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Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures.

Paper, as a foldable, pollution-free, and low-cost material, has become a suitable support substrate for producing flexible thermoelectric (TE) generators to realize waste heat recycling and the application of human-powered electronic devices. We propose a facile fabrication method to modify cellulose paper with inorganic TE powders via vacuum filtration, making a modified paper that possesses good thermoelectric properties. By connecting the modified paper to copper foils, flexible paper-based TE generators (PTGs) are fabricated. The obtained PTG with three units of P–N modules can generate an output voltage of ∼41.2 mV at a temperature difference of 50 K. Based on this modified paper, a thermal sensor that responds to heat sources, such as fingers, is proposed with a rapid response time of 0.25 s. This work offers a promising strategy for the simple fabrication of PTGs, paving the way for achieving the commercial application of self-powered wearable electronics.

Here we report the vertical paper analytical devices (vPADs) fabricated using the principles of quilling and kirigami. What differentiates the vPADs from conventional paper microfluidic devices is that the paper substrate used to fabricate the device is placed vertically to the device plane. The fabrication of vPADs with high precision is instrument-free, requiring no photolithography, printing or heating. Two- and three-dimensional vPADs are fabricated for multiplex colorimetric assays of four biochemical indicators and automated enzyme-linked immunosorbent assay of human myoglobin, respectively.

Unprecedented in the literature, levulinic acid (LA), one of the top value-added intermediates of chemical industry, is obtained from cigarette butts as cellulose feedstock by means of a one-pot hydrothermal process carried out at 200 °C for 2 h and catalysed by phosphoric acid. The protocol avoids the use of more aggressive and toxic H2SO4 and HCl, that are generally employed on several cellulose sources (e.g. sludge paper), thus minimizing corrosion phenomena of plants. Neither chemical pre-treatment of butts nor specific purification procedure of LA are required. Notably, by simply modifying acid catalyst (e.g. using CH3COOH), another top value-added fine chemical such as 5-hydroxymethylfuraldehyde (HMF) is obtained, thus widening the scope of the method. Being cigarette filters a waste available in quantities of megatonnes per year, they represent an unlimited at no cost source of cellulose, thus enabling the up-scale to an industrial level of LA production.

Worldwide, tropospheric ozone (O3) is a potential threat to wood production, but our understanding of O3 economic impacts on forests is still limited. To overcome this issue, we developed an approach for integrating O3 risk modelling and economic estimates, by using the Italian forests as a case study. Results suggested a significant impact of O3 expressed in terms of stomatal flux with an hourly threshold of uptake (Y = 1 nmol O3 m−2 leaf area s−1 to represent the detoxification capacity of trees), i.e. POD1. In 2005, the annual POD1 averaged over Italy was 20.4 mmol m−2 and the consequent potential damage ranged from 790.90 M€ to 2.85 B€ of capital value (i.e. 255–869 € ha−1, on average) depending on the interest rate. The annual damage ranged from 31.6 to 57.1 M€ (i.e. 10–17 € ha−1 per year, on average). There was also a 1.1% reduction in the profitable forest areas, i.e. with a positive Forest Expectation Value (FEV), with significant declines of the annual national wood production of firewood (− 7.5%), timber pole (− 7.4%), roundwood (− 5.0%) and paper mill (− 4.8%). Results were significantly different in the different Italian regions. We recommend our combined approach for further studies under different economic and phytoclimatic conditions.

Superabsorbent materials can absorb many times their weight in water, but are commonly derived from petroleum. Here, acylation of coagulated potato protein concentrate or soluble potato protein fruit juice yields an effective, mould-resistant, and biodegradable superabsorbent polymer.Superabsorbent polymers (SAP) are a central component of hygiene and medical products requiring high liquid swelling, but these SAP are commonly derived from petroleum resources. Here, we show that sustainable and biodegradable SAP can be produced by acylation of the agricultural potato protein side-stream (PPC) with a non-toxic dianhydride (EDTAD). Treatment of the PPC yields a material with a water swelling capacity of ca. 2400%, which is ten times greater than the untreated PPC. Acylation was also performed on waste potato fruit juice (PFJ), i.e. before the industrial treatment to precipitate the PPC. The use of PFJ for the acylation implies a saving of 320 000 tons as CO2 in greenhouse gas emissions per year by avoiding the industrial drying of the PFJ to obtain the PPC. The acylated PPC shows biodegradation and resistance to mould growth. The possibilities to produce a biodegradable SAP from the PPC allows for future fabrication of environment-friendly and disposable daily-care products, e.g. diapers and sanitary pads.

Cellulase (mainly endoglucanase, EG) has been used in pulp modification for improving paper quality through environmentally friendly process. But low activity in alkaline pH and high filter paper activity (FPA) were still obstacles for extending the cellulase application in papermaking industry. In the study, an alkali-tolerant EG gene of Bacillus subtilis Y106 was homologous over-expressed for obtaining suitable enzyme used in pulp modification. The engineering strain could produce the crude enzyme with more alkali-tolerant EG and little PFA. Potential of the crude enzyme in modification of different pulps were investigated. It was found that the enzyme could be used for improving drainage and strength properties of pulps from softwood, hardwood and non-wood materials, especially non-wood pulp such as wheat straw pulp. The underlying mechanisms of pulp modification and different effects on various types of pulps by the EG treatment were also discussed by studying the change in fibers characteristics and fiber bonding.

Organic electronics is emerging for large-area applications such as photovoltaic cells, rollable displays or electronic paper. Its future development and integration will require a simple, low-power organic memory, that can be written, erased and readout electrically. Here we demonstrate a non-volatile memory in which the ferroelectric polarisation state of an organic tunnel barrier encodes the stored information and sets the readout tunnel current. We use high-sensitivity piezoresponse force microscopy to show that films as thin as one or two layers of ferroelectric poly(vinylidene fluoride) remain switchable with low voltages. Submicron junctions based on these films display tunnel electroresistance reaching 1,000% at room temperature that is driven by ferroelectric switching and explained by electrostatic effects in a direct tunnelling regime. Our findings provide a path to develop low-cost, large-scale arrays of organic ferroelectric tunnel junctions on silicon or flexible substrates., Ferroelectric organic materials can be used for tunnel barriers in memory devices as a cheaper and eco-friendly replacement of their inorganic counterparts. Here, Tian et al. use poly(vinylidene fluoride) with 1–2 layer thickness to achieve giant tunnel electroresistance of 1,000% at room temperature.

Oil spill has led to severe environmental and ecological problems. Due to the harsh environmental conditions, the bioremediation technology is not successfully used to remedy the oil spill in marine environment. In this study, immobilization technology was used to immobilize bacteria on natural organic carriers (i.e., wood chips and maize straw). The higher surface area of in wood chips leads to larger biomass density (0.0242 gVSS/g) than that of maize straw of 0.0097 gVSS/g carrier. Compared with biodegradation efficiency of free bacteria (44.79%), the immobilized bacteria on wood chips and maize straw reached to 73.39% and 52.28%, respectively. The high biological activity of the immobilized bacteria can be also explained by nutrients, such as TN (total nitrogen) and TP (total phosphorus), released from wood chips and maize straw, which was 8.83 mg/g and 5.53 mg/g, 0.0624 mg/g and 0.0099 mg/g, respectively.

Flexible and crystallized indium–tin oxide (ITO) thin films were successfully obtained on plastic polyethylene terephthalate (PET) films with monolayered graphene as a platform. The highly crystalline ITO (c-ITO) was first fabricated on a rigid substrate of graphene on copper foil and it was subsequently transferred onto a PET substrate by a well-established technique. Despite the plasma damage during ITO deposition, the graphene layer effectively acted as a Cu-diffusion barrier. The c-ITO/graphene/PET electrode with the 60-nm-thick ITO exhibited a reasonable sheet resistance of ~45 Ω sq−1 and a transmittance of ~92% at a wavelength of 550 nm. The c-ITO on the monolayered graphene support showed significant enhancement in flexibility compared with the ITO/PET film without graphene because the atomically controlled monolayered graphene acted as a mechanically robust support. The prepared flexible transparent c-ITO/graphene/PET electrode was applied as the anode in a bulk heterojunction polymer solar cell (PSC) to evaluate its performance, which was comparable with that of the commonly used c-ITO/glass electrode. These results represent important progress in the fabrication of flexible transparent electrodes for future optoelectronics applications.

Mode-locked and Q-switched pulsed fiber laser sources with wavelengths of 1.55 μm are widely used in various fields. Gold nanorods (GNRs) have been applied in biomedicine and optics owing to their biocompatibility, easy fabrication, and unique optical properties. This paper presents the analysis of a saturable absorber based on a colloidal gold nanorod (GNR) thin film for dual-function passively mode-locked and Q-switched 1.55-μm fiber lasers. The colloidal GNR thin film possesses superior properties such as a wide operating wavelength range, large nonlinear absorption coefficient, and a picosecond-order recovery time. Its modulation depth and saturation intensity at 1.55 μm are 7.8% and 6.55 MW/cm2, respectively. Passive mode-locked or Q-switched laser operation is achieved by changing the number of GNR thin-film layers. The advantages of these high-quality GNRs in mode-locked and Q-switched fiber lasers with record-high slope efficiency are verified by conducting comprehensive material and laser dynamic analyses. The self-starting mode-locked fiber laser with an efficiency as high as 24.91% and passively Q-switched fiber laser with the maximum energy of 0.403 μJ are successfully demonstrated. This paper presents the novel demonstration of reconfigurable mode-locked and Q-switched all-fiber lasers by incorporating colloidal GNR thin films.

The present paper aims to quantify how human-made changes in the upstream exacerbate climate change impacts on water birds’ habitat in the downstream. To reduce climate change effects and design adaptation policies, it is important to identify whether human activities understate or overstate the effects of climate change in a region on its inhabitants. This paper also shows how human activities may magnify climate change impacts both locally and regionally. Land-use/land-cover change as the important sign of human-made destruction in an ecosystem was detected in the upstream of the Helmand basin over 40 years. Owing to conflicts in Afghanistan, studies on this basin are rare. The water bird’s habitat suitability maps during the study period were created using the maximum entropy model and the multi-criteria evaluation method. The post-classification method was applied to show the land-use/land-cover change over 40 years. These results were compared to the area of suitable habitat for water birds. The findings of these analyses indicated that the irrigated farming was expanded in the upstream despite climate change and water limitation, while the water birds’ habitat in the downstream was declined. These results revealed that the unsustainable pattern of farming and blocking water behind dams in the upstream exacerbated the negative effects of climate change on water birds’ habitat in the downstream. The significance of this study is to demonstrate the role of human in exacerbating climate change impacts both locally and regionally.

Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).

In this paper using Plasmon Resonances Management (PRM), a bi-functional meta-mirror is proposed in which, the meta-mirror can obtain two opposite properties in two different frequency ranges. In this method, an anisotropic unit cell with polarization conversion property is modified to have two plasmon resonances in both symmetric and anti-symmetric planes in a specific frequency. This allows the unit cell to have the property of unchanged polarization in that frequency. The meta-mirror is composed of this modified unit cell and its mirror as a chessboard arrangement and the incident wave on the meta-mirror is reflected as in-phase in that specific frequency i.e. specular reflection, while as out-of-phase in other frequencies i.e. RCS reduction. The designed meta-mirror in this paper demonstrates the RCS reduction in two side-bands from 4 to 9 GHz and 10.8 to 14.8 GHz while behaving as a specular reflection in the frequency around 10 GHz.

We investigate conducting characteristics of biochar derived from the pyrolysis of a paper at terahertz frequencies. Paper is annealed under temperatures ranging from 600 to 1000 °C to modify structural and electrical properties. We experimentally observe that the terahertz conductivity increases above 102 S/m as the annealing temperature increases up to 800 °C. From structural characterization using energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, Raman spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, we confirm that more graphitic biochars are produced in high annealing temperature, in agreement with the improvement of terahertz conductivity. Our results show that biochar can be a highly promising candidate to be used in paper-based devices operating at terahertz frequencies.

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

On-chip technology continuously explores and provides novel methods for high-throughput drug screening, such as droplet-array sandwiching technology. Droplet-array sandwiching technology involves upper and lower substrates with a droplet-array designed for a one-step process for drug screening, including cell-based screening. This technology is, however, limited to batch manipulation of the droplet-array. This paper proposes selective control of individual droplets, which allows different conditions for individual droplet pairs. Electrowetting-on-dielectric (EWOD) technology is introduced to control the height of the droplets so that the contact between droplet-pairs can be individually controlled. Circular patterns 4 mm in diameter composed of electrodes for EWOD and hydrophilic-hydrophobic patterns for droplet formation 4 μl in volume were developed. This paper presents and examines the selective control of the droplet height by EWOD for an applied voltage up to 160 V, followed by successful results of selective control of the contact and transport of substances.

Antimicrobial resistance (AMR) is a global health emergency. Complementary to developing new drugs, AMR can be monitored and controlled through cost-effective active surveillance of resistance. As an initiative to monitor resistance, countries all across the globe are collecting data using a variety of surveillance tools. Moreover, hospitals routinely collect the AMR data for treatment which is being stored in their Laboratory and Hospital Information systems (LIS-HIS). The generated clinical data is collected & stored in various formats, making it very difficult to analyze and generate national reports. To integrate the stored clinical data for predictive modeling and analysis, there is an immediate need for a one-stop data repository capable of importing and exporting data in simple data exchange formats (CSV/Excel). The paper highlights the design & development of i-DIA, a python-based web API to facilitate the interoperability of AMR data by automatically importing the bulk of medical data from CSV files into generic data management and analysis system. The i-DIA has been integrated and tested with the ICMR’s AMR surveillance network on in-house developed software, i-AMRSS. The i-AMRSS is presently collecting data from 31 laboratories across India and i-DIA has been used to import data generated from LIS & HIS of a few hospitals directly into the system. The paper also proposes the complete web-based framework (an extension of i-DIA) integrated with peer-to-peer system architecture.

Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Much effort has been expended on building cellular computational devices for different applications. Despite the significant advances, there are still several addressable restraints to achieve the necessary technological transference. These improvements will ease the development of end-user applications working out of the lab. In this study, we propose a methodology for the construction of printable cellular devices, digital or analogue, for different purposes. These printable devices are designed to work in a 2D surface, in which the circuit information is encoded in the concentration of a biological signal, the so-called carrying signal. This signal diffuses through the 2D surface and thereby interacts with different device components. These components are distributed in a specific spatial arrangement and perform the computation by modulating the level of the carrying signal in response to external inputs, determining the final output. For experimental validation, 2D cellular circuits are printed on a paper surface by using a set of cellular inks. As a proof-of-principle, we have printed and analysed both digital and analogue circuits using the same set of cellular inks but with different spatial topologies. The proposed methodology can open the door to a feasible and reliable industrial production of cellular circuits for multiple applications., Synthetic biology circuits are finding application in a wide range of computational devices, such as contaminant detection. Here, the authors design 2D paper circuits in which the spatial orientation of the cellular components specifies function.

A lack of access to sanitation not only has negative effects on the environment but also results in increased prevalence of disease and adversely affects health outcomes. This paper uses data from a survey in Liaoning Province in China to examine the relationship between general health knowledge and improved sanitation facility access. It also explores the effects of work experience and government subsidies on toilet renovation. Using Logit Regression and Duration Analysis, our results show that general health knowledge had a significant positive correlation with flush toilet use and a significant negative correlation with the average duration of traditional latrine pits use. The results of heterogeneity analysis show that work experience in cities can expand farmers’ horizons, which increases their general health knowledge. However, per capita government subsidies are insufficient and result in no significant impact on general health knowledge. This paper also discusses certain restrictions that prevent general health knowledge from having an effect on toilet renovation. The article finally proposes policy recommendations for rural areas in China to better promote the rural toilet revolution and our results can also provide useful guidelines for drawing up relevant policies in other developing countries.

Eucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for.

Neuromyelitis Optica and Multiple Sclerosis are idiopathic inflammatory demyelinating diseases of the central nervous system that currently are considered distinct autoimmune diseases, so differences in genetic susceptibility would be expected. This study aimed to investigate the HLA association with Neuromyelitis Optica by a systematic review with meta-analysis. The STROBE instrument guided research paper assessments. Thirteen papers published between 2009 and 2020 were eligible. 568 Neuromyelitis Optica patients, 41.4% Asians, 32.4% Latin Americans and 26.2% Europeans were analyzed. Only alleles of the DRB1 locus were genotyped in all studies. Neuromyelitis Optica patients have 2.46 more chances of having the DRB1*03 allelic group than controls. Ethnicity can influence genetic susceptibility. The main HLA association with Neuromyelitis Optica was the DRB1*03:01 allele in Western populations and with the DPB1*05:01 allele in Asia. Differences in the Multiple Sclerosis and Neuromyelitis Optica genetic susceptibility was confirmed in Afro descendants. The DRB1*03 allelic group associated with Neuromyelitis Optica has also been described in other systemic autoimmune diseases.

Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions. The current state-of-the-art deep learning models for radiology applications consider only pixel-value information without data informing clinical context. Yet in practice, pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes. To achieve a similar goal using deep learning, medical imaging pixel-based models must also achieve the capability to process contextual data from electronic health records (EHR) in addition to pixel data. In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020. We conducted a systematic search on PubMed and Scopus for original research articles leveraging deep learning for fusion of multimodality data. In total, we screened 985 studies and extracted data from 17 papers. By means of this systematic review, we present current knowledge, summarize important results and provide implementation guidelines to serve as a reference for researchers interested in the application of multimodal fusion in medical imaging.

Contains fulltext : 220691.pdf (Publisher’s version ) (Open Access) Invasive motor Cortex Stimulation (iMCS) was introduced in the 1990's for the treatment of chronic neuropathic orofacial pain (CNOP), although its effectiveness remains doubtful. However, CNOP is known to be a heterogeneous group of orofacial pain disorders, which can lead to different responses to iMCS. Therefore, this paper investigated (1) whether the effectiveness of iMCS is significantly different among different CNOP disorders and (2) whether other confounding factors can be impacting iMCS results in CNOP. A systematic review and meta-analysis using a linear mixed-model was performed. Twenty-three papers were included, totaling 140 CNOP patients. Heterogeneity of the studies showed to be 55.8%. A visual analogue scale (VAS) measured median pain relief of 66.5% (ranging from 0-100%) was found. Linear mixed-model analysis showed that patients suffering from trigeminal neuralgia responded significantly more favorable to iMCS than patients suffering from dysfunctional pain syndromes (p = 0.030). Also, patients suffering from CNOP caused by (supra)nuclear lesions responded marginally significantly better to iMCS than patients suffering from CNOP due to trigeminal nerve lesions (p = 0.049). No other confounding factors were elucidated. This meta-analysis showed that patients suffering from trigeminal neuralgia and patients suffering from (supra)nuclear lesions causing CNOP responded significantly more favorable than others on iMCS. No other confounding factors were found relevant.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In 2017, the WHO classification of tumours of the endocrine organs established the criteria for a NIFTP diagnosis. The present paper considers some aspects that are still debated or unresolved: the real incidence and clinical meaning of multifocal/multinodular lesions, the biological behaviour of micro-NIFTP, the sprinkling phenomenon and the corresponding modifications to the FNA reporting systems based on changes to the ROM. Moreover, the paper suggests possible scenarios for the clinical-pathological management of this entity. From the initial 1470 cases, a group of 68 NIFTPs was recruited in a 9 year-long period. The average age at diagnosis was 55 years. The average diameter of the lesion was 1.7 cm (0.1 cm–10 cm). In 41 cases (60.1%), the lesion was inserted in the context of a multinodular background. In 12 cases, the diagnosis was incidental and the pre- operative FNA was performed on a different target. In 10 out of 68 cases, there was a multifocal NIFTP; in 14.7% of patients, PTC-like nuclear features showed sprinkling phenomenon. The cytological revision allocated 21 cases (49%) to the SIAPEC TIR3 indeterminate class and a nuclear score 2 or 3 were identified in 25 smears. Multifocality is part of the spectrum of NIFTPs, that can arise in a multinodular background with variable sizes from microscopic lesions to very large ones. Cytopathological criteria such as an evaluation of the nuclear score may help the pathologists in promoting a NIFTP diagnosis in the preoperative setting.

There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n=2986) and B (n=3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved. TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [118C353] Ilkay Oksuz has been benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C353). However, the entire responsibility of the publication/paper belongs to the owner of the paper. The financial support received from TUBITAK does not mean that the content of the publication is approved in a scientific sense by TUBITAK.

Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author(s): Dai, Mingzhi; Wang, Weiliang; Wang, Pengjun; Iqbal, Muhammad Zahir; Annabi, Nasim; Amin, Nasir | Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.