Cultural crop-production practices are not only engineered to minimize pest incidence but also improve resource use efficiency and increase the diversity of habitat for beneficial insects that provide pollination services. With the increasing cultivation of industrial hemp and the benefits associated with the cultivation of multiple crops, its integration into a polyculture cropping system remains to be evaluated. We intercropped two pollinator-attractive crops, hemp and cowpea, with squash, a pollinator-dependent crop, to evaluate the impact of pollinator abundance and diversity on crop yield. Intercropping significantly increased the overall abundance of pollinators with 79.1% recorded from the intercropping systems compared to 21.9% in the monocropping systems. Sweat bees and bumble bees were the most abundant bees, and Squash+Cowpea was the most diverse cropping system. Intercropping significantly increased the yield of squash with higher squash yield (155%) in Hemp+Squash and (161%) in Squash+Cowpea than in squash monocrop. Also, intercropping resulted in higher hemp yield (64%) in Hemp+Cowpea and (165%) in Hemp+Squash compared to hemp monocrop. This study demonstrated that agricultural systems such as intercropping that are designed to attract pollinators are much more productive by not only improving crop yield but also growers’ returns on investments. Full article

In this paper, we propose a training method to reduce the VR sickness that occurs while viewing VR content with an HMD on. The proposed approach is a new method that involves pre-exposing users to VR sickness to enable them to adapt to VR sickness. In the proposed method, the training process was designed based on the features of existing studies related to exposure and adaptation to motion sickness and simulator sickness. The effectiveness of the proposed method was evaluated through experiments with 15 subjects (SSQ and VR sickness response were used in the analysis). As a result of the experiment, nausea was significantly decreased by 47%, and oculomotor discomfort was significantly decreased by 34% after the proposed training method. The VR sickness response decreased by 31%; however, this difference was not statistically significant. Furthermore, we analyzed the VR sickness response in two groups: those whose sickness decreased and those whose sickness increased. We confirmed that the decrease group (pre-experiment mean: 1.34 times, post-experiment mean: 0.58 times) had a larger change than the increase group (pre-experiment mean: 0.31 times, post-experiment mean: 0.42 times). Therefore, from the experimental results, it was confirmed that the proposed method is effective in reducing VR sickness. Full article

The aim of this work is research dedicated to the search for new bactericidal systems for use in cosmetic formulations, dermocosmetics, or the production of wound dressings. Over the last two decades, chitosan, due to its special biological activity, has become a highly indispensable biopolymer with very wide application possibilities. Reports in the literature on the antibacterial effects of chitosan are very diverse, but our research has shown that they can be successfully improved through chemical modification. Therefore, in this study, results on the synthesis of new chitosan-based Schiff bases, dCsSB-SFD and dCsSB-PCA, are obtained using two aldehydes: sodium 4-formylbenzene-1,3-disulfonate (SFD) and 2-pyridine carboxaldehyde (PCA), respectively. Chitosan derivatives synthesized in this way demonstrate stronger antimicrobial activity. Carrying out the procedure of grafting chitosan with a caproyl chain allowed obtaining compatible blends of chitosan derivatives with κ-carrageenan, which are stable hydrogels with a high swelling coefficient. Furthermore, the covalently bounded poly(ε-caprolactone) (PCL) chain improved the solubility of obtained polymers in organic solvents. In this respect, the Schiff base-containing polymers obtained in this study, with special hydrogel and antimicrobial properties, are very promising materials for potential use as a controlled-release formulation of both hydrophilic and hydrophobic drugs in cosmetic products for skin health. Full article

Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model’s fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. Full article

The correlation femtoscopy technique makes it possible to estimate the geometric dimensions and lifetime of the particle emission region after the collision of ions. Measurements of the emission region characteristics not only at midrapidity but also at backward (forward) rapidity can provide new information about the source and make it possible to impose constraints on the heavy-ion collision models. This work is devoted to revealing the dependence of the spatial and temporal parameters of the emission region of identical pions in Au+Au collisions at $\sqrt{s_{NN}}$ = 3 GeV from the fixed-target program of the STAR experiment. The extracted femtoscopic radii, $R_{out}$, $R_{side}$, $R_{long}$, $R_{out-long}^2$, and the correlation strength, $\lambda$, are presented as a function of collision centrality, pair rapidity, and transverse momentum. Physics implications will be discussed. Full article

The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become more robust with longer key lengths, they provide higher levels of security. However, non-invasive side channels, specifically through electromagnetic (EM) emanations, can expose confidential cryptographic details, thus presenting a novel solution to the pressing forensic challenge. This research delves into the capabilities of EM side-channel analysis (EM-SCA), specifically focusing on detecting both cryptographic key lengths and the algorithms employed utilizing a machine-learning-based approach, which can be instrumental for digital forensic experts during their investigations. Through meticulous data processing and analysis, the Support Vector Machine (SVM) model, among others, demonstrated a notable accuracy of 94.55% in distinguishing between AES and ECC cryptographic operations. This capability significantly enhances digital forensic methodologies, offering a novel avenue for noninvasively uncovering encrypted data’s cryptographic settings. By identifying key lengths and algorithms without invasive procedures, this research contributes substantially to the advancement of forensic investigations in encrypted environments. Full article

The morphology and crystal structure of Pt films grown by pulsed laser deposition (PLD) on yttria-stabilized zirconia (YSZ)at high temperatures Tg = 900 °C was studied for four different film thicknesses varying between 10 and 70 nm. During the subsequent growth of the capping layer, the thermal stability of the Pt was strongly influenced by the Pt film’s thickness. Furthermore, these later affected the film morphology, the crystal structure and hillocks size, and distribution during subsequent growth at Tg = 900 °C for a long duration. The modifications in the morphology as well as in the structure of the Pt film without a capping layer, named also as the as-grown and encapsulated layers in the bilayer system, were examined by a combination of microscopic and scattering methods. The increase in the thickness of the deposited Pt film brought three competitive phenomena into occurrence, such as 3D–2D morphological transition, dewetting, and hillock formation. The degree of coverage, film continuity, and the crystal quality of the Pt film were significantly improved by increasing the deposition time. An optimum Pt film thickness of 70 nm was found to be suitable for obtaining a hillock-free Pt bottom electrode which also withstood the dewetting phenomena revealed during the subsequent growth of capping layers. This achievement is crucial for the deposition of functional bottom electrodes in ferroelectric and multiferroic heterostructure systems. Full article

Since methods for reducing macromolecule entanglements have been developed, it has become possible to better understand the impact of polymer chain entanglement on the crystallization process. The article presents basic information about the disentangling of macromolecules and the characterization of the degree of entanglement. The basic knowledge of polymer crystallization was also presented. Then, it was discussed how polymers crystallize during their disentangling. Non-isothermal and isothermal crystallization experiments using disentangled polymers, and for comparison using entangled polymers, are described in more detail. The influence of disentangling on both nucleation and crystal growth is highlighted. It is also shown how the crystallization of polymers changes when macromolecules re-entangle. Full article

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care. Full article

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity. Full article

Membrane proteins constitute about 20% of the human proteome and play crucial roles in cellular functions. However, a complete understanding of their structure and function is limited by their hydrophobic nature, which poses significant challenges in purification and stabilization. Detergents, essential in the isolation process, risk destabilizing or altering the proteins’ native conformations, thus affecting stability and functionality. This study leverages single-particle cryo-electron microscopy to elucidate the structural nuances of membrane proteins, focusing on the SLAC1 bacterial homolog from Haemophilus influenzae (HiTehA) purified with diverse detergents, including n-dodecyl β-D-maltopyranoside (DDM), glycodiosgenin (GDN), β-D-octyl-glucoside (OG), and lauryl maltose neopentyl glycol (LMNG). This research not only contributes to the understanding of membrane protein structures but also addresses detergent effects on protein purification. By showcasing that the overall structural integrity of the channel is preserved, our study underscores the intricate interplay between proteins and detergents, offering insightful implications for drug design and membrane biology. Full article

Movement and muscle control are crucial for the survival of all free-living organisms. This study aimed to explore differential patterns of cortical and subcortical activation across different stages of muscle control using functional magnetic resonance imaging (fMRI). An event-related design was employed. In each trial, participants (n = 10) were instructed to gently press a button with their right index finger, hold it naturally for several seconds, and then relax the finger. Neural activation in these temporally separated stages was analyzed using a General Linear Model. Our findings revealed that a widely distributed cortical network, including the supplementary motor area and insula, was implicated not only in the pressing stage, but also in the relaxation stage, while only parts of the network were involved in the steady holding stage. Moreover, supporting the direct/indirect pathway model of the subcortical basal ganglia, their substructures played distinct roles in different stages of muscle control. The caudate nucleus exhibited greater involvement in muscle contraction, whereas the putamen demonstrated a stronger association with muscle relaxation; both structures were implicated in the pressing stage. Furthermore, the subthalamic nucleus was exclusively engaged during the muscle relaxation stage. We conclude that even the control of simple muscle movements involves intricate automatic higher sensory–motor integration at a neural level, particularly when coordinating relative muscle movements, including both muscle contraction and muscle relaxation; the cortical and subcortical regions assume distinct yet coordinated roles across different stages of muscle control. Full article

Coatomer Protein Complex-II (COPII) mediates anterograde vesicle transport from the endoplasmic reticulum (ER) to the Golgi apparatus. Here, we report that the COPII coatomer complex is constructed dependent on a small GTPase, Sar1, in spermatocytes before and during Drosophila male meiosis. COPII-containing foci co-localized with transitional endoplasmic reticulum (tER)-Golgi units. They showed dynamic distribution along astral microtubules and accumulated around the spindle pole, but they were not localized on the cleavage furrow (CF) sites. The depletion of the four COPII coatomer subunits, Sec16, or Sar1 that regulate COPII assembly resulted in multinucleated cell production after meiosis, suggesting that cytokinesis failed in both or either of the meiotic divisions. Although contractile actomyosin and anilloseptin rings were formed once plasma membrane ingression was initiated, they were frequently removed from the plasma membrane during furrowing. We explored the factors conveyed toward the CF sites in the membrane via COPII-mediated vesicles. DE-cadherin-containing vesicles were formed depending on Sar1 and were accumulated in the cleavage sites. Furthermore, COPII depletion inhibited de novo plasma membrane insertion. These findings suggest that COPII vesicles supply the factors essential for the anchoring and/or constriction of the contractile rings at cleavage sites during male meiosis in Drosophila. Full article

Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings. Full article

While digitalization offers new opportunities for small- and medium-sized enterprises (SMEs), it also introduces the phenomenon of the “digitalization paradox”. This paper develops a theoretical model comprising digitalization, digital technology–business alignment, external social capital, and SMEs’ performance, rooted in strategic alignment theory (SAT) and social capital theory (SCT). The necessary data for the study were obtained by distributing questionnaires to 352 small and medium-sized enterprises engaged in digital practices in China, and hierarchical regression analysis was employed to investigate the impact of digitalization on the performance of SMEs and its boundaries of influence. The results indicate an inverted U-shaped relationship between digitalization and SME performance, with both digital technology–business alignment and external social capital serving as positive moderators. Specifically, digital technology–business alignment and external social capital both enhance the positive impact of digitalization on the performance of SMEs and mitigate its negative effects. The findings enhance comprehension of the “digitalization paradox” and offer new insights and solutions for SMEs to navigate the opportunities and challenges of digitalization. Full article

Starch extraction from tubers and roots has long been an essential process, playing a crucial role in diverse industries ranging from alimentary to pharmacology. This review explores the different methods employed in starch extraction, including traditional techniques and the most innovative mechanical strategies. The methods show a good improvement in many aspects, such as an improvement in the efficiency of the process and an improvement in the yield, showing a value of 10.0–65.0% depending on the starch source. On the other hand, solvents such as NaOH are used in many mechanical processes for alkaline digestion to improve the extraction time. Ethanol and K₂S₂O₅ concentrations of 0.5% and 0.8% were used to prevent oxidation and modify some properties of the extracted starch. The use of many solvents has improved the optimization of the processes, providing the final extracted starch with more advantages and better quality. However, using enzymes such as cellulase in new and innovative ways has provided more advantages and a better efficiency and yield than the other methods. Each method has its advantages and challenges, highlighting the importance of understanding the diversity of different approaches and their impact on the yield, sustainability, environmental considerations, and quality of the extracted starch. As the world looks for more ecological approaches, this review shows the importance of critically evaluating the yield, efficiency, and environmental implications of the extraction methods, providing us with more ways of evaluating the methods used for starch extraction. The ecological impact is a crucial point when evaluating the innovation of a new extraction process, which is why methods such as ultrasound and pulsed electric-field-assisted techniques have been proposed. These methods have been presented as sustainable techniques called green technologies, offering more approaches and different advantages than the other methods. This review intends to investigate the complexities and considerations of starch extraction, providing a solid basis for decision-making regarding starch extraction. In a time where sustainability and product quality are crucial elements of industrial strategy formulation, an in-depth understanding of these methods becomes imperative to the development of responsible practices and efficiency in starch extraction. Full article

Cytoreductive surgery (CRS) combined with hyperthermic intrathoracic chemoperfusion (HITOC) is a promising treatment strategy for pleural mesothelioma (PM). The aim of this study was to evaluate the impacts of this multimodal approach in combination with systemic treatment on disease-free survival (DFS) and overall survival (OS). In this retrospective multicenter study, clinical data from patients after CRS and HITOC for PM at four high-volume thoracic surgery departments in Germany were analyzed. A total of 260 patients with MPM (220 epithelioid, 40 non-epithelioid) underwent CRS and HITOC as part of a multimodal treatment approach. HITOC was administered with cisplatin alone (58.5%) or cisplatin and doxorubicin (41.5%). In addition, 52.1% of patients received neoadjuvant and/or adjuvant chemotherapy. The median follow-up was 48 months (IQR = 38 to 58 months). In-hospital mortality was 3.5%. Both the resection status (macroscopic complete vs. incomplete resection) and histologic subtype (epithelioid vs. non-epithelioid) had significant impacts on DFS and OS. In addition, adjuvant chemotherapy (neoadjuvant/adjuvant) significantly increased DFS (p = 0.003). CRS and HITOC within a multimodal treatment approach had positive impacts on the survival of patients with epithelioid PM after macroscopic complete resection. The addition of chemotherapy significantly prolonged the time to tumor recurrence or progression. Full article

While the opioid crisis has justifiably occupied news headlines, emergency rooms are seeing many thousands of visits for another cause: cannabinoid toxicity. This is partly due to the spread of cheap and extremely potent synthetic cannabinoids that can cause serious neurological and cardiovascular complications—and deaths—every year. While an opioid overdose can be reversed by naloxone, there is no analogous treatment for cannabis toxicity. Without an antidote, doctors rely on sedatives, with their own risks, or ‘waiting it out’ to treat these patients. We have shown that the canonical synthetic ‘designer’ cannabinoids are highly potent CB1 receptor agonists and, as a result, competitive antagonists may struggle to rapidly reverse an overdose due to synthetic cannabinoids. Negative allosteric modulators (NAMs) have the potential to attenuate the effects of synthetic cannabinoids without having to directly compete for binding. We tested a group of CB1 NAMs for their ability to reverse the effects of the canonical synthetic designer cannabinoid JWH018 in vitro in a neuronal model of endogenous cannabinoid signaling and also in vivo. We tested ABD1085, RTICBM189, and PSNCBAM1 in autaptic hippocampal neurons that endogenously express a retrograde CB1-dependent circuit that inhibits neurotransmission. We found that all of these compounds blocked/reversed JWH018, though some proved more potent than others. We then tested whether these compounds could block the effects of JWH018 in vivo, using a test of nociception in mice. We found that only two of these compounds—RTICBM189 and PSNCBAM1—blocked JWH018 when applied in advance. The in vitro potency of a compound did not predict its in vivo potency. PSNCBAM1 proved to be the more potent of the compounds and also reversed the effects of JWH018 when applied afterward, a condition that more closely mimics an overdose situation. Lastly, we found that PSNCBAM1 did not elicit withdrawal after chronic JWH018 treatment. In summary, CB1 NAMs can, in principle, reverse the effects of the canonical synthetic designer cannabinoid JWH018 both in vitro and in vivo, without inducing withdrawal. These findings suggest a novel pharmacological approach to at last provide a tool to counter cannabinoid toxicity. Full article

Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated machine learning technique, leverages radiological imaging modalities for disease diagnosis and image classification tasks. Previous research on COVID-19 classification has encountered several limitations, including binary classification methods, single-feature modalities, small public datasets, and reliance on CT diagnostic processes. Additionally, studies have often utilized a flat structure, disregarding the hierarchical structure of pneumonia classification. This study aims to overcome these limitations by identifying pneumonia caused by COVID-19, distinguishing it from other types of pneumonia and healthy lungs using chest X-ray (CXR) images and related tabular medical data, and demonstrate the value of incorporating tabular medical data in achieving more accurate diagnoses. Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models were employed to extract features, which were then combined using early fusion for the classification of eight distinct classes. We leveraged the hierarchal structure of pneumonia classification within our approach to achieve improved classification outcomes. Since an imbalanced dataset is common in this field, a variety of versions of generative adversarial networks (GANs) were used to generate synthetic data. The proposed approach tested in our private datasets of 4523 patients achieved a macro-avg F1-score of 95.9% and an F1-score of 87.5% for COVID-19 identification using a Resnet-based structure. In conclusion, in this study, we were able to create an accurate deep learning multi-modal to diagnose COVID-19 and differentiate it from other kinds of pneumonia and normal lungs, which will enhance the radiological diagnostic process. Full article

The curative treatment of multiple solid tumors, including head and neck squamous cell carcinoma (HNSCC), utilizes radiation. The outcomes for HPV/p16-negative HNSCC are significantly worse than HPV/p16-positive tumors, with increased radiation resistance leading to worse locoregional recurrence (LRR) and ultimately death. This study analyzed the relationship between immune function and outcomes following radiation in HPV/p16-negative tumors to identify mechanisms of radiation resistance and prognostic immune biomarkers. A discovery cohort of 94 patients with HNSCC treated uniformly with surgery and adjuvant radiation and a validation cohort of 97 similarly treated patients were utilized. Tumor immune infiltrates were derived from RNAseq gene expression. The immune cell types significantly associated with outcomes in the discovery cohort were examined in the independent validation cohort. A positive association between high Th2 infiltration and LRR was identified in the discovery cohort and validated in the validation cohort. Tumor mutations in CREBBP/EP300 and CASP8 were significantly associated with Th2 infiltration. A pathway analysis of genes correlated with Th2 cells revealed the potential repression of the antitumor immune response and the activation of BRCA1-associated DNA damage repair in multiple cohorts. The Th2 infiltrates were enriched in the HPV/p16-negative HNSCC tumors and associated with LRR and mutations in CASP8, CREBBP/EP300, and pathways previously shown to impact the response to radiation. Full article

Neonates with a new diagnosis of anorectal malformation (ARM) present a unique challenge to the clinical team. ARM is strongly associated with additional midline malformations, such as those observed in the VACTERL sequence, including vertebral, cardiac, and renal malformations. Timely assessment is necessary to identify anomalies requiring intervention and to prevent undue stress and delayed treatment. We utilized a multidisciplinary team to develop an algorithm guiding the midline workup of patients newly diagnosed with ARM. Patients were included if born in or transferred to our neonatal intensive care unit (NICU), or if seen in clinic within one month of life. Complete imaging was defined as an echocardiogram, renal ultrasound, and spinal magnetic resonance imaging or ultrasound within the first month of life. We compared three periods: prior to implementation (2010–2014), adoption period (2015), and delayed implementation (2022); p ≤ 0.05 was considered significant. Rates of complete imaging significantly improved from pre-implementation to delayed implementation (65.2% vs. 50.0% vs. 97.0%, p = 0.0003); the most growth was observed in spinal imaging (71.0% vs. 90.0% vs. 100.0%, p = 0.001). While there were no differences in the rates of identified anomalies, there were fewer missed diagnoses with the algorithm (10.0% vs. 47.6%, p = 0.05). We demonstrate that the implementation of a standardized algorithm can significantly increase appropriate screening for anomalies associated with a new diagnosis of ARM and can decrease delayed diagnosis. Further qualitative studies will help to refine and optimize the algorithm moving forward. Full article