Effect of glycerol on the separation of nucleosomes and bent DNA in low ionic strength polyacrylamide gel electrophoresis

This paper seeks to understand extreme public transit riders in Beijing using both traditional household surveys and emerging new data sources such as Smart Card Data (SCD). We focus on four types of extreme transit behaviors: public transit riders who (1) travel significantly earlier than average riders (‘early birds’); (2) ride in unusual late hours (‘night owls’); (3) commute in excessively long distance (‘tireless itinerants’); and (4) make significantly more trips per day (‘recurring itinerants’). SCD are used to identify the spatiotemporal patterns of these four extreme transit behaviors. In addition, household surveys are employed to supplement the socioeconomic background and tentatively profile extreme travelers. While the research findings are useful to guide urban governance and planning in Beijing, our methodology and procedures can be extended to understand travel patterns elsewhere

Diverse Target and Contribution Scheduling for Domain Generalization

Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS

Rethinking Domain Generalization: Discriminability and Generalizability

Domain generalization (DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques tend to improve the feature generalizability by learning domain-invariant representations, inadvertently overlooking the feature discriminability. On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions. This challenge becomes particularly pronounced when domain-invariant features manifest reduced discriminability owing to the inclusion of unstable factors, \emph{i.e.,} spurious correlations. On the other hand, prevailing domain-invariant methods can be categorized as category-level alignment, susceptible to discarding indispensable features possessing substantial generalizability and narrowing intra-class variations. To surmount these obstacles, we rethink DG from a new perspective that concurrently imbues features with formidable discriminability and robust generalizability, and present a novel framework, namely, Discriminative Microscopic Distribution Alignment (DMDA). DMDA incorporates two core components: Selective Channel Pruning~(SCP) and Micro-level Distribution Alignment (MDA). Concretely, SCP attempts to curtail redundancy within neural networks, prioritizing stable attributes conducive to accurate classification. This approach alleviates the adverse effect of spurious domain invariance and amplifies the feature discriminability. Besides, MDA accentuates micro-level alignment within each class, going beyond mere category-level alignment. This strategy accommodates sufficient generalizable features and facilitates within-class variations. Extensive experiments on four benchmark datasets corroborate the efficacy of our method

Featured graphic. visualizing the minimum solution of the transportation problem of linear programming (TPLP) for beijing’s bus commuters

Link_to_subscribed_fulltex

Big data for intrametropolitan human movement studies : A case study of bus commuters based on smart card data

Unlike the data from traditional sources, there have not been standard ways to validate the quality and reliability of information derived from big data. This article argues that the theory of urban formation can be used to do the validation. In addition, the information derived from big data can be used to verify and even extend existing theories or hypotheses of urban formation. It proposes a general framework regarding how the theory of urban formation can be employed to validate information derived from smart card data and how the validated information can supplement other data to reveal spatial patterns of economic agglomeration or human settlements. Through a case study of Beijing, it demonstrates the usefulness of the framework. Additionally, it utilizes smart card data to delineate characteristics of subcenters defined by bus commuters of Beijing

Artemisinin Directly Targets Malarial Mitochondria through Its Specific Mitochondrial Activation

The biological mode of action of artemisinin, a potent antimalarial, has long been controversial. Previously we established a yeast model addressing its mechanism of action and found mitochondria the key in executing artemisinin's action. Here we present data showing that artemisinin directly acts on mitochondria and it inhibits malaria in a similar way as yeast. Specifically, artemisinin and its homologues exhibit correlated activities against malaria and yeast, with the peroxide bridge playing a key role for their inhibitory action in both organisms. In addition, we showed that artemisinins are distributed to malarial mitochondria and directly impair their functions when isolated mitochondria were tested. In efforts to explore how the action specificity of artemisinin is achieved, we found strikingly rapid and dramatic reactive oxygen species (ROS) production is induced with artemisinin in isolated yeast and malarial but not mammalian mitochondria, and ROS scavengers can ameliorate the effects of artemisinin. Deoxyartemisinin, which lacks an endoperoxide bridge, has no effect on membrane potential or ROS production in malarial mitochondria. OZ209, a distantly related antimalarial endoperoxide, also causes ROS production and depolarization in isolated malarial mitochondria. Finally, interference of mitochondrial electron transport chain (ETC) can alter the sensitivity of the parasite towards artemisinin. Addition of iron chelator desferrioxamine drastically reduces ETC activity as well as mitigates artemisinin-induced ROS production. Taken together, our results indicate that mitochondrion is an important direct target, if not the sole one, in the antimalarial action of artemisinins. We suggest that fundamental differences among mitochondria from different species delineate the action specificity of this class of drugs, and differing from many other drugs, the action specificity of artemisinins originates from their activation mechanism

Hepatocarcinoma Angiogenesis and DNA Damage Repair Response: An Update

Hepatocarcinoma is one of the most common lethal human malignant tumors, mainly because of active angiogenesis. This kind of high angiogenesis often accounts for early metastasis, rapid recurrence, and poor survival. Growing evidence has proved that hepatocarcinoma angiogenesis is closely associated with multiple risk factors, such as DNA damages resulting from hepatitis B and C virus infection, aflatoxin B1 exposure, ethanol intake, and obesity. Genetic alterations and genomic instability, probably resulting from low DNA damage repair response (DRR) and the following unrepaired DNA lesions, are also increasingly recognized as important risk factors of hepatocarcinoma angiogenesis. Dysregulation of DRRs and signaling to cell cycle checkpoints involving in DRR pathways may accelerate the accumulation of DNA damages and trigger the dysregulation of angiogenesis-related genes and the progression of hepatocarcinoma. In this review, we discussed DNA damages/DRRs and angiogenesis during hepatocarcinogenesis and their interactive regulations. Hopefully, the review will also remind the medical researchers and clinic doctors of further understanding and validating the values of DNA damages/DRRs in hepatocarcinoma angiogenesis

Modulation Design and Optimization for RIS-Assisted Symbiotic Radios

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR), the RIS acts as a secondary transmitter by modulating its information bits over the incident primary signal and simultaneously assists the primary transmission, then a cooperative receiver is used to jointly decode the primary and secondary signals. Most existing works of SR focus on using RIS to enhance the reflecting link while ignoring the ambiguity problem for the joint detection caused by the multiplication relationship of the primary and secondary signals. Particularly, in case of a blocked direct link, joint detection will suffer from severe performance loss due to the ambiguity, when using the conventional on-off keying and binary phase shift keying modulation schemes for RIS. To address this issue, we propose a novel modulation scheme for RIS-assisted SR that divides the phase-shift matrix into two components: the symbol-invariant and symbol-varying components, which are used to assist the primary transmission and carry the secondary signal, respectively. To design these two components, we focus on the detection of the composite signal formed by the primary and secondary signals, through which a problem of minimizing the bit error rate (BER) of the composite signal is formulated to improve both the BER performance of the primary and secondary ones. By solving the problem, we derive the closed-form solution of the optimal symbol-invariant and symbol-varying components, which is related to the channel strength ratio of the direct link to the reflecting link. Moreover, theoretical BER performance is analyzed. Finally, simulation results show the superiority of the proposed modulation scheme over its conventional counterpart.Comment: 16 pages,15 figure