Limited Number Research Articles

Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios

Summary Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and estimating seismic hazards. In our previous study, the authors proposed a simple and cost-effective method to estimate a SAF based on a deep neural network (DNN) model and microtremor horizontal-to-vertical spectral ratio (MHVR). Since the previous DNN model was based on the observed SAFs and MHVRs within a limited district in Japan, the applicability of the previous model to non-source regions with different site conditions was limited. This study explored the application of a transfer learning (TL) technique to adapt an existing (pre-trained) DNN model to new regions and a different database. The SAFs obtained through generalized spectral inversion technique (GIT) at the seismic observation stations (K-NET and KiK-net) in Japan were collated as the ground truth for site effects. MHVRs recorded at the stations in several districts of Japan were collected to construct a dataset for the development of the TL model. Subsequently, a TL model was constructed, leveraging the neural network layers and their weights from the pre-trained model while incorporating additional neural network layers to enhance the performance. During the training process, a total data set of 112 sites was divided into training set, validation set, and external test set by 1:1:5. Utilizing a cross-validation strategy, the residuals between pSAFs (pseudo-SAFs) estimated by the TL model and the observed SAFs were analyzed for the external test set containing 80 sites. The results showed that the TL model outperformed the pre-trained DNN model. The cross-validation results demonstrated that almost consistent prediction results were obtained regardless of any combination of 16 sites selected as the training set. Furthermore, by contrasting the influence of varying training set sizes on the performance of the TL model and comparing the TL model to a DNN model with an extended training set, the effectiveness of constructing the model with the limited number of data (16 sites) was ascertained. Finally, the effectiveness and limitations of the TL model were evaluated using MHVRs with peak frequencies falling outside the training set's range.

Indoor pollution control based on surrogate model for residential buildings

Individuals typically spend most of their lives indoors, predominantly in spaces like offices and residences. Consequently, prolonged indoor exposure underscores the critical significance of maintaining optimal indoor air quality (IAQ) to safeguard one's health. The primary impediment to attaining efficient regulation of Indoor Air Quality (IAQ) is the challenge of monitoring the IAQ parameters, particularly within the immediate vicinity of an individual's breathing space. Current heating, ventilation, and air conditioning systems lack the ability to rapidly predict and optimize the quality of indoor air. The objective of this study is to acquire the distribution features of indoor pollutants and precise indoor environment data in order to efficiently forecast and enhance the IAQ. To achieve this objective, a proposed surrogate model was developed using computational fluid dynamics (CFD). Notably, the Kriging surrogate model can rapidly predict IAQ while using a limited number of CFD runs. CFD are widely used as numerical simulation methods to obtain the accurate information. Surrogate models can rapidly forecast indoor environmental conditions using CFD simulation data simultaneously. Optimization algorithms can efficiently achieve desirable indoor ambient conditions, offering highly effective and intelligent control techniques for indoor atmospheric ventilation.

Factors Affecting Selection of TraineE for Neurointervention (FASTEN).

Neurointervention is a very competitive specialty in the United States due to the limited number of training spots and the larger pool of applicants. The training standards are continuously updated to ensure solid training experiences. Factors affecting candidate(s) selection have not been fully established yet. Our study aims to investigate the factors influencing the selection process. A 52-question survey was distributed to 93 program directors (PDs). The survey consisted of six categories: (a) Program characteristics, (b) Candidate demographics, (c) Educational credentials, (d) Personal traits, (e) Research and extracurricular activities, and (f) Overall final set of characteristics. The response rate was 59.1%. As per the programs' characteristics, neurosurgery was the most involved specialty in running the training programs (69%). Regarding demographics, the need for visa sponsorship held the greatest prominence with a mean score of 5.9 [standard deviation (SD) 2.9]. For the educational credentials, being a graduate from a neurosurgical residency and the institution where the candidate's residency training is/was scored the highest [5.4 (SD = 2.9), 5.4 (SD = 2.5), respectively]. Regarding the personal traits, assessment by faculty members achieved the highest score [8.9 (SD = 1)]. In terms of research/extracurricular activities, fluency in English had the highest score [7.2 (SD = 1.9)] followed by peer-reviewed/PubMed-indexed publications [6.4 (SD = 2.2)]. Our survey investigated the factors influencing the final decision when choosing the future neurointerventional trainee, including demographic, educational, research, and extracurricular activities, which might serve as valuable guidance for both applicants and programs to refine the selection process.

New Triazole-Based Potent Inhibitors of Human Factor XIIa as Anticoagulants.

Factor XIIa (FXIIa) functions as a plasma serine protease within the contact activation pathway. Various animal models have indicated a substantial role for FXIIa in thromboembolic diseases. Interestingly, individuals and animals with FXII deficiency seem to maintain normal hemostasis. Consequently, inhibiting FXIIa could potentially offer a viable therapeutic approach for achieving effective and safer anticoagulation without the bleeding risks associated with the existing anticoagulants. Despite the potential, only a limited number of small molecule inhibitors targeting human FXIIa have been documented. Thus, we combined a small library of 32 triazole and triazole-like molecules to be evaluated for FXIIa inhibition by using a chromogenic substrate hydrolysis assay under physiological conditions. Initial screening at 200 μM involved 18 small molecules, revealing that 4 molecules inhibited FXIIa more than 20%. In addition to being the most potent inhibitor identified in the first round, inhibitor 8 also exhibited a substantial margin of selectivity against related serine proteases, including factors XIa, Xa, and IXa. However, the molecule also inhibited thrombin with a similar potency. It also prolonged the clotting time of human plasma, as was determined in the activated partial thromboplastin time and prothrombin time assays. Subsequent structure-activity relationship studies led to the identification of several inhibitors with submicromolar activity, among which inhibitor 22 appears to demonstrate significant selectivity not only over factors IXa, Xa, and XIa, but also over thrombin. In summary, this study introduces novel triazole-based small molecules, specifically compounds 8 and 22, identified as potent and selective inhibitors of human FXIIa. The aim is to advance these inhibitors for further development as anticoagulants to provide a more effective and safer approach to preventing and/or treating thromboembolic diseases.

Fe-enriched two-phase multiferroic composites based on lead ferroniobate - titanate and modified nickel ferrite

Iron-enriched ferroelectrics are of particular interest as piezoactive components of piezoelectric-ferrite ME ceramics. This is due to the phase composition similarity and the reduction of expected interfacial doping effects during high-temperature calcination. This idea seems attractive, but laborious due to the significantly limited number of possible piezoelectric–ferrite combinations. Therefore, there is practically no data on such systems in the literature. ME composite ceramics based on the highly efficient Fe-containing Pb(Fe0.5Nb0.5)0.935Ti0.065O3 (PFNPT) ferroelectric has been studied. (100-х) wt.% Pb(Fe0.5Nb0.5)0.935Ti0.065O3 (PFNPT) + х wt.% Ni0.9Co0.1Cu0.1Fe1.9O4-d (NCCF) composite systems has been obtained by the solid-state method at 1050 °C, with no foreign phases. The specimens densities amounted to ∼80% of the theoretical. It has been shown that the ME conversion efficiency and other composites properties are significantly influenced by the PFNPT precursor pre-treatment method. The maximum value of the ME coefficient ΔΕ/ΔΗ = 75 mV/(cm Oe) has been observed for specimens with x = 50–60 made from PFNPT powder with the addition of Li2CO3. An increase in the ME composites sintering temperature to 1150 °C leads to the formation of Pb2Nb2O7 foreign phase with a pyrochlore structure along the grain boundaries. The ME coefficient ΔΕ/ΔΗ does not exceed 15 mV/(cm⋅Oe). There is an threefold decrease in the composites piezoelectric parameters (piezoelectric coefficients dij, gij), as well as in their magnetic properties (magnetizations MS, MR) due to piezophase degradation, which is presumably associated with a change in the cations distribution over A and B sublattices of the spinel structure.

The value of complaints mechanisms in the private labour regulation of GVCs: A case study of the Fair Labor Association

Can complaints mechanisms strengthen the private labour regulation of global value chains (GVCs)? This article empirically investigates the results produced by the Fair Labor Association's (FLA) complaints mechanism, which alone allows outcomes to be verified from the complainant's perspective. Although the vast majority of complainants find this mechanism to be valuable, it also has a number of limitations that seriously affect outcomes for workers. The authors utilize the concept of “grounding” to explore how national, social and corporate dimensions affect the performance of the FLA complaints mechanism for workers. The implications of these findings are then considered for complaints mechanisms in other GVCs.

Blueprint for Blue Carbon: Lessons from Seychelles for Small Island States

Blue carbon has been proposed as a nature-based solution for climate change mitigation; however, a limited number of published works and data and knowledge gaps hinder the development of small island developing states’ (SIDS) national blue carbon resources globally. This paper reviews the blue carbon ecosystems of Seychelles as a case study in the context of SIDS, comparing estimations by the Blue Carbon Lab and recent blue carbon (mangrove and seagrass) evaluations submitted to the Seychelles national government. Mangroves (2195 ha, 80% in Aldabra Atoll) and seagrasses (142,065 ha) dominate in Seychelles, with coral reefs having the potential for carbon sequestration (169,000 ha). Seychelles is on track to protecting its blue carbon, but these systems are threatened by rising sea levels, coastal squeeze, erosion, severe storms, and human activities. The importance of carbon inventories, accounting institutions, and continuous monitoring of blue carbon systems is discussed. Blue accounting is necessary for accurate accounting of carbon sequestration and carbon storage, generating carbon credits, and representing impactful reductions in greenhouse gases for NDCs. Challenges and opportunities include policy legislation regarding ownership rights, accreditation and certification for carbon credits, sustainable financing mechanisms like natural asset companies and blue tokens, local engagement for long-term success, and carbon market dynamics following COP27. The restoration and regulation of blue carbon resources for optimal ecosystem services delivery, carbon inventories, and blue carbon policy are recommended development priorities. Blue carbon ecosystems have the potential to contribute to NDCs of SIDS while simultaneously offering sustainable development pathways for local communities through the multiple ecosystem services they provide.

Real-time Fusion of Multi-Source Monitoring Data with Geotechnical Numerical Model Results using Data-driven and Physics-informed Sparse Dictionary Learning

Development of digital twins is emerging rapidly in geotechnical engineering, and it often requires real-time updating of numerical models (e.g., finite element model, FEM) using multiple sources of monitoring data (e.g., settlement and pore water pressure data). Conventional model updating, or calibration, often involves repeated executions of the numerical model, using monitoring data from a specific source or at limited spatial locations only. This leads to a critical research need of real-time model updating and predictions using a numerical model improved continuously by multi-source monitoring data. To address this need, a physics-informed machine learning method called multi-source sparse dictionary learning (MS-SDL) is proposed in this study. Originated from signal decomposition and compression, MS-SDL utilizes results from a suite of numerical models as basis functions, or dictionary atoms, and employs multi-source monitoring data to select a limited number of important atoms for predicting multiple, spatiotemporally varying geotechnical responses. As monitoring data are collected sequentially, no repeated evaluations of computational numerical models are needed, and an automatic and real-time model calibration is achieved for continuously improving model predictions. A real project in Hong Kong is presented to illustrate the proposed approach. Effect of monitoring data from different sources is also investigated.

Semi-supervised imbalanced multi-label classification with label propagation

Multi-label learning tasks usually encounter the problem of the class-imbalance, where samples and their corresponding labels are non-uniformly distributed over multi-label data space. It has attracted increasing attention during the past decade, however, there is a lack of methods capable of handling the imbalanced problem in a semi-supervised setting. This study proposes a label propagation technique to settle the semi-supervised imbalanced multi-label issue. Specially, we first utilize a collaborative manner to exploit the correlations from labels and instances, and learn a label regularization matrix to overcome the imbalanced problem in the labeled instance. After that, we extend to semi-supervised learning and explore to represent the similarity of instances with weighted graphs on labeled and unlabeled data. Then, the data distribution information and label correlations are fully utilized to design the loss function under the consistency assumption manner. At last, we present an iterative scheme to settle the optimization issue, thereby achieving label propagation to address the imbalanced challenge. Experiments on a variety of multi-label data sets show the favorable performance of the proposed method against related comparing approaches. Notably, the proposed method is also validated to be robust with a limited number of training instances.

Validation of age estimation methods through the scapula in a Mediterranean non-adult population.

Age estimation methods through the metric study of bones in non-adult individuals are a frequently used tool in Biological and Forensic Anthropology; however, few methodological validation studies are available, given the limited number of adequate samples for their study. In this context, the aim of this work is to test the effectiveness of the methods of Fazekas and Kósa (1978), Saunders et al. (1993), Rissech and Black (2007) and Cardoso et al. (2017), for age estimation through the measurements of the scapula, in the non-adult osteological collection of the Anthropology Laboratory of the University of Granada (Spain), one of the most representative identified non-adult collections internationally. For this purpose, nine different measurements of the scapula were taken from a total of 149 non-adult individuals, applying the regression formulae proposed by the different authors and verifying their accuracy. Results have shown the existence of significant differences between the real age and the estimated age in almost all the tested methods, with degrees of error that differ from those obtained in the original study populations. Based on the obtained results, it is recommended to use the method of Cardoso et al. in postnatal individuals, always adjusting the degrees of error with those obtained in this work.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation.

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

Anomaly detection in automated fibre placement: learning with data limitations

Introduction: Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However, the scarcity of such labelled data poses a challenge.Methods: To overcome this limitation, we present a comprehensive framework for defect detection and localization in Automated Fibre Placement. Our approach combines unsupervised deep learning and classical computer vision algorithms, eliminating the need for labelled data or manufacturing defect samples. It efficiently detects various surface issues while requiring fewer images of composite parts for training. Our framework employs an innovative sample extraction method leveraging AFP’s inherent symmetry to expand the dataset. By inputting a depth map of the fibre layup surface, we extract local samples aligned with each composite strip (tow).Results: These samples are processed through an autoencoder, trained on normal samples for precise reconstructions, highlighting anomalies through reconstruction errors. Aggregated values form an anomaly map for insightful visualization. The framework employs blob detection on this map to locate manufacturing defects.Discussion: The experimental findings reveal that despite training the autoencoder with a limited number of images, our proposed method exhibits satisfactory detection accuracy and accurately identifies defect locations. Our framework demonstrates comparable performance to existing methods, while also offering the advantage of detecting all types of anomalies without relying on an extensive labelled dataset of defects.

Robot-Assisted Free Flap Reconstruction for Oropharyngeal Cancer: A Systematic Review

Management of oropharyngeal cancer is undergoing a shift due to different profiles of patients, who tend to be younger and show better prognosis. Transoral robotic surgery is being established as the first-line approach as the procedure has shown minimal invasiveness and good oncological and functional outcomes, while avoiding extensive open surgery approaches and intensive chemoradiotherapy treatments. Naturally, the next step in the management of oropharyngeal cancer would be using the same technology for reconstruction to preserve the function and anatomy of the oropharynx. Free flaps, especially the radial forearm free flap and the anterolateral thigh flap, proved to be ideal for oropharyngeal reconstruction, and robotic assistance in these cases is yet to be explored. A limited number of studies have reported robot-assisted oropharyngeal reconstruction, so this review summarizes the currently available data and explores the feasibility, safety, and functional outcomes of this novel technique.

Efficacy of physical exercise intervention on children with acute lymphoblastic leukemia during treatment and rehabilitation: a systematic review and meta-analysis.

To systematically evaluate the impact of physical exercise intervention on children with acute lymphoblastic leukemia (ALL) during the treatment and rehabilitation consolidation periods. Randomized controlled trials (RCTs) were retrieved from PubMed, Scopus, Web of Science, CNKI, and Cochrane databases, with a search time range from database establishment to September 1, 2023. The quality of the included RCTs was evaluated using the Cochrane risk assessment tool, and a systematic evaluation was conducted using RevMan 5.4. The study has been registered with INPLASY (registration number: 202390100). A total of 12 RCTs including 423 subjects was included. The meta-analysis results showed that long-term exercise intervention can effectively improve the endurance performance (SMD = 1.37, 95% CI 0.45 to 2.29, p = 0.004), functional mobility (MD = - 1.17, 95% CI - 1.85 to - 0.49, p = 0.0008), cancer-related fatigue (CRF) (MD = - 1.25, 95% CI - 1.69 to - 0.80, p < 0.00001), and quality of life (QOL) (MD = 4.93, 95% CI 1.80 to 8.05, p = 0.002) of ALL children during the treatment and rehabilitation consolidation periods. Its promoting effect on the muscle strength (SMD = 0.53, 95% CI - 0.33 to 1.39, p = 0.23) and bone mineral density (BMD) (SMD = 0.48, 95% CI 0.20 to 0.77, p = 0.05) of the subjects was not significant. Further meta-analysis showed that exercise intervention with a duration of less than 1year (SMD = 0.91, 95% CI 0.55 to 1.28, p < 0.00001) rather than more than 1year (SMD = - 0.16, 95% CI - 0.61 to 0.29, p = 0.49) can effectively reduce subject BMD, while in terms of strength, exercise intervention can effectively improve strength during the treatment period (SMD = 0.97, 95% CI 0.40 to 1.54, p = 0.0008) rather than the consolidation period (SMD = - 0.27, 95% CI - 1.08 to 0.53, p = 0.51). Long-term regular exercise can effectively improve the endurance, functional mobility, CRF, and QOL of children with ALL in the rehabilitation and treatment consolidation stages. Their strength and BMD may be influenced by the timing of treatment and the intervention cycle, respectively. Considering the limited number of included literature and the instability of some outcome indicators, it is necessary to design more comprehensive and rigorous high-quality RCTs in the future to test the exercise efficacy of ALL children.

A systematic review of the performance of actigraphy in measuring sleep stages.

The accuracy of actigraphy for sleep staging is assumed to be poor, but examination is limited. This systematic review aimed to assess the performance of actigraphy in sleep stage classification of adults. A systematic search was performed using MEDLINE, Web of Science, Google Scholar, and Embase databases. We identified eight studies that compared sleep architecture estimates between wrist-worn actigraphy and polysomnography. Large heterogeneity was found with respect to how sleep stages were grouped, and the choice of metrics used to evaluate performance. Quantitative synthesis was not possible, so we performed a narrative synthesis of the literature. From the limited number of studies, we found that actigraphy-based sleep staging had some ability to classify different sleep stages compared with polysomnography.

Involvement of Vasoactive Intestinal Peptide Family Members in Diabetic Keratopathy

Diabetic keratopathy (DK) is a common ocular complication of diabetes, characterized by alteration of the normal wound-healing mechanism, reduction of epithelial hemidesmosomes, disruption of the basement membrane, impaired barrier function, reduced corneal sensitivity, corneal ulcers, and corneal edema. The limited number of clinical studies do not allow a full characterization of the pathophysiology of DK and, until now, effective therapeutic approaches have not been available. However, in recent years, neuropeptides gained great attention for their biochemical characteristics and therapeutic potential. This review focuses on the role of neuropeptides vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) in the eye and, in particular, in the cornea, in physiological conditions, or during DK, by providing an overview of this diabetes mellitus complication.

What are the factors affecting primary care choice when the access under health insurance scheme is limited?: a cross-sectional study in Bandung, Indonesia

BackgroundEnsuring equal access to primary care services is crucial, as the gateway to a higher level of care. Indonesia has been trying to increase financial access to medical care by administering national health insurance known as BPJS-Health (Badan Penyelenggara Jaminan Sosial Kesehatan) since 2014. However, BPJS-Health beneficiaries can only use their benefits at a limited number of registered primary care providers (BPJS-Health partners). This study investigated the geographical coverage of BPJS-Health and BPJS-Health beneficiaries’ primary care choices, based on their characteristics and healthcare preferences in the target areas of Bandung, Indonesia.MethodsThe setting of this cross-sectional study was the areas with low physical access to BPJS-Health partners but high physical access to non-BPJS-Health partners. Physical access was determined by spatial network analysis, resulting in a geographical coverage map. A total of 216 adults were recruited and they completed the questionnaire about their primary care choice. All participants had been registered with the BPJS-Health system and living in the study areas. Their participation in non-BPJS-Health was also evaluated. Participants’ choice of care was assessed in three different scenarios, when the individual was experiencing mild, chronic, and serious illnesses.ResultsBPJS-Health partners’ geographical coverage was unequally distributed in Bandung. Being registered with non-BPJS-Health company was negatively associated with the more frequent choice of using BPJS-Health partners’ services (AOR = 0.18; 95% CI, 0.06-0.58, P = 0.004) among BPJS-Health beneficiaries. For serious illnesses, having a high income was associated with choosing non-BPJS-Health partners and hospitals (AOR = 4.90; 95% CI, 1.16-20.77, P = 0.031). When dealing with mild and chronic illnesses, participants were concerned about the quality of treatment they would receive as a major factor in choosing a primary care provider. However, receiving better treatment quality was negatively associated with choosing BPJS-Health partners in all cases of illness severities.ConclusionsSociodemographic characteristics, healthcare preference factors, and health insurance status were associated with participants’ primary care choices in the target areas of Bandung, Indonesia. BPJS-Health partners’ coverage map and the preference factors are potentially important for policymakers, especially for the development of future BPJS-Health partnerships.

Identification and evolution of ICE-PmuST394: a novel integrative conjugative element in Pasteurella multocida ST394.

The emergence of macrolide and tetracycline resistance within Pasteurella multocida isolated from feedlot cattle and the dominance of ST394 in Australia was reported recently. To establish the genetic context of the resistance genes in P. multocida 17BRD-035, the ST394 reference genome, and conduct a molecular risk assessment of their ability to disperse laterally. A bioinformatic analysis of the P. multocida 17BRD-035 genome was conducted to determine if integrative conjugative elements (ICEs) carrying resistance genes, which hamper antibiotic treatment options locally, are in circulation in Australian feedlots. A novel element, ICE-PmuST394, was characterized in P. multocida 17BRD-035. It was also identified in three other isolates (two ST394s and a ST125) in Australia and is likely present in a genome representing P. multocida ST79 from the USA. ICE-PmuST394 houses a resistance module carrying two variants of the blaROB gene, blaROB-1 and blaROB-13, and the macrolide esterase gene, estT. The resistance gene combination on ICE-PmuST394 confers resistance to ampicillin and tilmicosin, but not to tulathromycin and tildipirosin. Our analysis suggests that ICE-PmuST394 is circulating both by clonal expansion and horizontal transfer but is currently restricted to a single feedlot in Australia. ICE-PmuST394 carries a limited number of unusual antimicrobial resistance genes but has hotspots that facilitate genomic recombination. The element is therefore amenable to hosting more resistance genes, and therefore its presence (or dispersal) should be regularly monitored. The element has a unique molecular marker, which could be exploited for genomic surveillance purposes locally and globally.

Prevalence and Predicting Factors of Pretreatment Dysphagia in Veterans With Head and Neck Cancer: A Pilot Study

Background: Dysphagia is a common sequela in individuals with a diagnosis of head and neck cancer. However, the prevalence and predicting factors for pretreatment dysphagia have not been well identified in the literature. A priori knowledge of the presence of a pretreatment dysphagia is advantageous and can have clinically significant implications for management of this patient population. The ability to predict pretreatment dysphagia in this population would allow more targeted interventions and potential improvements in clinical outcomes. Participants: This pilot study investigated the prevalence and predicting factors of pretreatment dysphagia in 67 veterans with head and neck cancer through the West Haven, CT, Veterans Affairs cancer database. Method: Data for this study were acquired via retrospective medical record review. Results: The prevalence of pretreatment dysphagia in our sample was found to be 49.3%. Age and pretreatment percutaneous endoscopic gastrostomy placement were found to have a statistically significant relationship with pretreatment dysphagia. Stage of cancer diagnosis and the diagnosis of pretreatment dysphagia were found to have a statistically significant relationship with posttreatment dysphagia. Conclusions: Findings offer important preliminary data and clinical considerations about this issue and serve as a foundation for future research. There are a limited number of factors associated with pretreatment dysphagia, and clinicians should consider a conservative referral approach when considering the need for dysphagia assessment prior to the initiation of oncologic treatment.

Codon-tRNA Coadaptation Bias for Identifying Strong Native Promoters in Komagataella phaffii.

Promoters are crucial elements for engineering microbial production strains used in bioprocesses. For the increasingly popular chassis Komagataella phaffii (formerly Pichia pastoris), a limited number of well-characterized promoters constrain the data-driven engineering of production strains. Here, we present an in silico approach for condition-independent de novo identification of strong native promoters. The method relies on tRNA-codon coadaptation of coding sequences in the K. phaffii genome and is based on two complementary scores: the number of effective codons and the tRNA adaptation index. Genes with high codon bias are expected to be translated efficiently and, thus, also be under control of strong promoters. Using this approach, we identified promising strong promoter candidates and experimentally assessed their activity using fluorescent reporter assays characterizing 50 promoters spanning a 76-fold difference in expression levels in a glucose medium. Overall, we report several promoters that should be added to the molecular toolbox for engineering of K. phaffii and present an approach for identifying promoters in microbial genomes.