Group-Level Multivariate Analysis in EasyEEG Toolbox: Examining the Temporal Dynamics Using Topographic Responses

Electroencephalography (EEG) provides high temporal resolution cognitive information from non-invasive recordings. However, one of the common practices–using a subset of sensors in ERP analysis is hard to provide a holistic and precise dynamic results. Selecting or grouping subsets of sensors may also be subject to selection bias, multiple comparison, and further complicated by individual differences in the group-level analysis. More importantly, changes in neural generators and variations in response magnitude from the same neural sources are difficult to separate, which limit the capacity of testing different aspects of cognitive hypotheses. We introduce EasyEEG, a toolbox that includes several multivariate analysis methods to directly test cognitive hypotheses based on topographic responses that include data from all sensors. These multivariate methods can investigate effects in the dimensions of response magnitude and topographic patterns separately using data in the sensor space, therefore enable assessing neural response dynamics. The concise workflow and the modular design provide user-friendly and programmer-friendly features. Users of all levels can benefit from the open-sourced, free EasyEEG to obtain a straightforward solution for efficient processing of EEG data and a complete pipeline from raw data to final results for publication

Stability Analysis of Delayed Genetic Regulatory Networks via a Relaxed Double Integral Inequality

Time delay arising in a genetic regulatory network may cause the instability. This paper is concerned with the stability analysis of genetic regulatory networks with interval time-varying delays. Firstly, a relaxed double integral inequality, named as Wirtinger-type double integral inequality (WTDII), is established to estimate the double integral term appearing in the derivative of Lyapunov-Krasovskii functional with a triple integral term. And it is proved theoretically that the proposed WTDII is tighter than the widely used Jensen-based double inequality and the recently developed Wiringter-based double inequality. Then, by applying the WTDII to the stability analysis of a delayed genetic regulatory network, together with the usage of useful information of regulatory functions, several delay-range- and delay-rate-dependent (or delay-rate-independent) criteria are derived in terms of linear matrix inequalities. Finally, an example is carried out to verify the effectiveness of the proposed method and also to show the advantages of the established stability criteria through the comparison with some literature

Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging

Deep learning has transformed computational imaging, but traditional pixel-based representations limit their ability to capture continuous, multiscale details of objects. Here we introduce a novel Local Conditional Neural Fields (LCNF) framework, leveraging a continuous implicit neural representation to address this limitation. LCNF enables flexible object representation and facilitates the reconstruction of multiscale information. We demonstrate the capabilities of LCNF in solving the highly ill-posed inverse problem in Fourier ptychographic microscopy (FPM) with multiplexed measurements, achieving robust, scalable, and generalizable large-scale phase retrieval. Unlike traditional neural fields frameworks, LCNF incorporates a local conditional representation that promotes model generalization, learning multiscale information, and efficient processing of large-scale imaging data. By combining an encoder and a decoder conditioned on a learned latent vector, LCNF achieves versatile continuous-domain super-resolution image reconstruction. We demonstrate accurate reconstruction of wide field-of-view, high-resolution phase images using only a few multiplexed measurements. LCNF robustly captures the continuous object priors and eliminates various phase artifacts, even when it is trained on imperfect datasets. The framework exhibits strong generalization, reconstructing diverse objects even with limited training data. Furthermore, LCNF can be trained on a physics simulator using natural images and successfully applied to experimental measurements on biological samples. Our results highlight the potential of LCNF for solving large-scale inverse problems in computational imaging, with broad applicability in various deep-learning-based techniques

Nonlinear Transport of Graphene in the Quantum Hall Regime

We have studied the breakdown of the integer quantum Hall (QH) effect with fully broken symmetry, in an ultra-high mobility graphene device sandwiched between two single crystal hexagonal boron nitride substrates. The evolution and stabilities of the QH states are studied quantitatively through the nonlinear transport with dc Hall voltage bias. The mechanism of the QH breakdown in graphene and the movement of the Fermi energy with the electrical Hall field are discussed. This is the first study in which the stabilities of fully symmetry broken QH states are probed all together. Our results raise the possibility that the v=6 states might be a better target for the quantum resistance standard.Comment: 15 pages,6 figure

Electrophysiological hallmarks for event relations and event roles in working memory

The ability to maintain events (i.e., interactions between/among objects) in working memory is crucial for our everyday cognition, yet the format of this representation is poorly understood. The current ERP study was designed to answer two questions: How is maintaining events (e.g., the tiger hit the lion) neurally different from maintaining item coordinations (e.g., the tiger and the lion)? That is, how is the event relation (present in events but not coordinations) represented? And how is the agent, or initiator of the event encoded differently from the patient, or receiver of the event during maintenance? We used a novel picture-sentence match-across-delay approach in which the working memory representation was “pinged” during the delay, replicated across two ERP experiments with Chinese and English materials. We found that maintenance of events elicited a long-lasting late sustained difference in posterior-occipital electrodes relative to non-events. This effect resembled the negative slow wave reported in previous studies of working memory, suggesting that the maintenance of events in working memory may impose a higher cost compared to coordinations. Although we did not observe significant ERP differences associated with pinging the agent vs. the patient during the delay, we did find that the ping appeared to dampen the ongoing sustained difference, suggesting a shift from sustained activity to activity silent mechanisms. These results suggest a new method by which ERPs can be used to elucidate the format of neural representation for events in working memory

The Patient's Guide to Psoriasis Treatment. Part 4: Goeckerman Therapy.

BackgroundThe Goeckerman regimen remains one of the oldest, most reliable treatment options for patients with moderate to severe psoriasis. Goeckerman therapy currently consists of exposure to ultraviolet B light and application of crude coal tar. The details of the procedure can be confusing and challenging to understand for the first-time patient or provider.ObjectiveTo present a freely available online guide and video on Goeckerman treatment that explains the regimen in a patient-oriented manner.MethodsThe Goeckerman protocol used at the University of California-San Francisco Psoriasis and Skin Treatment Center as well as available information from the literature were reviewed to design a comprehensive guide for patients receiving Goeckerman treatment.ResultsWe created a printable guide and video resource that covers the supplies needed for Goeckerman regimen, the treatment procedure, expected results, how to monitor for adverse events, and discharge planning.ConclusionThis new resource is beneficial for prospective patients planning to undergo Goeckerman treatment, healthcare providers, and trainees who want to learn more about this procedure. Online media and video delivers material in a way that is flexible and often familiar to patients

The Patient's Guide to Psoriasis Treatment. Part 2: PUVA Phototherapy.

BackgroundPUVA treatment is photochemotherapy for psoriasis that combines psoralen with UVA radiation. Although PUVA is a very effective treatment option for psoriasis, there is an absence of patient resources explaining and demonstrating the process of PUVA. Studies have shown that patients who viewed videos explaining the treatment procedures for various medical conditions had a greater understanding of their treatment and were more active participants in their health.ObjectiveTo present a freely available online guide and video on PUVA treatment designed for patient education on PUVA.MethodsThe PUVA treatment protocol used at the University of California-San Francisco Psoriasis and Skin Treatment Center as well as available information from the literature was reviewed to design a comprehensive guide for patients receiving PUVA treatment.ResultsWe created a printable guide and video resource that reviews the benefits and risks of PUVA, discusses the three types of PUVA (hand-foot soak, full body soak, and systemic), demonstrates the PUVA process, and provides practical tips for safe use.ConclusionOnline media and video delivers material in a way that is flexible and often familiar to patients. This new format is beneficial for prospective patients planning to undergo PUVA treatment, health-care providers, and trainees who want to learn more about this treatment