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We conducted a comprehensive analysis of possible perioperative complications following all-inside anterior cruciate ligament reconstruction (ACLR). Additional techniques and tips are proposed to prevent and manage complications.Complications following all-inside ACLR performed between December 2015 and December 2020 were retrospectively analysed. Altogether, 348 operations were performed and 275 patients were enrolled with a minimum 12-month follow-up period. Only semitendinosus autograft was utilised in most patients, and semitendinosus-gracilis autograft and allograft were used in five and 31 patients, respectively. Simultaneous meniscal repair, partial meniscectomy, and chondral surgery were performed in 29.5%, 21.1%, and 4.4% of patients, respectively. Complications were observed based on the patient's clinical condition, plain film, and magnetic resonance imaging. Clinical outcomes were assessed pre-operatively and at 12 months post-operatively, using the International Knee Documentation Committee form, Lysholm and Tegner activity scores, and KT1000 side-to-side difference.Intraoperative and post-operative complications developed in 65 patients (23.6%). The most common complication was cortical button malposition on the femoral side (19.3%). Intra-operative breakage of the retrograde drill was found in two cases (0.73%), with three cases (1.1%) of over-drilling with destruction of the outer cortex. Post-operatively, four (1.5%), 13 (4.7%), and 16 (5.8%) cases of infection, full-thickness re-rupture, and loss of extension, respectively, were recorded. Functional outcome scales showed significant post-operative improvement.Cortical button malposition was the most common but easily preventable complication. All-inside ACLR could be safe and promising after the suggested additional operative techniques and proper perioperative management which decrease complication rates and improve favourable outcomes.

The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more critical than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. In particular, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.

Purpose This study aimed to investigate the relationship between the artery diameter ratio (ADR) after recanalization and clinical outcomes. Methods Patients with middle cerebral artery occlusion confirmed by DSA from 1 January 2018, to 31 December 2019, were retrospectively analyzed. All patients confirmed TICI grade 2b or 3. The ADR was calculated as M2 segment diameter/M1 segment diameter. Multivariate regression analysis was used to describe clinical outcomes of two groups (ADR Results A total of 143 patients were included in the study, including 77 males and 66 females, with an average age of 67.79 ± 12 years. The NIHSS at discharge was significantly higher in the ADR 2 (AUC = 0.851) and DC (AUC = 0.805), and the best cutoff value are 0.6 (specificity 85.19%, sensitivity 75.81%) and 0.58 (specificity 65.96%, sensitivity 100%), respectively. Conclusion The low ADR is associated with poor outcomes. The decrease in ADR may be an indirect manifestation of the loss of cerebrovascular autoregulation.

Aggregated community-scale data could be harnessed to allow insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During Winter Storm Uri in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent wider collapse of the whole power grid when power demand overwhelmed supply. In this study, we collected community-scale big data (e.g., digital trace and crowdsourced data) in the context of the winter storm, which brought historically low temperatures, snow, and ice to the entire state of Texas. By analyzing this data, we can infer the extent of disparities in storm-related impacts on vulnerable populations. In addition to assessing disparate impacts of the managed power outage, this study also examined the extent of burst pipes and disrupted access to food. Using statistical and trend classification analyses, the results highlight the spatial and temporal patterns of impacts for subpopulations in Harris County, Texas, within which the city of Houston is located. In particular, findings show significant disparity in the extent and duration of power outages experienced by low-income and minority groups. This finding suggests that the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access were more severe for low-income and minority groups. These findings suggest implications for understanding the disparate impacts of extreme weather events on vulnerable populations; in particular, the findings could provide insights allowing infrastructure operators to enhance social equality considerations during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

P-xylene (p-C8H10) is extremely harmful and dangerous to human health due to high toxicity and strong carcinogenicity. Exploring sensitive material to effectively detect p-xylene is of importance. In this paper, perovskite single crystal (C4H9NH3)2PbI4 has been successfully synthesized via solution method. The obtained product was analyzed by single crystal X-ray diffraction. With the space group Pbca, orthorhombic (C4H9NH3)2PbI4 layered perovskite structure consists of an extended two-dimensional network of corner-sharing PbI6 octahedron. Single layer perovskite sheets of distorted PbI6 octahedron alternated with protonated n-butylammonium cation bilayers, which offers many advantages and provides the possibility of forming a gas sensor device based on the change of resistances. Density functional theory (DFT) simulations regarding the adsorption energy revealed that this organic–inorganic hybrid perovskite compound has excellent selectivity toward p-xylene compared with other gases including C2H5OH, C6H6, CH2Cl2, HCHO, CH3COCH3 and C7H8. The calculation of electron density, density of states and electron density difference showed the sensing mechanism of p-C8H10 is mainly derived from physical adsorption–desorption in view of electron transfer. For organic–inorganic hybrid perovskite (C4H9NH3)2PbI4, single layer perovskite sheets of distorted PbI6 octahedron alternated with protonated n-butylammonium cation bilayers (a), which offers many advantages and provides the possibility of forming a gas sensor device based on the change of resistances. Electron density difference calculation shows that adsorption of p-xylene molecule has an obvious effect on the electron distribution of (C4H9NH3)2PbI4, results in electrically polarization as a local manner (b).

Silicone breast implants are used worldwide for breast augmentation. After an axillary, periareolar or inframmary incision has been made to create an adequately sized pocket; the surgeon usually uses his or her fingers to insert the implant. The use of fingers makes the insertion process time-consuming, a few minutes or more. There are some complications, including need to ensure that the incision is long enough for the implant to be inserted, scar hypertrophy caused by implant insertion friction damage to the edge of incision, and the occasional need to ask the surgical assistant to lend more fingers to facilitate satisfactory insertion and placement. In addition, the use of gloved fingers to repeatedly push on the implant can increase the risk of contamination, postoperative silicone microleakage, and capsular contracture. To resolve these problems, we developed an improved silicone breast implant injector (reusable stainless steel 2007; single use polypropylene 2018) that can be used more easily than fingers and other “no touch” devices. From 2013 to 2017, the first author, a plastic surgeon at our clinic, used the 2007 reusable stainless steel injector to perform breast augmentations in 53 patients (Ave. age 23.8 years; range 19–67 years), 5 (8.8%) receiving 250-ml implants, 41 (77.4%) 251–300-ml implants, and 7 (13.8%) 301–400-ml implants. Overall, results were satisfactory except for two patients (3.7%) in whom capsular contracture occurred. There were no ruptures. Use of the injector made it possible to shorten the length of the incisions from the traditional 4–7 to 3–4 cm and expedited insertion time from a few minutes to a few seconds. This “no touch” insertion technique reduced implant damage caused by finger pushing, leading to a decrease in silicone microleakage and capsular contracture rate. It was performed with no friction trauma to the incision edge or harm to the surgeon’s fingers. It was found to be an effective alternative operative tool for the insertion of silicone breast implants. Level of evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Traditional speech recognition model based on deep neural network (DNN) and hidden Markov model (HMM) is a complex and multi-module system. In other words, optimization goals may differ between modules in traditional model. Besides, additional language resources are required, such as pronunciation dictionary and language model. To eliminate the drawbacks of traditional model, we hereby propose an end-to-end speech recognition method, where connectionist temporal classification (CTC) and attention are integrated for decoding. In our model, the complex modules are replaced by a single deep network. Our model mainly consists of encoder and decoder. The encoder is constructed by bidirectional long short-term memory (BLSTM) with a triangular structure for feature extraction. The decoder based on CTC-attention decoding utilizes advanced features extracted by shared encoder for training and decoding. The experimental results on the VoxForge dataset indicate that end-to-end method is superior to basic CTC and attention-based encoder-decoder decoding, and the character error rate (CER) is reduced to 12.9% without using any language model.