Altered Phase Research Articles

Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

SummaryIn recent years, a number of learning methods have been adopted for classifying the mammogram images, which helps the early detection and diagnosis of breast cancer. The breast lesion identification and categorization using mammography screening based on combined convolutional neural network and recursive neural network (CRNN) framework with parameters optimized using multi‐objective seagull optimization algorithm (BLIC‐CRNN‐MOSOA) is proposed in this article. Initially, the unnecessary noise components are taken away from the mammogram images and the quality of the images are enhanced based on altered phase preserving dynamic range compression filtering approach. Then, the deep CRNN model with weight parameters optimized using multi‐objective seagull optimization algorithm is adopted for classifying the mammogram images into three categories: (i) normal, (ii) benign, and (iii) malignant masses. The proposed BLIC‐CRNN‐MOSOA approach is executed in MATLAB platform, and its performance is compared with other deep learning classification approaches. Then the simulation performance of the proposed BLIC‐CRNN‐MOSOA method attains higher accuracy 99.67%, 98.38%, and 97.45%, higher sensitivity 98.33%, 89.34%, and 88.96%, higher specificity 93.15%, 91.25%, and 92.88% compared with existing methods, like BLIC‐FrCN, BLIC‐ICS‐ELM, and BLIC‐DCNN‐BO. By this, the proposed method achieves higher classification accuracy with less misclassified error. Finally, the simulation results show that the proposed method is more efficient than the other classification methods.

Daytime rest: Association with 24-h rest-activity cycles, circadian timing and cognition in older adults.

Growing epidemiological evidence points towardan association between fragmented 24-h rest-activity cycles and cognition in the aged. Alterations in the circadian timing system might at least partially account for these observations. Here, we tested whether daytime rest (DTR) is associated with changes in concomitant 24-h rest probability profiles, circadian timing and neurobehavioural outcomes in healthy older adults. Sixty-three individuals (59-82 years) underwent field actigraphy monitoring, in-lab dim light melatonin onsetassessment and an extensive cognitive test battery. Actimetry recordings were used to measure DTRfrequency, duration and timing and to extract 24-h rest probability profiles. As expected, increasing DTR frequency was associated not only with higher rest probabilities during the day, but also with lower rest probabilities during the night, suggesting more fragmented night-time rest. Higher DTR frequency was also associated with lower episodic memory performance. Moreover, later DTR timing went along with an advanced circadian phase as well as with an altered phase angle of entrainment between the rest-activity cycle and circadian phase. Our results suggest that different DTR characteristics, as reflective indices of wake fragmentation, are not only underlined by functional consequences on cognition, but also by circadian alteration in the aged.

Modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier using flamingo search optimization algorithm for disease diagnosis model

SummaryIn this article, the modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier with flamingo search optimization algorithm (MLBPFE‐ASRNNFSOAC‐DDM) is proposed for the disease diagnosis model. Here, breast cancer, diabetic, chronic kidney diseases diagnosis model is implemented. Initially, images are considered as dataset for disease diagnosis, which is given to the altered phase preserving dynamic range compression (APPDRC) scheme for preprocessing process. This APPDRC is used to preserve local features for boundary detection, thus; recovers image quality. Then the morphological, grayscale statistical and Haralick texture features are taken from preprocessed image with the help of modified local binary pattern process. The extracted features are given with attention segmented recurrent neural network (ASRNN) for classification. Then the weight parameters of ASRNN classifier are optimized by flamingo search optimization algorithm (FSOA), which increases the classification accuracy. This simulation process is accomplished at MATLAB platform. The proposed method, during Diabetic Diagnosis Model attains higher accuracy 16.4%, 21.45%, 30.38%, and 21.01% compared with the existing methods like FE‐ResNetV2DNNMSOC‐DDM and CMVHHO‐DKMLC‐DDM, CNN‐CAD‐DDM, and AD‐CFDRI‐SDL, respectively. During breast cancer Diagnosis Model attains higher accuracy 27.3%, 20.56%, 31.34%, and 25.13% compared with the existing methods like CMVHHO‐DKMLC‐BCM and SVMFE‐OPFPSOC‐BCM, MLPNN‐CNN‐BCM, and DCNN‐EL‐BCM, respectively.

Altered phase transformation behaviors and enhanced bending shape memory property of NiTi shape memory alloy via selective laser melting

Selective laser melting (SLM) of NiTi shape memory alloys (SMAs) was studied systematically for the resulting density and surface roughness. It was found that high laser energy density could not guarantee dense SLM NiTi SMAs. Furthermore, the phase composition and transformation temperatures varied remarkably with delicate change of laser power and scanning speed. Mechanical tests suggested that the SLM NiTi alloys made under the optimal process parameters (70 W in laser power and 105 mm/s in laser scanning speed) had an ultimate tensile strength of 788 MPa, an elongation of 7.43% in austenite state. Bending shape memory test revealed that 100% recovery rate was obtained in a sequent bending test (45°→90°→180°→270°→360°), and the bending deformation was fully recovered in ten-time cycle bending test at 180°, and recovery rate was 97% after ten-time cycle bending test at 360°. The excellent bending shape memory properties was attributed to high-density dislocations and Ti 2 Ni nanoprecipitates which impeded the occurrence of plastic deformation effectively. The findings in this study demonstrated that SLM NiTi parts had excellent shape memory properties and was promising for industrial applications. • The effect of process parameters on the density, surface roughness, and transformation behaviors for SLM NiTi were studied. • High laser energy density could not guarantee dense SLM NiTi SMAs. • Fully recovered bending deformation in ten-time cycle at 180°, and 97% recovery rate after ten-time cycle at 360°.

Postmitotic G1 phase survivin drives mitogen-independent cell division of B lymphocytes

B and T lymphocytes of the adaptive immune system undergo proliferative bursts to generate pools of antigen-specific cells for effective immunity. Here we show that in contrast to the canonical view that G1 progression signals are essential after mitosis to reenter S phase, B lymphocytes sustain several rounds of mitogen-independent cell division following the first mitosis. Such division appears to be driven by unique characteristics of the postmitotic G1 phase that has features of S and G2/M phases. Birc5 (survivin), a protein associated with chromosome segregation in G2/M, is expressed in the G1 phase of divided B cells and is necessary for mitogen-independent divisions. The partially active G1 phase and propensity for apoptosis inherited after each division may underlie rapid proliferation and cell death, which are hallmarks of B cell proliferative responses.

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

AbstractSeveral deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.

Fluidity is the way to life: lipid phase separation in bacterial membranes.

A hallmark of biological membranes is the dynamic localization of lipids and proteins. Lipids respond to temperature reduction below a critical point with phase separation, and poikilothermic animals and also bacteria adapt their lipid content to prevent gel phase formation in membranes. In a new study, Gohrbandt etal (2022) show that reduced membrane fluidity in bacterial cells causes reversible phase separation without membrane rupture invivo, highlighting the physical robustness of biological membranes.

Combining the advantages of radiomic features based feature extraction and hyper parameters tuned RERNN using LOA for breast cancer classification

This paper proposes Combining the Advantages of Radiomic features based Feature Extraction and Hyper Parameters tuned Recalling Enhanced Recurrent Neural Network (RERNN) using Lizard optimization Algorithm (LOA) for Breast cancer Classification. Here, breast cancer images are taken from the real time dataset collected from VPS hospital and then the images are preprocessed using Altered Phase Preserving Dynamic Range Compression (APPDRC) to remove the noises. Then the radiomic features, such as morphologic features, grayscale statistic features and Haralick texture features have been extracted utilizing Entropy Based Local Binary Pattern (ELBP). These extracted features have presented to Recalling Enhanced Recurrent Neural Network (RERNN) classifier. Hence, Lizard optimization Algorithm (LOA) is utilized to optimize the Recalling Enhanced Recurrent Neural Network (RERNN). The proposed approach is executed in MATLAB platform, then the performance is compared with different existing approaches. This approach is applicable in real time applications for screening the abnormalities of the breast cancer at initial stage, thus, determining the proper treatment to be given to the patient for decreasing deaths caused by breast cancer. The novelty or aim of this paper is to diagnose the breast cancer in early stage by extracting the radiomic features and to classify the types (Malignant, Benign, normal) of breast cancer with high accuracy by reducing the computational time and error rate. The simulation outcomes demonstrate that the proposed FE-APPDRC-ELBP-RERNN-LOA attains the accuracy of45.75%, 37.64%, 24.64 %is higher than the existing methods.

STZ-diabetic rat heart maintains developed tension amplitude by increasing sarcomere length and crossbridge density.

New Findings What is the central question of this study? In the papillary muscle from type I diabetic rats, does diabetes‐associated altered ventricular function result from changes of acto‐myosin interactions and are these modifications attributable to a possible sarcomere rearrangement? What is the main finding and its importance? For the first time, we showed that type‐I diabetes altered sarcomeric ultrastructure, as seen by transmission electron microscopy, consistent with physiological parameters. The diabetic condition induced slower timing parameters, which is compatible with a diastolic dysfunction. At the sarcomeric level, augmented β‐myosin heavy chain content and increased sarcomere length and crossbridges' number preserve myocardial stroke and could concur to maintain the ejection fraction. We investigated whether diabetes‐associated altered ventricular function, in a type I diabetes animal model, results from a modification of acto‐myosin interactions, through the in vitro recording of left papillary muscle mechanical parameters and examination of sarcomere morphology by transmission electron microscopy (TEM). Experiments were performed on streptozotocin‐induced diabetic and age‐matched control female Wistar rats. Mechanical isometric and isotonic indexes and timing parameters were determined. Using Huxley's equations, we calculated mechanics, kinetics and energetics of myosin crossbridges. Sarcomere length and A‐band length were measured on TEM images. Type I and III collagen and β‐myosin heavy chain (MHC) expression were determined by immunoblotting. No variation in resting and developed tension or maximum extent of shortening was evident between groups, but diabetic rats showed lower maximum shortening velocity and prolonged timing parameters. Compared to controls, diabetics also displayed a higher number of crossbridges with lower unitary force. Moreover, no change in type I and III collagen was associated to diabetes, but pathological rats showed a two‐fold enhancement of β‐MHC content and longer sarcomeres and A‐band, detected by ultrastructural morphometry. Overall, these data address whether a preserved systolic function accompanied by an altered diastolic phase results from a recruitment of super‐relaxed myosin heads or the phosphorylation of the regulatory light chain site in myosin. Although the early signs of diabetic cardiomyopathy were well expressed, the striking finding of our study was that, in diabetics, sarcomere modification may be a possible compensatory mechanism that preserves systolic function.

Textural and chemical characteristics of zircon, monazite, and thorite, Wadi Al-Baroud area, Eastern Desert of Egypt: Implication for rare metal pegmatite genesis

The Wadi Al-Baroud area is located close to the tectonic boundary between the Northern and Central Eastern Desert of Egypt. The exposed rocks include younger (post-tectonic) and older (syn-tectonic) granitoids and rare metal pegmatites (plugs and dikes). The rare metal pegmatites are comprised of feldspars, quartz, and mica, and occur chiefly within the barren younger granites and at their contact with the older granitoids. Strategic rare metal mineralization related to pegmatites includes mangano- to ferro-columbite, zircon, monazite-(Ce), yttrocolumbite, thorite, U-thorite, xenotime, euxenite-(Y), samarskite-(Y), and fergusonite-(Y) minerals that are frequently associated with mica and iron oxides. In this study, zircon, monazite, and thorite are used as potential monitors of the internal evolution of pegmatite and evolution of the granite-pegmatite system. In the younger A-type granite, zircon and thorite are common and form a solid solution with xenotime. In the pegmatite body, thorite forms a solid solution with coffinite, while zircon is Hf-rich pegmatitic type and forms two phases in the same grain - one is highly enriched in non-formula (Ca, Fe, Al, and Mn) and incompatible (Th, U, and Y) elements and the other is a chemically near-end member. In the pegmatite dike, zircon has the highest Hf and near-end-member stoichiometric compositions, while thorite is Y-rich uranothorite. Furthermore, monazite is a primary magmatic phase in the younger granites, but in pegmatite is sulfur-rich REE altered phase. Textural and chemical results show that these phases formed sequentially and intermittently through multi-genetic processes during the evolution from the granite to rare metal pegmatite and define two main chemical trends: 1) a continuous fractionation from the melt to pure exsolving hydrothermal fluid, and 2) a dissolution–recrystallization during fluid-saturated late-magmatic stages. Zircon displays a continuous Zr/Hf fractionation trend, indicating evolution from a silicate melt to an aqueous fluid. Besides, all pegmatite zircons form a solid solution with xenotime and this is a chemical fingerprint inherited from the granite zircon. Together, these suggest that the pegmatite was formed from a hydrous aluminosilicate melt and that high field strength elements (such as Nb, Ta, Y, Zr, U, and Th) can be mobilized during the magmatic to hydrothermal evolution of the alkaline granite-pegmatite system. Even in ore-barren granitic rocks, the high charge-to-ionic-radius ratios of these elements and generally incompatible nature during magmatic fractionation facilitate their extreme enrichment, even to ore-forming levels. Zircon, monazite, and thorite can provide effective exploration tools for identifying and characterizing rare metal-bearing pegmatites and insights into the mineralizing processes.

Insight into Ion Diffusion Dynamics/Mechanisms and Electronic Structure of Highly Conductive Sodium-Rich Na3+xLaxZr2-xSi2PO12 (0 ≤ x ≤ 0.5) Solid-State Electrolytes.

Solid-state electrolytes (SSEs) have attracted considerable attention as an alternative for liquid electrolytes to improve safety and durability. Sodium Super Ionic CONductor (NASICON)-type SSEs, typically Na3Zr2Si2PO12, have shown great promise because of their high ionic conductivity and low thermal expansivity. Doping La into the NASICON structure can further elevate the ionic conductivity by an order of magnitude to several mS/cm. However, the underlying mechanism of ionic transportation enhancement has not yet been fully disclosed. Herein, we fabricate a series of Na3+xLaxZr2-xSi2PO12 (0 ≤ x ≤ 0.5) SSEs. The electronic and local structures of constituent elements are studied via synchrotron-based X-ray absorption spectroscopy, and the ionic dynamics and Na-ion conduction mechanism are investigated by solid-state nuclear magnetic resonance spectroscopy. The results prove that La3+ ions exist in the form of phosphate impurities such as Na3La(PO4)2 instead of occupying the Zr4+ site. As a result, the increased Si/P ratio in the NASICON phase, accompanied by an increase in the sodium ion occupancy, makes a major contribution to the enhancement of ionic conductivity. The spin-lattice relaxation time study confirms the accelerated Na+ motions in the altered NASICON phase. Modifications on the Si/P composition can be a promising strategy to enhance the ionic conductivity of NASICON.

A New Method for a Polyethersulfone-Based Dopamine-Graphene (xGnP-DA/PES) Nanocomposite Membrane in Low/Ultra-Low Pressure Reverse Osmosis (L/ULPRO) Desalination.

Herein we present a two-stage phase inversion method for the preparation of nanocomposite membranes for application in ultra-low-pressure reverse osmosis (ULPRO). The membranes containing DA-stabilized xGnP (xGnP-DA-) were then prepared via dry phase inversion at room temperature, varying the drying time, followed by quenching in water. The membranes were characterized for chemical changes utilizing attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS). The results indicated the presence of new chemical species and thus, the inclusion of xGnP-DA in the polyethersulfone (PES) membrane matrix. Atomic force microscopy (AFM) showed increasing surface roughness (Ra) with increased drying time. Scanning electron microscopy (SEM) revealed the cross-sectional morphology of the membranes. Water uptake, porosity and pore size were observed to decrease due to this new synthetic approach. Salt rejection using simulated seawater (containing Na, K, Ca, and Mg salts) was found to be up to stable at <99.99% between 1–8 bars operating pressure. After ten fouling and cleaning cycles, flux recoveries of <99.5% were recorded, while the salt rejection was <99.95%. As such, ULPRO membranes can be successfully prepared through altered phase inversion and used for successful desalination of seawater.

Altered phase and nonphase EEG activity expose impaired maintenance of a spatial-object attentional focus in multiple sclerosis patients

Some of the anatomical and functional basis of cognitive impairment in multiple sclerosis (MS) currently remains unknown. In particular, there is scarce knowledge about modulations in induced EEG (nonphase activity) for diverse frequency bands related to attentional deficits in this pathology. The present study analyzes phase and nonphase alpha and gamma modulations in 26 remitting-relapsing multiple sclerosis patients during their participation in the attention network test compared with twenty-six healthy controls (HCs) matched in sociodemographic variables. Behavioral results showed that the MS group exhibited general slowing, suggesting impairment in alerting and orienting networks, as has been previously described in other studies. Time–frequency analysis of EEG revealed that the gamma band was related to the spatial translation of the attentional focus, and the alpha band seemed to be related to the expectancy mechanisms and cognitive processing of the target. Moreover, phase and nonphase modulations differed in their psychophysiological roles and were affected differently in the MS and HC groups. In summary, nonphase modulations can unveil hidden cognitive mechanisms for phase analysis and complete our knowledge of the neural basis of cognitive impairment in multiple sclerosis pathology.

Condensation of Ded1p Promotes a Translational Switch from Housekeeping to Stress Protein Production

SummaryCells sense elevated temperatures and mount an adaptive heat shock response that involves changes in gene expression, but the underlying mechanisms, particularly on the level of translation, remain unknown. Here we report that, in budding yeast, the essential translation initiation factor Ded1p undergoes heat-induced phase separation into gel-like condensates. Using ribosome profiling and an in vitro translation assay, we reveal that condensate formation inactivates Ded1p and represses translation of housekeeping mRNAs while promoting translation of stress mRNAs. Testing a variant of Ded1p with altered phase behavior as well as Ded1p homologs from diverse species, we demonstrate that Ded1p condensation is adaptive and fine-tuned to the maximum growth temperature of the respective organism. We conclude that Ded1p condensation is an integral part of an extended heat shock response that selectively represses translation of housekeeping mRNAs to promote survival under conditions of severe heat stress.

Influence of thermal oxidative aging on temperature induced self-healing transition of polymer modified bitumens

In this study, the fatigue-healing-fatigue (FHF) test is conducted to investigate influence pattern of temperature and thermal oxidative aging on the self-healing ability of styrene-butadiene-styrene (SBS) modified bitumen (SBS-MB) and polyethylene (PE) modified bitumen (PE-MB). Results display that the optimum healing temperature (OHT) of each bitumen (wherever unaged or aged) is in the phase transition (PT) zone, and is lower than its softening point, followed by its Newtonian flow temperature. Two types of healing crossover temperature points are found in the healing-temperature curves of polymer modified bitumen (PMB) samples with various aging degrees, indicating the complexity of self-healing transition phenomenon in the aged PMB samples. Hence, the evaluation of aging effects on the self-healing ability of PMBs should be performed in a wide temperature range. Furthermore, the self-healing ability of PMBs is gradually weakened with the deepening of aging extents. Notably, the thermal oxidative aging imparts totally different effects to the self-healing ability of SBS-MB and PE-MB at lower temperatures. Moreover, the results of softening point test, fluorescence microscopy test, frequency sweep test and differential scanning calorimeter test convey that the altered phase structure and polymer distribution of PMBs during aging elicit the healing-temperature curves shift and self-healing transition.

Effects of omega-O-acylceramide structures and concentrations in healthy and diseased skin barrier lipid membrane models

Ceramides (Cers) with ultralong (∼32-carbon) chains and ω-esterified linoleic acid, composing a subclass called omega-O-acylceramides (acylCers), are indispensable components of the skin barrier. Normal barriers typically contain acylCer concentrations of ∼10 mol%; diminished concentrations, along with altered or missing long periodicity lamellar phase (LPP), and increased permeability accompany an array of skin disorders, including atopic dermatitis, psoriasis, and ichthyoses. We developed model membranes to investigate the effects of the acylCer structure and concentration on skin lipid organization and permeability. The model membrane systems contained six to nine Cer subclasses as well as fatty acids, cholesterol, and cholesterol sulfate; acylCer content-namely, acylCers containing sphingosine (Cer EOS), dihydrosphingosine (Cer EOdS), and phytosphingosine (Cer EOP) ranged from zero to 30 mol%. Systems with normal physiologic concentrations of acylCer mixture mimicked the permeability and nanostructure of human skin lipids (with regard to LPP, chain order, and lateral packing). The models also showed that the sphingoid base in acylCer significantly affects the membrane architecture and permeability and that Cer EOP, notably, is a weaker barrier component than Cer EOS and Cer EOdS. Membranes with diminished or missing acylCers displayed some of the hallmarks of diseased skin lipid barriers (i.e., lack of LPP, less ordered lipids, less orthorhombic chain packing, and increased permeability). These results could inform the rational design of new and improved strategies for the barrier-targeted treatment of skin diseases.

Gain-of-Function MN1 Truncation Variants Cause a Recognizable Syndrome with Craniofacial and Brain Abnormalities

MN1 was originally identified as a tumor-suppressor gene. Knockout mouse studies have suggested that Mn1 is associated with craniofacial development. However, no MN1-related phenotypes have been established in humans. Here, we report on three individuals who have de novo MN1 variants that lead to a protein lacking the carboxyl (C) terminus and who presented with severe developmental delay, craniofacial abnormalities with specific facial features, and structural abnormalities in the brain. An invitro study revealed that the deletion of the C-terminal region led to increased protein stability, an inhibitory effect on cell proliferation, and enhanced MN1 aggregation in nuclei compared to what occurred in the wild type, suggesting that a gain-of-function mechanism is involved in this disease. Considering that C-terminal deletion increases the fraction of intrinsically disordered regions of MN1, it is possible that altered phase separation could be involved in the mechanism underlying the disease. Our data indicate that MN1 participates in transcriptional regulation of target genes through interaction with the transcription factors PBX1, PKNOX1, and ZBTB24 and that mutant MN1 impairs the binding with ZBTB24 and RING1, which is an E3 ubiquitin ligase. On the basis of our findings, we propose the model that C-terminal deletion interferes with MN1's interaction molecules related to the ubiquitin-mediated proteasome pathway, including RING1, and increases the amount of the mutant protein; this increase leads to the dysregulation of MN1 target genes by inhibiting rapid MN1 protein turnover.

Altered post-stroke propulsion is related to paretic swing phase kinematics

BackgroundGait propulsion is often altered following a stroke, with clear effects on anterior progression. Changes in the pattern of propulsion could potentially also influence swing phase mechanics. The purpose of the present study was to investigate whether post-stroke variability in paretic propulsion magnitude or timing influence paretic swing phase kinematics. Methods29 chronic stroke survivors participated in this study, walking on an instrumented treadmill at their self-selected and fastest-comfortable speeds. For each participant, we calculated several propulsion-related metrics derived from anteroposterior ground reaction force or from center of mass power, as well as knee flexion angle and circumduction displacement during the swing phase. We performed a series of linear mixed model analyses to determine whether the propulsion metrics for the paretic leg were related to paretic swing phase mechanics. FindingsA subset of the stroke survivors exhibited unusual braking forces late in the paretic stance phase, when strong propulsion typically occurs among uninjured controls. Beyond the effects of walking speed or walking condition, these braking forces were significantly linked with altered paretic swing phase mechanics. Specifically, large braking impulses were associated with reduced paretic knee flexion (p = 0.039) and increased paretic circumduction (p = 0.023). InterpretationThe present results suggest that braking forces late in stance are particularly indicative of deficits in the production of typical swing phase kinematics. This relationship suggests that therapies designed to address altered swing kinematics should also consider altered force generation in late stance, as these behaviors appear to be coupled.

A Similarity-Based Semi-analytical Solution for Recovery Performance Assessment of Unconventional Oil and Gas Reservoirs With Interfacial-Tension-Dependent Capillary Pressure Effects

Abstract Nanometer-pore characteristic of unconventional reservoirs significantly enlarges the impact of the role that capillary pressure (pc) plays on the recovery mechanism. A growing number of research has been focused on understanding the high pc effect in tight and ultra-tight reservoir systems, but mainly numerical simulation models and experimental studies are investigated. This work proposes an alternate, semi-analytical solution that readily applies to the analysis of production performance of multi-fractured horizontal wells in unconventional formations, capturing the phase behavior and fluid property-alteration effects under significant pc influence found in these systems. Capillary pressure is considered as interfacial-tension (IFT)-dependent function that impacts fluid flow in the sense of phase behavior and fluid properties. To arrive at the solutions, the similarity method is applied to the governing system of multiphase partial differential equations, and the resulting system of ordinary differential equations is solved simultaneously for pressure and saturation via shooting method coupled with Runge–Kutta solver. The proposed solution is validated by matching against numerically simulated data generated by an in-house simulator. Both oil-dominate and gas-dominate examples are presented. We also apply the proposed method to examine the sensitivity of production performance to degree of undersaturation and critical saturations. Results show that the altered fluid property and phase behavior due to IFT-dependent pc have ineligible effects on recovery performance of multi-fractured horizontal wells, and specific effects on production rates and producing gas–oil ratio also tie closely to the mobility of each phase.

Antifungal activity of titanium dioxide nanoparticles against Candida albicans

The unregulated release of titanium dioxide nanoparticles into the environment has raised concern, in particular due to the impact of the nanoparticles on indigenous micro-biome in our ecosystem. This paper reports a study on antifungal activity of titanium dioxide nanoparticles on a healthy growing fungi species, Candida albicans, a known opportunistic pathogen. A quantification of the total cell death was performed using a direct staining method, Trypan blue exclusion assay. Exposure to nanoparticles not only altered the growth rate, but also affected the onset and length of Candida albicans growth phases. The log and the onset of the death phase were shortened and accelerated, respectively. Up to 65% of the Candida albicans were killed after exposure to 100 μg/mL of the anatase titanium dioxide nanoparticles, while only 33% were killed with rutile. A higher dosage and incubation time of the nanoparticles increased their toxicity. Cells suffered from morphological changes upon the nanoparticle exposure, which correlates well with the results showing an altered growth phase culture.