Miracle-dz/UniFAR-FTUs huggingface dataset:
The utility of the subcuticular suture in hepatic resection

Aim of the study Despite recent technical progress and advances in the perioperative management of liver surgery, postoperative surgical site infection (SSI) is still one of the most common complications that extends hospital stays and increases medical expenses following hepatic surgery. Material and methods From 2001 to 2017 a total of 1180 patients who underwent hepatic resection for liver tumours were retrospectively analysed with respect to the predictive factor of superficial incisional SSI, using a propensity score matching by procedure (subcuticular or mattress suture). Results The incidence of superficial and deep incisional SSIs was found to be 7.1% (84/1180). By propensity score matching (PSM), 121 of the 577 subcuticular suture group patients could be matched with 121 of the 603 mattress suture group patients. Multivariate analysis demonstrated wound closure technique as the only independent risk factor that correlated significantly with the occurrence of superficial incisional SSIs (p = 0.038). C-reactive protein (CRP) levels on postoperative day 4 were significantly higher in patients with incisional SSIs than in those without (p < 0.001). Conclusions Wound closure technique with subcuticular continuous spiral suture using absorbable suture should be considered to minimise the incidence of incisional SSIs. Moreover, wounds should be carefully checked when CRP levels are high on postoperative day 4.
BronchoX: bronchoscopy exploration software for biopsy intervention planning

Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors’ solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea.
Fouling Mitigation and Wastewater Treatment Enhancement through the Application of an Electro Moving Bed Membrane Bioreactor (eMB-MBR)

High operational cost due to membrane fouling propensity remains a major drawback for the widespread application of membrane bioreactor (MBR) technology. As a result, studies on membrane fouling mitigation through the application of integrated processes have been widely explored. In this work, the combined application of electrochemical processes and moving bed biofilm reactor (MBBR) technology within an MBR at laboratory scale was performed by applying an intermittent voltage of 3 V/cm to a reactor filled with 30% carriers. The treatment efficiency of the electro moving bed membrane bioreactor (eMB-MBR) technology in terms of ammonium nitrogen (NH4-N) and orthophosphate (PO4-P) removal significantly improved from 49.8% and 76.7% in the moving bed membrane bioreactor (MB-MBR) control system to 55% and 98.7% in the eMB-MBR, respectively. Additionally, concentrations of known fouling precursors and membrane fouling rate were noticeably lower in the eMB-MBR system as compared to the control system. Hence, this study successfully demonstrated an innovative and effective technology (i.e., eMB-MBR) to improve MBR performance in terms of both conventional contaminant removal and fouling mitigation.
The Effect of Adherence to Screening Guidelines on the Risk of Alzheimer’s Disease in Elderly Individuals Newly Diagnosed With Type 2 Diabetes Mellitus

Objective: The aim of this study was to examine the possibility that type 2 diabetes and Alzheimer’s disease may share common behavioral protective factors such as adherence to type 2 diabetes treatment guidelines given that these two diseases have both epidemiological and metabolic similarities. Method: The method used in this study is a retrospective cohort study of 3,797 U.S. Medicare fee-for-service beneficiaries aged 66+ newly diagnosed with type 2 diabetes and without a prior record of Alzheimer’s disease based on the Health and Retirement Study. Results: Results of a left-truncated Cox model showed that adherence reduces the risk of Alzheimer’s disease by 20% to 24%. Other significant effects were college education (hazard ratio [HR]: 0.65; p value: .023), stroke (HR: 1.40; p value: .013), and 4+ limitations in physical functioning (HR: 1.33; p value: .008). Discussion: Risk of Alzheimer’s disease can be reduced by behavioral factors. Possible mechanisms may include earlier start of interventions to reduce blood glucose levels and improve insulin sensitivity.
Pedestrian Detection Based on Fusion of Millimeter Wave Radar and Vision

Pedestrian protection system plays an important role in perceptual system of unmanned vehicles and Advanced Drive Assistant System. In order to get more details information about surrounding objects, perceptual system of such kind intelligence system is usually equipped with different sensors, so technology to fuse information of heterogeneous sensors is very important. This paper proposed a novel way to fuse the information of radar and image of camera to realize pedestrian detection and acquire its dynamic information. Contribution of this paper are as following First, a new intra-frame cluster algorithm and an inter-frame tracking algorithm are put forward to extract valid target signal from original radar data with noise. Second, to realize radar-vision data space alignment, least square algorithm is used to get the coordinate transformation matrix. Then with the aid of projections of radar points, a flexible strategy to generate region of interest (ROI) is put forward. Furthermore, to further accelerate detection, an improved fast object estimation algorithm is proposed to acquire a more accurate potential target area based on ROI. At last, histogram of gradient (HOG) features of potential area are extracted and support vector machine is used to judge whether it's a pedestrian. The proposed approach is verified through real experimental examples and the trial results show this method is feasible and effective.
A road to obstetric fistula in Malawi: capturing women’s perspectives through a framework of three delays

Introduction Obstetric fistula (OF) is a devastating birth injury, which leaves a woman with leaking urine and/or feces accompanied by bad smell, a situation that has been likened to death itself. The condition is caused by neglected obstructed labor. Many factors underlie fistula formation, most of which are preventable. The main purpose of this study was to explore labor and childbirth experiences of women who developed OF with a focus on accessibility of care in the central region of Malawi. Methods We conducted semi-structured interviews with 25 women with OF at Bwaila Fistula Care Center in Lilongwe and in its surrounding districts. We interviewed 20 women at Bwaila Fistula Care Center; additional five women were identified through snowball sampling and were interviewed in their homes. Data were categorized using Nvivo 11 and were analyzed using thematic analysis. The three delays model by Thaddeus and Maine was used for data analysis. Findings The majority of women in our study suffered from OF with their subsequent pregnancies. All women experienced delays in one form or another consistent with the three-phase delays described by Thaddeus and Maine. Most of the participants (16) experienced two delays and 15 experienced second-phase delay, which was always coupled with the other; nine participants experienced delay while at the hospital. None of the participants experienced all three delays. Most decisions to seek health care when labor was complicated were made by mothers-in-law and traditional birth attendants. All but two delivered stillborn babies. Conclusion Testimonies by women in our study suggest the complexity of the journey to developing fistula. Poverty, illiteracy, inaccessible health facilities, negligence, lack of male involvement in childbirth issues, and shortage of staff together conspire to fistula formation. To prevent new cases of OF in Malawi, the above mentioned issues need to be addressed, more importantly, increasing access to skilled attendance at birth and emergency obstetric care and promoting girls’ education to increase their financial autonomy and decision-making power about their reproductive lives. Also men need to be educated and be involved in maternal and women’s reproductive health issues to help them make informed decisions when their spouses end up with a complicated labor or delivery.
Daily Maximum Erythemal Dose Rates in the Tropics

The maximum erythemal dose rate (EDRmax) at the Earth's surface tends to occur at local noon. However, clouds can make the timing of EDRmax significantly away from local noon. In fact, EDRmax and its time of occurrence depend mainly on the solar zenith angle at noon (SZAn), site's altitude, the total ozone column (TOC), cloud cover, cloud genera and aerosols. This work depicts the daily incidence of EDRmax for Belo Horizonte (19.92°S, 43.94°W, 858 m a.s.l.) in the Southern Hemisphere tropics for a period of five years (2005–2010). Daily values of EDRmax ranged from 0.063 W m−2 (1σ > 6.9%, Moderate UV‐Index of 3, winter) to 0.486 W m−2 (Extreme UVI of 19, summer). Indicative values of EDRmax for cloudless days were 0.336 W m−2 (summer, TOC = 258 Dobson Units), 0.311 W m−2 (fall, 260 DU), 0.253 W m−2 (spring, 274 DU) and 0.143 W m−2 (winter, 246 DU). Radiation enhancement events by clouds made EDRmax up to 45% higher than the reference EDRmax for cloudless summer skies at a time resolution of a few minutes. The main cloud genera to be associated with such events are Cumulus, Altocumulus, Altostratus and Stratocumulus. The EDR can also be significantly affected by aerosols, which attenuated on average 0.031 W m−2 (22%) of the erythemal UV in a case study at the site.
Distribution Discrepancy Maximization for Image Privacy Preserving

With the rapid increase in online photo sharing activities, image obfuscation algorithms become particularly important for protecting the sensitive information in the shared photos. However, existing image obfuscation methods based on hand-crafted principles are challenged by the dramatic development of deep learning techniques. To address this problem, we propose to maximize the distribution discrepancy between the original image domain and the encrypted image domain. Accordingly, we introduce a collaborative training scheme: a discriminator D is trained to discriminate the reconstructed image from the encrypted image, and an encryption model G_e is required to generate these two kinds of images to maximize the recognition rate of D, leading to the same training objective for both D and G_e. We theoretically prove that such a training scheme maximizes two distributions' discrepancy. Compared with commonly-used image obfuscation methods, our model can produce satisfactory defense against the attack of deep recognition models indicated by significant accuracy decreases on Casia-WebFace and LFW datasets.
Chronic Intermittent Ethanol Exposure Selectively Increases Synaptic Excitability in the Ventral Domain of the Rat Hippocampus

Many studies have implicated hippocampal dysregulation in the pathophysiology of alcohol use disorder (AUD). However, over the past twenty years, a growing body of evidence has revealed distinct functional roles of the dorsal (dHC) and ventral (vHC) hippocampal subregions, with the dHC being primarily involved in spatial learning and memory and the vHC regulating anxiety-and depressive-like behaviors. Notably, to our knowledge, no rodent studies have examined the effects of chronic ethanol exposure on synaptic transmission along the dorsal/ventral axis. To that end, we examined the effects of the chronic intermittent ethanol vapor exposure (CIE) model of AUD on dHC and vHC synaptic excitability. Adult male Long-Evans rats were exposed to CIE or air for 10 days (12 hrs/day; targeting blood ethanol levels of 175-225 mg%) and recordings were made 24 hours into withdrawal. As expected, this protocol increased anxiety-like behaviors on the elevated plus-maze. Extracellular recordings revealed marked CIE-associated increases in synaptic excitation in the CA1 region that were exclusively restricted to the ventral domain of the hippocampus. Western blot analysis of synaptoneurosomal fractions revealed that the expression of two proteins that regulate synaptic strength, GluA2 and SK2, was dysregulated in the vHC, but not the dHC, following CIE. Together, these findings suggest that the ventral CA1 region may be particularly sensitive to the maladaptive effects of chronic ethanol exposure and provide new insight into some of the neural substrates that may contribute to the negative affective state that develops during withdrawal. Highlights Chronic intermittent ethanol exposure produces robust increases in anxiety-like behavior in male Long Evans rats. Chronic intermittent ethanol exposure increases synaptic excitability in the ventral, but not the dorsal, domain of the hippocampus. These changes in excitability are associated with alterations in synaptoneurosomal expression of small conductance calcium-activated potassium channels and the GluA2 AMPA receptor subunit that are also restricted to the ventral hippocampus.
Use of Meta-Networks to Evaluate Key Agents, Knowledge and Resources in the Planning of Construction Projects

Abstract The article proposes a new approach to the identification of key agents, knowledge and resources required to complete tasks being performed as a part of construction projects. The author used the concept of meta-networks to model the relations between agents, knowledge, resources and tasks of a project. Up until now, the identification of key means of production employed a measure of performance of the project that was modelled using a meta-network. However, this measure is limited as it does not take into consideration the significance of individual tasks or the relations between them. The author thus proposed a structural modification of the performance measure for the purposes of identifying key agents, knowledge and resources of a planned project. A case study analysis has confirmed the application potential of the proposed approach. In practice, the results that were obtained can aid planners in evaluating the performance of a project’s plan. Information about key agents, knowledge and resources can constitute the basis for drafting alternative plans which would be more resistant to failure due to the possible loss of key means of production over the course of carrying out a project.
Optimal Low-power Workout State Classification for Multiple Configurations

The workout information of a user can be interpreted from sensor data by dividing the process into workout state classification, repetition count, and exercise type recognition procedures. Because the type of exercise and the number of repetitions can be determined only when the user is performing a workout, it is necessary to first ascertain whether the user is currently exercising or not. In this paper, we focus on the analysis for workout state recognition using inertial sensors. We collected the dataset and divided it into two sets of sensor data by using an accelerometer sensor only and an accelerometer sensor along with a gyroscope sensor; we then applied the sliding window and varied the size of the sliding window and strides. Thereafter, feature extraction was performed and finally, the performances of the recognizers were compared. The recognition system compared five different classifiers and voting methods, and showed that the recognition rate was over 90% with relatively low computational complexity.
Taxonomy‐based hierarchical analysis of natural mortality: polar and subpolar phocid seals

Abstract Knowledge of life‐history parameters is frequently lacking in many species and populations, often because they are cryptic or logistically challenging to study, but also because life‐history parameters can be difficult to estimate with adequate precision. We suggest using hierarchical Bayesian analysis (HBA) to analyze variation in life‐history parameters among related species, with prior variance components representing shared taxonomy, phenotypic plasticity, and observation error. We develop such a framework to analyze U‐shaped natural mortality patterns typical of mammalian life history from a variety of sparse datasets. Using 39 datasets from seals in the family Phocidae, we analyzed 16 models with different formulations for natural morality, specifically the amount of taxonomic and data‐level variance components (subfamily, species, study, and dataset levels) included in mortality hazard parameters. The highest‐ranked model according to DIC included subfamily‐, species‐, and dataset‐level parameter variance components and resulted in typical U‐shaped hazard functions for the 11 seal species in the study. Species with little data had survival schedules shrunken to the mean. We suggest that evolutionary and population ecologists consider employing HBA to quantify variation in life‐history parameters. This approach can be useful for increasing the precision of estimates resulting from a collection of (often sparse) datasets, and for producing prior distributions for populations missing life‐history data.
Plant Nutrient Resource Use Strategies Shape Active Rhizosphere Microbiota Through Root Exudation

Plant strategies for soil nutrient uptake have the potential to strongly influence plant–microbiota interactions, due to the competition between plants and microorganisms for soil nutrient acquisition and/or conservation. In the present study, we investigate whether these plant strategies could influence rhizosphere microbial activities via root exudation, and contribute to the microbiota diversification of active bacterial communities colonizing the root-adhering soil (RAS) and inhabiting the root tissues. We applied a DNA-based stable isotope probing (DNA-SIP) approach to six grass species distributed along a gradient of plant nutrient resource strategies, from conservative species, characterized by low nitrogen (N) uptake, a long lifespans and low root exudation level, to exploitative species, characterized by high rates of photosynthesis, rapid rates of N uptake and high root exudation level. We analyzed their (i) associated microbiota composition involved in root exudate assimilation and soil organic matter (SOM) degradation by 16S-rRNA-based metabarcoding. (ii) We determine the impact of root exudation level on microbial activities (denitrification and respiration) by gas chromatography. Measurement of microbial activities revealed an increase in denitrification and respiration activities for microbial communities colonizing the RAS of exploitative species. This increase of microbial activities results probably from a higher exudation rate and more diverse metabolites by exploitative plant species. Furthermore, our results demonstrate that plant nutrient resource strategies have a role in shaping active microbiota. We present evidence demonstrating that plant nutrient use strategies shape active microbiota involved in root exudate assimilation and SOM degradation via root exudation.
Identification, Characterization, and Expression Patterns of TCP Genes and microRNA319 in Cotton

The TEOSINTE BRANCHED 1, CYCLOIDEA, and PROLIFERATING CELL FACTORS (TCP) gene family is a group of plant-specific transcription factors that have versatile functions in developmental processes and stress responses. In this study, a total of 73 TCP genes in upland cotton were identified and characterizated. Phylogenetic analysis classified them into three subgroups: 50 belonged to PCF, 16 to CIN, and 7 to CYC/TB1. GhTCP genes are randomly distributed in 22 of the 26 chromosomes in cotton. Expression patterns of GhTCPs were analyzed in 10 tissues, including different developmental stages of ovule and fiber, as well as under heat, salt, and drought stresses. Transcriptome analysis showed that 44 GhTCP genes exhibited varied transcript accumulation patterns in the tested tissues and 41 GhTCP genes were differentially expressed in response to heat, salt, and drought stresses. Furthermore, three GhTCP genes of the CIN clade were found to contain miR319-binding sites. An anti-correlation expression of GhTCP21 and GhTCP54 was analyzed with miR319 under salt and drought stress. Our results lay the foundation for understanding the complex mechanisms of GhTCP-mediated developmental processes and abiotic stress-signaling transduction pathways in cotton.
Image Purification through Controllable Neural Style Transfer

Recently, progress in learning-by-synthesis proposed training models on synthetic images that can effectively reduce the cost of human and material resources. However, learning from synthetic images still cannot achieve the desired performance due to the different distribution of synthetic images compared to naturalistic images. Naturalistic images are composed of multiform light distribution, which is a characteristic of the outdoor scene. In an attempt to address this issue, previous methods learn a model to improve the realism of synthetic images. Differently from previous methods, this paper takes the first step towards purifying the naturalistic image to weaken the influence of light and convert the distribution of outdoor naturalistic image through a style transfer task to that of indoor synthetic image. This paper proposes therefore, a controlled neural style transfer network to preserve image structure, accelerate model convergence rate and adapt to multi-scale images. A mixed research approach (qualitative and quantitative) was adopted for the experiments carried out to demonstrate the possibility of purifying naturalistic images of complex distribution. Qualitatively, it compares the proposed method with baseline methods across several indoor and outdoor scenes of the LPW dataset. While quantitatively it evaluates the purified images by training models for gaze estimation on cross-dataset. Results show a significant improvement over using raw naturalistic images and when compared with the baseline methods.
An Exploration of Grit in a CS1 Context

Grit, defined as long-term focus and perseverance, has been proposed as a distinct personality trait that can help to predict success and achievement in challenging endeavours. In this work, we explore the concept of grit in computer science (CS) contexts and whether it has potential value to contribute to predictions of success. To validate the use of the scale in a CS context, we issued Duckworth's 12-item grit scale to 597 CS1 students. We confirm, using a factor analysis, that the items in the scale largely load as expected to two factors. We also evaluate the use of grit as a predictor and find little correlation between overall grit score and final course mark. However, we see a weak correlation between one component of grit -- perseverance of effort -- and final mark. These results suggest that grit, as a construct, may not have the same predictive power in CS1 as in other contexts but that perseverance in the face of difficulty contributes to success in CS1 and is a target of interest for further study.