Study on the Impact of Implementing an Exercise Program Using Fitlight Technology for the Development of Upper Limb Coordinative Abilities in Basketball Players.

The aim of this study was to evaluate the impact of implementing a basketball-specific exercise program using Fitlight technology on the coordinative abilities (reactive coordination, reactive reaction time, and reactive movement combination capacity) of U14 and U16 junior basketball players. This study included 70 male basketball players, 36 subjects U14 and 34 subjects U16, divided into two equal groups for each age category: the experimental group (EG) and the control group (CG). This study included an initial and a final test, between which, training was conducted over a period of 18 weeks. For the EG, the program aimed to develop coordinative abilities through an experimental program that utilized Fitlight technology, while the control groups underwent an identically timed program, but their training did not include the use of technology. Four tests were adapted and applied: the Reactive Reaction Test, Choice Reactive Reaction Test, Reactive Hand-Eye Coordination Test, and a test to evaluate the reactive capacity for combining movements. The results of this study showed statistically significant progress between the initial and final tests for the experimental group, p < 0.05. The Cohen's d values for the experimental groups were above 0.8, indicating a very large effect size, while for the control group, these values were small to medium. The comparative analysis of the experimental groups U14 and U16 and control groups shows statistically significant differences in favor of the experimental groups U14 and U16. This study highlights the effectiveness of implementing specific training programs that use modern technologies in developing coordinative abilities in the training and evaluation process of junior basketball players.

A minimally invasive, combined approach to the recurrent/recalcitrant sino-orbital mucocele.

PURPOSE: To evaluate the results of a minimally invasive combined endoscopic and eyelid crease/medial suprabrow incision approach in collaboration with oculoplastic and sinus surgeons for the treatment of recurrent/recalcitrant sino-orbital mucoceles. METHODS: Eighteen cases of recurrent/recalcitrant sino-orbital mucoceles, treated in collaboration with oculoplastic and sinus surgeons at the University of Michigan, were retrospectively reviewed. The recurrence of mucocele, reduction in proptosis, and complications were evaluated. RESULTS: The mean age at the time of surgery was 49 years (range: 17-76 years). All cases had a history of previous sinus or orbital surgeries for mucoceles. Among 18 cases, eight were due to chronic sinus infections, six due to trauma, three due to Schneiderian papilloma, and one case was secondary to an inflammatory sinus disease. Thirteen cases (72%) presented with orbital or facial cellulitis, while five cases (38%) experienced periocular swelling and limited extraocular motility. Following a mean follow-up of 19 months (range: 1-76 months)), recurrence was observed in two cases (11%): one in a cystic fibrosis patient with chronic sinusitis, and the other in a case of Schneiderian papilloma. The mean pre-operative proptosis in the affected eye was 2.78 mm, with an average decrease of 2.33 mm after surgery. Complications occurred in two cases, including one case of hypoesthesia in the forehead and one case of post-operative strabismus. CONCLUSION: Our series of 18 cases of recurrent/recalcitrant mucoceles, with only two cases of recurrence, demonstrates that this minimally invasive approach can be successfully employed for advanced sino-orbital disease, with a low rate of adverse outcomes and aesthetically pleasing results.

The predictive value of nontraditional lipid parameters for intracranial and extracranial atherosclerotic stenosis: a hospital-based observational study in China.

BACKGROUND: Ischemic strokes are primarily caused by intracranial and extracranial atherosclerotic stenosis. Nontraditional lipid parameters broaden traditional lipid profiles, better reflect the metabolism and interaction between different lipid components, and optimize the predictive ability of lipid profiles for atherosclerotic diseases. This research was carried out to investigate the predictive value of nontraditional lipid parameters for intracranial or extracranial atherosclerotic stenosis. METHODS: The investigation collected data from inpatients who underwent cervical vascular ultrasonography, carotid CTA, cerebral artery CTA or MRA, and brain MRI or CT from December 2014 to December 2021. The nontraditional lipid parameters were calculated by collecting traditional lipid parameters. To evaluate the predictive power of nontraditional lipid parameters, logistic regression and receiver operating characteristic curve (ROC) analyses were performed. RESULTS: Based on the inclusion and exclusion criteria, 545 patients were included. According to the imaging results, inpatients were divided into two groups, including no intracranial or extracranial atherosclerotic stenosis (n = 250) and intracranial or extracranial atherosclerotic stenosis (AS, n = 295). Among them, AS was further divided into three subgroups: intracranial atherosclerotic stenosis (ICAS), extracranial atherosclerotic stenosis (ECAS) and combined intracranial and extracranial atherosclerotic stenosis (IECAS). Logistic regression analysis showed that nontraditional lipid parameters, including the atherogenic index of plasma (AIP), TG/HDL-C, remnant cholesterol (RC), nonhigh-density lipoprotein cholesterol (non-HDL-C), lipoprotein combine index (LCI), atherogenic coefficient (AC), Castelli's index-I (CRI-I) and Castelli's index-II (CRI-II), were significantly correlated with intracranial or extracranial atherosclerotic stenosis (P < 0.05). Compared with other nontraditional lipid parameters, regardless of adjusting for potential confounding factors, AIP had a greater OR value in ICAS (OR = 4.226, 95% CI: 1.681-10.625), ECAS (OR = 2.993, 95% CI: 1.119-8.003) and IECAS (OR = 4.502, 95% CI: 1.613-12.561). ROC curve analysis revealed that nontraditional lipid parameters had good predictive power for intracranial or extracranial atherosclerotic stenosis. CONCLUSIONS: This Chinese hospital-based study demonstrates that nontraditional lipid parameters (AIP, LCI, RC, CRI-II, AC, CRI-I and non-HDL-C) are effective predictors of intracranial and extracranial atherosclerotic stenosis, of which AIP may be a significant risk factor for predicting atherosclerotic arterial stenosis in the intracranial or extracranial regions.

Development of Path Generation and Algorithm for Autonomous Combine Harvester Using Dual GPS Antenna.

Research on autonomous driving technology is actively underway to solve the facing problems in the agricultural field. Combine harvesters used in East Asian countries, including Korea, are tracked-type vehicles. The steering control system of the tracked vehicle has different characteristics from the wheeled vehicle used in the agricultural tractor. In this paper, a dual GPS antenna-based autonomous driving system and path tracking algorithm were developed for a robot combine harvester. An α-turn-type work path generation algorithm and a path tracking algorithm were developed. The developed system and algorithm were verified through experiments using actual combine harvesters. The experiment consisted of an experiment with harvesting work and an experiment without harvesting work. In the experiment without harvesting work, an error of 0.052 m occurred during working driving and 0.207 m during turning driving. In the experiment where the harvesting work was carried out, an error of 0.038 m occurred during work driving and 0.195 m during turning driving. As a result of comparing the non-work area and driving time to the results of manual driving, the self-driving experiment with harvesting work showed an efficiency of 76.7%.

A combined process of chemical precipitation and aerobic membrane bioreactor for treatment of citric acid wastewater.

The wastewater generated from citric acid production has a high organic loading content. The treatment and reuse of citric acid wastewater with high organic loading become extremely important. In this study, the performance of calcium hydroxide (Ca(OH)2) precipitation as a low-cost and environmentally friendly pre-treatment method and aerobic membrane bioreactor (MBR) combined treatment system was investigated for the treatment of citric acid (CA) wastewater. At the first step, optimization parameters such as agitation speed (100, 150, 200 rpm), temperature (30, 50, 70 °C), and reaction time (2, 4, 6 h) for Ca(OH)2 precipitation as a pre-treatment method were investigated using response surface methodology (RSM) to achieve maximum chemical oxygen demand (COD) removal. Experimental sets were designed using Box-Behnken Design. As a result of pre-treatment with Ca(OH)2 precipitation, a COD removal efficiency of 97.3% was obtained. Then, pre-treated CA wastewater was fed continuously to the MBR process for 10 days, which was the second stage of the combined process. As a result of the MBR process, 92.0% COD removal efficiency was obtained for 24 h HRT and 10 days SRT. In total, 99.8% COD removal efficiency was obtained when combined process was used and COD concentration decreased from 52,000-114 mg/L. For the treatment and reuse of wastewater from citric acid production, Ca(OH)2 precipitation and MBR combined treatment systems demonstrated an effective strategy.

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm.

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.

A comparison of functional and radiological outcome of combine compression antegrade intertrochanteric nail (InterTan) and proximal femoral nail anti-rotation II (PFNA-II) in elderly patients with intertrochanteric fractures.

Objective: To compare the functional and radiological outcome of combine compression interlocking intramedullary nail (InterTan) and proximal femoral nail anti-rotation II (PFNA-II) in the treatment of elderly patients with intertrochanteric fractures. Methods: As a retrospective cohort study, records of 88 patients with intertrochanteric fractures treated in our hospital from January 1st, 2019 to July 31st, 2021 were retrospectively reviewed. According to treatment records, it included 45 patients treated with InterTan (Group-A) and 43 patients treated with PFNA-II (Group-B). The operation safety and functional rehabilitation of the two groups were compared and analyzed. Results: This study included 88 patients with intertrochanteric fractures (mean [SD] age, 68.72 [0.10] years at baseline), of whom 52 (59.09%) were males and 36 (40.91%) were females. Operation time and intraoperative blood loss in Group-B were less than Group-A, while fracture healing time was shorter in Group-A. The fracture separation distance was measured four weeks after the operation. The widening rate of the fracture line in Group-A was lower than Group-B (4.4% vs.18.6%; P<0.05). The incidence of complications in Group-A was lower than Group-B (4.4% vs.18.6%; P<0.05). At three, six and twelve months after the operation, the Harris hip score of the two groups was higher than at discharge (P<0.05), with no significant difference between groups (P>0.05). Conclusions: We found no significant difference in the functional outcome in elderly patients with intertrochanteric fractures treated with InterTan and PFNA-II. Early fracture healing and reduced complication rate however has been noted with InterTan.

Using machine learning to predict heavy drinking during outpatient alcohol treatment.

BACKGROUND: Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. METHOD: We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. RESULTS: Models predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. CONCLUSION: Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.

Multivariate survival analysis in big data: A divide-and-combine approach.

Multivariate failure time data are frequently analyzed using the marginal proportional hazards models and the frailty models. When the sample size is extraordinarily large, using either approach could face computational challenges. In this paper, we focus on the marginal model approach and propose a divide-and-combine method to analyze large-scale multivariate failure time data. Our method is motivated by the Myocardial Infarction Data Acquisition System (MIDAS), a New Jersey statewide database that includes 73,725,160 admissions to nonfederal hospitals and emergency rooms (ERs) from 1995 to 2017. We propose to randomly divide the full data into multiple subsets and propose a weighted method to combine these estimators obtained from individual subsets using three weights. Under mild conditions, we show that the combined estimator is asymptotically equivalent to the estimator obtained from the full data as if the data were analyzed all at once. In addition, to screen out risk factors with weak signals, we propose to perform the regularized estimation on the combined estimator using its combined confidence distribution. Theoretical properties, such as consistency, oracle properties, and asymptotic equivalence between the divide-and-combine approach and the full data approach are studied. Performance of the proposed method is investigated using simulation studies. Our method is applied to the MIDAS data to identify risk factors related to multivariate cardiovascular-related health outcomes.

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal.

The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure-activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models' goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure-activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.

Biologics combined with conventional systemic agents for the treatment of children with severe psoriasis. Real-life data from the BiPe cohorts and a practice survey among French and Italian pediatric dermatologists.

Combined therapies involve the use of multiple drugs to increase efficacy and reduce the toxicity of individual treatments. We evaluated the use of combinations of conventional systemic therapies and biologics in children with psoriasis in daily practice. This two-part study used data from the 170 children in the Franco-Italian BiPe cohorts to evaluate the use, efficacy, and safety of combined conventional systemic-biologic therapies, and from a survey carried out among French and Italian dermatologists to better understand the reasons for using or avoiding these combinations. In total, 33 children (19.4%) from 13 dermatology centers received 48 combined conventional systemic-biologic therapies (cumulative duration: 43.6 years), including three triple combination therapies (acitretin-methotrexate, with a TNF-alpha inhibitor). A total of 14 different combinations were used, most frequently etanercept-acitretin (n = 10), adalimumab-acitretin (n = 7), adalimumab-methotrexate (n = 5), and ustekinumab-methotrexate (n = 5). The combined therapies were started at biologic initiation in 41 cases (85.4%), and after a period of biologic monotherapy in the remaining 7 cases. Mean PGA and PASI scores decreased between baseline and M3 with all the combinations used. Four serious adverse events were reported, all with favorable outcomes. The survey was completed by 61 dermatologists: 39 (63.9%) had previously used or planned to use the combined therapies, most commonly TNF-alpha inhibitors with acitretin or methotrexate. The main reason for using these treatments was to improve the outcome of biologic therapies in cases of partial efficacy or loss of efficacy. Combined therapies have been used frequently in the treatment of childhood psoriasis, in a range of clinical situations and in variable drug combinations, without significant toxicity. Although the use of these combined therapies needs to be clarified in future management guidelines, these combined therapies should be considered for the treatment of children with severe psoriasis, psoriatic arthritis, and recalcitrant disease.

The effect of oral contraceptive use on sympatic nerve activity in patients with polycystic ovary syndrome.

OBJECTIVE: High sympathetic nerve activity, is thought to be a cardiovascular risk factor, has been shown to correlate with circulating high testosterone levels. However its role on the pathophysiology of the polycystic ovarian syndrome has not been clearly established. The aim of our study is to compare the sympathetic nerve activity between polycystic ovarian syndrome and control group and to determine whether the use of oral contraceptives for contraception has any effect on this activity. MATERIALS AND METHODS: Our study has designed as a prospective, case-control study including patients diagnosed with polycystic ovarian syndrome. Antropometric measurements were calculated, hormone levels were measured and sympathetic skin response and heart rate variability measurements were performed in the laboratory to assess sympathetic activity before starting oral contraceptive pill therapy and at the end of 1 month follow up. RESULTS: In the baseline condition LH/FSH ratio, LH, DHEAS, and total testosterone levels were significantly higher in women with PCOS. After 1 month of treatment, reductions in Ferriman-Gallwey score, ovarian volumes, number of follicles, LH/FSH ratio, LH, and androgen hormone levels were statistically significant. The mean values of sympathetic skin response amplitude and R-R analysis-Valsalva test were higher in PCOS group as compared to the control group. However, these differences were not statistically significant. Similarly, no statistically significant difference in SSR latency and R-R analysis-standup test were demonstrated between PCOS and control groups. CONCLUSION: In conclusion, our study supports the increased sympatovagal activity in patients with polycystic ovarian syndrome.

Stereovision-Based Ego-Motion Estimation for Combine Harvesters.

Ego-motion estimation is a foundational capability for autonomous combine harvesters, supporting high-level functions such as navigation and harvesting. This paper presents a novel approach for estimating the motion of a combine harvester from a sequence of stereo images. The proposed method starts with tracking a set of 3D landmarks which are triangulated from stereo-matched features. Six Degree of Freedom (DoF) ego motion is obtained by minimizing the reprojection error of those landmarks on the current frame. Then, local bundle adjustment is performed to refine structure (i.e., landmark positions) and motion (i.e., keyframe poses) jointly in a sliding window. Both processes are encapsulated into a two-threaded architecture to achieve real-time performance. Our method utilizes a stereo camera, which enables estimation at true scale and easy startup of the system. Quantitative tests were performed on real agricultural scene data, comprising several different working paths, in terms of estimating accuracy and real-time performance. The experimental results demonstrated that our proposed perception system achieved favorable accuracy, outputting the pose at 10 Hz, which is sufficient for online ego-motion estimation for combine harvesters.

An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks.

The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs' observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters' vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency.

Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN.

Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working parameters are set according to the operator's experience and not adjusted during the operation, which leads to the harvest quality fluctuating greatly in a complex environment. In this paper, an impurity-detection system, including a grain-sampling device and machine vision system, was developed. Sampling device structure and impurity extraction algorithm were studied to enhance the impurity identification accuracy. To reduce the effect of impurity occlusion on visual recognition, an infusion-type sampling device was designed. The sampling device light source form was determined based on the brightness histogram analysis of a captured image under different light irradiations. The effect of sampling device structures on impurity visualization, grain distribution, and mass flow rate was investigated by the discrete element method (DEM). The impurity recognition algorithm was proposed based on Mask R-CNN, which mainly includes an impurity feature extraction network, an ROI generation network, and a target segmentation network. The test set experiment showed that the precision rate, recall rate, average precision, and comprehensive evaluation indicator of the impurity recognition model were 92.49%, 88.63%, 81.47%, and 90.52%, respectively. The conversion between impurity pixel number and its actual mass was realized according to the pixel density calibration test and impurity rate correction factor. The bench test result showed that the designed system has a good detection accuracy of 91.15~97.26% for the five varieties. The result relative error was in a range of 5.71~11.72% between the impurity-detection system and manual method in field conditions. The impurity-detection system could be applied to tracked rice combine harvesters to provide a reference for the adjustment of operating parameters.

Combine Harvester Bearing Fault-Diagnosis Method Based on SDAE-RCmvMSE.

In the fault monitoring of rolling bearings, there is always loud noise, leading to poor signal stationariness. How to accurately and efficiently identify the fault type of rolling bearings is a challenge. Based on multivariate multiscale sample entropy (mvMSE), this paper introduces the refined composite mvMSE (RCmvMSE) into the fault extraction of the rolling bearing. A rolling bearing fault-diagnosis method based on stacked auto encoder and RCmvMSE (SDAE-RCmvMSE) is proposed. In the actual environment, the fault-diagnosis method use the multichannel vibration signals of the bearing as the input of stacked denoising autoencoders (SDAEs) to filter the noise of the vibration signals. The features of denoise signals are extracted by RCmvMSE and the rolling bearing operation-state diagnosis is completed with a support-vector machine (SVM) model. The results show that in the original test data, the accuracy rates of SDAE-RCmvMSE, RCmvMSE, and commonplace features of vibration signals combined with SVM (CFVS-SVM) methods are 99.5%, 100%, and 96% respectively. In the data with noise, the accuracy rates of RCmvMSE and CFVS-SVM are 97.75% and 93.08%, respectively, but the accuracy of SDAE-RCmvMSE is still 100%.

Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester.

Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.

Communities and employers show a high level of preparedness in supporting working mothers to combine breastfeeding with work in rural Kenya.

Community Readiness Model (CRM) through pragmatic mixed methods design, combining quantitative CRM survey with qualitative data, was used to assess the level of preparedness and readiness among local leaders, employers and community members in supporting working mothers to combine breastfeeding with work. The study was conducted in one of the tea state farms in Kericho County of Kenya. A total of 17 purposively selected men (fathers), lactating mothers, peer educators, health professionals (doctors, nurses and nutritionists), tea plantation managers and grandmothers were interviewed. The CRM that has six different dimensions was applied to determine the stage of readiness to support working mothers to combine breastfeeding with work. Community Readiness Score (CRS) was calculated descriptively as mean ± standard deviation (SD). Thematic analysis using NVIVO software was used to analyse qualitative data. We found that the mean (±SD) CRS was 7.3 (1.9), which corresponded to the third highest level of the nine stages or the 'stabilization' stage of community readiness. Dimensionally, the mean CRS was the highest (8.3 ± 1.9) for leadership followed by community efforts (7.5 ± 2.1), whereas the lowest CRS was observed for knowledge of efforts (6.6 ± 2.3) and availability of resources (6.6 ± 1.9). In conclusion, high level of readiness to support working women to combine work with breastfeeding with suboptimal knowledge of efforts and availability of resources was observed in the area. Future interventions should focus on enabling the community to feel more comfortable and creating detailed and refined knowledge on combining breastfeeding with work.

Evaluation of assay and in-vitro dissolution profile of certain fixed-dose combination using green analytical method.

OBJECTIVES: The pharmaceutical industry and the National Regulatory Authorities (NRAs) are now focusing on the dissolution of multi-component drugs for quality control testing and predicting in vivo results to further consolidation of the biowaiver concept. The mixed formulation of Ciprofloxacin hydrochloride (CIP) and Metronidazole (MET) have been used as a model for simultaneous determination and obtaining in vitro dissolution profiles by using green analysis method namely (UV-CWT(Db4, a=490)). MATERIAL AND METHODS: The proposed method (UV-CWT(Db4, a=490)) includes UV detection combined with Continuous Wavelet Transform (CWT) with Daubechies family and the order of fourth at the scaling factor (a=490) has been used and validated for analyzing and obtaining the dissolution profiles of the fixed-dose combination (CIP-MET). RESULTS: The proposed method (UV-CWT(Db4, a=490)) has been validated effectively in accordance with ICH rules, regarding linearity, specificity, rigor, and preciseness of the working range (3.0-16.0µg/mL) for both (CIP) and (MET), respectively. As well as figures of merit were concluded. The dissolution profiles of CIP-MET tablets were acquired by the proposed (UV-CWT (Db4, a=490)) and HPLC reported methods were conveniently compared using the indicators f1 and f2 ("difference" and "similarity") the results ensured that there were no statistically differences between the methods. In addition, the green assessment tool, namely analytical eco-scale, evaluated and compared the greenness of the suggested method (UV-CWT(Db4, a=490)) and HPLC reported one. CONCLUSION: The suggested process (UV-CWT(Db4, a=490)) was considered as an excellent green, rapid, accurate, economical and minimum-steps method for simultaneously resolve and construct the dissolution curves of a fixed-dose combination drug (CIP-MET) in a short time and without the use of organic solvents, enabling significant labor and resource savings.

Combination molecular therapies for type 1 spinal muscular atrophy.

BACKGROUND: Data on combining molecular therapies that increase survival motor neuron protein for spinal muscular atrophy type 1 (SMA1) is lacking. METHODS: This was a retrospective study describing our centers' experiences in treating SMA1 patients with combination therapy. RESULTS: Five children received nusinersen and onasemnogene abeparvovec-xioi (onasemnogene). Four were receiving nusinersen prior to onasemnogene. Nusinersen was continued in three. Marked liver enzyme elevations resulted in prolonged corticosteroid treatment in two patients with hospitalization and liver biopsy in one; milder liver enzyme elevations were noted in the other two. One patient received onasemnogene first, and then nusinersen. No adverse effects were noted. All patients improved. CONCLUSIONS: Combination molecular therapy is tolerated in SMA1 patients. Further studies are needed to determine whether there are circumstances in which combination therapy would be more efficacious than either monotherapy. Prolonged corticosteroid use and liver toxicity monitoring may be necessary with onasemnogene therapy.