Simple Addition Research Articles

Increasing Motivation Increases Intelligibility Benefits of Perceptual Training in Dysarthria.

Perceptual training offers a promising, listener-targeted option for improving intelligibility of dysarthric speech. Cognitive resources are required for learning, and theoretical models of listening effort and engagement account for a role of listener motivation in allocation of such resources. Here, we manipulate training instructions to enhance motivation to test the hypothesis that increased motivation increases the intelligibility benefits of perceptual training. Across two data collection sites, which differed with respect to many elements of study design including age of speaker with dysarthria, dysarthria type and severity, type of testing and training stimuli, and participant compensation, 84 neurotypical adults were randomly assigned to one of two training instruction conditions: enhanced instructions or standard instructions. Intelligibility, quantified as percent words correct, was measured before and after training. Listeners who received the enhanced instructions achieved greater intelligibility improvements from training relative to listeners who received the standard instructions. This result was robust across data collection sites and the many differences in methodology. This study provides evidence for the role of motivation in improved understanding of dysarthric speech-increasing motivation increases allocation of cognitive resources to the learning process, resulting in improved mapping of the degraded speech signal. This provides empirical support for theoretical models of listening effort and engagement. Clinically, the results show that a simple addition to the training instructions can elevate learning outcomes.

Long-term clinical outcomes informed by artificial intelligence-enabled single-lead ECG detection of reduced ejection fraction

Abstract Background * Reduced left ventricular ejection fraction (LVEF) is linked to poor clinical outcomes (1, 2). * Despite its increasing prevalence, reduced LVEF is often undetected due to resource-limited echocardiography and lack of suitable point-of-care diagnostic tools. * Previous studies have shown that an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm can accurately identify patients with reduced LVEF, but may also offer novel insights on long-term prognosis (3, 4). Purpose We aimed to determine if an AI-ECG algorithm designed for detecting reduced LVEF can also independently predict 2-year major adverse cardiovascular events (MACE) and all-cause mortality. Method * A retrospective multicentre observational study investigating the ability of AI-ECG to predict long-term clinical outcomes (i.e. over two years' follow up). * Analysis included a total of 1,007 consecutive unselected patients attending for routine echocardiography, with a single-lead ECG recording performed at the same time. * An AI-ECG algorithm, designed to identify impaired LVEF, was applied to each single-lead ECG. * Occurrence of MACE and all-cause mortality associated with AI-ECG results (Figure 1) was investigated using Cox regression; evaluating performance of AI-ECG results as a classifier (0 or 1) and as a probability score (0 to 1). * The study was approved by the UK Health Research Authority (reference 21/LO/0051). Results * The mean age was 62.3 years. 52.4% of patients were male and 57.5% of patients were White. * Appropriately, patients with a positive AI-ECG had a higher MACE rate compared to those with a negative AI-ECG (34.2% vs. 11.9%; adjusted hazard ratio (aHR) 2.02; 95% CI, 1.46 – 2.81; p < 0.005), primarily driven by increased mortality (23% vs. 9.6%; p < 0.0001; aHR 1.65; 95% CI, 1.12 – 2.43) and heart failure hospitalization (14.5% vs. 1%; p < 0.0001) – Figure 2. * AI-ECG probability score (per 10% increase) was significantly associated with MACE (aHR 1.17; 95% CI, 1.09 – 1.25; p < 0.005) and all-cause mortality (aHR 1.11; 95% CI, 1.02 – 1.20; p = 0.01). * Importantly, sub-analysis of AI-ECG for those who have a normal LVEF (i.e. ≥50%) continued to show association between positive AI-ECG and MACE (27.2% vs. 11.9%; p < 0.0001; aHR 1.70, 95% CI 1.17 – 2.48) and all-cause mortality (20.4% vs. 9.6%; p < 0.0001; aHR 1.53, 95% CI 1.00 – 2.36). Conclusion An AI-ECG algorithm designed to identify impaired LVEF can identify patients at risk of MACE and all-cause mortality from single-lead ECG screening – regardless of their LVEF. Such results may enable further risk stratification for cardiovascular investigations through the simple addition of single-lead ECG recording.

Memorizing Swin-Transformer Denoising Network for Diffusion Model

Diffusion models have garnered significant attention in the field of image generation. However, existing denoising architectures, such as U-Net, face limitations in capturing the global context, while Vision Transformers (ViTs) may struggle with local receptive fields. To address these challenges, we propose a novel Swin-Transformer-based denoising network architecture that leverages the strengths of both U-Net and ViT. Moreover, our approach integrates the k-Nearest Neighbor (kNN) based memorizing attention module into the Swin-Transformer, enabling it to effectively harness crucial contextual information from feature maps and enhance its representational capacity. Finally, we introduce an innovative hierarchical time stream embedding scheme that optimizes the incorporation of temporal cues during the denoising process. This method surpasses basic approaches like simple addition or concatenation of fixed time embeddings, facilitating a more effective fusion of temporal information. Extensive experiments conducted on four benchmark datasets demonstrate the superior performance of our proposed model compared to U-Net and ViT as denoising networks. Our model outperforms baselines on the CRC-VAL-HE-7K and CelebA datasets, achieving improved FID scores of 14.39 and 4.96, respectively, and even surpassing DiT and UViT under our experiment setting. The Memorizing Swin-Transformer architecture, coupled with the hierarchical time stream embedding, sets a new state-of-the-art in denoising diffusion models for image generation.

Toward Water-Resistant, Tunable Perovskite Absorbers Using Peptide Hydrogel Additives.

In recent years, hydrogels have been demonstrated as simple and cheap additives to improve the optical properties and material stability of organometal halide perovskites (OHPs), with most research centered on the use of hydrophilic, petrochemical-derived polymers. Here, we investigate the role of a peptide hydrogel in passivating defect sites and improving the stability of methylammonium lead iodide (MAPI, CH3NH3PbI3) using closely controlled, in situ X-ray photoelectron spectroscopy (XPS) techniques under realistic pressures. Optical measurements reveal that a reduction in the density of defect sites is achieved by incorporating peptide into the precursor solution during the conventional one-step MAPI fabrication approach. Increasing the concentration of peptide is shown to reduce the MAPI crystallite size, attributed to a reduction in hydrogel pore size, and a concomitant increase in the optical bandgap is shown to be consistent with that expected due to quantum size effects. Encapsulation of MAPI crystallites is further evidenced by XPS quantification, which demonstrates that the surface stoichiometry differs little from the expected nominal values for a homogeneously mixed system. In situ XPS demonstrates that thermally induced degradation in a vacuum is reduced by the inclusion of peptide, and near-ambient pressure XPS (NAP-XPS) reveals that this enhancement is partially retained at 9 mbar water vapor pressure, with a reduced loss of methylammonium (MA+) from the surface following heating achieved using 3 wt % peptide loading. A maximum power conversion efficiency (PCE) of 16.6% was achieved with a peptide loading of 3 wt %, compared with 15.9% from a 0 wt % device, the former maintaining 81% of its best efficiency over 480 h storage at 35% relative humidity (RH), compared with 48% maintained by a 0 wt % device.

A Simple and Facile Conjugate Addition of Cyclic and Acyclic 1,3‐Diketones to β‐Nitrostyrenes

AbstractA simple synthesis of 3‐hydroxy‐2‐(2‐nitro‐1‐arylethyl)cyclohex‐2‐en‐1‐ones, 3‐hydroxy‐5,5‐dimethyl‐2‐(2‐nitro‐1‐arylethyl)cyclohex‐2‐en‐1‐ones and 4‐hydroxy‐3‐(2‐nitro‐1‐arylethyl)pent‐3‐en‐2‐ones via conjugate addition of cyclic 1,3‐diketones, 1,3‐cyclohexanedione and 5,5‐dimethyl‐1,3‐cyclohexanedione and acyclic 1,3‐diketone, pentane‐2,4‐dione to β‐Nitrostyrenes were reported. This methodology is pleasing with reliability and scalability with gram scale products that can be synthesized.

The effectiveness of perspective video modelling training on one‐stage word problem‐solving skills in children with autism spectrum disorder

AbstractSingle‐subject research can provide adequate justification when developing successful evidence‐based educational practices for children with autism spectrum disorder (ASD), which can help such students thrive academically. This research investigates whether point‐of‐view video modelling effectively improved the word problem‐solving addition performances of primary‐school‐aged students with ASD. The effectiveness of the intervention on each participant's ability to solve single‐digit addition by a single digit, effectively view the video on a tablet and generalise a learned skill was investigated by utilising a design that included several participant baselines. All participants saw an improvement in their ability to solve simple addition problems due to using point‐of‐view video modelling. Between the baseline and intervention phases, each participant's rate of digits correctly entered per minute and total number of steps completed significantly increased. A generalisation phase was performed at home. After receiving the intervention, people with ASD could independently complete word problems involving addition with a single digit. The findings suggest that this technology could practically support the education of the growing number of children and young people with ASD by mollifying the particular learning obstacles their impairment brings.

Study on the scattering characteristics of large double-spherical systems

To study the influence of multiple scattering factors on the scattering characteristics, the scattering properties of a sizable double-spherical system at random orientations were analyzed by comparison of the T matrix and simple addition in the S-, C-, and X-bands, including the backscatter section (Cbak), attenuation section (Cext) and scattering section (Csca), asymmetry factor (g), and single scattering albedo (w), with the change in the spherical distance (d) and particle radius (r). It was found that the spacing between the spheres is four times the sphere’s radius, which is a clear cut-off point for the influence of multiple scattering in a two-spherical system. Then, by fitting the relative difference multiples of the T matrix method and the simple addition method, the uniform expression of each optical parameter under multiple scattering conditions is obtained.

Enhancing Lymph Node Yield in Gastric Cancer Resection: Impact of Back Table Dissection on Number of Lymph Node Examined.

During gastric cancer resection, back table dissection (BTD) involves examination and separation of lymph node (LN) packets from the surgical specimen based on LN stations, which are sent to pathology as separately labeled specimens. With potential impact on clinical outcomes, we aimed to explore how BTD affects number of LNs examined. A retrospective review of a gastric cancer database was performed, including all cases of gastrectomy with D2 lymphadenectomy from January 2009 to March 2022. Back table dissection and conventional groups were compared using Mann-Whitney U and Fisher's exact tests. Multiple linear regression modeling was used to identify potential predictors of number of LN examined. A total of 174 patients were identified: 39 (22%) BTD and 135 (78%) conventional. More patients in the BTD group underwent neoadjuvant chemotherapy (62% vs 29%, P < .05). Compared to the conventional group, the BTD group had a greater number of LNs examined (42 [26-59] vs 21[15-33], median [IQR], P < .001), lower LN positivity ratio (.01 vs .07, P = .013), and greater number of LNs in patients with BMI >35 (32.5[27.5-39] vs 22[13-27], P = .041). A multiple linear regression model controlling for age, BMI, preoperative N stage, neoadjuvant chemotherapy, surgeon experience, and operative approach identified BTD as a significant positive predictor of number of LN examined (β = 19.7, P = .001). Back table dissection resulted in improved LN yield during gastric cancer resection. As a simple technical addition, BTD helps enhance pathology examination and improve surgeon awareness, which may ultimately translate to improve oncologic outcomes.

Machine learning private equity returns

In this paper, we use two machine learning techniques to learn the aggregated return time series of complete private capital fund segments. First, we propose Stochastic Discount Factor (SDF) model combination to determine the public factor exposure of private equity. Here, we describe our theoretical motivation to favor model combination over model selection. This entails that we apply simple coefficient averaging to obtain multivariate SDF models that mimic the factor exposure of all major private capital fund types. As a second step, we suggest componentwise L2 boosting to estimate the error-term time series associated with our factor models. The simple addition of the public factor model returns and the error terms then yields the total return time series. These return time series can be applied for proper integrated public and private risk management or benchmarking.

A Dirt(y) World in a Changing Climate: Importance of Heat Stress in the Risk Assessment of Pesticides for Soil Arthropods.

The rise in global temperatures and increasing severity of heat waves pose significant threats to soil organisms, disrupting ecological balances in soil communities. Additionally, the implications of environmental pollution are exacerbated in a warmer world, as changes in temperature affect the uptake, transformation and elimination of toxicants, thereby increasing the vulnerability of organisms. Nevertheless, our understanding of such processes remains largely unexplored. The present study examines the impact of high temperatures on the uptake and effects of the fungicide fluazinam on the springtail Folsomia candida (Collembola, Isotomidae). Conducted under non-optimum but realistic high temperatures, the experiments revealed that increased temperature hampered detoxification processes in F. candida, enhancing the toxic effects of fluazinam. High temperatures and the fungicide exerted synergistic interactions, reducing F. candida's reproduction and increasing adult mortality beyond what would be predicted by simple addition of the heat and chemical effects. These findings highlight the need to reevaluate the current ecological risk assessment and the regulatory framework in response to climate changes. This research enhances our understanding of how global warming affects the toxicokinetics and toxicodynamics (TK-TD) of chemicals in terrestrial invertebrates. In conclusion, our results suggest that adjustments to regulatory threshold values are necessary to address the impact of a changing climate.

Single- and two-pollutant Concentration-Response Functions for PM2.5 and NO2 for Quantifying Mortality Burden in Health Impact Assessments

ObjectiveHealth Impact Assessments (HIAs) for air pollutant mixtures are challenging because risk estimates are primarily derived from single-pollutant models. Combining risk estimates from multiple pollutants requires new approaches, as a simple addition of single pollutant risk estimates from correlated air pollutants may result in double counting. We investigated approaches applying concentration-response functions (CRFs) from single- and two-pollutant models in HIAs, focusing on long-term exposure to particulate matter with a diameter less than 2.5 micrometers (PM2.5) and nitrogen dioxide (NO2) and their associations with all-cause mortality. MethodsA systematic literature search of MEDLINE and EMBASE identified cohort studies employing single- and two-pollutant models of long-term exposure to PM2.5 and NO2 with all-cause mortality. Pooled CRFs were calculated through random-effects meta-analyses of risk estimates from single- and two-pollutant models. Coefficient differences were calculated by comparing single- and two-pollutant model estimates. Four approaches to estimating population-attributable fractions (PAFs) were compared: PM2.5 or NO2 single-pollutant models to represent the mixture, the sum of single-pollutant models, the sum of two-pollutant models and the sum of single-pollutant models from a larger body of evidence adjusted by coefficient difference. ResultsSeventeen papers reported both single and two-pollutant estimates. Pooled hazard ratios (HRs) for mortality from single- and two-pollutant models were 1.053 (95% confidence interval: 1.034-1.071) and 1.035 (1.014-1.057), respectively, for a 5 μg/m3 increase in PM2.5. HRs for a 10 μg/m3 increase in NO2 were 1.032 (1.014-1.049) and 1.024 (1.000-1.049) for single- and two-pollutant models, respectively. The average coefficient difference between single- and two-pollutant models was 0.017 for PM2.5 and 0.007 for NO2. Combined PAFs for the PM2.5-NO2 mixture using joint HRs from single- and two-pollutant model CRFs were 0.09 and 0.06, respectively. ConclusionUtilizing CRFs from two-pollutant models or applying the coefficient difference to a more extensive evidence base seems to mitigate the potential overestimation of mixture health impacts from adding single-pollutant CRFs.

Highly Interconnected Thermal Conduction Highway for Highly Thermally Conductive and Mechanically Strong Polymeric Composites.

Industrial implementation of highly thermally conductive polymeric composites has been hindered by several hurdles, such as the low intrinsic thermal conductivity (TC) of polymers, the use of expensive thermally conductive fillers, and difficulty in processing composites with high filler loading. In this study, we introduce a straightforward fabrication method for a high TC polymeric composite with a programmed internal structure of a highly interconnected thermal conduction highway (HITCH) by the simple addition of partially cured resin fragments into the conventional filler/resin combination. Critical variables, such as the concentration of the added resin fragments and the local concentration of hexagonal boron nitride (hBN) in the HITCH, as well as the packing density of the fragments, were systematically tuned to maximize the TC with the use of the least amount of the filler. Careful choice of the compositions enabled a significant TC enhancement of the composite by 2.6 times (6.5 W/mK) compared to the value of the conventional composite at the same overall concentration of hBN (∼2.5 W/mK). Finally, a composite with high TC (∼12 W/mK) and strong tensile strength (∼22.6 MPa), which is good enough for most practical thermal management applications, could be successfully fabricated with the use of the least amount of the filler (∼34 wt %). The comprehensive study of the HITCH composite here can be easily extended to other combinations with various fillers and matrices and may provide a library to researchers looking for advanced materials for future thermal management systems.

Deciphering the Benefits of Coordinated Binders in Si‐Based Anodes by Combined Operando/In Situ and Ex Situ X‐Ray Micro‐ and Nano‐Tomographies

AbstractThe simple addition of a Zn(II) precursor to a preoptimized poly(carboxylic acid) binder solution enhances the electrochemical performance and cycle life of silicon‐based electrodes. The binder/cation couple forms a cross‐linked coordinated binder that plays a key role in enhancing the mechanical and chemical stability of the electrode microstructure. The impact of the addition of the Zn precursor on the microstructural evolution of the electrode during cycling is investigated at different scales (from cell/electrode to silicon particle scale) using complementary operando, in situ, and ex situ X‐ray tomography techniques. Comparative analyses conducted on the reference and with Zn electrode formulations using operando and in situ X‐ray micro‐tomography allow for monitoring of the electrode morphological deformations along with the crack pattern formation and evolution during cycling. The benefits of the precursor addition include enhancing the mechanical stability of the electrode through a strengthened microstructure more apt to maintaining its integrity, as well as a better anchoring to the current collector leading to decreased electrical disconnections and capacity fade. Moreover, complementary ex situ X‐ray nano‐tomography measurements highlight the benefits of the precursor addition in terms of chemical stability with mitigated solid electrolyte interface (SEI) formation over the electrode cycling.

SiamMAF: A multipath and feature-enhanced thermal infrared tracker

Thermal infrared (TIR) images are visually blurred and low in information content. Some TIR trackers focus on enhancing the semantic information of TIR features, neglecting the equally important detailed information for TIR tracking. After target localization, detailed information can assist the tracker in generating accurate prediction boxes. In addition, simple element-wise addition is not a way to fully utilize and fuse multiple response maps. To address these issues, this study proposes a multipath and feature-enhanced Siamese tracker (SiamMAF) for TIR tracking. We design a feature-enhanced module (FEM) based on complementarity, which can highlight the key semantic information of the target and preserve the detailed information of objects. Furthermore, we introduce a response fusion module (RFM) that can adaptively fuse multiple response maps. Extensive experimental results on two challenging benchmarks show that SiamMAF outperforms many existing state-of-the-art TIR trackers and runs at a steady 31FPS.

Halogen-free epoxy flame retardant & promoter with diimidazole skeleton improves overall properties of epoxy resins

In order to solve the widespread problem in the field of flame retardancy epoxy(FREP), for the deterioration of the overall properties, we designed a novel structural flame retardant & promoter DOPO-CDI and synthesized it using a simple nucleophilic addition one-step method, which exhibits remarkable flame retardant and curing promotion, the flame retardant and promote mechanism of DOPO-CDI on neat EP is illustrated in Fig 0a, which obtained excellent overall properties shown in Fig 0b. Compared with neat epoxy, it has high flame retardancy, low additives results in an limiting oxygen index(LOI) of up to 37.5 % and a V-0 rating of UL-94, a lower curing exothermic peak temperature, a shorter curing time. And in terms of mechanical properties, the excellent strength of epoxy has been further enhanced, flexural strength and tensile strength are increased by 13.3 % and 21.6 % respectively. Moreover, the toughness of epoxy, which is generally lacking, has been greatly improved, in particular, compared with neat epoxy, the impact strength of epoxy with 3 wt% DOPO-CDI increased by 103.6 %. Unlike most FREP, glass transition temperature(Tg) of the epoxy with the introduction of DOPO-CDI raises to 165.6 °C, which is a significant improvement compared to that of the neat epoxy, in terms of combustion behavior, DOPO-CDI has a considerable inhibitory effect on the combustion exotherm and smoke emission. This work explores a way to balance processing, mechanical and thermal properties when preparing FREP, which has great potential for application.

Control of Dopamine Signal in High-Order Receptor Complex on Striatal Astrocytes.

The receptor-receptor interaction (RRI) of G protein-coupled receptors (GPCRs) leads to new functional entities that are conceptually distinct from the simple addition of signals mediated by the activation of the receptors that form the heteromers. Focusing on astrocytes, there is evidence for the existence of inhibitory and facilitatory RRIs, including the heteromers formed by the adenosine A2A and the dopamine D2 receptors, by A2A and the oxytocin receptor (OTR), and the D2-OTR heteromers. The possible involvement of these receptors in mosaicism has never been investigated in striatal astrocytes. By biophysical and functional approaches, we focused our attention on the existence of an A2A-D2-OTR high-order receptor complex and its role in modulating cytosolic calcium levels and endogenous glutamate release, when striatal astrocyte processes were stimulated with 4-aminopyridine. Functional data indicate a permissive role of OTR on dopamine signaling in the regulation of the glutamatergic transmission, and an inhibitory control mediated by A2A on both the D2-mediated signaling and on the OTR-facilitating effect on D2. Imaging biochemical and bioinformatic evidence confirmed the existence of the A2A-D2-OTR complex and its ternary structure in the membrane. In conclusion, the D2 receptor appears to be a hotspot in the control of the glutamate release from the astrocytic processes and may contribute to the regulation and integration of different neurotransmitter-mediated signaling in the striatum by the A2A-D2-OTR heterotrimers. Considering the possible selectivity of allosteric interventions on GPCRs organized as receptor mosaics, A2A-D2-OTR heterotrimers may offer selective pharmacological targets in neuropsychiatric disorders and neurodegenerative diseases.

Effect of multifunctional cationic polymer coatings on mitigation of broad microbial pathogens.

Infection control is critical for maintaining a healthy home, work, and hospital environment. We test a cationic polymer capable of capturing and eradicating viral and bacterial pathogens by applying the polymer to the air filtration material and textiles. The data suggest that the simple addition of cationic material can result in the improvement of an infectious resilient environment against viral and bacterial pathogens.

Cardiac Loading using Passive Left Atrial Pressurization and Passive Afterload for Graft Assessment.

Ex vivo machine perfusion or normothermic machine perfusion is a preservation method that has gained great importance in the transplantation field. Despite the immense opportunity for assessment due to the beating state of the heart, current clinical practice depends on limited metabolic trends for graft evaluation. Hemodynamic measurements obtained from left ventricular loading have garnered significant attention within the field due to their potential as objective assessment parameters. In effect, this protocol provides an easy and effective manner of incorporating loading capabilities to established Langendorff perfusion systems through the simple addition of an extra reservoir. Furthermore, it demonstrates the feasibility of employing passive left atrial pressurization for loading, an approach that, to our knowledge, has not been previously demonstrated. This approach is complemented by a passive Windkessel base afterload, which acts as a compliance chamber to maximize myocardial perfusion during diastole. Lastly, it highlights the capability of capturing functional metrics during cardiac loading, including left ventricular pulse pressure, contractility, and relaxation, to uncover deficiencies in cardiac graft function after extended periods of preservation times (˃6 h).

Sandmeyer Chlorosulfonylation of (Hetero)Aromatic Amines Using DABSO as an SO2 Surrogate.

Sulfonyl chlorides not only play a crucial role in protecting group chemistry but also are important starting materials in the synthesis of sulfonamides, which are in-demand motifs in drug discovery chemistry. Despite their importance, the number of different synthetic approaches to sulfonyl chlorides is limited, and most of them rely on traditional oxidative chlorination chemistry from thiol precursors. In this report, we disclose a novel Sandmeyer-type sulfonyl chloride synthesis from feedstock anilines and DABSO, used as a stable SO2 surrogate, in the presence of HCl and a Cu catalyst. The method works on a wide range of anilines and allows for the isolation of the sulfonyl chloride after aqueous workup or its direct conversion into the sulfonamide by simple addition of an amine after the completion of the Sandmeyer reaction. The scalability of this method was demonstrated on a 20 g scale, and the corresponding heterocyclic sulfonyl chloride was isolated in 80% yield and excellent purity.

Performance optimization of continuous-variable quantum teleportation with generalized photon-varying non-Gaussian operations

Continuous variable quantum teleportation provides a path to the long-distance transmission of quantum states. Photon-varying non-Gaussian operations have been shown to improve the fidelity of quantum teleportation when integrated into the protocol. However, for a given type of non-Gaussian operation, the achievable fidelity varies with the parameters associated with the operation. Previous work only focused on particular settings of the parameters, over which an optimization was missing. The potential of such operations is not fully uncovered. Given a fixed non-Gaussian operation, the achievable fidelity also varies with input states. An operation that increases the fidelity for teleporting one class of states might do the contrary for other classes of states. A performance metric, upon which an operation is optimized, suitable for different input states, is also missing. In this work, we build a framework for photon-varying non-Gaussian operations for multimode states, upon which we propose a performance metric suitable for arbitrary teleportation input states. We then apply the new metric to evaluate different types of non-Gaussian operations. Starting from simple multiphoton photon subtraction and photon addition, we find that increasing the number of ancillary photons involved in the operation does not guarantee performance improvement. We then investigate a generalization of the operations mentioned above, finding that operations that approximate a particular form provide the best improvement. The results provided here will be valuable for real-world implementations of quantum teleportation networks and applications that harness the non-Gaussianity of quantum states. Published by the American Physical Society 2024