Fabrication and characterization of a mcro/nanofluidic platform for electroporation.

For traditional electroporation devices, there are a number of problems associated with these devices such as insufficient understanding of its theoretical mechanism, low cell viability, inadequate electroporation efficiency, excess voltage applied to generate required electric field due to the large size of these devices and sample contamination. Although newly developed microfluidic electroporation devices have solved most of the above existing problems in traditional bulk electroporation devices, they appear to lack the ability to control the precise dose of biomolecules or genes transfecting into cells and, from a manufacturing perspective, the fabrication methods do not enable repeatable production of such devices on the large scale. Here, we introduce a new, repeatable method for fabricating 3-D Micro/Nanofluidic electroporation platforms and characterize these platforms to demonstrate their ability to electroporate live cells. Some of the new methods developed in this work include a direct-write fiber technique via three-axis robotic dispensing system, dry film resist photolithography, film-to-film bonding and replica molding to create the desired electroporation platform. A robotic dispensing system was utilized to control the fiber diameter, which was determined vii by the: 1) prescribed dispense time; 2) pressure of the dispensing system valve; 3) rate at which the stage traversed; 4) diameter of the dispensing tip; 5) polymer solution viscosity and surface tension; and, 6) programmed drawing length. Thin dry film photoresist was utilized to replace liquid photoresist in order to achieve high-quality film-to-film bonding after drawing nanofibers onto one substrate containing the thin-film structure. Polydimethylsiloxane (PDMS) was employed as the bulk material to fabricate the target micro/nano electroporation substrate using replica molding and micro/nanofibers etching. Characterization of the direct-write fiber technique via robotic dispensing system to acquire suspended and complex fibers of the required dimension repeatedly under prescribed conditions were completed. Combining this fiber direct-write method and traditional clean room techniques, a total of 18 micro- to nano-scale electroporation devices (6 for each group of 1 ìm, 500 nm, and 300 nm diameter) were successfully developed and mass produced in two weeks with relatively high repeatability (within 20% of the design). Finally, metrology and characterization studies were performed on the electroporation platforms to validate the micro/nanochannel’s existence and its connectivity to two micro-chambers. Furthermore, biomolecules and other fluorescent particles were successfully transported through the micro/nanochannel and transferred (via electroporation) into the cells. Preliminary results of electroporation experiment performed on this micro/nano-electroporation platform illustrated that the duration of the entire electroporation process was significantly shorter than times reported previously by other investigator’s nano-electroporation platforms

Sequential Recommendation with Diffusion Models

Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods require sampling from probability distributions and adopt auxiliary loss functions to optimize the model, which can capture the uncertainty of user behaviors and alleviate exposure bias. However, existing generative models still suffer from the posterior collapse problem or the model collapse problem, thus limiting their applications in sequential recommendation. To tackle the challenges mentioned above, we leverage a new paradigm of the generative models, i.e., diffusion models, and present sequential recommendation with diffusion models (DiffRec), which can avoid the issues of VAE- and GAN-based models and show better performance. While diffusion models are originally proposed to process continuous image data, we design an additional transition in the forward process together with a transition in the reverse process to enable the processing of the discrete recommendation data. We also design a different noising strategy that only noises the target item instead of the whole sequence, which is more suitable for sequential recommendation. Based on the modified diffusion process, we derive the objective function of our framework using a simplification technique and design a denoise sequential recommender to fulfill the objective function. As the lengthened diffusion steps substantially increase the time complexity, we propose an efficient training strategy and an efficient inference strategy to reduce training and inference cost and improve recommendation diversity. Extensive experiment results on three public benchmark datasets verify the effectiveness of our approach and show that DiffRec outperforms the state-of-the-art sequential recommendation models

NP-Hardness of Tensor Network Contraction Ordering

We study the optimal order (or sequence) of contracting a tensor network with a minimal computational cost. We conclude 2 different versions of this optimal sequence: that minimize the operation number (OMS) and that minimize the time complexity (CMS). Existing results only shows that OMS is NP-hard, but no conclusion on CMS problem. In this work, we firstly reduce CMS to CMS-0, which is a sub-problem of CMS with no free indices. Then we prove that CMS is easier than OMS, both in general and in tree cases. Last but not least, we prove that CMS is still NP-hard. Based on our results, we have built up relationships of hardness of different tensor network contraction problems.Comment: Jianyu Xu and Hanwen Zhang are equal contributors. 10 pages (reference and appendix excluded), 20 pages in total, 6 figure

Multi-Modality is All You Need for Transferable Recommender Systems

ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developing transferable RecSys and is also susceptible to the cold-start issue. In this paper, we unleash the boundaries of the ID-based paradigm and propose a Pure Multi-Modality based Recommender system (PMMRec), which relies solely on the multi-modal contents of the items (e.g., texts and images) and learns transition patterns general enough to transfer across domains and platforms. Specifically, we design a plug-and-play framework architecture consisting of multi-modal item encoders, a fusion module, and a user encoder. To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders. To ensure the robustness of user representations, we propose a novel noised item detection objective and a robustness-aware contrastive learning objective, which work together to denoise user sequences in a self-supervised manner. PMMRec is designed to be loosely coupled, so after being pre-trained on the source data, each component can be transferred alone, or in conjunction with other components, allowing PMMRec to achieve versatility under both multi-modality and single-modality transfer learning settings. Extensive experiments on 4 sources and 10 target datasets demonstrate that PMMRec surpasses the state-of-the-art recommenders in both recommendation performance and transferability. Our code and dataset is available at: https://github.com/ICDE24/PMMRec.Comment: ICDE'24 Accepte

Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthesized data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.Comment: 13 pages, 5 figures. Submitted to IEEE for possible publicatio

OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization

Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data. Nevertheless, recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL. The existing solutions based on cryptography involve heavy computation and communication overhead and are vulnerable to inference attacks. Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model. This paper explores to improve the accuracy of the widely deployed tree boosting algorithms satisfying differential privacy under vertical FL. Specifically, we introduce a framework called OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. Comprehensive evaluations show that OpBoost has a better performance on prediction accuracy of trained models compared with existing LDP approaches on reasonable settings. Our code is open source

Statistical Significance of Clustering Using Soft Thresholding

Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts. This challenge is especially serious, and very few methods are available, when the data are very high in dimension. Statistical Significance of Clustering (SigClust) is a recently developed cluster evaluation tool for high dimensional low sample size data. An important component of the SigClust approach is the very definition of a single cluster as a subset of data sampled from a multivariate Gaussian distribution. The implementation of SigClust requires the estimation of the eigenvalues of the covariance matrix for the null multivariate Gaussian distribution. We show that the original eigenvalue estimation can lead to a test that suffers from severe inflation of type-I error, in the important case where there are a few very large eigenvalues. This paper addresses this critical challenge using a novel likelihood based soft thresholding approach to estimate these eigenvalues, which leads to a much improved SigClust. Major improvements in SigClust performance are shown by both mathematical analysis, based on the new notion of Theoretical Cluster Index, and extensive simulation studies. Applications to some cancer genomic data further demonstrate the usefulness of these improvements

Safety, Feasibility, and Efficacy of Early Rehabilitation in Patients Requiring Continuous Renal Replacement: A Quality Improvement Study

Introduction: Early rehabilitation in critically ill patients is associated with improved outcomes. Recent research demonstrates that patients requiring continuous renal replacement therapy (CRRT) can safely engage in mobility. The purpose of this study was to assess safety and feasibility of early rehabilitation with focus on mobility in patients requiring CRRT. Methods: Study design was a mixed methods analysis of a quality improvement protocol. The setting was an intensive care unit (ICU) at a tertiary medical center. Safety was prospectively recorded by incidence of major adverse events including dislodgement of CRRT catheter, accidental extubation, bleeding, and hemodynamic emergency; and minor adverse events such as transient oxygen desaturation \u3e 10% of resting. Limited efficacy testing was performed to determine if rehabilitation parameters were associated with clinical outcomes. Results: A total of 67 patients (54.0 ± 15.6 years old, 44% women, body mass index 29.2 ± 9.3 kg/m2) received early rehabilitation under this protocol. The median days of CRRT were 6.0 (interquartile range [IQR], 2–11) and 72% of patients were on mechanical ventilation concomitantly with CRRT at the time of rehabilitation. A total of 112 rehabilitation sessions were performed of 152 attempts (74% completion rate). No major adverse events occurred. Patients achieving higher levels of mobility were more likely to be alive at discharge (P = 0.076). Conclusions: The provision of early rehabilitation in critically ill patients requiring CRRT is safe and feasible. Further, these preliminary results suggest that early rehabilitation with focus on mobility may improve patient outcomes in this susceptible population

Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy

ObjectiveTo test the feasibility and reliability of intracranial electrophysiological recordings in an acute status epilepticus model on laboratory swine.MethodIntrahippocampal injection of kainic acid (KA) was performed on 17 male Bama pigs (Sus scrofa domestica) weighing between 25 and 35 kg. Two stereoelectroencephalography (SEEG) electrodes with a total of 16 channels were implanted bilaterally along the sensorimotor cortex to the hippocampus. Brain electrical activity was recorded 2 h daily for 9–28 days. Three KA dosages were tested to evaluate the quantities capable of evoking status epilepticus. Local field potentials (LFPs) were recorded and compared before and after the KA injection. We quantified the epileptic patterns, including the interictal spikes, seizures, and high-frequency oscillations (HFOs), up to 4 weeks after the KA injection. Test–retest reliability using intraclass correlation coefficients (ICCs) were performed on interictal HFO rates to evaluate the recording stability of this model.ResultsThe KA dosage test suggested that a 10 μl (1.0 μg/μl) intrahippocampal injection could successfully evoke status epilepticus lasting from 4 to 12 h. At this dosage, eight pigs (50% of total) had prolonged epileptic events (tonic-chronic seizures + interictal spikes n = 5, interictal spikes alone n = 3) in the later 4 weeks of the video-SEEG recording period. Four pigs (25% of total) had no epileptic activities, and another four (25%) had lost the cap or did not complete the experiments. Animals that showed epileptiform events were grouped as E + (n = 8) and the four animals showing no signs of epileptic events were grouped as E– (n = 4). A total of 46 electrophysiological seizures were captured in the 4-week post-KA period from 4 E + animals, with the earliest onset on day 9. The seizure durations ranged from 12 to 45 s. A significant increase of hippocampal HFOs rate (num/min) was observed in the E+ group during the post-KA period (weeks 1, 2,4, p < 0.05) compared to the baseline. But the E-showed no change or a decrease (in week 2, p = 0.43) compared to their baseline rate. The between-group comparison showed much higher HFO rates in E + vs. E – (F = 35, p < 0.01). The high ICC value [ICC (1, k) = 0.81, p < 0.05] quantified from the HFO rate suggested that this model had a stable measurement of HFOs during the four-week post-KA periods.SignificanceThis study measured intracranial electrophysiological activity in a swine model of KA-induced mesial temporal lobe epilepsy (mTLE). Using the clinical SEEG electrode, we distinguished abnormal EEG patterns in the swine brain. The high test–retest reliability of HFO rates in the post-KA period suggests the utility of this model for studying mechanisms of epileptogenesis. The use of swine may provide satisfactory translational value for clinical epilepsy research