COMSOL Simulation of a BioMEMS Disease-diagnostic Lab-on-a-Chip Device

Efficient, portable and accurate Disease Diagnostic systems are in great demand for modern clinical testing industry. Such systems are especially needed for patients with complicated health conditions. For example, some patients may have a variety of diseases, conventional testing methods may not be able to check it out all at once. In case of an emergency, failure to accurately determine the cause could be fatal for the patients. In this poster, a complete lab-on-a-chip device for disease diagnosis based on BioMEMS (Bio-Micro-Electro-Mechanical-Systems) technology is proposed. The proposed efficient disease diagnosis system integrates micropump, micromixer and gel electrophoresis component into a single chip. An on-chip control circuitry is used to store the pre-programmed blood sampling, buffering and chemical delivery sequence. Based on the theoretical analysis, a set of optimized design parameters of the lab-on-chip system are suggested. The working principle of the efficient disease diagnostic system is discussed. COMSOL simulation is used to verify the function of the system. The proposed efficient disease diagnostic system offers excellent efficiency, accuracy and portability compared to traditional disease diagnostic procedure. If integrated with other testing chips, it could provide a useful tool for biomedical field and be crucial for micro total analysis system

HELLaMA: LLaMA-based Table to Text Generation by Highlighting the Important Evidence

Large models have demonstrated significant progress across various domains, particularly in tasks related to text generation. In the domain of Table to Text, many Large Language Model (LLM)-based methods currently resort to modifying prompts to invoke public APIs, incurring potential costs and information leaks. With the advent of open-source large models, fine-tuning LLMs has become feasible. In this study, we conducted parameter-efficient fine-tuning on the LLaMA2 model. Distinguishing itself from previous fine-tuning-based table-to-text methods, our approach involves injecting reasoning information into the input by emphasizing table-specific row data. Our model consists of two modules: 1) a table reasoner that identifies relevant row evidence, and 2) a table summarizer that generates sentences based on the highlighted table. To facilitate this, we propose a search strategy to construct reasoning labels for training the table reasoner. On both the FetaQA and QTSumm datasets, our approach achieved state-of-the-art results. Additionally, we observed that highlighting input tables significantly enhances the model's performance and provides valuable interpretability

Hybrid2^2 Neural ODE Causal Modeling and an Application to Glycemic Response

Hybrid models composing mechanistic ODE-based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual reasoning). The incorporation of mechanistic models also provides inductive bias in standard blackbox modeling approaches, critical when learning from small datasets or partially observed, complex systems. Unfortunately, as the hybrid models become more flexible, the causal grounding provided by the mechanistic model can quickly be lost. We address this problem by leveraging another common source of domain knowledge: \emph{ranking} of treatment effects for a set of interventions, even if the precise treatment effect is unknown. We encode this information in a \emph{causal loss} that we combine with the standard predictive loss to arrive at a \emph{hybrid loss} that biases our learning towards causally valid hybrid models. We demonstrate our ability to achieve a win-win, state-of-the-art predictive performance \emph{and} causal validity, in the challenging task of modeling glucose dynamics post-exercise in individuals with type 1 diabetes

A mechanism of lysosomal calcium entry

Lysosomal calcium (Ca2+) release is critical to cell signaling and is mediated by well-known lysosomal Ca2+ channels. Yet, how lysosomes refill their Ca2+ remains hitherto undescribed. Here, from an RNA interference screen in Caenorhabditis elegans, we identify an evolutionarily conserved gene, lci-1, that facilitates lysosomal Ca2+ entry in C. elegans and mammalian cells. We found that its human homolog TMEM165, previously designated as a Ca2+/H+ exchanger, imports Ca2+ pH dependently into lysosomes. Using two-ion mapping and electrophysiology, we show that TMEM165, hereafter referred to as human LCI, acts as a proton-activated, lysosomal Ca2+ importer. Defects in lysosomal Ca2+ channels cause several neurodegenerative diseases, and knowledge of lysosomal Ca2+ importers may provide previously unidentified avenues to explore the physiology of Ca2+ channels

Design, Modeling, and Analysis of a Novel Hydraulic Energy-Regenerative Shock Absorber for Vehicle Suspension

To reduce energy consumption or improve energy efficiency, the regenerative devices recently have drawn the public’s eyes. In this paper, a novel hydraulic energy-regenerative shock absorber (HERSA) is developed for vehicle suspension to regenerate the vibration energy which is dissipated by conventional viscous dampers into heat waste. At first, the schematic of HERSA is presented and a mathematic model is developed to describe the characteristic of HERSA. Then the parametric sensitivity analysis of the vibration energy is expounded, and the ranking of their influences is k1≫m2>m1>k2≈cs. Besides, a parametric study of HERSA is adopted to research the influences of the key parameters on the characteristic of HERSA. Moreover, an optimization of HERSA is carried out to regenerate more power as far as possible without devitalizing the damping characteristic. To make the optimization results more close to the actual condition, the displacement data of the shock absorber in the road test is selected as the excitation in the optimization. The results show that the RMS of regenerated energy is up to 107.94 W under the actual excitation. Moreover it indicates that the HERSA can improve its performance through the damping control

Recent advances in the structure, function and regulation of the volume-regulated anion channels and their role in immunity

Volume-regulated anion channels (VRACs) are heteromeric complexes formed by proteins of the leucine-rich repeat-containing 8 (LRRC8) family. LRRC8A (also known as SWELL1) is the core subunit required for VRAC function, and it must combine with one or more of the other paralogues (i.e. LRRC8B–E) to form functional heteromeric channels. VRACs were discovered in T lymphocytes over 35 years ago and are found in virtually all vertebrate cells. Initially, these anion channels were characterized for their role in Cl− efflux during the regulatory volume decrease process triggered when cells are subjected to hypotonic challenges. However, substantial evidence suggests that VRACs also transport small molecules under isotonic conditions. These findings have expanded the research on VRACs to explore their functions beyond volume regulation. In innate immune cells, VRACs promote inflammation by modulating the transport of immunomodulatory cyclic dinucleotides, itaconate and ATP. In adaptive immune cells, VRACs suppress their function by taking up cyclic dinucleotides to activate the STING signalling pathway. In this review, we summarize the current understanding of LRRC8 proteins in immunity and discuss recent progress in their structure, function, regulation and mechanisms for channel activation and gating. Finally, we also examine potential immunotherapeutic applications of VRAC modulation