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Necrotizing fasciitis (NF) is a rare but lethal soft-tissue infection. There is still a paucity of information regarding the diagnostic tools and therapeutic strategies for the treatment of this devastating disease. This study aims to identify important perioperative parameters related to necrotizing fasciitis and to assess their relevance in terms of identifying NF. We retrospectively analyzed patients who underwent surgical exploration for suspected necrotizing fasciitis at a tertiary referral center, to explore the clinical features and factors related to the presence of necrotizing fasciitis and mortality. Between 2010 and 2017, 88 patients underwent surgical exploration for suspected NF. The infection occurred in the lower extremities in 48 patients, in the thoracocervical region in 18 patients, and the perineum and abdomen in 22 patients. Histological evidence of NF was present in 59 of 88 patients. NF was associated with a longer hospital stay and ICU stay (p = 0.05 and 0.019 respectively) compared to patients without NF. ROC analysis showed that only macroscopic fascial appearance could discriminate patients with histological evidence of NF. Moreover, multivariate logistic regression revealed, that liver failure (p = 0.019), sepsis (p = 0.011), positive Gram stain (p = 0.032), and macroscopic fascial appearance (p <0.001) were independent prognostic parameters for histological evidence of NF. Intraoperative tissue evaluation by an experienced surgeon is the most important diagnostic tool in identifying necrotizing fasciitis. An intraoperative Gram stain is an independent prognostic tool and therefore its use can be recommended especially in case of clinical uncertainty.

The concept of three-dimensional (3D) cell culture has been proposed to maintain cellular morphology and function as in vivo. Among different approaches for 3D cell culture, microcarrier technology provides a promising tool for cell adhesion, proliferation, and cellular interactions in 3D space mimicking the in vivo microenvironment. In particular, microcarriers based on biopolymers have been widely investigated because of their superior biocompatibility and biodegradability. Moreover, through bottom-up assembly, microcarriers have opened a bright door for fabricating engineered tissues, which is one of the cutting-edge topics in tissue engineering and regeneration medicine. This review takes an in-depth look into the recent advancements of microcarriers based on biopolymers—especially polysaccharides such as chitosan, chitin, cellulose, hyaluronic acid, alginate, and laminarin—for 3D cell culture and the fabrication of engineered tissues based on them. The current limitations and potential strategies were also discussed to shed some light on future directions.

The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.

Background Patients undergoing bariatric surgery have a high incidence of non-alcoholic fatty liver disease (NAFLD). However, the effect of NAFLD or non-alcoholic steatohepatitis (NASH) on the weight loss and resolution of obesity-related disorders is a matter of debate. Methods In this study, we compare the long-term outcomes after bariatric with the presence of NAFLD in the liver biopsy at the time of surgery. Results The follow-up was available for 226 out of 288 patients. The mean follow-up time was 24.9 (± 13.6) months. The baseline histology showed that 112 patients (38.9%) had no NASH, 70 (24.3%) were borderline, and 106 (36.8%) had NASH. At follow-up, the mean BMI dropped from (52 ± 10.2) to (36.6 ± 8) kg/m 2 . Excess weight loss (EWL) was similar in all NAFLD groups. Type 2 diabetes mellitus dropped from 35.7 to 11.4%, hypertension from 65.6 to 36.7%, hyperlipidemia from 62.3 to 33%, and obstructive sleep apnea from 37.5 to 14.9%. Only hyperlipidemia was significantly associated with NASH compared to the groups with no NASH or borderline NASH ( p value = 0.002 and p value = 0.04, respectively) during the first two years of follow-up. Conclusion The beneficial effects of bariatric surgery are evident across all patients with NAFLD. Patients with NASH have comparable outcomes regarding weight loss and resolution of obesity-related comorbidities.

One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.

The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains.

The use of the eddy current technique (ECT) for the non-destructive testing of conducting materials has become increasingly important in the past few years. The use of the non-destructive ECT plays a key role in the ensuring the safety and integrity of the large industrial structures such as oil and gas pipelines. This paper introduce a novel ECT probe design integrated with the distributed ECT inspection system (DSECT) use for crack inspection on inner ferromagnetic pipes. The system consists of an array of giant magneto-resistive (GMR) sensors, a pneumatic system, a rotating magnetic field excitation source and a host PC acting as the data analysis center. Probe design parameters, namely probe diameter, an excitation coil and the number of GMR sensors in the array sensor is optimized using numerical optimization based on the desirability approach. The main benefits of DSECT can be seen in terms of its modularity and flexibility for the use of different types of magnetic transducers/sensors, and signals of a different nature with either digital or analog outputs, making it suited for the ECT probe design using an array of GMR magnetic sensors. A real-time application of the DSECT distributed system for ECT inspection can be exploited for the inspection of 70 mm carbon steel pipe. In order to predict the axial and circumference defect detection, a mathematical model is developed based on the technique known as response surface methodology (RSM). The inspection results of a carbon steel pipe sample with artificial defects indicate that the system design is highly efficient.

Microspores are reprogrammed by stress in vitro toward embryogenesis. This process is an important tool in breeding to obtain double-haploid plants. DNA methylation is a major epigenetic modification that changes in differentiation and proliferation. We have shown changes in global DNA methylation during microspore reprogramming. 5-Azacytidine (AzaC) cannot be methylated and leads to DNA hypomethylation. AzaC is a useful demethylating agent to study DNA dynamics, with a potential application in microspore embryogenesis. This work analyzes the effects of short and long AzaC treatments on microspore embryogenesis initiation and progression in two species, the dicot Brassica napus and the monocot Hordeum vulgare. This involved the quantitative analyses of proembryo and embryo production, the quantification of DNA methylation, 5-methyl-deoxy-cytidine (5mdC) immunofluorescence and confocal microscopy, and the analysis of chromatin organization (condensation/decondensation) by light and electron microscopy. Four days of AzaC treatments (2.5 μM) increased embryo induction, response associated with a decrease of DNA methylation, modified 5mdC, and heterochromatin patterns compared to untreated embryos. By contrast, longer AzaC treatments diminished embryo production. Similar effects were found in both species, indicating that DNA demethylation promotes microspore reprogramming, totipotency acquisition, and embryogenesis initiation, while embryo differentiation requires de novo DNA methylation and is prevented by AzaC. This suggests a role for DNA methylation in the repression of microspore reprogramming and possibly totipotency acquisition. Results provide new insights into the role of epigenetic modifications in microspore embryogenesis and suggest a potential benefit of inhibitors, such as AzaC, to improve the process efficiency in biotechnology and breeding programs.

Objective To determine prognostic value of bone marrow retention index (RI-bm) and bone marrow-to-liver ratio (BLR) measured on baseline dual-phase 18 F-FDG PET/CT in a series of newly diagnosed patients with diffuse large B-cell lymphoma (DLBCL) treated homogeneously with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. Patients and methods This prospective study enrolled 135 patients with newly diagnosed DLBCL. All patients underwent dual-phase 18 F-FDG PET/CT. The following PET parameters were calculated for both tumor and bone marrow: maximum standardized uptake value (SUVmax) at both time points (SUVmax early and SUVmax delayed), SUVmax increment (SUVinc), RI, and BLR. Patients were treated with R-CHOP regimen and response at end of treatment was assessed. Results The final analysis included 98 patients with complete remission. At a median follow-up of 22 months, 57 patients showed no relapse, 74 survived, and 24 died. The 2-year relapse-free survival (RFS) values for patients with higher and lower RI-bm were 20% and 65.1%, respectively (p < 0.001), and for patients with higher and lower BLR were 30.2% and 69.6%, respectively ( p  < 0.001). The 2-year overall survival (OS) values for patients with higher and lower RI-bm were 60% and 76.3%, respectively ( p  = 0.023), and for patients with higher and lower BLR were 57.3% and 78.6%, respectively (p = 0.035). Univariate analysis revealed that RI-bm and BLR were independent significant prognostic factors for both RFS and OS (hazard ratio [HR] = 4.02, p  < 0.001, and HR = 3.23, p  < 0.001, respectively) and (HR = 2.83, p  = 0.030 and HR = 2.38, p  = 0.041, respectively). Conclusion Baseline RI-bm and BLR were strong independent prognostic factors in DLBCL patients. Clinical relevance statement Bone marrow retention index (RI-bm) and bone marrow-to-liver ratio (BLR) could represent suitable and noninvasive positron emission tomography/computed tomography (PET/CT) parameters for predicting pretreatment risk in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) who were treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. Key Points • Bone marrow retention index (RI-bm) and bone marrow-to-liver ratio (BLR) are powerful prognostic variables in diffuse large B-cell lymphoma (DLBCL) patients . • High BLR and RI-bm are significantly associated with poor overall survival (OS) and relapse-free survival (RFS) . • RI-bm and BLR represent suitable and noninvasive risk indicators in DLBCL patients .

Machine translation plays a crucial role in bridging language gaps, especially in specialized domains such as software engineering. Traditional neural machine translation models, including Transformer and LSTM-based models, have shown significant progress in Chinese-to-English translation tasks. However, these models often face challenges in optimizing hyperparameters dynamically and handling diverse textual domains, leading to suboptimal translation accuracy and efficiency. To address these limitations, this study proposes an enhanced translation model, Adaptive Grey Wolf Optimization with Self-Attention and LSTM (AGWO-SALSTM). The proposed model integrates an adaptive Grey Wolf Optimization (AGWO) algorithm to dynamically fine-tune hyperparameters, optimizing learning rates, attention weights, and network configurations. The combination of self-attention and bidirectional LSTM enhances contextual understanding and sequential processing, leading to improved translation accuracy. The proposed AGWO-SALSTM is validated against three baseline models: Transformer, LSTM-Seq2Seq, and MT5, across four well-established datasets: PARACRAWL, WMT, UM-Corpus, and OPUS. Experimental results demonstrate that AGWO-SALSTM consistently outperforms the baseline models in terms of translation accuracy and efficiency. Specifically, the proposed model achieves an average translation accuracy of 95.56% with the highest accuracy recorded across all datasets, outperforming the closest competitor, MT5, which achieves 90.94%. Additionally, AGWO-SALSTM requires fewer iterations to converge to a stable state, with an average of 16–20 iterations, compared to the Transformer model, which requires up to 57 iterations.