Designing a touchless physical examination for a virtual Objective Structured Clinical Examination.

Purpose: Given the COVID-19 pandemic, many Objective Structured Clinical Examinations (OSCEs) have been adapted to virtual formats without addressing whether physical examination maneuvers can or should be assessed virtually. In response, we developed a novel touchless physical examination station for a virtual OSCE and gathered validity evidence for its use. Methods: We used a touchless physical examination OSCE station pilot-tested in a virtual OSCE in which Internal Medicine residents had to verbalize their approach to the physical examination, interpret images and videos of findings provided upon request, and make a diagnosis. We explored differences in performance by training year using ANOVA. In addition, we analyzed data using elements of Bloom's taxonomy of learning, i.e. knowledge, understanding, and synthesis. Results: Sixty-seven residents (PGY1-3) participated in the OSCE. Scores on the pilot station were significantly different between training levels (F=3.936, p = 0.024, ηp2 = 0.11). The pilot station-total correlation (STC) was r = 0.558, and the item-station correlations ranged from r = 0.115-0.571, with the most discriminating items being those that assessed application of knowledge (interpretation and synthesis) rather than recall. Conclusion: This touchless physical examination station was feasible, had acceptable psychometric characteristics, and discriminated between residents at different levels of training.

Objet: Compte tenu de la pandémie de COVID-19, de nombreux examens cliniques objectifs structurés (ECOS) ont été adaptés vers un format virtuel sans que l'on se questionne à savoir si les manœuvres d'examen physique peuvent ou doivent être évaluées virtuellement. Conséquemment, nous avons développé une nouvelle station d'examen physique sans contact pour un ECOS virtuel et recueilli des preuves de validité concernant son utilisation. Méthodes: Nous avons utilisé une station d'examen physique sans contact testée dans le cadre d'un ECOS virtuel pendant lequel les résidents en médecine interne devaient verbaliser leur approche concernant l'examen physique, interpréter des images et des vidéos d'examens fournis sur demande, et poser un diagnostic. Nous avons étudié les différences de rendement en fonction de l'année de formation à l'aide de l'ANOVA. En outre, nous avons analysé les données en utilisant les éléments de la taxonomie de l'apprentissage de Bloom, c'est-à-dire la connaissance, la compréhension et la synthèse. Résultats: Soixante-sept résidents (PGY1-3) ont participé à l'ECOS. Les scores de la station pilote étaient significativement différents entre les niveaux de formation (F=3.936, p=0.024, ηp2=0.11). La corrélation totale de la station pilote (STC) était de r=0,558, et les corrélations question-station variaient de r=0,115-0,571, les questions les plus discriminantes étant celles qui évaluaient l'application (interprétation et synthèse) plutôt que le rappel de connaissances. Conclusion: Cette station d'examen physique sans contact était réalisable, a présenté des caractéristiques psychométriques acceptables et a permis d'établir une discrimination entre les résidents de différents niveaux de formation.

A New Species of Scymnus (Coleoptera, Coccinellidae) from Pakistan with Mitochondrial Genome and Its Phylogenetic Implications.

In this study, a new species of the subgenus Pullus belonging to the Scymnus genus from Pakistan, Scymnus (Pullus) cardi sp. nov., was described and illustrated, with information on its distribution, host plants, and prey. Additionally, the completed mitochondrial genome (mitogenome) of the new species using high-throughput sequencing technology was obtained. The genome contains the typical 37 genes (13 protein-coding genes, two ribosomal RNAs, and 22 transfer RNAs) and a non-coding control region, and is arranged in the same order as that of the putative ancestor of beetles. The AT content of the mitogenome is approximately 85.1%, with AT skew and GC skew of 0.05 and -0.43, respectively. The calculated values of relative synonymous codon usage (RSCU) determine that the codon UUA (L) has the highest frequency. Furthermore, we explored the phylogenetic relationship among 59 representatives of the Coccinellidae using Bayesian inference and maximum likelihood methods, the results of which strongly support the monophyly of Coccinellinae. The phylogenetic results positioned Scymnus (Pullus) cardi in a well-supported clade with Scymnus (Pullus) loewii and Scymnus (Pullus) rubricaudus within the genus Scymnus and the tribe Scymnini. The mitochondrial sequence of S. (P.) cardi will contribute to the mitochondrial genome database and provide helpful information for the identification and phylogeny of Coccinellidae.

Large regions of homozygosity in prenatal diagnosis.

Chromosomal microarrays (CMA) incorporate single nucleotide polymorphisms to enable the detection of regions of homozygosity (ROH). Here, we retrospectively analyzed 6288 prenatal cases who performed CMA to explored the clinical implications of large ROH in prenatal diagnosis. We analyzed cases with ROH larger than 10 megabases and reviewed the ultrasound findings; karyotype results and pregnancy follow-up data. Cases with possible imprinting disorders were assessed by methylation-specific multiplex ligation-dependent probe amplification. In total, we identified 50 cases with large ROH and chromosomes 1 and 2 were the most affected. About 59.18% of the ROH cases had ultrasound abnormalities, with the most common findings being ultrasound soft-marker abnormalities. There were seven fetuses had ROH which covered almost the entire chromosome and four had terminal ROH that involved almost the entire long arm of the chromosomes, which indicated uniparental disomy (UPD), of which 70% showed abnormal ultrasound findings. Ten cases with multiple ROH on different chromosomes indicated the third to fifth degree of consanguinity. In this study, we highlighted the clinical relevance of large ROH related to UPD. The analysis of ROH allowed us to gain further understanding of complex cytogenetic and disease mechanisms in prenatal diagnosis.

YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION: In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.

Thermal expansion, lattice vibration, and isotope effect on hydrogen diffusion in BCC Fe, Cr, and W from first-principles calculations.

Fe, Cr, and W are important elements in the alloys of in-reactor materials and operate in high-temperature environments with thermal expansion. Their tritium-impeding abilities are crucial to the radiation safety of various nuclear reactors. In this study, first-principles density functional theory is combined with quasi-harmonic approximation to evaluate factors that can affect the interstitial formation energy and diffusion coefficient of hydrogen isotopes in body-centered cubic (BCC) Fe, Cr, and W, including thermal expansion, metal host lattice vibrations, phonon density-of-states (pDOS) coupling diffusing atoms, and isotope effects. Calculation results indicate that the interstitial formation energy decreases as lattice expansion increases, whereas the jump barriers remain almost constant. Thermal expansion, host lattice vibration, and pDOS coupling minimally affect the diffusion coefficients of hydrogen isotopes in Fe, Cr, and W. The diffusion coefficient ratios between hydrogen isotopes are higher than the inverse ratio of the square root of the isotope mass at low temperatures. However, they decrease to the inverse ratio of the square root of the isotope mass at temperatures exceeding 800 K. This study comprehensively investigates factors that affect the diffusion coefficients of hydrogen isotopes in BCC Fe, Cr, and W, thus providing a firm theoretical foundation for predicting the diffusion coefficients of tritium at different temperatures using protium/deuterium diffusion coefficients.

An aqueous alkaline zinc-sulfur flow battery.

We demonstrate a rechargeable aqueous alkaline zinc-sulfur flow battery that comprises environmental materials zinc and sulfur as negative and positive active species. Meanwhile, a nickel-based electrode is also obtained by a two-step process to decrease the polarization of the sulfur redox reaction, thus greatly improving the voltage efficiency of the system from 32% to 78% at 10 mA cm-2.

Macronutrient and PFOS bioavailability manipulated by aeration-driven rhizospheric organic nanocapsular assembly.

Ubiquitous presence of the extremely persistent pollutants, per- and polyfluoroalkyl substances, is drawing ever-increasing concerns for their high eco-environmental risks which, however, are insufficiently considered based on the assembly characteristics of those amphiphilic molecules in environment. This study investigated the re-organization and self-assembly of perfluorooctane sulfonate (PFOS) and macronutrient molecules from rhizospheric organic (RhO) matter induced with a common operation of aeration. Atomic force microscopy (AFM) with infrared spectroscopy (IR)-mapping clearly showed that, after aeration and stabilization, RhO nanocapsules (∼ 1000 nm or smaller) with a core of PFOS-protein complexes coated by "lipid-carbohydrate" layers were observed whereas the capsule structure with a lipid core surrounded by "protein-carbohydrate-protein" multilayers was obtained in the absence of PFOS. It is aeration that exerted the disassociation of pristine RhO components, after which the environmental concentration PFOS restructured the self-assembly structure in a conspicuous "disorder-to-order" transition. AFM IR-mapping analysis of faeces combined with quantification of component uptake denoted the decreased ingestion and utilization of both PFOS and proteins compared with lipids and carbohydrates when Daphnia magna were fed with RhO nanocapsules. RhO nanocapsules acted as double-edged swords via simultaneously impeding the bioaccessibility of hazardous PFOS molecules and macronutrient proteins; and the latter might be more significant, which caused a malnutrition status within merely 48 h. Elucidating the assembly structure of natural organic matter and environmental concentration PFOS, the finding of this work could be a crucial supplementation to the high-dose-dependent eco-effect investigations on PFOS.

[Research on fault diagnosis of patient monitor based on text mining].

The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer. Then, the bidirectional fault features were extracted and weighted by the improved BiGRU and attention mechanism respectively. Finally, the weighted loss function is used to reduce the impact of class imbalance on the model. To validate the effectiveness of the proposed method, this paper uses the patient monitor fault dataset for verification, and the macro F1 value has achieved 91.11%. The results show that the model built in this study can realize the automatic classification of fault text, and may provide assistant decision support for the intelligent fault diagnosis of the patient monitor in the future.

Characterization of Four Complete Mitogenomes of Monolepta Species and Their Related Phylogenetic Implications.

Monolepta is one of the diverse genera in the subfamily Galerucinae, including 708 species and 6 sub-species worldwide. To explore the information on the mitogenome characteristics and phylogeny of the section "Monoleptites", especially the genus Monolepta, we obtained the newly completed mitochondrial genomes (mitogenomes) of four Monolepta species using high-throughput sequencing technology. The lengths of these four new mitochondrial genomes are 16,672 bp, 16,965 bp, 16,012 bp, and 15,866 bp in size, respectively. All four mitochondrial genomes include 22 transfer RNA genes (tRNAs), 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (rRNAs), and one control region, which is consistent with other Coleoptera. The results of the nonsynonymous with synonymous substitution rates showed that ND6 had the highest evolution rate, while COI displayed the lowest evolution rate. The substitution saturation of three datasets (13 PCGs_codon1, 13 PCGs_codon2, 13 PCGs_codon3) showed that there was no saturation across all datasets. Phylogenetic analyses based on three datasets (ND1, 15 genes of mitogenomes, and 13 PCGs_AA) were carried out using maximum likelihood (ML) and Bayesian inference (BI) methods. The results showed that mitogenomes had a greater capacity to resolve the main clades than the ND1 gene at the suprageneric and species levels. The section "Monoleptites" was proven to be a monophyletic group, while Monolepta was a non-monophyletic group. Based on ND1 data, the newly sequenced species whose antennal segment 2 was shorter than 3 were split into several clades, while, based on the mitogenomic dataset, the four newly sequenced species had close relationships with Paleosepharia. The species whose antennal segment 2 was as long as 3 were split into two clades, which indicated that the characteristic of "antennal segment 2 as long as 3" of the true "Monolepta" evolved multiple times in several subgroups. Therefore, to explore the relationships among the true Monolepta, the most important thing is to perform a thorough revision of Monolepta and related genera in the future.

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

BACKGROUND: With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed. RESULTS: Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task. CONCLUSIONS: The inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

Comparative of the effectiveness and safety of biological agents, small molecule drugs, and microbiome therapies in ulcerative colitis: Systematic review and network meta-analysis.

BACKGROUND: Biological agents are commonly used for the first-line treatment of ulcerative colitis (UC). However, small-molecule drugs and microbiome therapies are now being used as new treatments for ulcerative colitis. We aimed to compare the relative efficacy and safety of biologics, small-molecule drugs, and microbiome therapies for the treatment of patients with moderate-to-severe ulcerative colitis. METHODS: We searched the Cochrane, Embase, and PubMed databases from their inception to December 2022. RCTs that recruited patients with moderate-to-severe ulcerative colitis treated with biological agents, small-molecule drugs, and microbiome therapies. Efficacy outcomes were induction of clinical remission and mucosal healing; safety outcomes were adverse events and serious adverse events. A network meta-analysis with multivariate consistency model random-effect meta-regression was done, with rankings based on surface under the cumulative ranking curve (SUCRA) values. Higher SUCRA scores correlate with better efficacy, whereas lower SUCRA scores correlate with better safety. RESULTS: A total of 31 RCTs comprising 7933 UC patients were included in our studies. A risk of bias assessment showed a low risk of bias for most of the included studies. Upadacitinib ranked highest for induction of clinical remission (SUCRA, 0.83) and mucosal healing (SUCRA, 0.44). Moreover, no treatments were found to increase the occurrence of adverse events compared with placebos. Ustekinumab ranked lowest for adverse events (SUCRA 0.26) and probiotic ranked lowest for serious adverse events (0·21), whereas tofacitinib ranked highest for adverse events (0·43) and upadacitinib ranked highest for serious adverse events (0·43). CONCLUSION: In this systematic review and network meta-analysis, we found upadacitinib to be ranked highest for the induction of clinical remission and mucosal healing, but the worst performing agent in terms of adverse events in UC patients. Probiotics were the best-performing agent for safety outcomes. More trials of direct comparisons are needed to inform clinical decision-making with greater confidence.

A deep generative framework with embedded vector arithmetic and classifier for sample generation, label transfer, and clustering of single-cell data.

Multiple-source single-cell datasets have accumulated quickly and need computational methods to integrate and decompose into meaningful components. Here, we present inClust (integrated clustering), a flexible deep generative framework that enables embedding auxiliary information, latent space vector arithmetic, and clustering. All functional parts are relatively modular, independent in implementation but interrelated at runtime, resulting in an all-in general framework that could work in supervised, semi-supervised, or unsupervised mode. We show that inClust is superior to most data integration methods in benchmark datasets. Then, we demonstrate the capability of inClust in the tasks of conditional out-of-distribution generation in supervised mode, label transfer in semi-supervised mode, and spatial domain identification in unsupervised mode. In these examples, inClust could accurately express the effect of each covariate, distinguish the query-specific cell types, or segment spatial domains. The results support that inClust is an excellent general framework for multiple-task harmonization and data decomposition.

A Molecular Dynamics Simulation Study on Enhancement of Mechanical and Tribological Properties of Nitrile-Butadiene Rubber with Varied Contents of Acrylonitrile.

The molecular models of nitrile-butadiene rubber (NBR) with varied contents of acrylonitrile (ACN) were developed and investigated to provide an understanding of the enhancement mechanisms of ACN. The investigation was conducted using molecular dynamics (MD) simulations to calculate and predict the mechanical and tribological properties of NBR through the constant strain method and the shearing model. The MD simulation results showed that the mechanical properties of NBR showed an increasing trend until the content of ACN reached 40%. The mechanism to enhance the strength of the rubber by ACN was investigated and analyzed by assessing the binding energy, radius of gyration, mean square displacement, and free volume. The abrasion rate (AR) of NBR was calculated using Fe-NBR-Fe models during the friction processes. The wear results of atomistic simulations indicated that the NBR with 40% ACN content had the best tribological properties due to the synergy among appropriate polarity, rigidity, and chain length of the NBR molecules. In addition, the random forest regression model of predicted AR, based on the dataset of feature parameters extracted by the MD models, was developed to obtain the variable importance for identifying the highly correlated parameters of AR. The torsion-bend-bend energy was obtained and used to successfully predict the AR trend on the new NBR models with other acrylonitrile contents.

[Analysis of copy number variation in AZF region of Y chromosome in patients with spermatogenic failure].

OBJECTIVE: To explore the characteristics of copy number variation (CNV) within the Y chromosome azoospermia factor (AZF) region in patients with spermatogenesis disorders in the Shenzhen area. METHODS: A total of 123 patients with spermatogenesis disorders who had visited Shenzhen People's Hospital from January 2016 to October 2022 (including 73 patients with azoospermia and 50 patients with oligozoospermia) and 100 normal semen males were selected as the study subjects. The AZF region was detected with multiplex ligation-dependent probe amplification (MLPA), and the correlation between the CNV in the AZF region and spermatogenesis disorders was analyzed using the chi-square test or Fisher's exact test. RESULTS: 19 CNV were detected among 53 patients from the 223 samples, including 20 cases (27.40%, 20/73) from the azoospermia group, 19 cases (38%, 19/50) from the oligozoospermia group, and 14 cases (14%, 14/100) from the normal control group. In the azoospermia, oligozoospermia, and normal control groups, the detection rates for CNV related to the AZFa region (including AZFab and AZFabc) were 5.48% (4/73), 2.00% (1/50), and 0 (0/100), respectively. The detection rates for the AZFb region (including the AZFbc region) were 6.85% (5/73), 0 (0/50), and 0 (0/100), respectively. The detection rates for gr/gr deletions in the AZFc region were 2.74% (2/73), 6.00% (3/50), and 9.00% (9/100), respectively, and those for b2/b4 deletions in the AZFc region were 2.74% (2/73), 10.00% (5/50), and 0 (0/100), respectively. The detection rates for complex rearrangements in the AZFc region were 6.85% (5/73), 18.00% (9/50), and 3.00% (3/100), respectively. Statistical analysis showed no significant difference in the detection rate of gr/gr deletions between the three groups (Fisher's Exact Test value = 2.712, P = 0.249); the differences in the detection rate of b2/b4 deletions between the three groups were statistically significant (Fisher's Exact Test value = 9.489, P = 0.002); the differences in the detection rate of complex rearrangements in the AZFc region between the three groups were statistically significant (Fisher's Exact Test value = 9.493, P = 0.006). In this study, a rare AZFa region ARSLP1 gene deletion (involving SY86 deletion) was detected in a patient with oligozoospermia. CONCLUSION: CNV in the AZFa and AZFb regions have a severe impact on spermatogenesis, but partial deletion in the AZFa region (ARSLP1 gene deletion) has a minor impact on spermatogenesis. The b2/b4 deletion and complex rearrangement in the AZFc region may be risk factors for male infertility. The gr/gr deletion may not serve as a risk factor for male infertility in the Shenzhen area.

A meta-analysis of microbiome therapies for hepatic encephalopathy.

Microbiome therapies may be reported to be effective in hepatic encephalopathy (HE). We thus did a meta-analysis of randomized controlled trials to assess the effect of microbiome therapies for HE. We systematically searched PubMed, Web of Science, EMBASE, and Cochrane Library for randomized controlled trials that compared the different treatments for HE including probiotics, symbiotics, and fecal microbiota transplant (FMT). Meta-analysis was performed to calculate pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Twenty-one studies met our inclusion criteria (N = 1746 participants). Probiotics, synbiotics and FMT significantly reversed minimal HE (MHE) (OR: 0.41, 95% CI: 0.19-0.90, P = 0.03), reduced overt HE (OHE) development (OR, 0.41; 95% CI: 0.28-0.61 P < 0.00001)and the frequency of serious adverse events(SAEs) (OR:0.14, 95% CI: 0.04-0.47, P = 0.001), meanwhile decreased ammonia levels (WMD: -9.26, 95% CI: -16.92 to -1.61; P = 0.02), NCT level (MD = -4.41, 95% CI: -0.87 to -0.22, P = 0.04) and hospitalization rates (OR, 0.38; 95% CI: 0.19-0.79, P = 0.009) compared with placebo/no treatment. Finally, we conclude that microbiome therapies were more effective in improving MHE and preventing progression to OHE, reducing the frequency of SAEs, and decreasing ammonia levels, NCT level, and hospitalization rates when compared to placebo/no treatment.

A novel fractional discrete grey model with variable weight buffer operator and its applications in renewable energy prediction.

With the continuous depletion of global fossil energy, optimizing the energy structure has become the focus of attention of all countries. With the support of policy and finance, renewable energy occupies an important position in the energy structure of the USA. Being able to predict the trend of renewable energy consumption in advance plays a vital role in economic development and policymaking. Aiming at the small and changeable annual data of renewable energy consumption in the USA, a fractional delay discrete model of variable weight buffer operator based on grey wolf optimizer is proposed in this paper. Firstly, the variable weight buffer operator method is used to preprocess the data, and then, a new model is constructed by using the discrete modeling method and the concept of fractional delay term. The parameter estimation and time response formula of the new model are deduced, and it is proved that the new model combined with the variable weight buffer operator satisfies the new information priority principle of the final modeling data. The grey wolf optimizer is used to optimize the order of the new model and the weight of the variable weight buffer operator. Based on the renewable energy consumption data of solar energy, total biomass energy and wind energy in the field of renewable energy, the grey prediction model is established. The results show that the model has better prediction accuracy, adaptability and stability than the other five models mentioned in this paper. According to the forecast results, the consumption of solar and wind energy in the USA will increase incrementally in the coming years, while the consumption of biomass will decrease year by year.

Photosynthesis-Mediated Intracellular Biomineralization of Gold Nanoparticles inside Chlorella Cells towards Hydrogen Boosting under Green Light.

Engineering living microorganisms to enhance green biomanufacturing for the development of sustainable and carbon-neutral energy strategies has attracted the interest of researchers from a wide range of scientific communities. In this study, we develop a method to achieve photosynthesis-mediated biomineralization of gold nanoparticles (AuNPs) inside Chlorella cells, where the photosynthesis-dominated reduction of Au3+ to Au0 allows the formed AuNPs to locate preferentially around the thylakoid membrane domain. In particular, we reveal that the electrons generated by the localized surface plasmon resonance of AuNPs could greatly augment hypoxic photosynthesis, which then promotes the generation and transferring of photoelectrons throughout the photosynthetic chain for augmented hydrogen production under sunlight. We demonstrate that the electrons from AuNPs could be directly transferred to hydrogenase, giving rise to an 8.3-fold enhancement of Chlorella cells hydrogen production independent of the cellular photosynthetic process under monochromatic 560 nm light irradiation. Overall, the photosynthesis-mediated intracellular biomineralization of AuNPs could contribute to a novel paradigm for functionalizing Chlorella cells to augment biomanufacturing.

Lymphangiomatosis presented with melena and chylous ascites: A case report.

INTRODUCTION: Lymphangioma, a rare benign tumor of the lymphatic system, is called lymphangiomatosis when it involves >1 organ, which is more rarely complicated with thoracic obstruction, the relationship between them remains unclear. With the development of enteroscopy, clinicians know more about small intestinal lymphangioma and attempt to treat it through enteroscopic injection sclerotherapy(EIS). PATIENT CONCERNS: A 59-year-old male firstly manifested with gastrointestinal bleeding after a gastric perforation, who was diagnosed with lymphangiomatosis by balloon-assisted enteroscopy and abdomen CT showing >1 organ with multiple cysts besides the small intestine. The patient received an EIS, then the melena disappeared. Surprisingly he came back because of refractory ascites confirmed to be chylous by chemical tests 7 months later. DIAGNOSIS: Lymphangiography could not determine the location of lymphatic leakage, Ultrasonography showed stenosis of the left cervical part of the thoracic duct. INTERVENTION: On the condition that medical treatment is ineffective, thoracic duct exploration and lysis of fibrous adhesion were performed. OUTCOMES: Ascites significantly reduced at last. LESSONS: Lymphangiomatosis is the malformation of the lymphatic system involving multiple organs, it has a possibility to be associated with thoracic obstruction. Capsule endoscopy and enteroscopy are effective methods to diagnose small intestinal lymphangioma, and EIS is an effective therapy.

scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data.

BACKGROUND: With the rapid accumulation of scRNA-seq data, more and more automatic cell type identification methods have been developed, especially those based on deep learning. Although these methods have reached relatively high prediction accuracy, many issues still exist. One is the interpretability. The second is how to deal with the non-standard test samples that are not encountered in the training process. RESULTS: Here we introduce scCapsNet-mask, an updated version of scCapsNet. The scCapsNet-mask provides a reasonable solution to the issues of interpretability and non-standard test samples. Firstly, the scCapsNet-mask utilizes a mask to ease the task of model interpretation in the original scCapsNet. The results show that scCapsNet-mask could constrain the coupling coefficients, and make a one-to-one correspondence between the primary capsules and type capsules. Secondly, the scCapsNet-mask can process non-standard samples more reasonably. In one example, the scCapsNet-mask was trained on the committed cells, and then tested on less differentiated cells as the non-standard samples. It could not only estimate the lineage bias of less differentiated cells, but also distinguish the development stages more accurately than traditional machine learning models. Therefore, the pseudo-temporal order of cells for each lineage could be established. Following these pseudo-temporal order, lineage specific genes exhibit a gradual increase expression pattern and stem cell associated genes exhibit a gradual decrease expression pattern. In another example, the scCapsNet-mask was trained on scRNA-seq data, and then used to assign cell type in spatial transcriptomics that may contain non-standard sample of doublets. The results show that the scCapsNet-mask not only restored the spatial map but also identified several non-standard samples of doublet. CONCLUSIONS: The scCapsNet-mask offers a suitable solution to the challenge of interpretability and non-standard test samples. By adding a mask, it has the advantages of automatic processing and easy interpretation compared with the original scCapsNet. In addition, the scCapsNet-mask could more accurately reflect the composition of non-standard test samples than traditional machine learning methods. Therefore, it can extend its applicability in functional analysis, such as fate bias prediction in less differentiated cells and cell type assignment in spatial transcriptomics.