High Discrimination Capability Research Articles

Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies

SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers.

Multi-scale deep learning ensemble for segmentation of endometriotic lesions

AbstractUltrasound is a readily available, non-invasive and low-cost screening for the identification of endometriosis lesions, but its diagnostic specificity strongly depends on the experience of the operator. For this reason, computer-aided diagnosis tools based on Artificial Intelligence techniques can provide significant help to the clinical staff, both in terms of workload reduction and in increasing the overall accuracy of this type of examination and its outcome. However, although these techniques are spreading rapidly in a variety of domains, their application to endometriosis is still very limited. To fill this gap, we propose and evaluate a novel multi-scale ensemble approach for the automatic segmentation of endometriosis lesions from transvaginal ultrasounds. The peculiarity of the method lies in its high discrimination capability, obtained by combining, in a fusion fashion, multiple Convolutional Neural Networks trained on data at different granularity. The experimental validation carried out shows that: (i) the proposed method allows to significantly improve the performance of the individual neural networks, even in the presence of a limited training set; (ii) with a Dice coefficient of 82%, it represents a valid solution to increase the diagnostic efficacy of the ultrasound examination against such a pathology.

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis

Dual-responsive luminescent dye@MOF hybrids for ratiometric trace detection, colorimetric visualization, and precise discrimination of tetracycline antibiotics

In this work, dual-responsive luminescent Her@Zn-Bim hybrids are developed by herniarin (Her) dye encapsulation engineering within a layered host MOF (Zn-Bim). The pore confinement effect of the Zn-Bim host effectively isolates the guest dye molecules, therefore affording bright blue dye emission for Her@Zn-Bim hybrids. Upon exposure to tetracycline antibiotics (TCs), the hybrid undergoes a significant quenching of the blue emission, concurrent with the appearance of a distinct green emission. This dual-emission response is very fast and sensitive, facilitating real-time ratiometric detection of TCs with a low limit of detection of 7.81 nM. Notably, the ratiometric luminescence response manifests as a readily observable blue-to-green color switching, enabling straightforward and colorimetric visualization of TCs. Visual and semiquantitative monitoring of TCs is achieved using a readily available smartphone installed with a color-analysis app. Furthermore, in conjunction with principal component analysis, the Her@Zn-Bim hybrids demonstrate efficient discrimination of four TCs with high structural and chemical similarities, even in complex mixture systems. The mechanisms underlying the luminescent responses of the hybrids towards TCs are carefully elucidated. These hybrid sensors, featuring high sensitivity, fast response, colorimetric visualization, and precise discrimination capability, hold promising potential for practical applications in sensing TCs.

ALI: The adaptive levels of interval method for processing laser speckle images with superior activity extraction and discrimination capabilities

This paper describes the development of a new laser speckle imaging method for extracting speckle patterns' temporal activity information with superior extraction and discrimination capabilities. It is based on a novel mechanism which uses the speckle images, in an unconventional manner, through adapting the levels of their interval, to deal with the scattering medium that contains regions with different activity scales. This mechanism overcomes the discrimination inability of the other processing methods when they deal with such a medium and speeds up the process of activity map calculation. The developed speckle processing method was evaluated using numerical and graphical evaluation tests, the results showing the efficacy of using the adapting mechanism for the levels of interval. Experimental tests were also undertaken on the proposed method to validate its ability with real speckle images, obtained through a series of experiments performed using a laser speckle imaging system. The experimental results show the high discrimination and activity extraction capabilities of this method against the speckle processing methods and validate its ability to deal with multi-spectral real speckle images. The developed method achieved a faster processing performance in comparison with traditional methods when processing temporal information for the speckle images and calculating the map of their activity.

Multi-Mode Long-Wavelength GaAs/AlGaAs Quantum Well Infrared Photodetectors for Circular Polarization Detection

We present an integrated device combining a double L-shaped chiral metasurface with long-wavelength GaAs/AlGaAs quantum well infrared photodetectors (QWIPs), achieving a circular polarized extinction ratio (CPER) as high as 45 in the long-wavelength infrared range of 7–9 μm. The unit of the chiral metasurface array consists of two structurally identical L-shaped gold structures with central symmetry. The CPER of the double L-shaped QWIPs is 14 times higher than that of a single L-shaped QWIP. The device operates in three modes within the detection band: the microcavity mode, the surface plasmon polariton (SPP) mode, and the hybrid mode. The double L-shaped chiral structure selects and reflects a small portion of left-handed circularly polarized light (LCP), while the majority enters the device and excites SPP modes with the bottom gold grating layer, leading to an absorption enhancement. In contrast, right-handed circularly polarized light (RCP) is mostly reflected with limited excitation of SPP waves. QWIPs exhibit a peak absorption of 0.8 and a coupling efficiency of 2700% in the active region of the quantum well due to the combined effects of the microcavity and SPP modes, in which the SPP mode plays a dominant role. The proposed device maintains high circular polarization discrimination capability under large incident angles and can be applied in spectral imaging.

An innovative MedIcaL dEcision Support tool for neurological ouTcome predictiOn from post-resuscitatioN Electrocardiograms (MILESTONE)

Abstract Background The prediction of poor neurological outcome after Out-of-Hospital Cardiac Arrest (OHCA) represents a major challenge in post-resuscitation care. This is currently based on a complex multiparametric prognostication approach and the existing decision support tools are characterized by unsatisfactory performance. Purpose The aim of the present study is to develop a machine learning (ML) tool for clinical decision support in the prediction of neurological outcome in patients who have experienced an OHCA. We present an innovative MedIcaL dEcision Support tool for neurological ouTcome predictiOn from post-resuscitatioN Electrocardiograms (MILESTONE), which can be used in a post-resuscitation setting. Methods MILESTONE uses an ECG image as an input from which it extracts features by computer vision techniques. Those features, used to feed the ML model for the prediction of the neurological outcome, were retrieved from a dataset of 1341 ECGs collected from a Utstein-style OHCA registry: 431 post-ROSC ECGs of patients with poor neurological outcome (defined according to the Cerebral Performance Category > 2) at discharge (class 1); 568 post-ROSC ECGs of patients with good neurological outcome (CPC≤2) and 342 ECGs of healthy subjects (class 0). Image features were extracted by several computer vision algorithms derived from the scale-invariant feature transform (SIFT) method. The extracted features, patient’s age and gender were used to train an XGBoost classifier. Nested cross-validation was applied to optimize the model’s parameters and to test its generalization capability. Class weightage technique was introduced to make our classifier more prone to detect positive cases. Model’s performance was evaluated by the balanced accuracy, the Area Under the Curve of the Receiver Operating Characteristic curve (AUC-ROC), and the F1-score in both the validation and the test sets. Results Our model achieved an AUC-ROC and F1-score of 88.5±4.1% and 78.2±7.3% respectively in the hold-out set (Figure). MILESTONE reached a balanced accuracy of over 80% as a proof of the robustness of the results and the high discrimination capability for both classes, although it has an intended bias towards the identification of patients with worse outcomes. In terms of explainability, MILESTONE has the potential to correlate machine-readable ECG features with more well-known macro features used in clinical practice such as QT intervals, peak shapes, etc. Conclusions We present the first use of computer vision-based ECG features able to predict poor neurological outcome in patients resuscitated from OHCA. Poor neurological outcome represents a major challenge in post-resuscitation care and this innovative and flexible tool showed an extremely promising performance. Moreover, this tool can be re-trained either for different pathologies or on any time length of ECG recording thus continuously improving its performance.

Confirmatory structural validation and refinement of the Recurrent Urinary Tract Infection Symptom Scale.

To confirm the structural validity of the Recurrent Urinary Tract Infection Symptom Scale (RUTISS), determining whether a bifactor model appropriately fits the questionnaire's structure and identifying areas for refinement. Used in conjunction with established clinical testing methods, this patient-reported outcome measure addresses the urgent need to validate the patient perspective. A clinically and demographically diverse sample of 389 people experiencing recurrent UTI across 37 countries (96.9% female biological sex, aged 18-87 years) completed the RUTISS online. A bifactor graded response model was fitted to the data, identifying potential items for deletion if they indicated significant differential item functioning (DIF) based on sociodemographic characteristics, contributed to local item dependence or demonstrated poor fit or discrimination capability. The final RUTISS comprised a 3-item symptom frequency section, a 1-item global rating of change scale and an 11-item general 'rUTI symptom and pain severity' subscale with four sub-factor domains measuring 'urinary symptoms', 'urinary presentation', 'UTI pain and discomfort' and 'bodily sensations'. The bifactor model fit indices were excellent (root mean square error of approximation [RMSEA] =0.041, comparative fit index [CFI] =0.995, standardised root mean square residual [SRMSR] =0.047), and the mean-square fit statistics indicated that all items were productive for measurement (mean square fit indices [MNSQ] =0.64 - 1.29). Eighty-one per cent of the common model variance was accounted for by the general factor and sub-factors collectively, and all factor loadings were greater than 0.30 and communalities greater than 0.60. Items indicated high discrimination capability (slope parameters >1.35). The 15-item RUTISS is a patient-generated, psychometrically robust questionnaire that dynamically assesses the patient experience of recurrent UTI symptoms and pain. This brief tool offers the unique opportunity to enhance patient-centred care by supporting shared decision-making and patient monitoring.

Performance comparison of water Cherenkov detectors with different geometries

In the framework of the development of the SWGO experiment, we have performed a detailed study of the single unit of an extensive air shower observatory based on an array of water Cherenkov detectors. Indeed, one of the possible water Cherenkov detector unit configurations for SWGO consists of tanks, and to reach a high detection efficiency and discrimination capability between gamma-ray and hadronic air showers, different tank designs are under investigation. In this study, we consider and compare double-layer tanks with several sizes, shapes and number of photo-multiplier tubes (PMTs). Muons, electrons, and gamma-rays with energies typical of secondary particles in extensive air showers have been simulated entering the tanks with zenith angles from 0 to 60 deg. The comparison between different tank geometries was evaluated considering the number of photoelectrons produced by the PMTs, the detection efficiency, and the time resolution of the measurement of the first photon. We will show that with circular and hexagonal tanks better performance can be obtained than with the square ones, and that it improves for smaller tanks as expected.

Volatolomic approach by HS-SPME/GC–MS and chemometric evaluations for the discrimination of X-ray irradiated mozzarella cheese

In this work, an untargeted screening of the volatile profile of X-ray irradiated mozzarella cheese was carried out to study the possible radio-induced modifications. A Central Composite Design (CCD) for Response Surface Methodology (RSM) was employed to optimise the HS-SPME analysis of volatile organic compounds (VOCs). The optimised HS-SPME conditions, in terms of sample amount (5.0 g), extraction temperature (50 °C) and extraction time (75 min), were used to analyse non-irradiated and irradiated samples at three dose levels, 1.0, 2.0, 3.0 kGy. Partial Least Squares-Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were applied to explore the variation of volatile profile with respect to the X-ray irradiation treatment. Both methods highlighted a high discriminant capability with excellent values of accuracy, specificity and sensitivity, demonstrating the effectiveness of the volatolomic approach to evaluate the variations induced by the treatment and allowing to select a total of 35 VOCs as potential irradiation markers.

A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics

Malignant melanoma is the most invasive skin cancer and is currently regarded as one of the deadliest disorders; however, it can be cured more successfully if detected and treated early. Recently, CAD (computer-aided diagnosis) systems have emerged as a powerful alternative tool for the automatic detection and categorization of skin lesions, such as malignant melanoma or benign nevus, in given dermoscopy images. In this paper, we propose an integrated CAD framework for rapid and accurate melanoma detection in dermoscopy images. Initially, an input dermoscopy image is pre-processed by using a median filter and bottom-hat filtering for noise reduction, artifact removal, and, thus, enhancing the image quality. After this, each skin lesion is described by an effective skin lesion descriptor with high discrimination and descriptiveness capabilities, which is constructed by calculating the HOG (Histogram of Oriented Gradient) and LBP (Local Binary Patterns) and their extensions. After feature selection, the lesion descriptors are fed into three supervised machine learning classification models, namely SVM (Support Vector Machine), kNN (k-Nearest Neighbors), and GAB (Gentle AdaBoost), to diagnostically classify melanocytic skin lesions into one of two diagnostic categories, melanoma or nevus. Experimental results achieved using 10-fold cross-validation on the publicly available MED-NODEE dermoscopy image dataset demonstrate that the proposed CAD framework performs either competitively or superiorly to several state-of-the-art methods with stronger training settings in relation to various diagnostic metrics, such as accuracy (94%), specificity (92%), and sensitivity (100%).

Organic heterostructured scintillators with a high pulse shape discrimination capability

Recently, we have developed a new type of the scintillation material, namely organic composite scintillators. It is a non-scintillating transparent gel composition (a polysiloxane elastomer) containing grains of organic single crystals. The grains are obtained after directional crystallization of the material by crushing a crystalline ingot under a layer of liquid nitrogen. This approach removes technological restrictions on the area of the entrance window of a scintillator, does not require the growth of structurally perfect single crystals and their subsequent mechanical treatment. In contrast to organic single crystals and liquids, these materials are not continuous media but heterostructured ones.The ability of single crystals and liquids to separate signals from radiations with different specific energy losses dE/dx is well known. Due to the peculiarities of the creation and recombination of triplet-excited (T) states in these objects is also well studied. Such information on heterostructured scintillation materials, for which the grain size can limit migration of T-states, is practically absent.We discuss the results of the investigation of the luminescence spectra upon excitation by light into the T-states absorption region and the relative light yield of composite scintillators containing p-terphenyl grains (both activated and non-activated) and trans-stilbene grains. We used the grain fractions from 0.04 to 1.0 mm. We obtained and studied both single-layer samples (the thickness of a sample practically corresponded to the grain size of the scintillation material) and samples of 5 mm thick. The research results showed that samples with grain fractions of less than 0.04, 0.06 and between 0.06 and 0.1 mm have low intensity of the delayed fluorescence and relative light output values. We obtained that to separate radiations with different dE/dx by the shape of the radioluminescence pulse it is advisable to use grain fractions larger than 0.1 mm. We also discuss the physical processes that can lead to such results.

Ancient and nonuniform loss of olfactory receptor expression renders the shark nose a de facto vomeronasal organ.

Cartilaginous fishes are renowned for a keen sense of smell, a reputation based on behavioral observations and supported by the presence of large and morphologically complex olfactory organs. At the molecular level, genes belonging to the four families coding for most olfactory chemosensory receptors in other vertebrates have been identified in a chimera and a shark, but it was unknown whether they actually code for olfactory receptors in these species. Here we describe the evolutionary dynamics of these gene families in cartilaginous fishes using genomes of a chimera, a skate, a sawfish and eight sharks. The number of putative OR, TAAR and V1R/ORA receptors is very low and stable whereas the number of putative V2R/OlfC receptors is higher and much more dynamic. In the catshark Scyliorhinus canicula, we show that many V2R/OlfC receptors are expressed in the olfactory epithelium in the sparsely distributed pattern characteristic for olfactory receptors. In contrast, the other three vertebrate olfactory receptor families are either not expressed (OR) or only represented with a single receptor (V1R/ORA and TAAR). The complete overlap of markers of microvillous olfactory sensory neurons with panneuronal marker HuC in the olfactory organ suggests the same cell type specificity of V2R/OlfC expression as for bony fishes, i.e. in microvillous neurons. The relatively low number of olfactory receptors in cartilaginous fishes compared to bony fishes could be the result of an ancient and constant selection in favor of a high olfactory sensitivity at the expense of a high discrimination capability.

3D Mosaic Carbon Nanofiber Sensors for Room‐Temperature Detection of Methane and Other Dissolved Gases in Transformer Oil

AbstractHydrocarbons and carbon oxides are typical dissolved gases in transformer oil that reflect the latent pitfalls, on‐line monitoring of their concentrations can effectively evaluate the operating status of the power transformer. However, these low‐concentration targets (especially for CH4) show high chemical inertness at room temperature, challenging the sensitive performance of current commonly used chemiresistive gas sensors. Herein, a strategy by combining traditional inorganic semiconductors to carbon nanofiber (CNF) via electrospinning–annealing route is described. Three optimized 3D mosaic films, CNFs scaffold incorporated with WO3, SnO2 and MoS2 nanoparticles, are obtained. Due to the large specific surface area of the 3D network, and the synergic and heterojunction effects between nanoparticles and CNFs, all three sensors exhibit high response to CH4 at room temperature, and also record distinguishable signals toward H2, C2H4, CO and CO2, revealing the three sensors are cross‐sensitive to the five analytes. Accordingly, preliminary discrimination of five dissolved gases is realized by principle component analysis. This study provides an effective and extendable solution of preparing room‐temperature chemiresistive sensors for the detection of CH4 and other gases, and offers a strategy for the construction of sensor array to achieve a high discrimination capability.

Segregation of apple cultivars on the basis of main fruit physical and chemical properties and antioxidant activity

This study aims to analyze the essential physicochemical properties and antioxidant activity of 25 apple commercial cultivars grown in Serbia during 2019 and 2020 in order to segregate the apples with the best size, shape, and also nutritious and health promoting values. The results showed that 'Morens Jonagore' and 'Red Boscop' had similar and the highest fruit weight (FW) whereas 'Golden Delicious', 'Hired', 'Red Chief' , and 'Morens Jonagored' had the highest and similar fruit length (L). The lowest both these values were found in 'Akane'. 'Red Boscop' had the highest fruit diameter (D), whereas the lowest had 'Galaxy'. Twenty-two or 88% of the examined cultivars have a D greater than 70 mm. Both fruit shape indexes [sphericity (φ) and aspect ratio (R$_{a}$)] were the highest and similar in 'Akane' and 'Budimka' and the lowest in 'Red Chief'. Peel was a better source of dry matter (DM), total phenolic content (TPC), and total flavonoid content (TFC) and had higher antioxidant capacity than flesh. Moroever, pH juice was higher in peel. In contrast, titratable acidity (TA) was higher in flesh in comparison with peel, whereas ash content was statistically similar in both peel and flesh tissues. 'Akane' had the highest total anthocyanin content (TAc), whereas the lowest was found in 'Red Boscop'. Old cultivar 'Budimka' had the highest TPC and TFC in both peel and flesh, whereas 'Ginger Gold' had the lowest TPC and TFC and also the lowest antioxidant power. Peel of 'Red Chief' and flesh of 'Braeburn' had the best antioxidant activity. Among others, correlation analysis showed that the DM content in the peel is a crucial factor that determines the DM, soluble solids content (SSC), TPC, TFC, and DPPH free-radical scavenging activity values in the flesh. The principal component analysis (PCA) showed high discrimination capabilities of variables measured and revealed that a more nutritious peel may be darker or redder while a more nutritious flesh may have a lighter color and lower SSC.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19.The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Stratifying sudden death risk in adults with drug-resistant focal epilepsy: The SUDEP-CARE score.

A clinical risk score for sudden unexpected death in epilepsy (SUDEP) in patients with drug-resistant focal epilepsy could help improve prevention. A case-control study was conducted including (i) definite or probable SUDEP cases collected by the French National Sentinel Mortality Epilepsy Network and (ii) control patients from the French national research database of epilepsy monitoring units. Patients with drug-resistant focal epilepsy were eligible. Multiple logistic regressions were performed. After sensitivity analysis and internal validation, a simplified risk score was developed from the selected variables. Sixty-two SUDEP cases and 620 controls were included. Of 21 potential predictors explored, seven were ultimately selected, including generalized seizure frequency (>1/month vs. <1/year: adjusted odds ratio [AOR] 2.6, 95% confidence interval [CI] 1.25-5.41), nocturnal or sleep-related seizures (AOR 4.49, 95% CI 2.68-7.53), current or past depression (AOR 2.0, 95% CI 1.19-3.34) or the ability to alert someone of an oncoming seizure (AOR 0.57, 95% CI 0.33-0.98). After internal validation, a clinically usable score ranging from -1 to 8 was developed, with high discrimination capabilities (area under the receiver operating curve 0.85, 95% CI 0.80-0.90). The threshold of 3 has good sensitivity (82.3%, 95% CI 72.7-91.8), whilst keeping a good specificity (82.7%, 95% CI 79.8-85.7). These results outline the importance of generalized and nocturnal seizures on the occurrence of SUDEP, and show a protective role in the ability to alert someone of an oncoming seizure. The SUDEP-CARE score is promising and will need external validation. Further work, including paraclinical explorations, could improve this risk score.

A low-cost photonic band gap (PBG) microstrip line resonator for dielectric characterization of liquids

Abstract In this paper, hybrid integration of planar microstrip line and Photonic Band Gap (PBG) structure is proposed for the dielectric characterization of liquids. To implement the PBG structure of the microstrip line, a microfluidic channel with periodic form is introduced into the substrate and filled with different liquids. Based on this configuration, the operation principle of the sensor is based on a frequency shift due to the variation in the center of the bandgap, which in turn changes with the variation of the permittivity of LUT filled in the microfluidic channel. The proposed sensor exploits the behavior of the bandgap as a reflector to construct a resonant structure sensitive to the variation in LUT permittivity. The dimensions of the planar structure are optimized to achieve high precision and discrimination capability. The different empirical expressions describing the complex permittivity with the measured parameters were carried out. To validate the proposed concept, the sensor prototype is designed, fabricated, and tested. The frequency shift related to a change of 3.2 in LUT permittivity corresponds to 180 MHz around 6 GHz. The resonant-mode sensor spans a permittivity range from 1 to 80 with a precision better than 7.2%. The proposed sensor is simple in design and low cost, which may be applied in different applications at the industrial.

Validation of the AGCU Expressmarker 20 + 20Y kit: A 6-dye multiplex assay for forensic application

The AGCU Expressmarker 20 + 20Y Kit is a newly devised short tandem repeat (STR) multiplex system that simultaneously analyses a set of 19 autosomal STR loci, 20 Y chromosomal STR loci and the amelogenin locus with six-dye fluorescent labelling. Here, the AGCU Expressmarker 20 + 20Y system was validated following the guidelines published by the Scientific Working Group on DNA Analysis Methods (SWGDAM), including PCR-condition, sensitivity, mixtures, species specificity, models of inhibition, precision, stutter percentage, concordance, population genetic studies, performance on three kinds of degraded DNA and a type of casework samples. The results indicated that the kit had high sensitivity when there was a small amount of DNA (0.0625 ng), more than one male (minor: major=1:19), or a mixture of males and females (male: female=1:32), models of inhibition (250 μM hematin, 1500 ng/μL humic acid and more than 100 ng/μL tannic acid) and degraded samples. The kit showed high precision level with standard deviation of allele size no more than 0.0930. Furthermore, this system was also tested in 444 random male samples of Chinese Han and Hui population, showing its high discrimination capability in Han and Hui population. Meanwhile, the system was applicable to the case of the AMELY abnormality. In short, the kit was verified and proved to be a robust, reliable and suitable tool for human identification and casework samples.

Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation.

Unplanned readmission after transsphenoidal resection of pituitary adenoma can occur in up to 10% of patients but is unpredictable. To develop a reliable system for predicting unplanned readmission and create a validated method for stratifying patients by risk. Data sets were retrospectively collected from the National Surgical Quality Improvement Program and 2 tertiary academic medical centers. Eight machine learning classifiers were fit to the National Surgical Quality Improvement Program data, optimized using Bayesian parameter optimization and evaluated on the external data. Permutation analysis identified the relative importance of predictive variables, and a risk stratification system was built using the trained machine learning models. Readmissions were accurately predicted by several classification models with an area under the receiving operator characteristic curve of 0.76 (95% CI 0.68-0.83) on the external data set. Permutation analysis identified the most important variables for predicting readmission as preoperative sodium level, returning to the operating room, and total operation time. High-risk and medium-risk patients, as identified by the proposed risk stratification system, were more likely to be readmitted than low-risk patients, with relative risks of 12.2 (95% CI 5.9-26.5) and 4.2 (95% CI 2.3-8.7), respectively. Overall risk stratification showed high discriminative capability with a C-statistic of 0.73. In this multi-institutional study with outside validation, unplanned readmissions after pituitary adenoma resection were accurately predicted using machine learning techniques. The features identified in this study and the risk stratification system developed could guide clinical and surgical decision making, reduce healthcare costs, and improve the quality of patient care by better identifying high-risk patients for closer perioperative management.