiveness of the proposed method, which shows a promising performance. The code is available at https://github.com/hooldylan/IIDCL. [ABSTRACT FROM AUTHOR]