Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP.

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-ß-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Linear ubiquitin chains remodel the proteome and influence the levels of hundreds of regulators in Drosophila.

Ubiquitin controls many cellular processes via its post-translational conjugation onto substrates. Its use is highly variable due to its ability to form poly-ubiquitin with various topologies. Among them, linear chains have emerged as important regulators of immune responses and protein degradation. Previous studies in Drosophila melanogaster found that expression of linear poly-ubiquitin that cannot be dismantled into single moieties leads to their own ubiquitination and degradation or, alternatively, to their conjugation onto proteins. However, it remains largely unknown which proteins are sensitive to linear poly-ubiquitin. To address this question, here we expanded the toolkit to modulate linear chains and conducted ultra-deep coverage proteomics from flies that express non-cleavable, linear chains comprising 2, 4, or 6 moieties. We found that these chains regulate shared and distinct cellular processes in Drosophila by impacting hundreds of proteins. Our results provide key insight into the proteome subsets and cellular pathways that are influenced by linear poly-ubiquitin with distinct lengths and suggest that the ubiquitin system is exceedingly pliable.

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms.

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Single-cell transcriptome profiling reveals cell type-specific variation and development in HLA expression of human skin.

Skin, the largest organ of body, is a highly immunogenic tissue with a diverse collection of immune cells. Highly polymorphic human leukocyte antigen (HLA) molecules have a central role in coordinating immune responses as recognition molecules. Nevertheless, HLA gene expression patterns among diverse cell types within a specific organ, like the skin, have yet to be thoroughly investigated, with stromal cells attracting much less attention than immune cells. To illustrate HLA expression profiles across different cell types in the skin, we performed single-cell RNA sequencing (scRNA-seq) analyses on skin datasets, covering adult and fetal skin, and hair follicles as the skin appendages. We revealed the variation in HLA expression between different skin populations by examining normal adult skin datasets. Moreover, we evaluated the potential immunogenicity of multiple skin populations based on the expression of classical HLA class I genes, which were well represented in all cell types. Furthermore, we generated scRNA-seq data of developing skin from fetuses of 15 post conception weeks (PCW), 17 PCW, and 22 PCW, delineating the dynamic expression of HLA genes with cell type-dependent variation among various cell types during development. Notably, the pseudotime trajectory analysis unraveled the significant variance in HLA genes during the evolution of vascular endothelial cells. Moreover, we uncovered the immune-privileged properties of hair follicles at single-cell resolution. Our study presents a comprehensive single-cell transcriptomic landscape of HLA genes in the skin, which provides new insights into variation in HLA molecules and offers a clue for allogeneic skin transplantation.

Aged polystyrene microplastics exacerbate alopecia associated with tight junction injuries and apoptosis via oxidative stress pathway in skin.

Microplastics (MPs) are pervasive pollutants in the natural environment and contribute to increased levels of illness in both animals and humans. However, thespecific impacts of MPs on skin damage and alopeciaare not yet well understood. In this study, we have examined the effects of two types of polystyrene MPs (pristine and aged) on skin and hair follicle damage in mice. UV irradiation changed the chemical and physical properties of the aged MPs, including functional groups, surface roughness, and contact angles. In both in vivo and in vitro experiments, skin and cell injuries related to oxidative stress, apoptosis, tight junctions (TJs), alopecia, mitochondrial dysfunction, and other damages were observed. Mechanistically, MPs and aged MPs can induce TJs damage via the oxidative stress pathway and inhibition of antioxidant-related proteins, and this can lead to alopecia. The regulation of cell apoptosis was also observed, and this is involved in the ROS-mediated mitochondrial signaling pathway. Importantly, aged MPs showed exacerbated toxicity, which may be due to their elevated surface irregularities and altered chemical compositions. Collectively, this study suggests a potential therapeutic approach for alopecia and hair follicle damage caused by MPs pollution.

Multimodal joint deconvolution and integrative signature selection in proteomics.

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data.

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

Modulating macrophage phenotype for accelerated wound healing with chlorogenic acid-loaded nanocomposite hydrogel.

Wound healing involves distinct phases, including hemostasis, inflammation, proliferation, and remodeling, which is a complex and dynamic process. Conventional preparations often fail to meet multiple demands and provide prompt information about wound status. Here, a pH/ROS dual-responsive hydrogel (OHA-PP@Z-CA@EGF) was constructed based on oxidized hyaluronic acid (OHA), phenylboronic acid-grafted ε-polylysine (PP), chlorogenic acid (CA)-loaded ZIF-8 (Z-CA), and epidermal growth factor (EGF), which possesses intrinsic antibacterial, antioxidant, and angiogenic capacities. Due to the Schiff base and Phenylboronate ester bonds, the hydrogel exhibited excellent mechanical properties, strong adhesion, good biodegradability, high biocompatibility, stable rheological properties, and self-healing ability. Moreover, introducing Z-CA as an initiator and nanofiller led to the additional cross-linking of hydrogel through coordination bonds, which further improved the mechanical properties and antioxidant capabilities. Bleeding models of liver and tail amputations demonstrated rapid hemostatic properties of the hydrogel. Besides, the hydrogel regulated macrophage phenotypes via the NF-κB/JAK-STAT pathways, relieved oxidative stress, promoted cell migration and angiogenesis, and accelerated diabetic wound healing. The hydrogel also enabled real-time monitoring of the wound healing stages by colorimetric detection. This multifunctional hydrogel opens new avenues for the treatment and management of full-thickness diabetic wounds.

Tetraspanin CD82 Correlates with and May Regulate S100A7 Expression in Oral Cancer.

Many metastatic cancers with poor prognoses correlate to downregulated CD82, but exceptions exist. Understanding the context of this correlation is essential to CD82 as a prognostic biomarker and therapeutic target. Oral squamous cell carcinoma (OSCC) constitutes over 90% of oral cancer. We aimed to uncover the function and mechanism of CD82 in OSCC. We investigated CD82 in human OSCC cell lines, tissues, and healthy controls using the CRISPR-Cas9 gene knockout, transcriptomics, proteomics, etc. CD82 expression is elevated in CAL 27 cells. Knockout CD82 altered over 300 genes and proteins and inhibited cell migration. Furthermore, CD82 expression correlates with S100 proteins in CAL 27, CD82KO, SCC-25, and S-G cells and some OSCC tissues. The 37-50 kDa CD82 protein in CAL 27 cells is upregulated, glycosylated, and truncated. CD82 correlates with S100 proteins and may regulate their expression and cell migration. The truncated CD82 explains the invasive metastasis and poor outcome of the CAL 27 donor. OSCC with upregulated truncated CD82 and S100A7 may represent a distinct subtype with a poor prognosis. Differing alternatives from wild-type CD82 may elucidate the contradictory functions and pave the way for CD82 as a prognostic biomarker and therapeutic target.

Research on aquatic microcosm: Bibliometric analysis, toxicity comparison and model prediction.

Recently, aquatic microcosms have attracted considerable attention because they can be used to simulate natural aquatic ecosystems. First, to evaluate the development of trends, hotspots, and national cooperation networks in the field, bibliometric analysis was performed based on 1841 articles on aquatic microcosm (1962-2022). The results of the bibliometric analysis can be categorized as follows: (1) Aquatic microcosm research can be summarized in two sections, with the first part focusing on the ecological processes and services of aquatic ecosystems, and the second focusing on the toxicity and degradation of pollutants. (2) The United States (number of publications: 541, proportion: 29.5%) and China (248, 13.5%) are the two most active countries. Second, to determine whether there is a difference between single-species and microcosm tests, that is, to perform different-tier assessments, the recommended aquatic safety thresholds in risk assessment [i.e., the community-level no effect concentration (NOECcommunity), hazardous concentrations for 5% of species (HC5) and predicted no effect concentration (PNEC)] were compared based on these tests. There was a significant difference between the NOECcommunity and HC5 (P < 0.05). Moreover, regression models predicting microcosm toxicity values were constructed to provide a reference for ecological systemic risk assessments based on aquatic microcosms.

Cycloplatinated (II) Complex Based on Isoquinoline Alkaloid Elicits Ferritinophagy-Dependent Ferroptosis in Triple-Negative Breast Cancer Cells.

The development and optimization of metal-based anticancer drugs with novel cytotoxic mechanisms have emerged as key strategies to overcome chemotherapeutic resistance and side effects. Agents that simultaneously induce ferroptosis and autophagic death have received extensive attention as potential modalities for cancer therapy. However, only a limited set of drugs or treatment modalities can synergistically induce ferroptosis and autophagic tumor cell death. In this work, we designed and synthesized four new cycloplatinated (II) complexes harboring an isoquinoline alkaloid C∧N ligand. On screening the in vitro activity of these agents, we found that Pt-3 exhibited greater selectivity of cytotoxicity, decreased resistance factors, and improved anticancer activity compared to cisplatin. Furthermore, Pt-3, which we demonstrate can initiate potent ferritinophagy-dependent ferroptosis, exhibits less toxic and better therapeutic activity than cisplatin in vivo. Our results identify Pt-3 as a promising candidate or paradigm for further drug development in cancer treatment.

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Precisely Controlled Polymerization of Two-Dimensional Conducting Polymers in Quasi-Liquid Layer Enables Ultrahigh Sensing Performance.

Gas sensors based on conducting polymers offer great potential for high-performance room temperature applications due to their cost-effectiveness, high-sensitivity, and operational advantage. However, their current performance is limited by the deficiency of control in conventional polymerization methods, leading to poor crystallinity and inconsistent material properties. Here, the quasi-liquid layer (QLL) on the ice surface acts as a self-regulating nano-reactor for precise control of thermodynamics and kinetics in the polymerization, resulting in a 7.62 nm thick two-dimensional (2D) polyaniline (PANI) film matching the QLL thickness. The ultra-thin film optimizes the exposure of active sites, enhancing the detection of analyte gases at low concentrations. It is validated by fabricating a chemiresistive gas sensor with the 2D PANI film, demonstrating stable room-temperature detection of ammonia down to 10 ppt in ambient air with an impressive 10% response. This achievement represents the highest sensitivity among sensors of this kind while maintaining excellent selectivity and repeatability. Moreover, the QLL-controlled polymerization strategy offers an alternative route for precise control of the polymerization process for conducting polymers, enabling the creation of advanced materials with enhanced properties.

Selective CK1α degraders exert antiproliferative activity against a broad range of human cancer cell lines.

Molecular-glue degraders are small molecules that induce a specific interaction between an E3 ligase and a target protein, resulting in the target proteolysis. The discovery of molecular glue degraders currently relies mostly on screening approaches. Here, we describe screening of a library of cereblon (CRBN) ligands against a panel of patient-derived cancer cell lines, leading to the discovery of SJ7095, a potent degrader of CK1α, IKZF1 and IKZF3 proteins. Through a structure-informed exploration of structure activity relationship (SAR) around this small molecule we develop SJ3149, a selective and potent degrader of CK1α protein in vitro and in vivo. The structure of SJ3149 co-crystalized in complex with CK1α + CRBN + DDB1 provides a rationale for the improved degradation properties of this compound. In a panel of 115 cancer cell lines SJ3149 displays a broad antiproliferative activity profile, which shows statistically significant correlation with MDM2 inhibitor Nutlin-3a. These findings suggest potential utility of selective CK1α degraders for treatment of hematological cancers and solid tumors.

High-sensitivity hybrid MoSe2/AgInGaS quantum dot heterojunction photodetector.

Zero-dimensional (0D)-two-dimensional (2D) hybrid photodetectors have received widespread attention due to their outstanding photoelectric performances. However, these devices with high performances mainly employ quantum dots that contain toxic elements as sensitizing layers, which restricts their practical applications. In this work, we used eco-friendly AgInGaS quantum dots (AIGS-QDs) as a highly light-absorbing layer and molybdenum diselenide (MoSe2) as a charge transfer layer to construct a 0D-2D hybrid photodetector. Notably, we observed that MoSe2 strongly quenches the photoluminescence (PL) of AIGS-QDs and decreases the decay time of PL in the MoSe2/AIGS-QDs heterojunction. The MoSe2/AIGS-QDs hybrid photodetector demonstrates a responsivity of 14.3 A W-1 and a high detectivity of 6.4 × 1011 Jones. Moreover, the detectivity of the hybrid phototransistor is significantly enhanced by more than three times compared with that of the MoSe2 photodetector. Our work suggests that 0D-2D hybrid photodetectors with multiplex I-III-VI QDs provide promising potential for future high-sensitivity photodetectors.

Photothermal Conversion of the Oleophilic PVDF/Ti3 C2 Tx Porous Foam Enables Non-Aqueous Liquid System Applicable Actuator.

Various physical and chemical reaction processes occur in non-aqueous liquid systems, particularly in oil phase systems. Therefore, achieving efficient, accurate, controllable, and cost-effective movement and transfer of substances in the oil phase is crucial. Liquid-phase photothermal actuators (LPAs) are commonly used for material transport in liquid-phase systems due to their remote operability and precise control. However, existing LPAs typically rely on materials like hydrogels and flexible polymers, commonly unsuitable for non-aqueous liquids. Herein, a 3D porous poly(vinylidene fluoride) (PVDF)/Ti3 C2 Tx actuator is developed using a solvent displacement method. It demonstrates directional movement and controlled material transport in non-aqueous liquid systems. When subject to infrared light irradiation (2.0 W cm-2 ), the actuator achieves motion velocities of 7.3 and 6 mm s-1 vertically and horizontally, respectively. The actuator's controllable motion capability is primarily attributed to the foam's oil-wettable properties, 3D porous oil transport network, and the excellent photothermal conversion performance of Ti3 C2 Tx , facilitating thermal diffusion and the Marangoni effect. Apart from multidimensional directions, the actuator enables material delivery and obstacle avoidance by transporting and releasing target objects to a predetermined position. Hence, the developed controllable actuator offers a viable solution for effective motion control and material handling in non-aqueous liquid environments.