Subtractive Approaches Research Articles

Recent progress of SPIDER: Aspects of subtractive approaches to existing building’s performance improvement

This contribution reports about methodology, progress and preliminary findings of a recent exploratory Research and Development project, pertaining to the (semi-)automated thermal retrofit of existing building’s envelopes. Thereby, the potential of robots, which autonomously identify areas of facades that can be used for inserting openings into the existing wall material, is examined. The idea is based on the fact that masonry walls of historic masonry walls (especially of Gründerzeit buildings built between 1850 and 1918) often have been structurally over-dimensioned. As such, the spare thickness of the walls could be used for thermal insulation purposes. The initial idea to implement air cavities for insulation purposes is in correspondence with the predominant functional principle of most insulation materials.

Designing a Multi-Epitope Vaccine against Chlamydia trachomatis by Employing Integrated Core Proteomics, Immuno-Informatics and In Silico Approaches.

Simple SummaryChlamydia trachomatis is the most common cause of blindness, ectopic pregnancy, and bacterial sexually transmitted infections. These diseases affect mostly young women but can also infect men and women of all ages. It is not difficult to treat, but it can lead to more significant health problems if left untreated. There is no licensed vaccine available for this pathogen at present. Hence, a vaccine that can control and prevent C. trachomatis infections is designed in this study by using different immuno-informatics approaches. However, the designed vaccine is the result of computational approaches; therefore, experimental validation is required to prove its effectiveness.Chlamydia trachomatis, a Gram-negative bacterium that infects the rectum, urethra, congenital sites, and columnar epithelium of the cervix. It is a major cause of preventable blindness, ectopic pregnancy, and bacterial sexually transmitted infections worldwide. There is currently no licensed multi-epitope vaccination available for this pathogen. This study used core proteomics, immuno-informatics, and subtractive proteomics approaches to identify the best antigenic candidates for the development of a multi-epitope-based vaccine (MEBV). These approaches resulted in six vaccine candidates: Type III secretion system translocon subunit CopD2, SctW family type III secretion system gatekeeper subunit CopN, SycD/LcrH family type III secretion system chaperone Scc2, CT847 family type III secretion system effector, hypothetical protein CTDEC_0668, and CHLPN 76kDa-like protein. A variety of immuno-informatics tools were used to predict B and T cell epitopes from vaccine candidate proteins. An in silico vaccine was developed using carefully selected epitopes (11 CTL, 2 HTL & 10 LBL) and then docked with the MHC molecules (MHC I & MHC II) and human TLR4. The vaccine was coupled with Cholera toxin subunit B (CTB) adjuvant to boost the immune response. Molecular dynamics (MD) simulations, molecular docking, and MMGBSA analysis were carried out to analyze the molecular interactions and binding affinity of MEBV with TLR4 and MHC molecules. To achieve the highest level of vaccine protein expression, the MEBV was cloned and reverse-translated in Escherichia coli. The highest level of expression was achieved, and a CAI score of 0.97 was reported. Further experimental validation of the MEBV is required to prove its efficacy. The vaccine developed will be useful in preventing infections caused by C. trachomatis.

New putative therapeutic targets against Serratia marcescens using reverse vaccinology and subtractive genomics

The Gram-negative bacillus Serratia marcescens, a member of Enterobacteriaceae family, is an opportunistic nosocomial pathogen commonly found in hospital outbreaks that can cause infections in the urinary tract, bloodstream, central nervous system and pneumonia. Because S. marcescens strains are resistant to several antibiotics, it is critical the need for effective treatments, including new drugs and vaccines. Here, we applied reverse vaccinology and subtractive genomic approaches for the in silico prediction of potential vaccine and drug targets against 59 strains of S. marcescens. We found 759 core non-host homologous proteins, of which 87 are putative surface-exposed proteins, 183 secreted proteins, and 80 membrane proteins. From these proteins, we predicted seven candidates vaccine targets: a sn-glycerol-3-phosphate-binding periplasmic protein UgpB, a vitamin B12 TonB-dependent receptor, a ferrichrome porin FhuA, a divisome-associated lipoprotein YraP, a membrane-bound lytic murein transglycosylase A, a peptidoglycan lytic exotransglycosylase, and a DUF481 domain-containing protein. We also predicted two drug targets: a N(4)-acetylcytidine amidohydrolase, and a DUF1428 family protein. Using the molecular docking approach for each drug target, we identified and selected ZINC04259491 and ZINC04235390 molecules as the most favorable interactions with the target active site residues. Our findings may contribute to the development of vaccines and new drug targets against S. marcescens. Communicated by Ramaswamy H. Sarma

An Intelligent Approach to Predict the Viscosity of Water/Glycerin Containing Cu Nanoparticles: Neuro-Fuzzy Inference System (ANFIS) Model

The ability to approximate the nanofluid properties such as viscosity, thermal conductivity, and specific heat capacity will greatly assist in the modeling and design of nanofluidic systems. The purpose of this study was to present an adaptive neuro-fuzzy inference system (ANFIS) model for estimating the viscosity of Water/Glycerin nanofluid-containing Cu nanoparticles. The model inputs consist of two variables of temperature and volume concentration of nanofluids which have a great influence on the nanofluid viscosity. The experimental data were divided into two categories: training (three-quarters) and testing (a quarter of the data). The grid partition and subtractive clustering approaches were employed to determine the ANFIS configuration. The mean value of the relative error of 5.18% and the root mean square error of 0.0794 were obtained by comparing the target and model output values for the testing data. Proper matching of ANFIS prediction results with the test data set indicates the validity of the model. In addition, an empirical correlation was developed based on the form presented in the literature. The constants of the equation were determined by the genetic algorithm (GA) searching technique. The comparison of the prediction accuracy of the two models showed the complete superiority of the ANFIS.

A shape control strategy for wire arc additive manufacturing of thin-walled aluminium structures with sharp corners

• Self-overlapping in WAAM of thin-walled structures with sharp corners is revealed. • A corner cross-section model for WAAM is established. • Travel/welding speeds at corners are optimized by using the developed model. • An improved deposition quality at corners can be achieved through using the proposed shape control strategy. Wire Arc Additive Manufacturing (WAAM) of large thin-walled aluminium structures in the aerospace field has significant advantages over traditional subtractive approaches. The technique is based on arc welding and wire filler material, and the thin-walled structures are built up layer-by-layer. However, for the parts with sharp corners, the shape control poses a challenge due to the occurring of an inevitable self-overlapping of the deposition at corners. Since aluminium alloys are sensitive to heat, the undesired materials stacked at corners with excessive heat input reducing not only the shape accuracy of the deposited structures, but the stability of the deposition process also. In this paper, a shape control strategy was proposed to reduce shape errors through local optimization of deposition parameters at corners. A corner cross-section model correlating the relationship between the process parameter and bead profile in the self-overlapping area was firstly established. Accordingly, the parameter of the welding speeds at the corner areas were locally optimized with the proposed bead model. The performance of the present corner cross-section model was then investigated in terms of corners with the angle degree varied from 15° to 60°. Finally, the proposed shape control strategy was implemented through experiments on a thin-walled aluminium structure with varied sharp corners. It can be found that the accumulative height errors varied with the change of the angle degree of the corners. At the corner with the angle of 60°, the height error reduced from 3.56 mm to 1.75 mm corresponding to the average building height of 55.2mm. More than 50 % in error reduction was achieved indicating the considerable improvement of the proposed control strategy. This is critical in process planning to achieve better shape accuracy and material efficiency in the WAAM of thin-walled aluminium structures with sharp corners.

Direct Optical Lithography of Colloidal Metal Oxide Nanomaterials for Diffractive Optical Elements with 2π Phase Control.

Spatially patterned dielectric materials are ubiquitous in electronic, photonic, and optoelectronic devices. These patterns are typically made by subtractive or additive approaches utilizing vapor-phase reagents. On the other hand, recent advances in solution-phase synthesis of oxide nanomaterials have unlocked a materials library with greater compositional, microstructural, and interfacial tunability. However, methods to pattern and integrate these nanomaterials in real-world devices are less established. In this work, we directly optically pattern oxide nanoparticles (NPs) by mixing them with photosensitive diazo-2-naphthol-4-sulfonic acid and irradiating with widely available 405 nm light. We demonstrate the direct optical lithography of ZrO2, TiO2, HfO2, and ITO NPs and investigate the chemical and physical changes responsible for this photoinduced decrease in solubility. Micron-thick layers of amorphous ZrO2 NPs were patterned with micron resolution and shown to allow 2π phase control of visible light. We also show multilayer patterning and use it to fabricate features with different thicknesses and distinct structural colors. Upon annealing at 400 °C, the deposited ZrO2 structures have excellent optical transparency across a wide wavelength range (0.3-10 μm), a high refractive index (n = 1.84 at 633 nm), and are optically smooth. We then fabricate diffractive optical elements, such as binary phase diffraction gratings, that show efficient diffractive behavior and good thermal stability. Different oxide NPs can also be mixed prior to patterning, providing a high level of material tunability. This work demonstrates a general patterning approach that harnesses the processability and diversity of colloidal oxide nanomaterials for use in photonic applications.

Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis.

Background Mycobacterium leprae and Mycobacterium lepromatosis are gram-positive bacterial pathogens and the causative agents of leprosy in humans across the world. The elimination of leprosy cannot be achieved by multidrug therapy alone, and highlights the need for new tools and drugs to prevent the emergence of new resistant strains.Methods In this study, our contribution includes the prediction of vaccine targets and new putative drugs against leprosy, using reverse vaccinology and subtractive genomics. Six strains of Mycobacterium leprae and Mycobacterium lepromatosis (4 and 2 strains, respectively) were used for comparison taking Mycobacterium leprae strain TN as the reference genome. Briefly, we used a combined reverse vaccinology and subtractive genomics approach.Results As a result, we identified 12 common putative antigenic proteins as vaccine targets and three common drug targets against Mycobacterium leprae and Mycobacterium lepromatosis. Furthermore, the docking analysis using 28 natural compounds with three drug targets was done.ConclusionsThe bis-naphthoquinone compound Diospyrin (CID 308140) obtained from indigenous plant Diospyros spp. showed the most favored binding affinity against predicted drug targets, which can be a candidate therapeutic target in the future against leprosy.

Subtractive proteomics and immunoinformatics approaches to explore Bartonella bacilliformis proteome (virulence factors) to design B and T cell multi-epitope subunit vaccine.

Bartonella bacilliformis a gram-negative facultative aerobe responsible for the Carrion's disease widely distributed in Ecuador, Peru, and Colombia with a high mortality rate when no specific treatment is received. B bacilliformis is transmitted by Sand fly (Lutzomyia verrucarum) to healthy individuals. Immunoinformatic and subtractive proteomics approaches were employed in this study to prioritize the best candidates for vaccine designing. These approaches resulted in five vaccine candidates, flagellar biosynthetic protein (Uniprot ID: A1UTU1), heme exporter protein C (UniProt ID: A1UU82), Cytochrome c-type biogenesis protein (Uniprot ID: A1URZ7), Hemin ABC transporter (Uniprot ID: A1US20) and Phosphatidate cytidylyltransferase (Uniprot ID: A1USE3). The mentioned proteins are antigenic and essential for pathogen survival. A range of immune-informatics tools was applied for the prediction of B and T cell epitopes for the vaccine candidate proteins. In-silico vaccine was constructed using carefully evaluated epitopes and consequently modeled for docking with human Toll-like receptor 4. TLR-4 agonist 50S ribosomal protein L7/L12 (UniproKB ID; P9WHE3) was linked to the vaccine as an adjuvant to boost immune response towards the vaccine. For stability evaluation of the vaccine-TLR-4 docked complex, MD simulations were performed. The final vaccine was back-translated and cloned in Eschericia coli to attain the maximal expression of the vaccine protein. The maximal expression was ensured, and the CAI score of 0.96 was reported. The current vaccine requires future experimental validation to confirm its effectiveness. The vaccine developed will be helpful to protect against B bacilliformis associated infections.

Identification of Potential Drug Targets in Helicobacter pylori Using In Silico Subtractive Proteomics Approaches and Their Possible Inhibition through Drug Repurposing.

The class 1 carcinogen, Helicobacter pylori, is one of the World Health Organization’s high priority pathogens for antimicrobial development. We used three subtractive proteomics approaches using protein pools retrieved from: chokepoint reactions in the BIOCYC database, the Kyoto Encyclopedia of Genes and Genomes, and the database of essential genes (DEG), to find putative drug targets and their inhibition by drug repurposing. The subtractive channels included non-homology to human proteome, essentiality analysis, sub-cellular localization prediction, conservation, lack of similarity to gut flora, druggability, and broad-spectrum activity. The minimum inhibitory concentration (MIC) of three selected ligands was determined to confirm anti-helicobacter activity. Seventeen protein targets were retrieved. They are involved in motility, cell wall biosynthesis, processing of environmental and genetic information, and synthesis and metabolism of secondary metabolites, amino acids, vitamins, and cofactors. The DEG protein pool approach was superior, as it retrieved all drug targets identified by the other two approaches. Binding ligands (n = 42) were mostly small non-antibiotic compounds. Citric, dipicolinic, and pyrophosphoric acid inhibited H. pylori at an MIC of 1.5–2.5 mg/mL. In conclusion, we identified potential drug targets in H. pylori, and repurposed their binding ligands as possible anti-helicobacter agents, saving time and effort required for the development of new antimicrobial compounds.

Teacher sociolinguistic backgrounds: a multilinguistic domain approach to understand teacher agency and language planning outcomes

ABSTRACT This paper examines the unintended consequences of state language policy and planning (LPP) that adopt subtractive approaches on teachers’ subsequent receptivity to policy fine-tuning. A comparative approach is adopted in this statistical study of two strategic contexts, where the influence of the world’s two leading languages—English and Mandarin—manifests in the home language conversion patterns of ethnic Chinese teachers of Hong Kong and Singapore. The interplay among state, education, and family linguistic domains provides the framework to understand how teachers exercise agency underpinned by their sociolinguistic background (childhood home language—CHL) and home language conversion preference (home language as adult—HL). The results show that teacher CHL–HL conversion preferences underlie their response to state LPP initiatives and influence LPP outcomes in the education domain. The results are theorized in terms of the prevailing values in Hong Kong and Singapore that shape teacher agency, the unintended outcomes of subtractive LPP in education, and the probable outcomes on the linguistic vitality of local and dominant languages in Hong Kong, with the interplay between future subtractive LPP and teacher agency.

Guidelines to compare additive and subtractive manufacturing approaches under the energy demand perspective

In order to characterise the environmental performance of additive manufacturing (AM) processes, comparative analyses are required. Different manufacturing approaches (such as additive and subtractive ones), besides adopting different equipment, use different kinds and amounts of material. Therefore, the material-related flow has to be followed throughout the entire product life. Differences in environmental impact arise at each step of the life cycle: material production, manufacturing, use, disposal, and transportation. A life cycle-based methodology able to take due account of all the factors of influence on the total energy demand for the production of metal components is given in this paper. Decision support tools for identifying the most sustainable manufacturing route (subtractive versus AM-based approaches) are presented for different scenarios. The aim of the present paper is to contribute to the debate concerning the environmental impact characterisation of AM processes.

Guidelines to compare additive and subtractive manufacturing approaches under the energy demand perspective

Guidelines to compare additive and subtractive manufacturing approaches under the energy demand perspective

A permeable-membrane microchannel heat sink made by additive manufacturing

Microchannel heat sinks are capable of removing dense heat loads from high-power electronic devices with low thermal resistance, but suffer from high pressure drops due to the small channel dimensions. Features that reduce the pressure drop, such as manifolds, increase fabrication complexity and are constrained by traditional subtractive manufacturing approaches. Additive manufacturing technologies offer improved design freedom and reduced geometric restrictions, expanding the types of features that can be produced and integrated into a heat sink. In this work, a novel permeable membrane microchannel (PMM) heat sink geometry is proposed and fabricated using direct metal laser sintering (DMLS) of an aluminum alloy (AlSi10Mg). In this PMM design, the cooling fluid is forced through thin, porous walls that act as both conducting fins and membranes that allow flow through their fine internal flow features for efficient heat exchange. The design leverages the ability of this fabrication process to incorporate complex, arbitrarily curved structures having internal porosity to enhance heat transfer and reduce pressure drop across the heat sink. The PMM heat sink geometry is benchmarked against a low-pressure-drop manifold microchannel (MMC) heat sink. A reduced-order model is used to explore the relative performance trends between the designs. Both heat sinks are experimentally characterized at flow rates of 50–500 mL/min using deionized water as the working fluid. At a constant pumping power of 0.018 W, the permeable membrane microchannel design offers both lower thermal resistance (17% reduction) and lower pressure drop (28% reduction) compared to the manifold microchannel heat sink.

Putative vaccine candidates and drug targets identified by reverse vaccinology and subtractive genomics approaches to control Haemophilus ducreyi, the causative agent of chancroid.

Chancroid is a sexually transmitted infection (STI) caused by the Gram-negative bacterium Haemophilus ducreyi The control of chancroid is difficult and the only current available treatment is antibiotic therapy; however, antibiotic resistance has been reported in endemic areas. Owing to recent outbreaks of STIs worldwide, it is important to keep searching for new treatment strategies and preventive measures. Here, we applied reverse vaccinology and subtractive genomic approaches for the in silico prediction of potential vaccine and drug targets against 28 strains of H. ducreyi We identified 847 non-host homologous proteins, being 332 exposed/secreted/membrane and 515 cytoplasmic proteins. We also checked their essentiality, functionality and virulence. Altogether, we predicted 13 candidate vaccine targets and three drug targets, where two vaccines (A01_1275, ABC transporter substrate-binding protein; and A01_0690, Probable transmembrane protein) and three drug targets (A01_0698, Purine nucleoside phosphorylase; A01_0702, Transcription termination factor; and A01_0677, Fructose-bisphosphate aldolase class II) are harboured by pathogenicity islands. Finally, we applied a molecular docking approach to analyse each drug target and selected ZINC77257029, ZINC43552589 and ZINC67912117 as promising molecules with favourable interactions with the target active site residues. Altogether, the targets identified here may be used in future strategies to control chancroid worldwide.

The Role of re-design for Additive Manufacturing on the Process Environmental Performance

At present, economic and technological design criteria for products and processes should be matched with the minimization of environmental impact objectives. Manufacturing, material production, and product design are strictly connected stages. The choice of a production system over another could result in significant material and energy/resource savings, particularly if the component has been properly designed for manufacturing. In this scenario, Additive Manufacturing, which has been identified as a potential disruptive technology, gained an increasing interest for the creation of complex metal parts. The paper focuses on the tools, based on the holistic modelling of additive and subtractive approaches, which could be used to identify the production route allowing the lowest energy demand or CO2 emissions. The models account for the main process variables as well as the impacts due to the re-design for AM for the creation of components made of Ti-6Al-4V.

Environmental modelling of aluminium based components manufacturing routes: Additive manufacturing versus machining versus forming

Additive Manufacturing represents, by now, a viable alternative for metal-based components production. Therefore the designer, often, has to select among three options at process design stage: subtractive, mass conserving, and additive approaches. The selection of a given process, besides affecting the manufacturing step impact, influences significantly the impact related to the material production step. If the process enables a part weight reduction (as the Additive Manufacturing approaches do) even the use phase is affected by the manufacturing approach selection. The present research provides a comprehensive environmental manufacturing approaches comparison for components made of aluminum alloys. Additive manufacturing (Selective Laser Sintering), machining, and forming processes are analyzed and compared by means of Life Cycle Assessment techniques. The effect of weight reduction enabled by additive approach is considered. The paper aims at highlighting the strong link between manufacturing approach selection and material use. In this respect, a thorough environmental analysis of the pre-manufacturing step is developed. Moreover, the influence of eco-attributes aluminium variability on the comparative analysis results is studied. The paper, therefore, contributes to the development of a methodology for manufacturing approaches comparison, providing guidelines for green manufacturing approach selection. Results reveal that, for the analyzed case studies, the Additive Manufacturing is a sustainable solution for aluminium components only under a specific scenario: high complexity shapes, significant weight reduction, and application in transportation systems.

Precise Patterning of Large‐Scale TFT Arrays Based on Solution‐Processed Oxide Semiconductors: A Comparative Study of Additive and Subtractive Approaches

AbstractPrecise patterning of solution‐processed oxide semiconductors is critical for cost‐effective, large‐scale, and high throughput fabrication of circuits and display application. In this paper, demonstration and comparison are made using the additive and subtractive patterning strategies to precisely fabricate wafer‐scale thin film transistor arrays (1600 devices), which are based on high‐quality solution‐processed indium zinc oxide (IZO) and indium gallium zinc oxide (IGZO). The IZO and IGZO TFTs exhibit field‐effect mobility up to 8.0 and 5.2 cm2 V−1 s−1 when using the additive method, whereas the highest mobility of 24.2 and 13.7 cm2 V−1 s−1 for IZO and IGZO TFTs is achieved when using the subtractive method. The X‐ray photoelectronic spectroscopy studies and quantitative 2D device simulations together reveal that good device performance is attributed to moderate shallow donor‐like states (providing electrons) from oxygen vacancy and few accepter‐like states (trapping electrons) resulted from the dense structural framework of MO bonds. After examining the uniformity and reliability of the devices, the solution‐patterned inverters are demonstrated using negative‐channel metal oxide semiconductors, which show full swing output transfer characteristics and thus provide a promising method for solution‐based fabrications of circuits.

Introducing a novel mesh following technique for approximation-free robotic tool path trajectories

Abstract Modern tools for designing and manufacturing of large components with complex geometries allow more flexible production with reduced cycle times. This is achieved through a combination of traditional subtractive approaches and new additive manufacturing processes. The problem of generating optimum tool-paths to perform specific actions (e.g. part manufacturing or inspection) on curved surface samples, through numerical control machinery or robotic manipulators, will be increasingly encountered. Part variability often precludes using original design CAD data directly for toolpath generation (especially for composite materials), instead surface mapping software is often used to generate tessellated models. However, such models differ from precise analytical models and are often not suitable to be used in current commercially available path-planning software, since they require formats where the geometrical entities are mathematically represented thus introducing approximation errors which propagate into the generated toolpath. This work adopts a fundamentally different approach to such surface mapping and presents a novel Mesh Following Technique (MFT) for the generation of tool-paths directly from tessellated models. The technique does not introduce any approximation and allows smoother and more accurate surface following tool-paths to be generated. The background mathematics to the new MFT algorithm are introduced and the algorithm is validated by testing through an application example. Comparative metrology experiments were undertaken to assess the tracking performance of the MFT algorithms, compared to tool-paths generated through commercial software. It is shown that the MFT tool-paths produced 40% smaller errors and up to 66% lower dispersion around the mean values. Highlights This work presents a technique for generation of tool-paths from tessellated models. The technique does not introduce approximations to surface following tool-paths. Comparative metrology experiments were carried out to assess the technique. The technique produces smaller errors and lower dispersion around the mean values. The developed method is aligned with the growing use of surface mapping techniques.

Methylation-assisted bisulfite sequencing to simultaneously map 5fC and 5caC on a genome-wide scale for DNA demethylation analysis.

Active DNA demethylation is mediated by ten-eleven translocation (TET) proteins that progressively oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). We have developed a methylation-assisted bisulfite sequencing (MAB-seq) method that enables direct genome-scale mapping and quantification of 5fC and 5caC marks together at single-base resolution. In bisulfite sequencing (BS), unmethylated cytosine residues (Cs), 5fCs and 5caCs, are converted to uracil and cannot be discriminated from each other. The pretreatment of the DNA with the CpG methylation enzyme M.SssI, which converts only the Cs to 5mCs, protects Cs but not 5fCs and 5caCs, which enables direct detection of 5fCs and 5caCs as uracils. Here we also describe an adapted version of the protocol to perform reduced-representation MAB-seq (RRMAB-seq) that provides increased coverage on CpG-rich regions, thus reducing the execution costs and increasing the feasibility of the technique. The main advantage of MAB-seq is to reduce the number of chemical/enzymatic DNA treatments required before bisulfite treatment and to avoid the need for prohibitive sequencing coverage, thus making it more reliable and affordable than subtractive approaches. The method presented here is the ideal tool for studying DNA demethylation dynamics in any biological system. Overall timing is ∼3 d for library preparation.

2D Psychoacoustic modeling of equivalent masking for automatic speech recognition

Noise robustness has long been one of the most important goals in speech recognition. While the performance of automatic speech recognition (ASR) deteriorates in noisy situations, the human auditory system is relatively adept at handling noise. To mimic this adeptness, we study and apply psychoacoustic models in speech recognition as a means to improve robustness of ASR systems. Psychoacoustic models are usually implemented in a subtractive manner with the intention to remove noise. However, this is not necessarily the only approach to this challenge. This paper presents a novel algorithm which implements psychoacoustic models additively. The algorithm is motivated by the fact that weak sound elements that are below the masking threshold are the same for the human auditory system, regardless of the actual sound pressure level. Another important contribution of our proposed algorithm is a superior implementation of masking effect. Only those sounds that fall below the masking threshold are modified, which better reflects physical masking effects. We give detailed experimental results showing relationships between the subtractive and additive approaches. Since all the parameters of the proposed filters are positive or zero, they are named 2D psychoacoustic P-filters. Detailed theoretical analysis is provided to show the noise removal ability of these filters. Experiments are carried out on the AURORA2 database. Experimental results show that the word recognition rate using our proposed feature extraction method has been effectively increased. Given models trained with clean speech, our proposed method achieves up to 84.23% word recognition on noisy data.