Background: Pediatric obesity is prevalent and challenging to treat. Although family-centered behavioral management is the gold standard, many families face structural inequities to its access and efficacy. Identifying ways to manage pediatric obesity within primary care is needed. Methods: This feasibility study included three sequential trials of peer-led group sessions occurring biweekly or monthly between 3/2016 and 2/2017. Parent–child dyads were recruited from a large academic primary care clinic via mailed invitations, prioritizing patients living in local zip codes of historical disinvestment. Eligible patients were 6 to 12 years with a body mass index ≥85th percentile, with parent and child interest in making healthy lifestyle changes, and English speaking. Results: 27 dyads participated, 77% were non-Hispanic Black. Retention and attendance rates were highest in the initial four-session biweekly pilot , high in the full six-session biweekly cohort , and moderate in the monthly cohort . Families reported high satisfaction with the sessions (4.75/5). Qualitative comments suggested social connections had motivated behavior change in some families. Conclusion: Parent-led group sessions for pediatric weight management show promise in engaging families. A future large trial is needed to assess behavior change and anthropometric outcomes. Obesity is a highly prevalent condition of childhood  with significant racial/ethnic disparities  . Childreth percentile associated with a decrease in cardiometabolic risk  34.6 to 49.9). Baseline child BMI was 24.2 (SD 21.7 to 28.8) with a baseline BMI z-score of 1.97 ± 0.44.Baseline parent BMI of those who completed the biweekly and monthly cohorts , high in biweekly cohort  and moderate in the monthly cohort , however they also wanted a “content expert” to co-lead. One focus group participant suggested that we rotate the parent lead; this suggestion was incorporated into the pilot and feasibility trial.Participants in the monthly and biweekly group sessions reported high overall satisfaction (4.75 out of 5) with the group sessions. They rated the nutritional information to be useful (4.72 out of 5). Participants also enjoyed having the group sessions led by a peer (4.64 out of 5) and felt connected to the other parents (4.63 out of 5).At each session, participants’ data on steps and habits such as fruit/vegetable consumption and screen time were compiled and shared back with them. Results for parents and children would be compiled separately. Below are a few examples of the data shared with dyads .n = 14, 7 via phone and 7 in-person), families reported more engagement in their primary care home after completion of the STEP IN study. A few families mentioned it seemed the primary care clinic “truly cared about them” to offer this program. Most participants said they enjoyed the interaction. Several who did not know each other before became friends and have spent time together outside of the sessions. Many families were receptive to follow up in primary care or a weight-management clinic.In exit interviews . Parents and children had fun competing over step counts and servings of fruits and vegetables  were more likely to change over the course of six months compared to the three months required for the biweekly sessions. We also found that six months allowed for more chances of life stressors, such as an illness or death in the family, to change families’ priorities. While we found better attendance with biweekly sessions, one confounding factor is the biweekly cohort had many sessions over summer break from school. Sessions appear to be poorly attended at the end of school semesters (May and December) and in the winter months (December and January) due to snow and cold. An additional confounder was session five for the monthly cohort had to be rescheduled due to weather, yielding 50% attendance. Overall, attendance rates compare very favorably with the clinic appointment no-show rate of 25% for all visit types, and no-show rates as high as 67–75% for weight follow up and nutrition-only visits. The high attendance rate is also crucial given the participants in the full six session cohorts were not previously engaged with an intensive behavior and lifestyle program. Our study visits’ high attendance rates and the high level of engagement of participants at the sessions speak to the value participants see in these sessions for gaining knowledge, feeling supported, and improving their child’s health and their own health. The recent clinical practice guidelines from the American Academy of Pediatrics for the treatment of children with obesity recommend intensive behavior and lifestyle treatment as a key intervention for children six years and older, with a minimum of 26 contact hours recommended . The preThis feasibility study has a few important limitations and challenges. First, recruitment was best achieved by letters mailed directly to families (as compared to fliers in the clinic), however there was a low response rate even to letters (about 1%). Optimization of the recruitment strategy will be needed for a larger trial. Finding engaging activities for the children was sometimes challenging and dependent upon the childcare provider’s engagement with the material. An additional challenge was the unpredictable attendance by siblings of a wide age range. Obtaining catering for the shared meal that was healthy, within budget, and enjoyed by the participants was a challenge. While the exact content of STEP IN may need to be customized to other neighborhood or populations, the structure of the peer led sessions could be applied to other groups facing structural disadvantages and high rates of pediatric obesity. We also found that parents with low literacy were not able to participate fully in these sessions, limiting the generalizability of this format of intervention. Finally, the size of the study was not powered to assess the impact of the intervention on objectively measured behavior change or BMI. We did not find a statistically significant change in dyads’ BMI.Since the study was initially conducted, we have explored models for providing this type of engagement intervention to clinic patients who are in a pre-contemplative or contemplative readiness to change mindset. Because Medicaid does not reimburse for group-level or peer-level counseling in Ohio, and because the program relies on having a full-time program manager to run, we have paused additional study of this intervention. If others are considering group pediatric weight management sessions, we recommend avoiding scheduling the sessions during the end of the academic semester (December and May). Future research should also look for ways to involve children in the learning and sharing their learning while still separating parents and children for part of the session as parents valued this time to connect with one another. Parents noted they would like if their children received their own certificate of completion. We would also consider adding an additional session to discuss sleep hygiene, based on many participants indicating they struggle with healthy sleep habits for themselves and their children.This feasibility study of peer-led group sessions showed strong promise and a larger study should be conducted to assess whether the intervention can improve diet and physical activity habits of families who are predominantly low-income and identify as non-Hispanic Black. More frequent sessions (every other week) were found to be better attended than less frequent (monthly). The opportunity for peer support and connection was one key to the success of this low-intensity, family-focused intervention for pediatric weight management based in the primary medical home.


Water pollution due to heavy metal ions has become a persistent and increasing problem globally. To combat this, carbonaceous materials have been explored as possible adsorbents of these metal ions from solution. The problem with using these materials on their own is that their lifespan and, therefore, usability is reduced. Hence the need to mask them and an interest in using polymers to do so is picked. This introduces an improvement into other properties as well and opens the way for more applications. This work gives a detailed review of the major carbonaceous materials, graphene and graphene oxide, outlining their origin as well as morphological studies. It also outlines the findings on their effectiveness in removing heavy metal ions from water, as well as their water absorption properties. The section further reports on graphene/polymer and graphene oxide/polymer composites previously studied and their morphological as well as thermal properties. Then the work done in the absorption and adsorption capabilities of these composites is explored, thereby contrasting the two materials. This enables us to choose the optimal material for the desired outcome of advancing further in the utilization of carbonaceous material-based polymer composites to remove heavy metal ions from water. Having access to a clean water supply is a basic need in South Africa, as well as the rest of the world. Water is one of the most crucial elements in the sustenance of life on Earth, and as such, its preservation is important. There is a growing concern regarding the availability of usable water in South Africa, as well as on a global scale. In 2007, the World Health Organisation (WHO) issued a report stating that around 1.1 billion people did not have access to a supply of clean water globally . All theThe main reasons for water scarcity in South Africa are climate change, an increasing population, irresponsible water usage, and poor wastewater treatment methods. Climate change causes global warming, which in turn results in delayed rainfall that brings about draught and a deficit in the water supply . Then wh3 and more [Amongst the different contaminants found in water are heavy metals. By definition, these are metals that have a specific density of 5 g/cmand more . Exampleand more .One heavy metal of particular interest is lead, which is a greyish material that naturally occurs on the earth’s crust in very small amounts. Although lead occurs naturally in the environment, industrial activities such as manufacturing, acid metal plating, ammunition development, paints and dyes formations, ceramic and glass forming, as well as fossil fuel burning increase lead concentrations deposited on the earth’s surface. The residues from these industries contain lead ions which eventually make their way into drinking water through transporting agents such as water, wind, and gravity. Research shows that adults consume 35 to 50% of lead through drinking water, and the consumption rate of children may be over 50%. Moreover, lead is not biodegradable, and this means that it would accumulate on living cells and cause fatal damage to the liver, kidneys, reproductive system, and brain, upon consuming water that has it ,11. The Throughout the years, a variety of procedures have been formulated to remove heavy metals and, specifically lead from solution. Amongst others, these methods include coagulation and precipitation, membrane filtration, ion exchange, and absorption. However, most of these methods have financial implications, and/or their execution fails for various reasons and, as such, they have not been accepted for commercial usage ,13,14. IAdsorption refers to a process whereby gasses or liquids accumulate on a substrate surface that is in solid or liquid form . The phaBy definition, carbonaceous materials or carbon-based materials refer to solid materials that have carbon as a main component . These mGraphene (GN), or fully hydrogenated graphene, is a form of carbon that has a single layer of atoms having a two-dimensional honeycomb lattice arrangement in its nanostructure. The derivation of its name is from graphite, and the suffix -ene alludes to the fact that it has numerous double bonds. Graphene possesses useful properties such as heat conduction, electricity conduction, high tensile strength, and its ability to absorb light in all wavelengths. In material science, it gained popular usage and attraction due to its high specific area, which causes ease of processability and allows for tailoring of its properties into what is required ,43. FiguMorphology is the study of form via structure, size, and shape. It gives an indication of the physical and chemical properties of nanostructured materials, making it very crucial in material studies . MorpholIn AFM analyses, it was established that the hexagonal growths are visible on the surface of the substrate. The thickness of the surface increased as the vegetable oils were allowed to interact with the substrate and form graphene. This brought about the assumption that graphene will almost always form hexagonal growths on the surface of the substrate, and this would influence the arrangement of its particles. −1 and a D peak at 1360 cm−1. These peaks represented the presence of more than ten layers of graphene structures and a depiction of the disordered nature of these layers, respectively. The ultra-sonicated graphene showed a second-order peak at 960 cm−1, whereas pristine graphene did not have this peak. This implied, according to the authors, that pristine graphene has thicker layers as compared to ultra-sonicated graphene. From this, it was concluded that the process of ultra-sonication in preparing graphene results in thinner layers of product, which might disperse easily in a polymer matrix.Other morphology studies of graphene have utilized X-ray diffraction (XRD), Transmission electron microscopy (TEM), and Raman spectroscopy together with SEM characterization techniques. Monti et al. , in theiIn terms of thermal properties, there really have not been studies that focus on the thermal stability and melting and crystallization of graphene. The current focus has been on the thermal conductivity of graphene. This might be because graphene consists only of carbon in its structure, so it might be simple to deduce its thermal stability, melting, and crystallization properties.Graphene has been proven to have very poor water intake abilities. This surely is because of its structure, which consists mainly of carbon and no other functional groups that may aid absorption. There really is not much to look at in the way graphene is used in water absorption studies. The consensus here is that the introduction of graphene to any material will lower the water intake properties ,49. ThisIt has already been established that graphene does not possess good water intake properties. As such, researchers shy away from using it as an adsorptive material for heavy metals in aqueous solutions. The already reviewed structure of graphene, which shows it to consist of only carbon, also contributes to its inability to adsorb heavy metals from water. Due to these already established parameters, it would only make sense that there is no progressive and tangible work so far attributed to the removal of heavy metals from water by graphene. There are some reviews and little work done in this regard on the feasibility of graphene being used as a selective adsorbent of heavy metals in water ,51,52. MThe current work on graphene on its own has been remarkable and progressive; most researchers have contributed profusely to the general understanding of the structure of graphene. With more understanding, it becomes eminent that the water uptake and metal ion intake properties are very weak for graphene. On top of this, it is considerably costly to purchase or manufacture. Hence not many researchers have opted to utilize it. All research points towards graphene oxide in this regard, as it seems to be a promising substitute.The functionalization and exfoliation of graphite and graphene forms graphene oxide (GO). This is a carbonaceous carbon allotrope consisting of a single graphite oxide layer. The synthesis of GO is done by chemically treating graphite in an oxidation reaction while continuously exfoliating or dispersing it in a solvent. To this day, the precise structure of GO has not been described conclusively. However, a series of models have surfaced that aim to describe its structure. The common aspect of all these proposed models is that they all suggest that GO consists of oxygen-containing functional groups of variety, with specific areas of attachment on the structure ,54,55. ISeveral morphology studies have been conducted on GO in order to verify its structure and/or synthesis. Shang et al.  looked i−1, C-O-C (ester) at 1226 cm−1, and C=O in carboxylic acid and carbonyl moieties (carbonyl) at 1719 cm−1. There were also bands observed at 3330 and 1396 cm−1, which were attributed to the O-H stretching mode and deformation vibration of intercalated water, respectively. The authors stated that GO was successfully synthesized in light of these observations. Furthermore, they pointed out that the observed FTIR and XPS results implied that GO is amphiphilic, has a hydrophobic basal plane, and is hydrophilic at the edges. In another study by Ghosh et al. [−1 which was attributed to the stretching of the O-H bond and portrayed the presence of a number of OH groups on the surface of GO. A band occurred at 1760 cm−1, indicating the C=O stretching, and another at 1403 cm−1, which represented the presence of carboxy groups in GO. The Raman spectroscopy and XRD results also validated the successful synthesis of GO. In Raman analysis, a spectrum was obtained with two main features: The G band and the D band. The G band was said to represent the first-order scattering of phonons by sp2 carbon atoms, while the D band was representative of the breathing mode of photons. The G band was found at 1591 cm−1 and was a broader band compared to the D band, which was found at 1341 cm−1 and was more apparent. The extensive oxidation of graphite to graphene oxide caused a distraction of the sp2 character and formed defects in the GO sheets, thereby resulting in the said shifts in bands. Then, their XRD results showed a diffraction peak at 11.41°, with a calculated interlayer spacing of 0.741 nm. This, according to the authors, was indicative of the intercalation of water molecules into the graphite layers and the formation of oxygen-containing functional groups between the layers of graphite. Several other authors arrived at similar findings when analyzing the morphology of GO [From the results above, the AFM images showed the average lateral sizes or thickness of GO sheets to be 1 µm and a height of 0.919 nm. This was indicative of the monolayer structure of the GO sheets, according to the authors. The XPS analysis resulted in a C1 spectrum having four different peaks. These were: C=C (284.3 eV), C-O (286.3 eV), C=O (287.6 eV), and COOH (289.0 eV). The peaks were a portrayal of the existence of oxygen-containing functional groups in the structure of GO. Then in FTIR analysis, a spectrum was obtained with the following peaks and their functional groups: C-O  at 1045 cmh et al. , the morh et al. , where tgy of GO ,62,63. AThermal properties refer to the physical properties of a material that are related to heat conductivity. Briefly put, these are properties that a material exhibits when put under a fluctuating heat cycle. Four major components of thermal analysis have been established, namely: heat capacity, thermal expansion, thermal conductivity, and thermal stress . A very The area of study that looks at the water intake properties of GO, according to my knowledge, is still very novel, and as such, there have been minimal outputs in this field ,68. As aAlthough there has not been much work published ,70 that Following this, the authors discovered that adsorption had proceeded by chemisorption for the most part of the experiment. Chemisorption is a type of adsorption occurring on a chemical basis, with a possibility of an irreversible reaction between the adsorbent and the adsorbate. Then they did kinetic modeling to illustrate the mechanism involved in GO adsorbing these heavy metal ions. The Langmuir and Freundlich models were used, and the following −1), KL represents the enthalpy of adsorption (L·mg−1), KF and n are the Freundelich constants related to the adsorption capacity and adsorption intensity, respectively. The qmax values obtained for all the metal ions implied that 1 g of GO could adsorb around 5 mmol of divalent metal ions. This makes GO the highest adsorbing material currently, amongst all the other materials, according to the authors. Moreover, the constants obtained on the Freundlich model were between 1 and 10, and this implied that adsorption was favored under the studied conditions. Upon fitting the isotherms from data derived from the table above, the authors obtained Langmuir and Freundelich plots shown in Where qmax is the maximum amount of metal ions adsorbed per unit mass of GO at high equilibrium ion concentration  (PLA), poly (ε-caprolactone) (PCL), poly (PHBV), and polybutylene succinate (PBS), amongst others. From this, it follows that the recent strides in polymeric composites of GN and GO are reviewed.Graphene/polymer composites have attracted quite a substantial amount of research attention over the past years. The many useful applications of graphene have sparked the interest of researchers to develop new and advanced materials. Most of these composites have been developed and used for improving specific properties in polymer matrices. These include compatibility and miscibility, thermal behavior and materials, and water purification, amongst others. Most researchers have opted to investigate single polymer/graphene composites instead of binary polymer/graphene composites. This implies that the work towards using binary polymer systems is still relatively new and, therefore, quite niche.The morphology of polymer composites with graphene as a filler has been one area of particular interest to researchers. Gao et al.  investigThe study of the morphology of graphene/polymer composites gains more traction each day as more researchers are picking an interest in the field. The current knowledge so far gives an exciting and fruitful incite which paves the way to more advanced applications and advancements in the study of polymers filled with graphene. In time, the structure of graphene will surely be optimized and tailored for specific and useful needs.Thermal analysis is very important in analyzing polymer/GN composites. This is mostly done through differential scanning calorimetry (DSC) and thermogravimetric analyses (TGA). DSC helps in understanding the melting and crystallization behavior, whereas TGA assesses the thermal degradation and stability. Very limited studies have been published that look at the thermal properties of GN/polymer composites.The widely used characterization technique here is DSC. Generally, the literature suggests that the presence of GN in the matrix of a polymer will increase the degree of crystallinity of that polymer ,72. Wu eFrom the table, it is clear that both the neat PLA and the PLA/GN composite exhibited the Tg, which did not differ that much in terms of position. The second transition obtained for both samples was the cold crystallization peak. This transition shifted a bit to a higher temperature for the composite, as the table portrays. The observation was said to imply that the presence of graphene nanosheets reduces chain mobility in PLA and hinders its crystal growth. The melting transition was split into two peaks for both the neat PLA and the PLA/G composite, and its position did not change that much. So, although there were no attributions to the presence of the double melting peak for pure PLA, this could be explained by polymer science principles. That is, even though PLA did not show any crystallization peak upon cooling, it might have crystallized, but the peak was not prominent. The crystals formed from the cooling and the ones formed during cold crystallization would be different types of crystals. These two types of crystals would then melt at slightly different temperatures, thereby resulting in a double melting peak for PLA. In addition, the cold- crystallization of PLA might have resulted in the formation of two types of crystals, namely, the metastable and perfect crystals. These crystals would then melt at different temperatures, which would also result in two melting peaks for PLA. The melting transition of PLA did not change position upon GN addition, which showed that the GN did not have any significant influence on the melting process of PLA. Lastly, the degree of crystallinity of PLA was not influenced by the addition of GN. Perhaps if the analyses were done at increasing GN loadings, then there would be a visible trend in the effect of GN loading on the polymer matrix of PLA. However, on a general basis, carbonaceous materials are known to have significant effects on polymer matrices, even in small quantities. This might be the reason why the study was conducted at 1 wt.% GO content. Then Gao et al.  conducteThe findings in this regard were that firstly, the melting temperature of PCL was not affected by the added graphene, as it remained in the range of 56–60 °C for both composites. Then during cooling, PCL, together with its composites with rGO and CCG, exhibited crystallization transitions. The crystallization temperature of PCL had a significant shift with the addition of graphene for both composites. The neat PCL polymer crystallized at 26 °C, while in the PCL/rGO composite, it crystallized at 39 °C and in the PCL/CCG at 35 °C. These observations were said to indicate that the incorporation of graphene nanosheets into the PCL matrix affects the polymer microstructure by acting as multiple nucleation centers for crystallization. In addition, in our view, graphene nanosheets might have increased the crystallinity of PCL, and the number of crystalline chains increased in the matrix of PCL.These observations work to ascertain the assumption that the presence of graphene in a polymer matrix has a direct effect on only the degree of crystallinity. However, most research has been on one-polymer composites. Perhaps a bi-polymer composite analysis would yield different and more interesting outcomes.Thermal degradation analyses provide insight into the thermal stability of a polymeric material. This information is very important if we wish to deduce the possible applications of that material. It has been generally discovered that the presence of graphene improves the thermal stability in a polymer matrix/matrices. Rodill et al.  made oneThese observations by other researchers could prove useful in the study of using graphene to reinforce polymeric materials. The world needs more materials that can withstand high temperatures, and this research field promises just that.−2) but the lowest average flake thickness (1.6 nm) exhibited the highest maximum water intake (around 1.5%) among the composites. This was attributed to the fact that the high surface area and less thickness in the graphene would make it more permeable and more susceptible to being porous. It is very much clear that the size and surface area of particles has a direct influence on their water intake properties. The fact that the saturation point of intake was reached after a minimal amount of time shows that graphene really does have a low water intake capability. The maximum intake of 15% for the materials used is a very low water intake percentage for both the polymer and graphene. Wang et al. [Some polymers are naturally hydrophilic, and as such, researchers would fill them with graphene to assess the change, if any, in their water intake properties. Prolongo et al.  assessedg et al.  looked aFrom these results, it can clearly be seen that the absorption of pure PVA, taken after 24 h, was 105.2%, with a measured contact angle of 36°. The addition of graphene reduces the water intake and thus improves the barrier properties in the composites. This is seen by the absorption degree dropping to 59.8 and 48.8%, respectively, for 0.5 and 1 wt.% graphene loadings. The contact angle increased with the addition of graphene loading . These observations clearly imply that the introduction of graphene induces and increases hydrophobicity and reduces hydrophilicity in a polymer matrix. These observations are both advantageous and disadvantageous depending on the desired objective. If the intent is to strengthen or rather reinforce the surface properties of a polymer matrix, then graphene is ideal and conducive as a filler. However, if we require to develop materials that can absorb water and probably adsorb heavy metals from solution, then graphene might just not be the go-to filler.Unfortunately, the inefficacy of graphene in removing heavy metal ions from solution has caused a lot of researchers to refrain from exploring its polymer composites in this regard. According to our knowledge, there really is no tangible work so far attributed to the removal of heavy metal ions from solution by graphene/polymer composites. The findings presented by researchers in analysing the water intake properties of graphene-filled polymer composites just show that the inclusion of graphene yields a higher hydrophobicity. As already mentioned, it will all depend on the specific desired outcome of the prepared materials. Particularly, if we require to enhance the metal adsorption properties of polymer composites, we need to investigate fillers that have the desired functional groups, such as oxygen, for example. In light of the above-mentioned views, it becomes important that we review the properties of GO/polymer composites. The contrasts made in this regard, in comparison to graphene and other carbonaceous materials, imply that GO deserves all the attention. The oxygen-containing functional groups on its surface are the primary concern and reason to investigate the feasibility and work done so far in using GO/polymer composites as selective adsorbents of metal ions in water.The reactive sites present in GO make it ideal for structural modification and tailoring, whereby its structure can be manipulated for different purposes. This ease of processability aspect of GO has attracted the need to formulate and analyse GO/polymer composites by researchers. Although little research has been done into biodegradable polymer-based composites with GO, there has been quite a variety of papers reporting on the development and properties of GO/polymer composites in general. This has been for the purposes of adsorption of pollutants, electrical conductivity, and the improvement of materials’ properties. This section focuses on the strides made in the development and characterization of GO/polymer composites ,82,83,84w/w PVA/GO composites. Herein they observed the GO to be dispersed on the surface of the polymer matrix without being embedded deeply into the matrix. This was attributed to the smaller interlayer distances and smaller defects, thereby making it hard for the matrix to be broken down. Their EDS study showed that the carbon composition was 60%, whilst oxygen was 40% in the polymer composites. This makes sense as the GO and PVA consist mainly of carbon. In XRD studies, the peaks became more intense with an increasing GO content for a typical PVA/GO composite. This alluded to the idea that the crystallinity of the composites increased with an increase in GO content, according to the authors. Chen et al. [The limited morphology studies previously conducted on GO/polymer composites utilize SEM and TEM mostly for analysis. Generally, SEM images are not that clear at high magnifications, hence the need to supplement them with TEM, which provides clearer images at higher magnifications. The heat sensitivity of most polymers contributes to this aspect of SEM, meaning these polymers would be thermally unstable at high temperatures and result in blurred images. Pinto et al.  used SEMn et al.  looked iConducting thermal studies on polymer/GO composites is very important. This is done through the differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). DSC aids in understanding the melting and crystallization behavior, whereas TGA assesses the thermal stability. There has been a limited number of published work that investigates the thermal properties of GO/polymer composites ,80,81.Xu et al.  analyzedFrom the curves in These observations were said to imply that GO caused the molecular chains in PVC to relax with ease. Furthermore, the authors observed that the addition of 0.1 wt.% GO increased the melting enthalpy of PVC from 58.66 J/g for the neat polymer to 62.9 J/g for the composite. This was attributed to the reinforcing effect that GO had on PVC at that loading. Increasing the GO loading, however, resulted in the melting enthalpy of PVC dropping, whereby the lowest enthalpy (29.81 J/g) was when 1 wt.% GO was used. This was attributed to the weak interaction that existed between PVC and GO as the GO loading increased. The authors further stated that the formation of agglomerates of GO might have influenced this behavior. To expand further from known DSC interpretations: at low GO loadings (around 0.1 wt.%), the GO sheets might have completely covered the polymer matrix and dispersed evenly. However, when the content of GO was increased, it might have dispersed and covered all of the available surface area on the polymer matrix, such that it still remained. The remaining GO then formed agglomerates and exhibited particle-to-particle interactions. This could have been the reason for the observed agglomeration and poor dispersion, which in turn affected the melting enthalpy. The melting transition of PVC did not seem to be influenced by the addition of GO. This might imply that the GO did not have that much of an influence on the crystallinity of PVC at the loadings used. In addition, the PVC used could have been mostly amorphous.Xu et al.  investigIt is clear that graphene oxide-based polymer composites are still not that widely explored. Findings from different researchers in this regard are promising and can only be improved from here. GO seems to have a higher affinity towards certain polymers, and this could be very beneficial in helping to develop materials to assist in a number of problems such as pollution, drug administration, and electrical conductivity, amongst others.In analyzing the water intake properties of polymer/GO composites, the generally held opinion is that the oxygen containing functional groups in most polymers and GO influence the water intake properties profusely. These groups have been proven to up the absorption rate in these materials, and as such, the minimally studied GO/polymer composites, so far, are considered very hydrophilic. In one study, Ghosh et al.  investigThe above-presented work clearly shows that the water intake properties of graphene oxide and graphene oxide/polymer composites surpass those of graphene and graphene/polymer composites. The main contributing factor here would be the oxygen containing functional groups present on GO, and the assumption is that these groups would assist a great deal in metal ion adsorption. So as a step further, it would only be fitting to explore strides made in the metal adsorption capabilities of graphene oxide/polymer composites.w/w GO:bipolymercomposition) were used to treat heavy metal contaminated water surfaces. The authors developed the composites and investigated the effect of pH and dosage, ionic strength and associate ions, and contact time on the adsorption of Cr(III), Pb(II), and As(V) ions. In investigating the effect of pH and dosage, a pH range of 2 to 10 was used, and the results were summarised in GO/polymer composites, as adsorbents for heavy metal ions in solution, have had very minimal research outputs ,84 thus 43− ions, Pb(II) removal by increasing the concentrations of Ca2−, K+, and Mg2+, and lastly, the adsorption of As(V) was reduced by the introduction of the anionic species CO32− and PO43−, respectively. The reasons put forth were that the removal of Cr(III) is not affected by any other ionic species but only PO43− due to inner sphere surface complexation at the interface of the adsorbent and metal. The reduction in Pb(II) adsorption is due to the ionic strength and charge of the Ca2+, K+, and Mg2+ ions being close, which might reduce the interaction between Pb(II) and the GO/polymer adsorbent. Lastly, the disturbance in As(V) metal ions by the CO32− and PO43− species was said to be because these ionic species were competing with As (V) for adsorption sites via inner-sphere surface complexation. Concerning the contact time, as expected, metal ion adsorption would increase with an increase in contact time with the adsorbent. Modeling was done in this regard, and the Langmuir isotherm proved more applicable to the scenario, with constants calculated supporting a high adsorption capability. From this, the authors tried to determine the mechanism involved in the adsorption of these heavy metals onto the prepared composites. This was done using FTIR and XPS after the adsorption of heavy metals. It was established that after adsorption, new peaks were formed in FTIR, which portrayed the presence of metal ion and oxygen bonds for all the analyzed heavy metals. This brought about the assumption that adsorption took place on the surface of the adsorbent as the oxygens interacted with the heavy metal ions. Furthermore, the XPS analyses also resulted in newly formed peaks, as well as the shifting of peaks, signifying the presence of newly formed bonds. These bonds were still said to be between the oxygen groups on the adsorbent and the adsorbates . To give it more context, the authors went on to propose that the adsorption process was mostly caused by surface complexation instead of electrostatic contact. The proposed mechanism for the adsorption is shown in Concerning the adsorbent dosage, it was expected that increasing the dosage would result in an increased degree of adsorption. Then in investigating the effect of ionic strength and associated ions, different ionic substances were added to the adsorbent/adsorbate mixture to mimic the natural state with which the water is found if it is contaminated. The findings in this regard were that firstly Cr (III) removal was only deterred by POIt was further established from the modelling that the introduction of a polymer matrix/matrices to a GO-based adsorbent would increase the adsorption capability. This is obviously due to the presence of oxygen containing functional groups on some polymers, which would act as adsorption sites to trap the metal ions. Although there has not been much work done in using these composites for heavy metal removal, the chemistry coupled with recent work really proves that GO-based polymer composites possess excellent metal adsorption properties. In one other study by Peng et al. , the anaThe above report on GO/polymer composites is very minimal, and this is because there has not been much work done on this front, especially where biodegradable polymers are used. So, it would be fitting to identify this area as a knowledge gap and deem it only logical that the gap is explored fully and means to close it are put in place. This would contribute a great deal to the ongoing developments in material science and scientific research in general.This work aimed to review and contrast graphene and graphene oxide and their polymer composites in terms of metal ion adsorption and other properties. Insight into the strides made so far in this field of research has been provided with utmost certainty. In terms of structure and morphology, recent work has established that the main difference between GO and GN is the presence of oxygen-containing functional groups on GO, whereas GN only consists of carbon. Coming to the morphology of the composites, it was established that the layered structures of both GO and GN assist with adhesion and enveloping the polymer matrix/matrices. As such, we can say that both GO and GN have a compatibilizing effect on polymer matrices. From there, the thermal properties were reviewed, starting with melting and crystallization, then thermal stability. The work done on the thermal properties in this regard is also positive and promising. In melting and crystallization, the introduction of GO and GN into a polymer matrix/matrices is said to improve the miscibility of the blend composites and increase the crystallinity. This, of course, can open the way to many uses and applications. The thermal stability is also improved with the introduction of these two fillers, according to thealready-statedd literature. Then we looked at the water absorption capabilities of GN and GO, together with their polymeric composites. Here it was established, through the literature, that the water intake properties of GN are not at all satisfactory, even when used in polymer composites. This is particularly disadvantageous. As such, GO seems to be a suitable stand-in candidate. That is, both GO on its own and in polymer composites have been proven to show great water intake properties. The idea is that the oxygen containing functional groups on GO assist a great deal with this. Then the recent work done in metal ion adsorption shows that GN and its composites are not at all suitable adsorbents of heavy metals in solution. The lack of adsorption sites is the main reason here, and as such, GO would need to step in again. Both neat GO and GO/polymer composites have been shown to have excellent metal ion adsorption capabilities. This is one area that can be further explored, as there are gaps in it. Firstly, as already stated, the GO on its own has a shorter life span, so the gap lies in the need to mask it. Then, the masking that has taken place currently mostly utilizes a single polymer. We need to look into using binary polymer systems to mask GO, and improve other properties. Lastly, a very big gap remains in the type of polymers used. There has not been much research directed to the formulation and usage of completely biodegradable polymer/GO composites for the purposes of removing heavy metal ions from solution. These polymers are important as most of them contain oxygen in their structures, which could aid a great deal in trapping metal ions from water. In addition, as most biodegradable polymers are either too brittle or too rubbery on their own, blending them would assist in remedying this and balancing out the properties to obtain optimal materials. It is very clear now which direction should be taken from here in order to advance further the knowledge already acquired to optimize the usage of GO and its composites.As for the future perspectives regarding the usage of graphene and graphene oxide-based materials for water treatment, it is clear that graphene oxide is the most suited because of the oxygen-containing groups. The limitation with graphene is the absence of these functional groups that would act as adsorption sites and improve the adsorption rate and efficacy. Even if polymers that have these functional groups were used with graphene to form composites, the adsorption rate would surely be lower as compared to materials containing graphene oxide. Therefore, as it stands, there are many countries trying to explore the usage of graphene oxide-based materials in water treatment applications. However limitations arise in the policies. The toxicity of graphene oxide in the human body is a concept that is currently widely debated, although in some countries, it is already allowed to use these materials. This would influence the idea of ulitizing GO-based composites for water treatment, and then focus on developing methods to remove the GO traces in the water. This is already being explored as well, and we believe it could be the optimal remedy, as the absorption capabilities of GO, and its polymeric composites are crucial in water treatment.


RSV is an extremely common respiratory pathogen and a leading cause of infant hospitalisation. An estimated 1 in 7 infants will develop an RSV lower respiratory tract infection (LRTI) requiring medical attention. The majority of infants hospitalised have no comorbidities and were born at term. Nirsevimab is the only preventative option designed to provide protection to all infants from RSV LRTI, from the start of their first RSV season, for the duration of that season.In the HARMONIE trial conducted in the UK, France, and Germany (EudraCT 2022-000099-20), we evaluated the impact of nirsevimab on all cause LRTI hospitalisations, as well as RSV LRTI specifically. Analyses looked at the efficacy of nirsevimab across subgroups that constitute the all infant population, all of whom are vulnerable throughout their first RSV season.Individually randomised infants  received a single intramuscular injection of nirsevimab , or no intervention (standard of care) before or during the RSV season. Following a single physical visit participants were monitored remotely for all cause LRTI hospitalisation . Efficacy was evaluated through the RSV season. Adverse events (AEs) continue to be monitored for 365 days.8058 infants were randomized, 4037 to the nirsevimab group and 4021 to the no intervention group. Efficacy against RSV LRTI was 83.21% (CI 67.77-92.04%) across all countries. Efficacy was consistent across infant subgroups, in those born at term (≥ 37 weeks: 84.41% (CI 64.92-94.10%)) or prematurely (< 37 weeks: 78.31% (CI 33.49-94.69%)) and not impacted by infant weight at randomisation (< 5kg: 82.12% CI 59.14-93.30% and ≥ 5kg 85.16% CI 57.01-96.25%). Efficacy against all cause LRTI hospitalisation was 58.04% (39.693- 71.19).A single dose of nirsevimab given before or during the RSV season demonstrated a significant and sustained impact on RSV LRTI hospitalisations for the entire RSV season. Consistent efficacy was shown across subgroups comprising the all infant population.The potential impact of nirsevimab was reinforced by a reduction in all cause LRTI hospitalisations.Saul N. Faust, FRCPCH PhD, AstraZeneca, Janssen, Pfizer, Moderna, GlaxoSmithKline, Novavax, Sanofi, Seqirus, Medimmune, Merck, MSD, Iliad and Valneva: Advisor/Consultant|AstraZeneca, Janssen, Pfizer, Moderna, GlaxoSmithKline, Novavax, Sanofi, Seqirus, Medimmune, Merck, MSD, Iliad and Valneva: Investigator Katrina Cathie, MBE, FRCPCH, AstraZeneca: Advisor/Consultant|GSK: Advisor/Consultant|Iliad: Advisor/Consultant|Janssen: Advisor/Consultant|MedImmune: Advisor/Consultant|Merck: Advisor/Consultant|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant|Valneva: Advisor/Consultant SB Drysdale, FRCPCH, PhD, AstraZeneca: Advisor/Consultant|iLiAD: Advisor/Consultant|Janssen: Advisor/Consultant|Moderna: Advisor/Consultant|MSD: Advisor/Consultant|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant|Valneva: Advisor/Consultant S Royal, FRCGP, Sanofi: Advisor/Consultant C Felter, MD, Sanofi: Employee NC Vassilouthis, MD, Sanofi: Employee Mathieu Bangert, PhD, Sanofi: Staff member K Mari, PhD, Sanofi: Employee R Nteene, MD, Sanofi: Employee M Roberts, MD, Sanofi: Employee P Tissieres, MD, Baxter: Advisor/Consultant|PAion: Advisor/Consultant|Sanofi: Advisor/Consultant|Sedana: Advisor/Consultant


Structural stabilities were confirmed for all these heterostructures, indicating their feasibility in fabrication. BP/BP/MoS2 and BlueP/BlueP/MoS2 lowered the bandgaps further, making them suitable for a greater range of applications, with respect to the bilayers BP/MoS2 and BlueP/MoS2, respectively. Their absorption coefficients were remarkably improved in a wide spectrum, suggesting the better performance of photodetectors working in a wide spectrum from mid-wave (short-wave) infrared to violet. In contrast, the bandgaps in BP/graphene/MoS2 and BlueP/graphene/MoS2 were mostly enlarged, with a specific opening of the graphene bandgap in BP/graphene/MoS2, 0.051 eV, which is much larger than usual and beneficial for optoelectronic applications. Accompanying these bandgap increases, BP/graphene/MoS2 and BlueP/graphene/MoS2 exhibit absorption enhancement in the whole infrared, visible to deep ultraviolet or solar blind ultraviolet ranges, implying that these asymmetrically graphene-sandwiched heterostructures are more suitable as graphene-based 2D optoelectronic devices. The proposed 2D trilayer vdW heterostructures are prospective new optoelectronic devices, possessing higher performance than currently available devices.Constructing two-dimensional (2D) van der Waals (vdW) heterostructures is an effective strategy for tuning and improving the characters of 2D-material-based devices. Four trilayer vdW heterostructures, BP/BP/MoS Bilayeronfirmed ,25. Graponfirmed . MoS2/MXoS2/WSe2 , and alloS2/WSe2 , showingoS2/WSe2 . The typ regions . Blue-ph regions . BlueP/G regions . The pho regions  or an el regions . BP/MoS2etectors . Many BP devices . BlueP/M ability . In addiectivity . Graphenectivity . It is a2/MX2/MoS2 exhibit a smaller electron effective mass and a semiconductor-to-metal transition under tensile strain [2/MX2/MoS2 were further calculated, showing that these trilayer heterostructures would be prospective to construct highly sensitive field effect transistors for nano-biomolecules sensing as well as for pH sensing [2 monolayer is encapsulated by two graphitic carbon nitride monolayers, revealing their magnetic metallic characteristics [2/SiC/MoS2 and SiC/MoS2/SiC, and predicted their strain-induced tunable band structure promising for future optoelectronic devices [2-based trilayer heterostructures demonstrated the excellent ability to accelerate the separation of photo-generated electron-hole pairs and the controllable power conversion efficiency indicating great potential for photocatalysis and photovoltaics [2-graphene-based trilayer heterostructures are of unique optoelectronic properties tunable by external electric field and thus of great potential for solar energy harvesting and conversion [2/graphene/graphene and graphene/PtSe2/graphene, revealing their usefulness to guide the design of high-performance field effect transistors [2/graphene/MoS2 p-g-n heterostructure photodetector, which achieved a wide detection wavelength range of 400–2400 nm and a short rise/fall time [2/graphene/SnS2 p–g–n junctions, and achieved extraordinary responsivity as high as 2600 A/W, detectivity as good as 1013 Jones with fast photoresponse in the range of 405–1550 nm [As an extension to the above idea, trilayer vdW heterostructures have recently been attracting more and more attention, since one more layer might open more space for tuning the properties further ,41. Simue strain . Device  sensing . Bafekryeristics  probably devices . Calculavoltaics . BN/grapvoltaics ,48. Xu enversion . Externansistors . In an eall time . Li et a–1550 nm . It is t2, phosphorene and/or graphene are simulated in terms of structural, optical and optoelectronic properties. It is suggested that sophisticated 2D trilayer vdW heterostructures can provide further optimized characters for a new generation of optoelectronic devices. In this work, we attempt to construct and study trilayer vdW heterostructures with asymmetric structures and/or three different types of 2D materials. Four kinds of trilayer heterostructures containing MoS2, BlueP/BlueP/MoS2, BP/graphene/MoS2 and BlueP/graphene/MoS2, which are structurally asymmetric and/or composed of more than two different types of 2D materials. The first-principles calculation was performed using the tool of Vienna ab initio simulation package (VASP) [−5 eV and 0.01 eV/A, respectively. When optimizing the trilayer atomic structures, 1 × 7 × 1, 3 × 3 × 1, 2 × 2 × 1 and 3 × 3 × 1 k-point sets were adopted for BP/BP/MoS2, BlueP/BlueP/MoS2, BP/graphene/MoS2 and BlueP/graphene/MoS2, respectively. When calculating the electronic structures, 5 × 21 × 1, 16 × 16 × 1, 5 × 5 × 1 and 8 × 8 × 1 k-point sets were adopted for the four structures, respectively. When calculating the optical properties, 8 × 21 × 1, 20 × 20 × 1, 10 × 10 × 1 and 13 × 13 × 1 k-point sets were adopted for the four structures, respectively. The above-mentioned k-point sets were actually determined by convergence tests: increasing the k-point set scale until a state in which the calculation results do not change beyond the convergence standard any longer. It reflects that calculations for structural, electronic and optical properties need more and more computational cost and accuracy.In this study, we designed four trilayer vdW heterostructures BP/BP/MoSe (VASP)  within De (VASP) . The gene (VASP) ,55. Due e (VASP)  was adopEb) is calculated using the following equation: Ehts represents the overall energy of the heterostructure and EA, EB, and EC represent the energy of each monolayer in its free state. The work function, used as the key parameter to deduce the band alignment, is described byEvac is the energy of a stationary electron in the vacuum and Ef is the Fermi level.To establish the stability of the proposed vdW heterostructures, we should in principle evaluate their vibrational frequencies to seek real and positive phonon parameters. Nevertheless, most of the first-principles simulations on vdW heterostructures took the binding energy as the figure of merit to characterize the structure stability ,27,30,36ω represents the circular frequency of light. The real part The macroscopic optical response function is usually measured by the complex dielectric function den rule . Based oden rule ,(3)α(ω)2 heterostructure is constructed from a 2 monolayer and a 5 × 1 supercell bilayer-BP, while the BlueP/BlueP/MoS2 heterostructure is formed by stacking a 3 × 3 supercell bilayer-BlueP on a 3 × 3 supercell MoS2 monolayer. After structure optimization calculation, we obtain the rectangular lattice constants a = 22.227 and b = 3.278 Å for BP/BP/MoS2, and the hexagonal lattice constants a = b = 9.642 Å for BlueP/BlueP/MoS2. For BP/graphene/MoS2, bilayer BP/graphene cells are first constructed by matching 3 × 1 BP with 2. For BlueP/graphene/MoS2, a 3 × 3 supercell monolayer BlueP, 4 × 4 supercell graphene, and 3 × 3 supercell monolayer MoS2 are successively stacked. The optimized rectangular lattice constants of BP/graphene/MoS2 are a = 10.256 Å and b = 8.907 Å, and the hexagonal ones of BlueP/graphene/MoS2 are a = b = 9.776 Å. The simulated models of BP/BP/MoS2, BlueP/BlueP/MoS2, BP/graphene/MoS2 and BlueP/graphene/MoS2 are depicted in Due to the surface relaxation and interfacial mismatch, the trilayer vdW heterostructure supercells must be carefully set, for which we here extend monolayer cells individually to satisfy the length matching between different layers. The BP/BP/MoS2 [2 [2 [2 and BlueP/graphene/MoS2 might be easier to be fabricated than BP/BP/MoS2 and BP/graphene/MoS2, although BlueP-related 2D materials seem currently farther away from practical manufacture than BP-related ones.The trilayer vdW heterostructures will have different energies with different layer spacing. 2 , graphen2 [2 , graphen2 [2  bilayer 2 [2 [2  trilayer2 [2 [2 , and the2 [2 [2 ,46. Comb2 and BlueP/BlueP/MoS2 calculated using the PBE method. BP/BP/MoS2 exhibits the conduction band minimum (CBM) located between the Y and G points and mostly contributed by MoS2 (red lines) as well as the valance band maximum (VBM) located at the high-symmetry G point and mostly contributed by BP (blue lines). These are qualitatively consistent with our calculation of the bilayer counterpart BP/MoS2 , which are already lower than the free monolayer ones , whereas the VBM is located at the high-symmetry K point and is mostly contributed by BlueP (blue lines), consistent with the calculated results of the bilayer counterpart BlueP/MoS2 , which are already decreased compared with the corresponding free monolayer ones , suggesP 0.9 eV ). Signifent with ). Simila1.908 eV ). The whdW force . A wholedW force ,61; band2 and BlueP/graphene/MoS2 trilayer vdW heterostructures calculated using the PBE method. What is immediately significant is that the BP/graphene/MoS2 structure opens the bandgap of graphene to 0.051 eV, which is over one order of magnitude larger than the opening by the reported bilayer heterostructures such as graphene/BP (~0.0013 eV) [2 (~0.0007 eV) [2 (~0.0048 eV) [2 opens the bandgap of graphene by 0.0093 eV, similar to many other heterostructures. The effectiveness of the graphene bandgap opening in BP/graphene/MoS2 can hardly be attributed to strain, because the strains of graphene in both structures are close to each other (3.2% and 2.8%) and close to those of the reported structures such as graphene/BP (3%) [2 monolayers and possible built-in electric fields. The remarkable graphene bandgap opening suggests potential applications in temperature-sensitive devices [2 and BlueP/BlueP/MoS2. There seem to be two types of bandgap engineering mechanisms in trilayer vdW heterostructures, providing wider space for novel device design.0013 eV) , graphen0013 eV) , and gra0007 eV) , and rem0048 eV) , graphen0048 eV) , and gra0048 eV) . BlueP/g/BP (3%) . We beli devices . On the  devices  and Blue devices ), which 2 and BlueP/BlueP/MoS2, as shown in 2 and BlueP/graphene/MoS2, where the DOS also increases due to the insertion of a single graphene monolayer with respect to BP/MoS2 and BlueP/MoS2 bilayer heterostructures. It evidences the effectiveness of the proposed trilayer vdW heterostructures in optimizing the performance of 2D material systems.We also calculated the local density of state (LDOS) and total density of states (TDOS) of BP/BP/MoS2 and BlueP/BlueP/MoS2 heterostructures, p-type bilayer-BP(BlueP) (fEcloser to VBM) and n-type MoS2 (fE closer to CBM) form type-II band alignment, as is expected. This band alignment has carrier confinement effects [2 shows a more obvious type-II band alignment with a larger offset on the valence band and will produce a stronger carrier confinement effect compared with BP/MoS2 [2 and BlueP/graphene/MoS2 heterostructures naturally show a type-I quantum-well-like band alignment, providing the potential to construct nano-scaled bipolar transistors, field-effect transistors and photon transistors, which may need three different energy structures. In contrast to the Schottky contact of graphene/MoS2 and graphene/BP(BlueP), here, semiconductor heterojunctions are formed. By applying an electric field across this semiconductor heterojunction, the graphene bandgap can be further flexibly tuned to exhibit different band alignments, which is of significance for the construction of new types of field-effect photodetectors [The band alignment of the vdW heterostructure is important in the design of new 2D-material structures. It is thus necessary to specifically pick up the band alignment data from the proceeding band structures with the help of calculating the work function. In  effects , favorin BP/MoS2 ,40. Withetectors .2 and BlueP/BlueP/MoS2 trilayer heterostructures are shown in 2 is much stronger (reaching 105 cm−1) than that of BlueP/BlueP/MoS2 in the near-infrared range, but the latter displays more significant absorption in the ultraviolet range. They can thus be applied effectively in different scenes. As a whole, these results make up for the shortage of monolayer 2D materials in optical characteristics and are beneficial for optoelectronic devices with the need for light absorption enhancement and absorption range broadening.On the optical absorption properties of 2D materials, many studies have been devoted to monolayer and bilayer vdW heterostructures, but little research has been carried out on trilayer ones, let alone a comparison between the bilayer and trilayer heterostructures . The abs2, and the trilayer heterostructures BP/graphene/MoS2 and BlueP/graphene/MoS2. The inset shows that the two kinds of trilayer heterostructures have stronger light absorption in the range below 0.8 eV than the related bilayer heterostructures, so they are better choices for graphene-based 2D materials to realize photodetectors widely responding to short-, mid- and long-wave infrared light. In the range of 2.2–6 eV, BP/graphene/MoS2 shows remarkably higher light absorption than the related bilayer heterostructures, making it able to realize better graphene-based optoelectronic devices working in a wide range from visible to deep ultraviolet. BlueP/graphene/MoS2 shows better light absorption characteristics than the related bilayer heterostructures in the range of 4–6 eV, so it is more suitable for use as a graphene-based solar-blind ultraviolet photodetector. These asymmetric graphene-sandwiched vdW heterostructrues are thus proved to be effective in tuning and improving the performance of graphene-based 2D materials on the basis of guaranteeing the excellent electronic properties of graphene.2, BlueP/BlueP/MoS2, BP/graphene/MoS2 and BlueP/graphene/MoS2. The atomic structures of these heterostructures are all stable and look feasible to prepare. For BP/BP/MoS2 and BlueP/BlueP/MoS2, all the bandgaps were tuned to be smaller, to effectively compensate for the shortage of corresponding monolayers and to satisfy a wider spectrum of applications with respect to the bilayer structures BP/MoS2 and BlueP/MoS2, respectively. Their higher DOS, much stronger light absorption and more obvious type-II band alignment will realize better performance of optoelectronic devices working in a wide spectrum from mid-wave (short-wave) infrared to violet. In contrast, for BP/graphene/MoS2 and BlueP/graphene/MoS2, most of the bandgaps were enlarged, implying different tuning mechanisms related to vdW interaction. Specifically in BP/graphene/MoS2, the graphene bandgap was well opened to 0.051 eV, more than one order of magnitude larger than usual, indicating more space for graphene tuning in trilayer vdW heterostructures. Accompanying the bandgap increase, both BP/graphene/MoS2 and BlueP/graphene/MoS2 exhibit absorption enhancement in wide spectral ranges, suggesting that these trilayer vdW heterostructures show better prospects as graphene-based 2D material optoelectronic devices with broad responses in the short- to long-wave infrared, visible to deep ultraviolet and solar-blind ultraviolet bands. It is concluded that the proposed 2D trilayer vdW heterostructures are potentially applicable as novel optoelectronic devices.Using the first-principles calculation based on DFT theory, we proposed and simulated trilayer 2D vdW heterostructures: BP/BP/MoS


Pseudomonas is rare. Our objective was to investigate outcomes of patients with Pseudomonas endocarditis by treatment strategy and identify factors associated with treatment failure.Infective endocarditis secondary to Pseudomonas endocarditis at 8 hospitals were identified between July 2010 and December 2022. Unless the cultured valve yielded a Gram-negative pathogen, patients with blood cultures growing Gram-positive bacteria or yeast for >24 hours were excluded. Combination therapy was defined as receipt of ≥2 in vitro active agents for ≥72 hours. Failure was a defined as death or microbiologic failure by day 42; microbiologic failures included escalation of antimicrobial therapy following treatment-emergent resistance, increased vegetation size, or blood cultures positive for ≥14 days.Adult patients with definitive P. aeruginosa was the underlying pathogen in 97% (33/34) of patients. 65% (22/34) and 35% (12/34) received combination and monotherapy, respectively. Patients managed with combination therapy had comparable rates of prosthetic valve endocarditis , a vegetation size > 1cm  and no receipt of cardiac surgical intervention despite an indication  compared to those who received monotherapy. There was no significant difference in 42-day failure rates , 90-day mortality  or median hospital length of stay  between patients who receivedcombination therapy and monotherapy. Patients who experienced 42-day failure were more likely to have multi-drug resistant (MDR) Pseudomonas on initial blood cultures  and were less likely to be initially managed with piperacillin/tazobactam or cefepime  compared to those who experienced clinical cure.34 patients met the inclusion criteria. 32% (11/34) were people who inject drugs and 12% (4/34) were organ transplant recipients. Combination therapy versus monotherapyFactors associated with clinical failurePseudomonas endocarditis to date. We did Not identify a benefit to combination therapy; however, the overall sample size was small. Endocarditis due to MDR P. aeruginosa was associated with a higher risk of clinical failure.To our knowledge, this is the largest study of Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support


Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry (MS) experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here, we leveraged recent advances in ribosome profiling and MS to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly expressed to be detected by shotgun MS at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for 4 noncanonical proteins in MS data, which were also supported by evolution and translation data. These results illustrate the power of MS to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly expressed proteins. Ribosome profiling experiments suggest pervasive translation of short ORFs outside of annotated protein-coding genes. However, shotgun mass spectrometry experiments typically detect only a small fraction of the predicted protein products. This study shows that shotgun mass spectrometry has limited utility at this task due to its intrinsically low sensitivity for small, low-abundance proteins. Ribosome profiling (ribo-seq) experiments indicate that genomes are pervasively translated outside of annotated coding sequences . This “nOver the past decade, numerous studies have attempted to identify noncanonical proteins using bottom-up “shotgun” proteomics in which MS/MS spectra from a digested protein sample are matched to predicted spectra from a protein database ,17. ThesEven given the low rates of detection of noncanonical proteins predicted by ribo-seq, there are suggestions ,22 that Saccharomyces cerevisiae. He and colleagues [S. cerevisiae and investigate the biological and statistical factors affecting detection of noncanonical proteins.Several recent MS studies have aimed to improve detection of short, lowly expressed proteins in lleagues  used a clleagues  also uselleagues  searchedlleagues  integratUsing the MSFragger program , we searFig 1A). For noncanonical ORFs considered alone, it was not possible to generate a substantial list of detected proteins at a 1% FDR because too many decoys were detected relative to targets at all confidence thresholds . When the full proteome was considered together, 4,389 proteins were found at a 1% FDR, including 4,371 canonical proteins and 18 noncanonical . However, 10 noncanonical decoys also passed the 1% FDR expect score threshold, implying an estimated 56% FDR among these 18 noncanonical proteins. Thus, using a 1% proteome-wide FDR threshold, rather than a class-specific FDR strategy, results in a list of inferred noncanonical proteins of which a large fraction are false positives, as cautioned by Nesvizhskii [Among canonical ORFs considered alone, 4,391 of 5,968 had proteins detected at a 1% FDR . The small enrichment of targets above decoys gives little confidence in detection of noncanonical ORF products at the level of individual proteins but leaves open the possibility that MS data could contain a weak biological signal.In general, there is a trade-off in target-decoy approaches such that setting a weaker confidence threshold results in a longer list of proteins inferred as detected, but with a higher FDR. In the case of yeast noncanonical proteins, the decoy/target ratio never went below 60% for any list of inferred detected target proteins larger than 10, and this ratio also did not converge to 1 even with thresholds set to allow 10,000 target proteins to pass . In contrast, targets substantially outnumber decoys at all ranks for noncanonical ORFs . We reasoned that this bias could be explained by the short length of noncanonical proteins. Indeed, many predicted peptides derived from noncanonical ORFs include the starting methionine, while decoys, consisting of reversed sequences from the protein database, are more likely to end with methionine . To eliminate this large systematic difference, we constructed an alternative decoy database in which decoys for noncanonical proteins were reversed only after the leading methionine. When this database is used, the number of noncanonical targets and decoys at each rank is close to equal , and the target/decoy ratio converges to one as confidence thresholds are lowered . This behavior is consistent with expectations for a well-constructed decoy set. We therefore repeated our initial analysis using the alternative decoy set  and used it for all subsequent analyses.However, there is an alternative explanation for why targets are found at somewhat higher rates than decoys across a large range of confidence thresholds: decoy bias . The accFig 2G). We therefore sought to examine the strongest hits to determine if we could identify evidence that any were genuine detections. Two noncanonical proteins had peptides with stronger expect scores than any decoys . We gave the ORFs encoding these proteins systematic names YMR106W-A and YFR035W-A following SGD conventions [S1 Fig). Only a single YMR106W-A peptide was found at this threshold, but 3 additional YMR106W-A peptides had stronger matches than the next strongest decoy . Moreover, YMR106W and YFR035W-A both had translation rates (in-frame ribo-seq reads per codon) greater than 99% of noncanonical ORFs in the Wacholder and colleagues dataset [Using the alternative decoy set and standard MSFragger analysis, we remained unable to construct an FDR-controlled list of noncanonical proteins at a 10% FDR threshold because decoys were still detected at a similar rate as targets . In a multiple sequence alignment with other species in the Saccharomyces genus, the full span of the YFR035W-A amino acid sequence aligns between all species . Thus, evolutionary, translation, and proteomics evidence all indicate that unannotated ORF YFR035W-A is a better candidate for a conserved protein-coding gene than annotated ORF YFR035C.YMR106W-A is located 27 nt away from a Ty1 long terminal repeat. No homologs outside lleagues . It is tposition . This isposition . YFR035Wposition ,38, so iposition , but thi−5, similar to the level at which YMR106W-A was detected, or else the strongest candidates if another program was used.Aside from YMR106W-A and YFR035W-A, the standard MSFragger approach did not confidently detect proteins encoded by noncanonical ORFs supported by ribo-seq. We therefore considered some reasons we could miss noncanonical proteins present in the data and employed alternative approaches to test these possibilities. For each approach, we determined whether a substantial list of noncanonical ORFs could be constructed with FDR of 10% at the protein level. If not, we further investigated peptides with MSFragger expect scores <10Fig 4A). The only noncanonical proteins detected at a 10−5 expect score threshold were the same two as in the standard analysis.First, we hypothesized that a mismatch between the environmental conditions in which the ribo-seq and MS datasets were constructed may explain the low number of detected noncanonical proteins. To investigate this possibility, we reduced our analysis to consider only ribo-seq and MS experiments conducted on cells grown in YPD at 30°C. The target/decoy ratio looked similar to the analysis on the full dataset, with no noncanonical protein detection list generatable with a 10% FDR . The only noncanonical proteins detected at a 10−5 e-value threshold  were YMR106W-A and YFR035W-A, also found by MSFragger. We then applied the machine learning based MS2Rescore algorithm [Fig 4C). We also performed a search using MaxQuant [Fig 4D), with only 3 peptides given stronger scores than the strongest decoy; two belonged to YMR106W-A and one to a different hypothetical protein we named YPR195C-A following SGD conventions. However, this hypothetical protein was identified from a peptide found only once, showed no evidence of conservation in the Saccharomyces genus, and was not translated at high levels (Table 1); we therefore conclude that it may not be a genuine detection.Next, to ensure that our results were not specific to the search program MSFragger, we repeated our analysis using MS-GF+ . The patlgorithm  to rescoMaxQuant , which uMaxQuant  to indicFig 4E), and there were no PSMs with expect scores below 10−5.Work in other species has shown that use of multiple proteases, rather than trypsin alone, can improve detection of small or noncanonical proteins ,45. We t−5 expect score .One challenge in MS proteogenomics is that expanding searches to larger sequence database sizes raises the threshold for detection, which could limit discoveries . To redu−5 expect score threshold . Adding acetylation of lysine or N-terminal acetylation as variable modifications did not improve target/decoy ratios overall , but a single hit with an expect score of 8.37 × 10−6 was found, which we named YOR109W-A following SGD convention. However, this hypothetical protein was identified from a peptide found only once, showed no evidence of conservation in the Saccharomyces genus, and was translated at lower levels than other noncanonical protein detections (Table 1); we therefore conclude that it may not be a genuine detection.Next, we hypothesized that noncanonical proteins could have been missed from our searches due to posttranslational modification or cleavage. Allowing for phosphorylation of threonine, serine, or tyrosine as variable modifications did not improve the decoy/target ratio or yield detection of any noncanonical phosphorylated peptides at a 10Fig 4I), but a single additional noncanonical peptide was identified with a relatively strong expect score of 2.78 × 10−6 . This peptide was from the ORF YIL059C, annotated as “dubious” on SGD, indicating that, in the view of SGD, the ORF is “unlikely to encode a functional protein.” YIL059C is in the 88th percentile of translation rate and 99th percentile of length among noncanonical ORFs, at 366 nt (Table 1). It overlaps on the opposite strand of the ORF YIL060W, classified as “verified” on SGD. However, the references listed in support of YIL060W are all based on full deletion experiments, which would disturb both ORFs and therefore do not distinguish between them [Fig 5A), and YIL060W was not detected in our MS analysis of canonical ORFs. Given that the YIL059C peptide had one non-enzymatic end, we tested whether it could be a signal peptide using the TargetP program [Fig 5B), providing additional support that this is a genuine detection. Searching for homologs using TBLASTN, BLASTP, and BLASTN in the NCBI databases and TBLASTN and BLASTN in Saccharomyces genus genomes at a 10−4 e-value threshold, YIL059C and YIL060W have detected DNA homologs only in Saccharomyces species S. paradoxus, S. mikatae, and S. jurei. There was an intact protein alignment of YIL059C between S. cerevisiae and S. jurei , while YIL060W has no homologs that fully align in any species . YIL059C is located adjacent, and on the opposite strand, to a Ty2 long terminal repeat. These observations are consistent with a transposon-mediated horizontal transfer of YIL059C prior to divergence between S. cerevisiae and S. mikatae, followed by loss in S. paradoxus and S. mikatae and preservation in S. cerevisiae and S. jurei. We do not rule out a role for YIL060W, but all considered evidence provides greater support for the biological significance of YIL059C.Allowing for peptides to have one end that is not an enzymatic cut site to search for potential cleavage products did not improve target/decoy ratios overall . Thus, the Sun and colleagues results do not differ from ours because more high-confidence noncanonical PSMs were found. Rather, the difference is in statistical approach for FDR estimation. Sun and colleagues controlled FDR at a 1% proteome-wide level, rather than controlling a noncanonical-specific FDR as in our analysis. Moreover, Sun and colleagues analyzed multiple distinct datasets separately, each at a 1% FDR, and then constructed a combined list containing any protein found at 1% FDR in at least one analysis. Merging lists of detected proteins each constructed at 1% FDR is expected to generate a list with an FDR much higher than 1% [Finally, we wanted to investigate a class of noncanonical ORFs not present in the Wacholder and colleagues translated ORF dataset: noncanonical ORFs that overlap a canonical ORF on the same strand. These ORFs are difficult to identify by ribo-seq because it is challenging to distinguish noncanonical ORF-associated ribo-seq reads from those of the canonical gene; however, some proteins encoded by noncanonical ORFs that overlap canonical ORFs have been identified in previous analyses ,51, incllleagues , who fou than 1% .−5. We assigned it systematic name YNL155C-A following SGD conventions (Table 1).The stable translation product of YNL155C-A was supported by 2 distinct peptides, which together were detected 12 times with expect scores below the best decoy score of 5.69 × 10−7, with the strongest value of 5.77 × 10−9 .This 255-bp ORF overlaps canonical gene YNL156C for 57 of 255 bases. Its translation product was not identified in the Sun and colleagues’ analysis [Fig 6B). There also appears to be a triplet periodic pattern in a frame distinct from both YNL156C and YNL155C-A at the locus, suggesting that all 3 frames may be translated. Excluding the overlapping region, there are 265 reads per codon on the ORF that map to the first position of a codon in the YNL155C-A reading frame; this would put it in the 99.6th percentile of translation rate among translated noncanonical ORFs in the Wacholder and colleagues dataset. No homologs were found in more distantly related species in a TBLASTN search against the NCBI nonredundant protein database, but YNL155C-A was well conserved across Saccharomyces . As only 19 of 75 codons of YNL155C-A overlap YNL156C-A , the strong amino acid conservation across the length of the full protein  indicates purifying selection on YNL155C-A itself. Thus, proteomic, translation, and evolutionary evidence all support YNL155C-A as a protein-coding gene.In our analysis, only one overlapping ORF had associated PSMs with expect scores stronger than 10analysis . A clear−6 MSFragger expect score threshold). While 69% of canonical proteins have at least one peptide detected at this threshold, based on simulations, only 22% of noncanonical proteins would have a detectable peptide at this detection rate. These results illustrate how the short length of noncanonical proteins and the low numbers of potential tryptic peptides that result limit noncanonical detection.We sought to understand why the large majority of proteins predicted from translated noncanonical ORFs remained undetected across multiple computational search strategies. A major difference between canonical and noncanonical proteins is length: The average canonical protein is 503 residues compared to only 31 among noncanonical proteins. Short size can affect protein detection probability through distinct mechanisms: Shorter sequences provide fewer distinct peptides when digested, and the sample preparation steps of the MS experiment may be biased against small proteins . To inveS6 Fig). We therefore hypothesized that technical biases other than the number of potential tryptic peptides further limit the MS detectability of small proteins.Still, the larger challenge in noncanonical protein detection is not just the low number of possible peptides but the much lower detection rate among them than among canonical peptides. Our analyses only detect a handful of noncanonical proteins, far below the 22% that would be expected if lack of potential tryptic peptides was the only limitation. This is because, as a group, noncanonical proteins almost completely lack the high-confidence PSMs that support numerous canonical protein detections . We observe a division between canonical ORFs shorter versus longer than 150 nt. Among 27 canonical yeast ORFs shorter than 150 nt, none of 280 theoretically detectable peptides were detected at a 10−6 expect score threshold. This detection rate is significantly below expectation given the overall 6% rate at which canonical peptides are detected , suggesting that there may be technical biases limiting detection of proteins that are this short. As 83% of noncanonical ORFs  are shorter than 150 nt, short length can partially explain the low detectability of noncanonical ORF peptides. In contrast, however, among canonical ORFs longer than 150 nt, shorter lengths were associated with higher probabilities that a peptide was detected . This is likely due to a trend of higher translation rates among shorter ORFs , which is also observed among noncanonical ORFs . This observation suggests that short size should not be a barrier to detection of peptides encoded by noncanonical ORFs longer than 150 nt. There are 3,080 such ORFs, potentially encoding 35,392 detectable peptides, yet only one peptide was found at a 10−6 expect score threshold .To investigate this hypothesis, we calculated the peptide detection rate, out of all theoretically detectable peptides, among different ORF size classes . For example, 20% of canonical peptides scoring above 0.9 were detected at a 10−6 expect score threshold, compared to only 0.17% of peptides scoring below 0.1, an 85-fold increase. The distribution of detectability scores was similar between canonical and noncanonical peptides overall, with the major difference being that 6.3% of canonical peptides scored above .9 compared to only 3% of noncanonical peptides . If canonical peptides had the same distribution of scores as noncanonical peptides, the number of detected canonical peptides would be 83% of those found in actuality. Thus, amino acid composition does increase the difficulty in detection of noncanonical peptides, but this is a relatively small effect.In addition to length, noncanonical ORFs also differ from canonical ORFs in amino acid composition . The ami8 Fig). Among noncanonical proteins overall, only 11% are predicted to have transmembrane domains, below the 20% of canonical proteins predicted to have one. However, among noncanonical proteins longer than 50 amino acids, there is an excess of proteins with transmembrane domains that make up more than 20% of the protein . Thus, for some larger noncanonical proteins we would otherwise expect to be more likely to be detected, transmembrane domains likely hinder their detection.Transmembrane proteins are also more difficult to detect by MS . To dete−6 expect score threshold, at both the protein  and peptide  levels. As with protein length, we can use the canonical ORFs to infer an approximate detection limit: Among 439 canonical ORFs with fewer than 3 in-frame reads per codon, only 3 of 9,253 theoretically detectable peptides were detected at a 10−6 threshold. Thus, almost all canonical peptides, with only these 3 exceptions, are found among ORFs with reads per codon above 3 and longer than 150 nt. Yet, only 448 noncanonical ORFs  are in this category . Thus, almost all noncanonical ORFs are outside the limits in which canonical ORF products are detected by MS.Besides length and sequence composition, a major difference between canonical and noncanonical ORFs is expression level, and this too can affect the probability a protein is detected in MS data . We ther−6 expect score threshold under the assumption that detection probability depends only on translation rate. This probability was estimated as the peptide detection rate among canonical ORFs with a similar translation rate to the transient ORF . Given these estimates, the expected total count of detected peptides for the 448 ORFs was 5.41. In reality, a single peptide was detected . To see whether observing only a single detection was surprising, we simulated the distribution of peptide detection counts under the estimated detection probabilities. The 95% confidence interval of noncanonical peptide detections ranged from 1 to 10. Thus, the single observed detection of a noncanonical peptide at a 10−6 expect score threshold is within range of expectations.For the 448 noncanonical translated ORFs displaying length and expression levels amenable to detection (longer than 150 nt and at least 3 reads per codon), we estimated the probability a peptide would be detected at a 10Table 1) were classified as evolutionarily transient. Among the 103 evolutionarily transient canonical ORFs, none were detected at a 10−5 expect score threshold, and similar numbers of ORFs and decoys were found at weaker thresholds .Wacholder and colleagues identified a class of rapidly evolving, evolutionarily novel ORFs termed “transient ORFs.” Despite lacking long-term evolutionary conservation, transient ORFs can express proteins that have major effects on phenotype . Of 18,9S. cerevisiae that plays a role in the yeast mating pathway [Table 2). These results indicate that MS detection appears to miss the entire class of evolutionary transient ORFs, whether canonical or not, including even those known to play important biological roles.Five transient canonical ORFs have been characterized in some depth , includi pathway . Yet, noBottom-up MS is an attractive approach for validating noncanonical ORFs supported by ribosome profiling due to the ease of testing large lists of predicted proteins but is limited by low sensitivity. Analyzing 3 MS experiments optimized to find small proteins, we identified 3 noncanonical proteins expressed from ORFs identified as translated in a recent analysis of yeast ribosome profiling studies . We additionally found MS evidence for an ORF not initially identified by ribo-seq, YNL155C-A, due to overlapping a canonical ORF on the same strand. All 4 proteins were translated at rates much higher than typical noncanonical ORFs, providing independent evidence that they are genuine protein-coding genes; 3 also showed evidence of evolutionary conservation. These findings illustrate the power of using proteomic, translation, and evolutionary evidence in combination to identify undiscovered genes at high confidence even in a well-annotated model organism.Nevertheless, the vast majority of ribo-seq-supported noncanonical ORFs showed no evidence of detection in MS datasets. We show that the low rates of detection of noncanonical ORFs can be explained primarily by their short size and low translation rate: Canonical ORFs at similar sizes and translation rates are also very rarely detected. The general amino acid composition of noncanonical ORFs, and the abundance of transmembrane domains among the longest ones, further contribute to hindering detection. As these factors explain the differences in detectability between canonical and noncanonical ORFs, little else about the biology of noncanonical ORFs can be inferred from their lack of detection in MS data. We cannot conclude that proteins expressed from noncanonical ORFs are less stable than canonical proteins, that they are targeted for degradation at higher rates, or that they are less likely to be functional, except to the extent that low expression already justifies these inferences.A majority of the yeast noncanonical translatome, and a small portion of the canonical, consist of evolutionarily young ORFs with little evolutionary conservation, classified as “evolutionary transient ORFs” in the Wacholder and colleagues dataset . No tranThere is considerable variability across studies that attempt to detect noncanonical proteins using MS, with some reporting detection of hundreds of proteins, while others, as in this study, find many fewer ,36,60–62How, then, can we use shotgun MS experiments to help us understand the biology of translated noncanonical ORFs and their potential protein products? We draw several lessons that may be applicable beyond yeast. For small-scale discovery of new protein-coding genes, the shotgun MS approach still provides value. Most noncanonical detections identified in this study were found on the basis of just 1 or 2 PSMs; additional support that these were genuine detections was provided from translation and evolution data. This suggests that further MS experiments conducted in a wide range of conditions will likely yield new discoveries of proteins that can be detected only rarely. To maximize these rare discoveries, it will be helpful to analyze MS data using different parameters, considering in particular a wide variety of posttranslational modifications. Given the short length of most noncanonical proteins, and that some noncanonical proteins lack tryptic peptides suitable for detection, it will likely also be helpful to use multi-enzyme digests or alternatives to digestion to maximize the probability that each noncanonical protein has at least one detectable peptide . HoweverAll MS data files were taken from 3 studies. The He and colleagues  dataset Searches were conducted using the MSFragger program . Unless Unless otherwise specified, FDR was calculated in a class-specific manner  by dividing the number of decoy proteins within the class that were detected at the expect score threshold from the number of target proteins in the class detected at the threshold. A protein was considered detected at a given expect score threshold if had at least one unique PSM with an expect score below the threshold. Decoys were either default (reverse of protein database sequence) or reversed after the starting methionine, as indicated. Peptides were excluded if they belonged to more than one predicted protein. Peptides were also excluded from supporting noncanonical proteins if the exact peptide sequence existed in a canonical protein, regardless of whether it would be a tryptic peptide of that protein. PSMs were excluded if the MSFragger hyperscore was less than 3 above the score for the next best peptide, in order to avoid using PSMs that did not uniquely support a single protein.2Rescore [In 2 analyses, searches were instead conducted either using the MS-GF+ program  or MaxQu2Rescore  was thenS. cerevisiae genome. All reads are mapped to ribosome P-sites as described by Wacholder and colleagues. A read was considered to map to an ORF only if the inferred P-site mapped to the first position of a codon in the reading frame of the ORF. The total read count for an ORF is the sum of reads mapping over all first codon positions, and the translation rate is the read count divided by the number of codons in the ORF.All ribo-seq data were taken from the analysis in Wacholder and colleagues . These d−4 e-value threshold to consider a match a homolog. BLAST searches conducted on NCBI databases were done on the NCBI website. Searches of the yeast genomes collected by Shen and colleagues [Saccharomyces species genomes were conducted on genomes acquired from the following sources: S. paradoxus from Liti and colleagues [S. arboricolus from Liti and colleagues [S. jurei from Naseeb and colleagues [S. mikatae, S. uvarum, S. eubayanus, and S. kudriavzevii from Scannell and colleagues [BLAST analyses were conducted with default settings and a 10lleagues  were conlleagues . TBLASTNlleagues , S. arbolleagues   were required, up to 2 missed cleavages were allowed, clipping to the N-terminal methionine was a variable modification, and methionine oxidation was a variable modification. As in the MSFragger analysis, peptides were restricted to 7 to 50 amino acids and peptide masses from 350 to 1,800 Da. Out of this list of theoretical peptides, the peptides that were detected in the MS analysis at a 10We used simulations to estimate the proportion of noncanonical proteins that would be detected if noncanonical peptides had the same detection probability as noncanonical proteins. Each simulation was conducted by randomly selecting noncanonical peptides at the canonical peptide detection rate and counting the proportion of noncanonical proteins with at least one selected peptide. The reported proportion is the average over 10 simulations.S. cerevisiae model provided with the program was used for scoring. Peptides were binned by AP3 detectability score, with 10 intervals evenly spaced between 0 and 1, to construct −6) within the bin. We then took the expected value of canonical peptide detections if the frequency distribution of canonical peptides among bins matched that of noncanonical peptides and divided this count by the number of canonical peptides actually detected.Every tryptic peptide was assessed for estimated detectability using the AP3 algorithm . SettingEvery protein was assessed for transmembrane domains using TMHMM . For eacS1 FigTwo spectra with stronger MSFragger expect scores than all decoys are shown. At top, the full protein sequence is shown, with peptide coverage colored in red. Spectra were visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange  for the (PNG)Click here for additional data file.S2 FigA single PSM (top left) had a stronger MSFragger expect score than all decoys, while 3 others had stronger scores than the next strongest decoy. At top, the full protein sequence is shown, with peptide coverage colored in red. Spectra were visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange for the USIs given in (PNG)Click here for additional data file.S3 FigA single PSM had a stronger MSFragger expect score than all decoys when a single non-enzymatic end was allowed. At top, the full protein sequence is shown, with peptide coverage colored in red. The spectrum was visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange for the USI given in (PNG)Click here for additional data file.S4 FigPXD028623); we combined all PSMs from 11 different output files to create the plot. Each output file contains the PSMs passing a 1% FDR threshold, set at the whole-proteome level , in analyses conducted by Sun and colleagues using pFind. Each output file was individually thresholded at a 1% FDR in the Sun and colleagues’ analysis and noncanonical proteins passing this threshold in any file were inferred to be detected. Lower scores indicate higher confidence given by the MS algorithm. The observation that decoys and claimed noncanonical detections have similar scores suggests that many claimed noncanonical detections (indicated in blue) may be false positives. Merging multiple lists of inferred detections that were each individually generated at a 1% FDR is expected to result in a combined list with a much higher FDR [For each protein and decoy passing the detection threshold in the Sun and colleagues  study, tgher FDR , which, (PNG)Click here for additional data file.S5 FigSpectra for the 4 strongest matches for YNL155C-A. At top, the full protein sequence is shown, with peptide coverage colored in red. The spectra were visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange for the USIs given in (PNG)Click here for additional data file.S6 FigThe best peptide-spectrum match MSFragger expect score for each noncanonical protein and decoy in the database. Lower scores indicate stronger matches. The data underlying this Figure can be found in (PNG)Click here for additional data file.S7 FigA) Average log in-frame ribo-seq read count per codon among canonical ORFs of different size classes. (B) Average log in-frame ribo-seq read count per codon among noncanonical ORFs of different size classes. The data underlying this Figure can be found in ((PNG)Click here for additional data file.S8 FigThe proportion of peptides detected, among all eligible for detection, for canonical proteins binned by proportion of amino acids in predicted transmembrane domains. Predictions were made using TMHMM . The fir(PNG)Click here for additional data file.S9 FigPredicted proteins and decoys detected in MS data at a range of expect-score thresholds, among canonical proteins identified as evolutionarily transient by Wacholder and colleagues , using t(PNG)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.