Target Application Research Articles

Investigation into designing VLSI of a flexible architecture for a deep neural network accelerator

Deep learning, a branch of AI, makes use of specialised neural networks. Computational acceleration of high-performance deep neural network algorithms continues to necessitate efficient architecture, high memory bandwidth, parallel processing, and resources, despite decades of study on such algorithms. Excessive space requirements are a problem for DNN implementations caused by resource-intensive components like activation functions (AF) and multiply-and-accumulate (MAC) units. In addition, edge-AI applications necessitate a densely packed, power-hungry, high-throughput DNN accelerator. If we want to build a DNN accelerator that uses little power and occupies little space, we need to optimise the MAC architecture, the AF, and the network complexity so that data flows efficiently. In addition, providing functional configurability while working with restricted chip surface is a difficulty for DNN hardware designs based on ASICs. This dissertation explores the efficient and low-power VLSI architecture of DNN accelerators, addressing the hardware implementation of DNN and targeting applications with limited resources. In order to assess MAC and non-linear AF operations, we investigate and enhance the CORDIC architecture. The poor throughput is a major downside of CORDIC-based architectures, even though they are area and power efficient. Consequently, we suggest a pipelined design for CORDIC-based MAC and AF that focuses on performance. Due to the increased hardware resource consumption that comes with pipeline stages, this study investigates the mutual exclusivity of CORDIC stages and investigates in depth the accuracy variation related to the number of stages needed to achieve high throughput.

Data-Driven Decision Support Tool Co-Development with a Primary Health Care Practice Based Learning Network

Background The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities’ Practice Based Learning Network (PBLN) data-driven decision support tool co-development project. Methods We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in terms of six stages: population-level descriptive-exploratory study, PBLN team engagement, decision support tool problem selection, sandbox case study 1: individual-level risk predictions, sandbox case study 2: population-level planning predictions, project recap and next steps decision. Results The population-level study provided an initial point of engagement to consider how clients are (not) represented in EHR data and to inform problem selection and methodological decisions thereafter. We identified three initial meaningful types of decision support, with target application areas: risk prediction/screening, triaging specialized program referrals, and identifying care access needs. Based on feasibility and expected impact, we started with the goal to support earlier identification of mental health decline after diabetes diagnosis. As discussions deepened around clinical use cases associated with example prediction task set ups, the target problem evolved towards supporting the upstream task of organizational planning and advocacy for adequate mental health care service capacity to meet incoming needs. Conclusions This case study contributes towards a tool to support diabetes and mental health care, as well as lays groundwork for future CHC EHR-based decision support tool initiatives. We share lessons learned and reflections from our process that other primary health care organizations may use to inform their own co-development initiatives.

Peptide-conjugated Nanoparticle Platforms for Targeted Delivery, Imaging, and Biosensing Applications.

Peptides have become an indispensable tool in engineering of multifunctional nanostructure platforms for biomedical applications such as targeted drug and gene delivery, imaging and biosensing. They can be covalently incorporated into a variety of nanoparticles (NPs) including polymers, metallic nanoparticles, and others. Using different bioconjugation techniques, multifunctional peptide-modified NPs can be formulated to produce therapeutical and diagnostic platforms offering high specificity, lower toxicity, biocompatibility, and stimuli responsive behavior. Targeting peptides can direct the nanoparticles into specific tissues for targeted drug and gene delivery and imaging applications due to their specificity towards certain receptors. Furthermore, due to their stimuli-responsive features, they can offer controlled release of therapeutics into desired sites of disease. In addition, peptide-based biosensors and imaging agents can provide non-invasive detection and monitoring of diseases including cancer, infectious diseases, and neurological disorders. In this review, we covered the design and formulation of recent peptide-based NP platforms, as well as their utilization in in vitro and in vivo applications such as targeted drug and gene delivery, targeting, sensing, and imaging applications. In the end, we provided the future outlook to design new peptide conjugated nanomaterials for biomedical applications.

Study on the applicability of novel molecular targets of Vibrio species by pan-genome mining in the field of nucleic acid detection

Vibrio spp. are ubiquitous in marine areas worldwide and can cause diseases in humans. Aqueous environments favor the transfer of genes between strains, resulting in the loss of strain-specific target genes. This study aimed to explore new molecular targets for the detection of Vibrio strains at the species level using pan-genome analysis for nucleic acid detection and differentiation of major pathogenic Vibrio in aquatic products. Pan-genome analysis and PCR validation revealed a total of 10 specific targets: three for Vibrio parahaemolyticus, one for Vibrio alginolyticus, three for Vibrio vulnificus, three for Vibrio fluvialis. We constructed primers for the selected genes, and we verified their specificity using various Vibrio species and non-Vibrio strains. After systematic optimization, a novel molecular target-based 4-multiplex PCR (4-mPCR) method was developed, exhibiting a detection limit of 103 CFU/mL for pure cultures and 104 CFU/g for fish samples. The initial inoculum was approximately 1 Log10 CFU/g, and all samples were positive after 4 h of enrichment. Not only that, the feasibility of the mined new targets in the field of nucleic acid detection was further validated by the 4-mqPCR and LAMP techniques. These results suggest that novel targets mined by pan-genomic have good generalizability for use in a variety of nucleic acid detection techniques and can help improve the accuracy of nucleic acid-based detection techniques in the detection of pathogenic Vibrio species in aquatic samples, with potential applications for food safety investigations and disease diagnosis.

Enhanced Sequence-Specific DNA Recognition Using Oligodeoxynucleotide-Benzimidazole Conjugates.

Synthetic modification of oligodeoxynucleotides (ODNs) via conjugation to nucleic acid binding small molecules can improve hybridization and pharmacokinetic properties. In the present study, five Hoechst 33258 derived benzimidazoles were conjugated to T rich ODNs and their hybridization effectiveness was tested. Thermal denaturation studies revealed significant stabilization of complementary duplexes by ODN-benzimidazole conjugates, with the extent of stabilization being highly dependent on the length of the linker between DNA and benzimidazole. The increases in thermal stability were determined to be due to the binding of the benzimidazole moiety to the duplex. Circular dichroism and molecular modeling studies provided insights toward the influence of conjugation on duplex structure and how linker length impacts placement of the benzimidazole moiety in the minor groove. Furthermore, thermal denaturation studies with the complementary strand containing a single base mismatch or being RNA revealed that covalent conjugation of benzimidazoles to an ODN also enhances the sequence specificity. The fundamental studies reported herein provide a strategy to improve the stability and specificity properties of the ODN probes, which can be of use for targeting and diagnostics applications.

Power-efficient mass transfer of a novel pulsating multi-packed bed liquid - liquid extractor

This paper presents the development and performance evaluation of a new pulsating packed bed contactor designed for optimized diffusion-controlled liquid-liquid operations. Targeting applications like aromatics separation in petroleum, phenol removal from wastewater, and heat-sensitive material extraction (e.g., antibiotics), the contactor offers substantial advantages over traditional mechanical systems – notably, lower power consumption for equivalent production rates. Using the widely studied extraction of acetic acid from toluene-acetic acid by water as a model system, this work comprehensively investigates the factors impacting mass transfer within the pulsating contactor. Key variables explored include physical properties of the feed solution, oscillation frequency and amplitude, initial concentration of solute, flow rates, bed height, packing diameter, and bed spacing. Results reveal a significant enhancement in mass transfer with increasing frequency, amplitude, initial concentration, continuous phase flow rate, and dispersed phase flow rate. Interestingly, while increasing bed height initially improves mass transfer up to a specific point, further height increase exhibits a diminishing return. Packing sphere diameter shows minimal impact, while bed spacing has a negligible effect. A novel dimensionless equation effectively correlates the pulsating multi-packed bed data for broader applicability. To assess economic viability, the study also measures the mechanical power consumption under various operating conditions. Finally, diverse potential applications across pharmaceuticals, chemicals, biochemicals, petroleum, and nuclear fuel reprocessing are highlighted, showcasing the broad utility of this innovative mass transfer technology.

Race matters more than racial identity disclosure when evaluating applicant diversity statements

AbstractThe present research investigated whether a target applicant's race and disclosure of their race in a personal diversity statement influenced White evaluators' perceptions of the applicant's egalitarian motivations and their likelihood of contributing to organizational diversity and inclusion outcomes. In Study 1 (N = 206), participants evaluated a diversity statement that was ostensibly written by a White or Black applicant who either referenced or did not reference his race within the statement. Participants judged Black applicants as more internally motivated to be egalitarian and White applicants as more externally motivated, regardless of whether they disclosed their race in the statement. Participants also judged Black applicants as more likely to contribute to diversity and inclusion outcomes than White applicants. Study 2 (N = 257) aimed to replicate Study 1 and tested a strengthened race disclosure condition. We again saw little evidence of race disclosure impacting evaluations of applicants: Black applicants were judged as more internally motivated, less externally motivated, and more likely to contribute to diversity and inclusion compared to White applicants. Study 3 (N = 297) aimed to further replicate and expand on these results by testing a disclosure manipulation wherein the applicant discussed the personal importance/centrality of his race. Once again, applicant race (and not disclosure) demonstrated consistent effects on applicant evaluations. Our results highlight flaws in the personal diversity statement evaluation process, such that factors beyond statement content (i.e., applicant race) influenced perceptions and outcomes of the applicants. Practical implications and solutions for applicant evaluation processes are discussed.

Coupled dislocations and fracture dynamics at finite deformation: model derivation, and physical questions

A continuum mechanical model of coupled dislocation based plasticity and fracture at finite deformation is proposed. Motivating questions and target applications of the model are sketched.

Hepatocellular carcinoma biomarkers screening based on hydrogel photonic barcodes with tyramine deposition amplified ELISA

Hepatocellular carcinoma (HCC), as one of the most lethal cancers, significantly impacts human health. Attempts in this area tends to develop novel technologies with sensitive and multiplexed detection properties for early diagnosis. Here, we present novel hydrogel photonic crystal (PhC) barcodes with tyramine deposition amplified enzyme-linked immunosorbent assay (ELISA) for highly sensitive and multiplexed HCC biomarker screening. Because of the abundant amino groups of acrylic acid (AA) component, the constructed hydrogel PhC barcodes with inverse opal structure could facilitate the loading of antibody probes for subsequent detection of tumor markers. By integrating tyramine deposition amplified ELISA on the barcode, the detection signal of tumor markers has been enhanced. Based on these features, it is demonstrated that the hydrogel PhC barcodes with tyramine deposition amplified ELISA could realize highly sensitive and multiplexed detection of HCC-related biomarkers. It was found that this method is flexible, sensitive and accurate, suitable for multivariate analysis of low abundance tumor markers and future cancer diagnosis. These features make the newly developed PhC barcodes an innovation platform, which possesses tremendous potential for practical application of low abundance targets.

Digital application for drug product potency target evaluation in biopharmaceutical manufacturing.

Biopharmaceutical manufacturing entails a series of highly regulated steps. The manufacturing of safe and efficacious drug product (DP) requires testing of critical quality attributes (CQAs) against specification limits. DP potency concentration, which measures the dosage strength of a particular DP, is a CQA of great interest. In order to minimize the DP potency out-of-specification (OOS) risk, sterile fill finish (SFF) process adjustments may be needed. Varying the potency targets can be one such process adjustment. To facilitate such evaluation, data acquisition and statistical calculations are required. Regularly conducting the OOS risk assessment manually using commercial statistical software can be tedious, error-prone, and impractical, especially when several alternate potency targets are under consideration. In this work, the development of a novel framework for OOS risk assessment and deployment of cloud-based statistical software application to facilitate the risk assessment are presented. This application is intended to streamline the assessment of alternate potency targets for DP in biologics manufacturing. The major aspects of this potency targeting application development are presented in detail. Specifically, data sources, pipeline, application architecture, back-end and front-end development as well as application verification are discussed. Finally, several use cases are presented to highlight the application's utility in biologics manufacturing.

PD-L1 Expression in Neoplastic and Immune Cells of Thymic Epithelial Tumors: Correlations with Disease Characteristics and HDAC Expression.

Programmed death-ligand 1 (PD-L1) expression in neoplastic and immune cells of the tumor microenvironment determines the efficacy of antitumor immunity, while it can be regulated at the epigenetic level by various factors, including HDACs. In this study, we aim to evaluate the expression patterns of PD-L1 in thymic epithelial tumors (TETs), while we attempt the first correlation analysis between PD-L1 and histone deacetylases (HDACs) expression. Immunohistochemistry was used to evaluate the expression of PD-L1 in tumor and immune cells of 91 TETs with SP263 and SP142 antibody clones, as well as the expressions of HDCA1, -2, -3, -4, -5, and -6. The PD-L1 tumor proportion score (TPS) was higher, while the immune cell score (IC-score) was lower in the more aggressive TET subtypes and in more advanced Masaoka-Koga stages. A positive correlation between PD-L1 and HDAC-3, -4, and -5 cytoplasmic expression was identified. Higher PD-L1 expression in neoplastic cells and lower PD-L1 expression in immune cells of TETs characterizes more aggressive and advanced neoplasms. Correlations between PD-L1 and HDAC expression unravel the impact of epigenetic regulation on the expression of immune checkpoint molecules in TETs, with possible future applications in combined therapeutic targeting.

Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries.

Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues. Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.

Programming self-assembling magnetic microrobots with multiple physical and chemical intelligence

Medical microrobots represent the cutting-edge of biomedical research, showcasing their potential as versatile tools. They exhibit promise in acting as carriers for cancer cell therapy, effectively delivering drugs, and as manipulators equipped for biosensing, offering mobility and adaptability. Despite these advancements, the intricate challenge of creating a microrobot that seamlessly integrates various physical and chemical functionalities persists. This includes the fusion of selective sensing, manipulation capabilities, carrier functionality, precise time-based actuators for motion control, and adaptive shaping. Addressing these complexities remains an ongoing endeavor. In this context, our work introduces a pioneering magnetic microrobot founded on CaCO3 microparticles (MPs) synthesized alongside polyethylenimine (CaCO3-PEI), forming the core body. This is combined with Fe3O4 nanoparticles (NPs) enveloped in glutaraldehyde (Fe3O4-Glu), constituting the propulsive engine. The synergy of these elements enables the microrobot to execute multimodal motions, orchestrating its movement with finesse. This dynamic capability follows a “deliver-and-return” pattern for precise targeting applications with real-world relevance. Furthermore, the Fe3O4-Glu/CaCO3-PEI microrobots demonstrated remarkable proficiency in the targeted identification, manipulation, and transportation of cancer cells through the strategic integration of specific antibodies onto their structure. Within the realm of selective cancer cell detection, these microrobots adeptly function as dynamic mobile immunosensors. The versatile utility of the Fe3O4-Glu/CaCO3-PEI microrobots extends to their role as carriers for drugs and imaging agents, facilitated by the mediation of extracellular pH modulation in cancer cells orchestrated by CaCO3. This innovative work introduces a novel “on-the-fly” concept, revolutionizing the landscape of robotics programmed with multifaceted chemical and physical intelligences.

Experimental validation of igneous rocks as filler material for packed bed system dedicated to small CSP plants 1MWel

This study is based on an experimental evaluation of four igneous rocks as storage materials for a packed bed system. This system will be in conjunction with a 1 Mel Concentrated Solar Power (CSP) plant employing an Organic Rankine Cycle (ORC). The target temperature of the application ranges from 180 °C to 300 °C, which will be achieved through a Linear Fresnel solar field. In this endeavor, this work aims to identify the potential of locally collected rocks: dolerite, leucogabbro, wehrlite, and troctolite, for application in this medium-temperature thermal energy storage (TES) system. To assess their suitability, a prior short-term thermal stability tests were performed by applying five cycles using thermogravimetric and dilatometry techniques. Afterward, a durability test was conducted by subjecting the rocks to 380 cycles within a temperature range of 100 °C to 300 °C, under an air atmosphere. At the end of the test, an evaluation was assessed to analyze the evolution of mineralogical and structural stability. Furthermore, this study examined the development of critical thermophysical and mechanical properties for sensible storage materials. We concluded that, the hydrothermal alterations occurred in mafic rocks leucogabbro and dolerite, enhancing their thermomechanical properties. In contrast, ultra-mafic rocks, wehrlite and, troctolite maintained a stable mineralogical composition and structure, preserving their main properties after the durability test. Dolerite, wehrlite and troctolite are considered suitable for an air-packed bed system. For our target application, wehrlite presents the most potential rock as it combines high thermophysical and mechanical properties, along with stable petrographic characteristics.

A novel strategy for the design of compositionally complex alloys for advanced nuclear applications

The development of compositionally complex alloys (CCAs), including medium- and high-entropy alloys (M/HEAs), which can offer exceptional mechanical properties, thermal stability, and corrosion resistance, has largely been driven by trial-and-error, and designing CCAs for specific properties and commercial applications remains an unresolved challenge. This study introduces a novel approach for designing CCAs with specific properties for target applications. It presents a TiVCrFeZrTa high-entropy master alloy (HEMA), composed primarily of refractory elements, with low neutron capture, minimal transmutation, and reduced gamma activity, targeting nuclear applications. Utilizing a natural-mixing guided design (NMGD) methodology, the process identifies the crystalline structures of the HEMA's phases, allowing for the recreation of solid solution phases while avoiding brittle intermetallic phases. The resultant Ti55Zr30Ta6V5Fe2Cr2 CCA exhibits as-cast ductility allowing 86 % thickness reduction during cold-rolling, high tensile yield strengths up to 1360 MPa and fracture toughness up to 36 MPa m1/2, contingent on thermomechanical treatment. This approach demonstrates how tailored property calculations and NMGD can expedite the identification of CCA compositions, streamlining the development process and reducing reliance on extensive, costly trial-and-error experimentation.

Computational analysis of electrode structure and configuration for efficient and localized neural stimulation.

Neuromodulation technique using electric stimulation is widely applied in neural prosthesis, therapy, and neuroscience research. Various stimulation techniques have been developed to enhance stimulation efficiency and to precisely target the specific area of the brain which involves optimizing the geometry and the configuration of the electrode, stimulation pulse type and shapes, and electrode materials. Although the effects of electrode shape, size, and configuration on the performance of neural stimulation have individually been characterized, to date, there is no integrative investigation of how this factor affects neural stimulation. In this study, we computationally modeled the various types of electrodes with varying shapes, sizes, and configurations and simulated the electric field to calculate the activation function. The electrode geometry is then integratively assessed in terms of stimulation efficiency and stimulation focality. We found that stimulation efficiency is enhanced by making the electrode sharper and smaller. A center-to-vertex distance exceeding 100µm shows enhanced stimulation efficiency in the bipolar configuration. Additionally, the separation distance of less than 1mm between the reference and stimulation electrodes exhibits higher stimulation efficiency compared to the monopolar configuration. The region of neurons to be stimulated can also be modified. We found that sharper electrodes can locally activate the neuron. In most cases, except for the rectangular electrode shape with a center-to-vertex distance smaller than 100µm, the bipolar electrode configuration can locally stimulate neurons as opposed to the monopolar configuration. These findings shed light on the optimal selection of neural electrodes depending on the target applications.

Effect of Suboptimal Disease Control on Patient Quality of Life: Real-World Data from the Observational IBD-PODCAST Canada Trial

BackgroundThe real-world application of STRIDE-II treatment targets to identify whether disease control is optimal in Crohn’s disease (CD) and ulcerative colitis (UC) is not well known.AimsThis study aimed to estimate proportions of patients with suboptimally controlled CD and UC in real-world Canadian healthcare settings and the impact on quality of life (QoL).MethodsThe noninterventional, multicenter, observational IBD-PODCAST Canada study comprised a single study visit involving routine assessments, patient- and clinician-completed questionnaires, and a retrospective chart review. Primary outcomes were proportions of patients with STRIDE-II-based red flags indicative of suboptimal disease control and mean ± standard deviation Short Inflammatory Bowel Disease Questionnaire (SIBDQ) scores. Secondary outcomes included proportions of patients and clinicians subjectively reporting suboptimal control.ResultsAmong 163 enrolled patients from 10 sites, 45/87 patients with CD (51.7%; 95% CI: 40.8%, 62.6%) and 33/76 patients with UC (43.3%; 95% CI: 32.1%, 55.3%) had suboptimal disease control based on STRIDE-II criteria. Suboptimal control was subjectively reported at lower proportions (patients: CD, 15.0%; UC, 18.6%; clinicians: CD, 19.5%; UC, 25.0%). Numerically lower SIBDQ scores were observed with suboptimal control (CD, 43.0 ± 10.8; UC, 42.5 ± 12.0) than with optimal control (CD, 58.2 ± 7.2; UC, 57.8 ± 6.6).ConclusionsApproximately 50% (CD) and 40% (UC) of patients from real-world Canadian practices had suboptimal disease control based on STRIDE-II criteria. Suboptimal control was underestimated by patients and clinicians and accompanied by reduced QoL, suggesting further efforts to implement STRIDE-II treat-to-target strategies are needed.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound’s aggregation propensity.Graphical

Solubility of seven amino acids in two acetate ionic liquids from 298.15 K to 328.15 K

The number of studies on the solubilities of amino acids in pure ionic liquids is currently very scarce. Focusing on fundamental studies for the recovery of added-value compounds from fisheries and aquaculture discards, in this work the solubility of several amino acids in acetate ionic liquids and their mixtures with water were determined at temperatures ranging from 298.15 K to 328.15 K and atmospheric pressure. Sharing the acetate anion, solubilities were higher in the ionic liquid with 1-ethyl-3-methylimidazolium than with 1-hexyl-3-methylimidazolium cation. Increasing temperatures resulted in enhanced solubilisation. Solubilities in the ionic liquids ranked thus: Pro > OH-Pro > Lys·HCl > Ala > Gly > Cys, with an intermediate position of Arg (the amino acid most influenced by the hydrophobic environment and temperature). In the case of the mixtures with water, the ranking was Pro > Lys·HCl > Ala > Arg > OH-Pro > Gly > Cys. The van't Hoff equation adequately correlated experimental solubilities in the range of temperatures studied, and the process of dissolution was found endothermic and entropy-driven in all the cases. The ionic liquid 1-ethyl-3-methylimidazolium should be considered for the target application and the water content a design variable to analyse.

Let it stick: Strategies and applications for intracellular plasma membrane targeting of proteins in Saccharomyces cerevisiae.

Lipid binding domains and protein lipidations are essential features to recruit proteins to intracellular membranes, enabling them to function at specific sites within the cell. Membrane association can also be exploited to answer fundamental and applied research questions, from obtaining insights into the understanding of lipid metabolism to employing them for metabolic engineering to redirect fluxes. This review presents a broad catalog of membrane binding strategies focusing on the plasma membrane of Saccharomyces cerevisiae. Both lipid binding domains (pleckstrin homology, discoidin-type C2, kinase associated-1, basic-rich and bacterial phosphoinositide-binding domains) and co- and post-translational lipidations (prenylation, myristoylation and palmitoylation) are introduced as tools to target the plasma membrane. To provide a toolset of membrane targeting modules, respective candidates that facilitate plasma membrane targeting are showcased including their in vitro and in vivo properties. The relevance and versatility of plasma membrane targeting modules are further highlighted by presenting a selected set of use cases.