Abstract Bioplastics were produced by mixing starch with carboxymethyl cellulose (CMC) during the manufacturing process. The physical characteristics of the bioplastics were investigated using Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Tensile strength, elongation, and Young's modulus tests were utilized to assess the mechanical characteristics of bioplastics. The bioplastic with the highest tensile strength was BP3 (7.03 ± 0.341 N/mm²), whereas BP0 had a tensile strength of 1.57 ± 0.111 N/m². The addition of CMC increased the viscosity of the solution and, consequently, the strength of the bioplastic. The range of bioplastic hydrophobicity was approximately 128.32% to 323.74%. FTIR, XPS, and XRD indicated that the physical mixing utilized during synthesis did not result in the addition of functional groups other than the native functional groups of the substances since no chemical reaction occurred. The thermal behavior investigation revealed that increasing the amount of CMC added to TPS can increase the presence of O-H functional groups in bioplastics, contributing to an increase in the glass transition temperature. Furthermore, both bioplastics broke down at approximately 250°C.

Abstract The purpose of this research is the detect of presence and location of defects in a reinforced plate, using piezoelectric patches, for structural health monitoring. In this study, an array of piezoelectric parts is used, each of these parts produces ultrasonic guided waves in structure, called actuator and other parts receive the waves, called sensors. The amount of energy received from piezoelectric pieces will be different after passing the distance between actuators and sensors in healthy and damaged paths. This difference is used to detect the presence and location of the defects. To check the state of defect, the visualization of the damage probability function is used and the damage index is calculated based on continuous wavelet transformation. The presence of rib in the structure causes the amount of energy received to be different compared to the path without ribs. Therefore, reinforcements are recognized as defects in the visualization of probability function. In this research, the amount of energy received from piezoelectric sensors is equated with the amount of energy of an unribbed structure, using correction coefficients, so that only real defects of the structure can be recognized as damages. In this research, the selected structure is a ribbed aluminum plate, which is widely used in the aviation industries. The defect placed in the structure is considered as a gap in different locations, and the obtained results show that the method of correcting the coefficients improves the accuracy of identifying the defect presence and location.

Abstract A biological trial was carried out with 256 numbers of sex-separated day-old broiler chicks distributed to four experimental groups with eight replicates, each replicate consisting of eight chicks. The experimental basal diet supplemented with zinc oxide (T1) at 80 ppm and three levels of zinc alginate complex at 56,45 and 34 ppm were designated as dietary treatments (T2, T3 and T4). At the end of 35 days T4, T3 and T2 groups had significantly (P < 0.01) higher body weight and FCR compared to the zinc oxide group and average daily feed intake (g/bird) was comparable among the zinc alginate complex groups and the zinc oxide-supplemented group. The tibial zinc was significantly (P < 0.01) increased with a low level of inclusion of zinc alginate complex compared to the zinc oxide group. However, serum zinc was not statistically significant, but the lower inclusion of zinc alginate complex at 34 ppm had numerically higher serum zinc concentration than the zinc oxide group. The zinc alginate complex at 34 ppm and 56 ppm significantly (P < 0.05) increased the dry matter digestibility and crude protein digestibility. At 34 ppm and 45 ppm significantly (P > 0.05) increased tibial bone length. The ileal villi height and ileal villi height to crypt depth ratio (VH: CD) were significantly (P < 0.01) increased in the zinc alginate complex. Overall, these findings underscore the potential benefits of zinc alginate complex in enhancing nutrient utilization, and growth performance in poultry farming.

Background: Speech-in-noise hearing is impaired in early symptomatic Alzheimer's disease. However, most tests involve the use of verbal stimuli where performance measures may be confounded by linguistic and cultural factors. Non-verbal auditory measures may overcome these issues. Methods: 158 cognitively healthy, 26 mild cognitively impaired and 28 participants with Alzheimer's disease dementia underwent evaluation using the Addenbrookes Cognitive Examination (3rd Edition), pure-tone audiometry, speech-in-noise hearing testing with digits and sentences and non-verbal auditory short-term memory for basic sound features. Group-level differences were assessed after adjusting for age, sex and educational attainment. Multinomial logistic regression and receiver operator characteristic metrics were used to test the fit of the model to the diagnosis using verbal and non-verbal auditory variables. Results: Non-verbal measures provided a better fit to diagnosis (cognitively normal, mild cognitive impairment or Alzheimer's disease dementia) (Akaike Information Criteria >10) using logistic regression as compared to verbal measures. There were no statistically significant differences using receiver operator characteristic measures. Conclusions: Non-verbal auditory measures are as good as verbal speech-in-noise measures at discriminating between cognitively healthy, mild cognitively impaired and people with Alzheimer's disease dementia.

Importance: Nurses work related stress and sickness absence are high. The consequences of sickness absence are severe for health systems efficiency and productivity. Objective: To measure the association between nurse staffing configurations and sickness absence in hospital ward nursing teams. Design: Retrospective case control study using hospital routinely collected data Setting: Four general acute care hospitals in England Participants: 3,583,586 shifts worked or missed due to sickness absence by 18,674 registered nurses (RN) and nursing assistant (NA) staff working in 116 hospital units. Exposure: Nursing team skill mix; temporary staffing hours; understaffing; proportion of long shifts (12+ hours) worked; full-time/part-time work status in the previous 7 days. Main outcome: Episodes of sickness absence, defined as a sequence of sickness days with no intervening days of work. Results: There were 43,097 sickness episodes. In our reduced parsimonious model, being exposed to a skill mix that was richer in RNs was associated with lower RN sickness absence (OR= 0.98; 95% CI = 0.96 0.99). For each 10% increase in proportion of hours worked as long shifts worked in the previous 7 days odds of sickness were increased by 2% (OR = 1.02; 95% CI = 1.02 1.03) for RNs. Part-time work for RNs was associated with higher sickness absence (OR = 1.09; 95% CI = 1.04 1.15). When RN staffing over the previous week was below average, the odds of sickness absence for NAs increased by 2% for every 10% increase in understaffing across the period (OR = 1.02; 95% CI = 1.01 1.03). For RNs there was a significant interaction between part-time work and RN understaffing, whereby short staffing in the previous week increased sickness absence for full time staff but not among those working part time. NA understaffing was not associated with sickness absence for any staffing group. Conclusions and Relevance: Working long shifts and working on understaffed wards increases the risk of sickness absence in nursing teams. Adverse working conditions for nurses, already known to pose a risk to patient safety, may also create risks for nurses and the possibility of further exacerbating staff shortages.

The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for the predicting the crystallization propensities of proteins based on their sequences. In this work, we benchmark the power of open protein language models (PLMs) through the TRILL platform, a bespoke framework democratizing the usage of PLMs for the task of predicting crystallization propensities of proteins. By comparing LightGBM / XGBoost classifiers built on the embedding representations learned by different PLMs, such as ESM2, Ankh, ProtT5- XL, ProstT5, with the performance of state-of-the-art sequence-based methods like DeepCrystal, ATTCrys and CLPred, we identify the most effective methods for predicting crystallization outcomes. The LightGBM classifiers utilizing embeddings from ESM2 model with 30 and 36 transformer layers and 150 and 3,000 million parameters respectively have performance gains by 3 - 5% then all compared models for various evaluation metrics, including AUPR (Area Under Precision-Recall Curve), AUC (Area Under the Receiver Operating Characteristic Curve), and F1 score on independent test sets. Furthermore, we fine-tune the ProtGPT2 model available via TRILL to generate crystallizable proteins. Starting with 3, 000 generated proteins and through a step of filtration processes including consensus of all open PLM- based classifiers, sequence identity through CD-HIT, secondary structure compatibility, aggregation screening, homology search and foldability evaluation, we identified a set of 5 novel proteins as potentially crystallizable.

Humans and animals exhibit a remarkable ability to selectively filter out irrelevant information based on context. However, the neural mechanisms underlying this context-dependent selection process remain elusive. Recently, the issue of discriminating between two prevalent selection mechanisms--input modulation versus selection vector modulation--with neural activity data has been highlighted as one of the major challenges in the study of individual variability underlying context-dependent decision-making (CDM). Here, we investigated these selection mechanisms through low-rank neural network modeling of the CDM task. We first showed that only input modulation was allowed in rank-one neural networks and additional dimensions of network connectivity were required to endow neural networks with selection vector modulation. Through rigorous information flow analysis, we gained a mechanistic understanding of why additional dimensions are required for selection vector modulation and how additional dimensions specifically contribute to selection vector modulation. This new understanding then led to the identification of novel neural dynamical signatures for selection vector modulation at both single neuron and population levels readily testable in experiments. Together, our results provide a rigorous theoretical framework linking network connectivity, neural dynamics and selection mechanisms, paving the way towards elucidating the circuit mechanisms when studying individual variability in context-dependent computation.

Sporadic Alzheimer's disease (sAD) arises from a complex interplay between genetic and environmental factors that remains poorly understood, making it challenging to develop accurate cell models. To address this problem, by hypothesing that the early disease sAD states can be characterised by transcriptomic fingerprints, we assessed the effect on Aβ aggregation in human neuroblastoma cells a set of genes obtained by analysing snRNA-seq data from post-mortem AD patients. We then validated the most effective genes in human iPSC-derived cortical neurons, and selected FBXO2, a gene encoding a subunit of the ubiquitin protein ligase complex SCF, for further analysis. We found that early downregulation of FBXO2 in human iPSC-derived cortical neurons resulted in Aβ aggregation, tau hyperphosphorylation, and structural and functional neuronal network impairment. Based on these results, we report a neuronal sAD model (FBXO2 KD sAD) that recapitulates a set of molecular hallmarks of sAD. We suggest that this strategy can be expanded towards the generation of panels of preclinical stem cell-derived models that recapitulate the molecular complexity of the broad spectrum of AD patients.

Patterning of cell fates is central to embryonic development, tissue homeostasis, and disease. Quantitative analysis of patterning reveals the logic by which cell-cell interactions orchestrate changes in cell fate. However, it is challenging to quantify patterning when graded changes in identity occur over complex 4D trajectories, or where different cell states are intermingled. Furthermore, comparing patterns across multiple individual embryos, tissues, or organoids is difficult because these often vary in shape and size. Here we present a toolkit of computational approaches to tackle these problems. These strategies are based on measuring properties of each cell in relation to the properties of its neighbours to quantify patterning, and on using embryonic landmarks in order to compare these patterns between embryos. We use this toolkit to characterise patterning of cell identities within the caudal lateral epiblast of E8.5 embryos, revealing local patterning in emergence of early mesoderm cells that is sensitive to inhibition of Notch activity.