Tunable Valley and Spin Splittings in VSi2N4 Bilayers.

The control and manipulation of the valley and spin degrees of freedom have received great interest in fundamental studies and advanced information technologies. Compared with magnetic means, it is highly desirable to realize more energy-efficient electric control of valley and spin. Using the first-principles calculations, we demonstrate tunable valley and spin degeneracy splittings in VSi2N4 bilayers, with the aid of the layered structure and associated electric control. Depending on different interlayer magnetic couplings and stacking orders, the VSi2N4 bilayers exhibit a variety of combinations of valley and spin degeneracies. Under the action of a vertical electric field, the degeneracy splittings become highly tunable for both the sign and the magnitude. As a result, a series of anomalous Hall currents can be selectively realized with varied indices of valley and spin. These intriguing features offer a practical way for designing energy-efficient devices based on the couplings between multiple electronic degrees of freedom.

Improved sensitivity and temporal resolution in perfusion FMRI using velocity selective inversion ASL.

PURPOSE: This work aims to investigate the utility of velocity selective inversion pulses for perfusion weighted functional MRI. METHODS: Tracer kinetic properties of velocity selective inversion (VSI) pulses as an input function for an arterial spin labeling (ASL) experiment were characterized in a group of healthy participants. Numerical simulations were conducted to search for a robust set of timing parameters for FMRI time series acquisition with maximal signal to noise ratio efficiency. The performance of three VSI pulse sequences with different timing parameters was compared with a pseudocontinuous ASL sequence in a simple FMRI experiment conducted on healthy participants. RESULTS: The fit to the tracer kinetic model yielded arterial CBV of 1.24% ± 0.52% and 0.45 ± 0.11% and perfusion rates of 60.8 ± 32.3 and 34.4 ± 5.4 mL/min/100 g for gray and white matter, respectively. Bolus arrival times were estimated as 75.7 ± 21 ms and 349 ± 78 ms for gray and white matter, respectively. The FMRI experiments showed that VSI pulses yield comparable sensitivity to PCASL with similar timing parameters (TR = 4 s). However, VSI pulses could be used at a faster acquisition speed (TR = 3 s) and were more sensitive to neuronal activity than PCASL pulses, as evidenced by the 31% higher Z scores obtained on average in the active regions. CONCLUSION: VSI pulses can be very beneficial for perfusion weighted functional MRI because of their tracer kinetic characteristics, which allow a faster acquisition rate while maintaining an efficient labeling input function.

Vascular architecture mapping for early detection of glioblastoma recurrence.

OBJECTIVE: Treatment failure and inevitable tumor recurrence are the main reasons for the poor prognosis of glioblastoma (GB). Gross-total resection at repeat craniotomy for GB recurrence improves patient overall survival but requires early and reliable detection. It is known, however, that even advanced MRI approaches have limited diagnostic performance for distinguishing tumor progression from pseudoprogression. The novel MRI technique of vascular architectural mapping (VAM) provides deeper insight into tumor microvascularity and neovascularization. In this study the authors evaluated the usefulness of VAM for the monitoring of GB patients and quantitatively analyzed the features of neovascularization of early- and progressed-stage GB recurrence. METHODS: In total, a group of 115 GB patients who received overall 374 follow-up MRI examinations after standard treatment were retrospectively evaluated in this study. The clinical routine MRI (cMRI) protocol at 3 Tesla was extended with the authors' experimental VAM approach, requiring 2 minutes of extra time for data acquisition. Custom-made MATLAB software was used for calculation of imaging biomarker maps of macrovascular perfusion from perfusion cMRI as well as of microvascular perfusion and architecture from VAM data. Additionally, cMRI data were analyzed by two board-certified radiologists in consensus. Statistical procedures included receiver operating characteristic (ROC) analysis to determine diagnostic performances for GB recurrence detection. RESULTS: Overall, cMRI showed GB recurrence in 89 patients, and in 28 of these patients recurrence was detected earlier with VAM data, by 1 (20 patients) or 2 (8 patients) follow-up examinations, than with cMRI data. The mean time difference between recurrence detection with VAM and cMRI data was 147 days. During this time period the mean tumor volume increased significantly (p < 0.001) from 9.7 to 26.8 cm3. Quantitative analysis of imaging biomarkers demonstrated microvascular but no macrovascular hyperperfusion in early GB recurrence. Therefore, ROC analysis revealed superior diagnostic performance for VAM compared with cMRI. CONCLUSIONS: This study demonstrated that the targeted assessment of microvascular features using the VAM technique provided valuable information about early neovascularization activity in recurrent GB that is complementary to perfusion cMRI and may be helpful for earlier and more precise monitoring of patients suffering from GB. This VAM approach is compatible with existing cMRI protocols. Prospective clinical trials are necessary to investigate the clinical usefulness and potential benefit of increased overall survival with the use of VAM in patients with recurrent GB.

Estimation of microvascular capillary physical parameters using MRI assuming a pseudo liquid drop as model of fluid exchange on the cellular level.

AIM: One of the most important microvasculatures' geometrical variables is number of pores per capillary length that can be evaluated using MRI. The transportation of blood from inner to outer parts of the capillary is studied by the pores and the relationship among capillary wall thickness, size and the number of pores is examined. BACKGROUND: Characterization of capillary space may obtain much valuable information on the performance of tissues as well as the angiogenesis. METHODS: To estimate the number of pores, a new pseudo-liquid drop model along with appropriate quantitative physiological purposes has been investigated toward indicating a package of data on the capillary space. This model has utilized the MRI perfusion, diffusion and relaxivity parameters such as cerebral blood volume (CBV), apparent diffusion coefficient (ADC), ΔR 2 and Δ R 2 * values. To verify the model, a special protocol was designed and tested on various regions of eight male Wistar rats. RESULTS: The maximum number of pores per capillary length in the various conditions such as recovery, core, normal-recovery, and normal-core were found to be 183 ± 146, 176 ± 160, 275 ± 166, and 283 ± 143, respectively. This ratio in the normal regions was more than that of the damaged ones. The number of pores increased with increasing mean radius of the capillary and decreasing the thickness of the wall in the capillary space. CONCLUSION: Determination of the number of capillary pore may most likely help to evaluate angiogenesis in the tissues and treatment planning of abnormal ones.

Clinical translation of ferumoxytol-based vessel size imaging (VSI): Feasibility in a phase I oncology clinical trial population.

PURPOSE: To assess the feasibility of acquiring vessel size imaging (VSI) metrics using ferumoxytol injections and stock pulse sequences in a multicenter Phase I trial of a novel therapy in patients with advanced metastatic disease. METHODS: Scans were acquired before, immediately after, and 48 h after injection, at screening and after 2 weeks of treatment. ΔR2 , ΔR2*, vessel density (Q), and relative vascular volume fractions (VVF) were estimated in both normal tissue and tumor, and compared with model-derived theoretical and experimental estimates based on preclinical murine xenograft data. RESULTS: R2 and R2* relaxation rates were still significantly elevated in tumors and liver 48 h after ferumoxytol injection; liver values returned to baseline by week 2. Q was relatively insensitive to changes in ΔR2*, indicating lack of dependence on contrast agent concentration. Variability in Q was higher among human tumors compared with xenografts and was mostly driven by ΔR2 . Relative VVFs were higher in human tumors compared with xenografts, while values in muscle were similar between species. CONCLUSION: Clinical ferumoxytol-based VSI is feasible using standard MRI techniques in a multicenter study of patients with lesions outside of the brain. Ferumoxytol accumulation in the liver does not preclude measurement of VSI parameters in liver metastases. Magn Reson Med 77:814-825, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Virus Sensitivity Index of UV disinfection.

A new concept of Virus Sensitivity Index (VSI) is defined as the ratio between the first-order inactivation rate constant of a virus, ki, and that of MS2-phage during UV disinfection, kr. MS2-phage is chosen as the reference virus because it is recommended as a virus indicator during UV reactor design and validation by the US Environmental Protection Agency. VSI has wide applications in research, design, and validation of UV disinfection systems. For example, it can be used to rank the UV disinfection sensitivity of viruses in reference to MS2-phage. There are four major steps in deriving the equation between Hi/Hr and 1/VSI. First, the first-order inactivation rate constants are determined by regression analysis between Log I and fluence required. Second, the inactivation rate constants of MS2-phage are statistically analysed at 3, 4, 5, and 6 Log I levels. Third, different VSI values are obtained from the ki of different viruses dividing by the kr of MS2-phage. Fourth, correlation between Hi/Hr and 1/VSI is analysed by using linear, quadratic, and cubic models. As expected from the theoretical analysis, a linear relationship adequately correlates Hi/Hr and 1/VSI without an intercept. VSI is used to quantitatively predict the UV fluence required for any virus at any log inactivation (Log I). Four equations were developed at 3, 4, 5, and 6 Log I. These equations have been validated using external data which are not used for the virus development. At Log I less than 3, the equation tends to under-predict the required fluence at both low Log I such as 1 and 2 Log I. At Log I greater than 3 Log I, the equation tends to over-predict the fluence required. The reasons for these may very likely be due to the shoulder at the beginning and the tailing at the end of the collimated beam test experiments. At 3 Log I, the error percentage is less than 6%. The VSI is also used to predict inactivation rate constants under two different UV disinfection scenarios such as under sunlight and different virus aggregates. The correlation analysis shows that viruses will be about 40% more sensitive to sunlight than to UV254. On the other hand, virus size of 500 nm will reduce their VSI by 10%. This is the first attempt to use VSI to predict the required fluence at any given Log I. The equation can be used to quantitatively evaluate other parameters influencing UV disinfection. These factors include environmental species, antibiotic-resistant bacteria or genes, photo and dark repair, water quality such as suspended solids, and UV transmittance.

Fuzzy Logic Based Controller for a Grid-Connected Solid Oxide Fuel Cell Power Plant.

This paper describes a mathematical model of a solid oxide fuel cell (SOFC) power plant integrated in a multimachine power system. The utilization factor of a fuel stack maintains steady state by tuning the fuel valve in the fuel processor at a rate proportional to a current drawn from the fuel stack. A suitable fuzzy logic control is used for the overall system, its objective being controlling the current drawn by the power conditioning unit and meet a desirable output power demand. The proposed control scheme is verified through computer simulations.

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound.

Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks, it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public 'Breast Ultrasound Images' dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Moreover, our model significantly outperformed other models in McNemar's test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.

VISCERAL SENSITIVITY INDEX (VSI-IT): Italian Adaptation and Validation.

The Visceral Sensitivity Index (VSI) represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with irritable bowel syndrome (IBS) and chronic inflammatory bowel diseases (IBD)-such as ulcerative colitis and Crohn's disease. However, an Italian version of the instrument is not yet available for the Italian-speaking population. This study utilized a national sample of 500 individuals divided into four groups: (a) patients with Crohn's disease, (b) patients with ulcerative colitis, (c) patients with IBS, and (d) healthy controls (individuals without any diagnoses) to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, this research applied a questionnaire and the VSI through an online format to 500 participants. Confirmatory Factor Analysis (CFA) revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach's α = 0.949) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with interoceptive awareness, type of disease, and gastrointestinal symptom severity, confirming its role in enhancing the understanding and management of IBD and IBS in Italy.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter.

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system's reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor's increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array's operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.

Differences in visio-spatial intelligence between non-athletes and netball players.

There is conflicting evidence regarding whether athletes have better visio-spatial skills than non-athletes. This gap may result from athletes' superiority in only some visio-spatial abilities (VSS), rather than all areas of vision. The aim of this study was to determine whether there is a significant difference in the visio-spatial intelligence between female netball players (n = 40) and non-athletes (n = 40) when comparing six visual skills (accommodation facility, saccadic eye movement, speed of recognition, peripheral awareness, hand-eye coordination, and visual memory). Following an optometric evaluation, the participants were assessed in six distinct established tests, including the hart near far rock, saccadic eye movement, evasion, accumulator, ball wall toss tests, and flash memory, to evaluate the VSS components of non-athletes and premier league netball players. For five of the six tests, there was a statistically significant (p ≤ 0.05) difference between netball players and non-athletes. Conversely, there is no concrete evidence that netball players have better visual memory than non-athletes (p = 0.277). When compared to non-athletes, netball players have significantly improved accommodation facility (p < .001), saccadic eye movements (p < .001), speed of recognition (p < .001), peripheral awareness (p < .001), and hand-eye coordination (p < .001), but not visual memory (p = 0.277). The findings that netball players perform better on a certain VSS have broad ramifications for theories of sport vision, the optimal way to choose tests, and the creation of VSS testing batteries for specific sports.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems.

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms.

Enhancing low voltage ride through capability of grid connected DFIG based WECS using WCA-PSO tuned STATCOM controller.

This paper investigates the utilization of a STATCOM to enhance the LVRT capability of a 9 MW DFIG based Wind Power Plant (WPP) during grid faults. The STATCOM under investigation is tuned using the Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO) and a hybrid algorithm of both WCA and PSO. Simulations are carried out in MATLAB programming software, using SimScape toolbox. Performance analysis is done by investigating the ability of the test system to ride through voltage sags on the grid side, with incorporation of the STATCOM tuned using WCA, PSO and further with the hybrid WCA-PSO algorithm. To confirm effectiveness of the proposed algorithm, simulation results for the three scenarios are compared. Results show that LVRT capability of the Danish power system was met for L-G faults. However, without a STATCOM, the WPP could not ride through LLL-G faults. When STATCOM was incorporated, LVRT capability requirements were met. Voltage fluctuations reduce from 17% to 3%, without STATCOM and with WCA-PSO tuned model, respectively, during L-G faults. During LLL-G faults, voltage magnitude fluctuates from 60% to 25%, without STATCOM and with WCA-PSO tuned model respectively. WCA-PSO tuned STATCOM also resulted in the least voltage, active and reactive power overshoots.

Vegetation Changing Patterns and Its Sensitivity to Climate Variability across Seven Major Watersheds in China.

Climate changes have profound impacts on vegetation and further alter hydrological processes through transpiration, interception, and evaporation. This study investigated vegetation's changing patterns and its sensitivity to climate variability across seven major watersheds in China based on a hybrid regionalization approach and a novel, empirical index-Vegetation Sensitivity Index (VSI). Vegetation showed linearly increasing trends in most of the seven watersheds, while decreases in vegetation were mostly found in the source regions of the Yangtze River Basin (YZRB) and Yellow River Basin (YRB), the forest and grassland areas of the Songhua River Basin (SHRB) and Liao River Basin (LRB), the Yangtze River Delta, and the Pearl River Delta during the growing season. The selected watersheds can be categorized into 11 sub-regions, and the regionalization result was consistent with the topography and vegetation types; the characteristics of vegetation dynamics were more homogeneous among sub-regions. Vegetation types such as forests and shrubland in the central parts of the YZRB were relatively more vulnerable to climate variations than the grasslands and alpine meadows and tundra (AMT) in the source regions of the YZRB and YRB and the Loess Plateau of the YRB. In arid and semi-arid regions, precipitation had a profound impact on vegetation, while, at low latitudes, solar radiation was the main controlling factor. Such comprehensive investigations of the vegetation-climate relationship patterns across various watersheds are expected to provide a foundation for the exploration of future climate change impacts on ecosystems at the watershed scale.

Ectopic expression γ-glutamylcysteine synthetase of Vicia sativa increased cadmium tolerance in Arabidopsis.

Glutathione (GSH) is a multifunctional essential biothiol, and its metabolism is important for plant against toxic metals and metalloids. γ-Glutamylcysteine (γ-EC), which is catalyzed by γ-Glutamylcysteine synthetase (γ-ECS), is a rate-limiting intermediate in GSH synthesis. Here, a γ-ECS gene (Vsγ-ECS) from Vicia sativa was cloned, and its function in modulating Cd tolerance was studied. Vsγ-ECS is a chloroplast localization protein, and the expression of Vsγ-ECS was upregulated by Cd stress in root of V. sativa. Heterologous expression of Vsγ-ECS (35S::Vsγ-ECS) in Arabidopsis enhanced the Cd tolerance of plants through improved primary root length, fresh weight, chlorophyll content and low degree of oxidation associated with reduced H2O2 and lipid peroxidation. However, the Cd accumulation of Arabidopsis had no effect on Vsγ-ECS overexpression. Further analysis showed that the increased Cd tolerance in 35S::Vsγ-ECS was mainly due to the capacity of increasing GSH synthesis that improved Cd chelation by GSH and phytochelatins (PCs) and alleviated the oxidative stress caused by Cd stress. In summary, a γ-ECS was characterized from V. sativa, and it demonstrated a property for increasing GSH and PC synthesis to protect plants from Cd poisoning.

Modeling & implementation of DRLA based partially shaded solar system integration with 3-ϕ conventional grid using constant current controller.

Renewable Energy Resources (RERs) are widely used on the concern of global environment protection. Solar energy systems play an important role in the generation of electrical energy, remarkably minimize the utilization of nonrenewable fuel sources. Solar energy can be extracted and transformed into electrical energy via solar photovoltaic process. Several traditional, soft computing, heuristic, and meta-heuristic maximum power point tracking (MPPT) techniques have been developed to extract Maximum Energy Point (MEP) from the solar photovoltaic modules under different atmospheric conditions. In this manuscript, the combination of reinforcement learning algorithm (RLA) and deep learning algorithm (DLA) called deep Reinforcement Learning Algorithm based MPPT (DRLAMPPT) is proposed under partial shading conditions (PSC) of the solar system. DRLAMPPT can deal with continuous state spaces, in contrast to RL it can be operated only with discrete action state spaces. In this proposed DRLAMPPT, deep deterministic policy gradient (DDPG) solves the problem of continuous state spaces are involved to reach the GMEP in photovoltaic systems especially under PSC. In DRLAMPPT, the representative's strategy is parameterized by an artificial neural network (ANN), which uses sensory information as input and directly sends out control signals. This work develops a 2 kW solar photovoltaic power plant comprises of a photovoltaic array, DC/DC step-up converter, 3-Φ Pulse Width Modulated Voltage Source Inverter (PWM-VSI) integrated with conventional power grid using Constant Current Controller (CCC Effectiveness of the proposed DRLAMPPT with CCC can be validated through an experimental setup and with MATLAB. Simulation and tested at different input conditions of solar irradiance. Experimental results prove that, in comparison to existing MPPTs, suggested DRLAMPPT not only attains the best efficiency and also adopts the change in environmental conditions of the photovoltaic system at a much faster rate and able to reach the GMEP within 0.8 s under PSC. Experimental and simulation results also prove that suggested CCC with LC filter makes the inverter output voltage and the grid voltage are in phase at the lower value of THD i.e. 1.1% and 0.98% respectively.

Growth performance, haematological assessment and chemical composition of Nile tilapia, Oreochromis niloticus (Linnaeus, 1758) fed different levels of Aloe vera extract as feed additives in a closed aquaculture system.

A 105-day experimental trial was conducted to assess different levels of dietary Aleo vera extract supplementation on water quality parameters, proximate composition, growth performance and haematological parameters of fry Oreochromis niloticus. Four different percentages of dietary leaf extract powder of Aleo vera (ALE) with a basal feed, designated as, i.e., T0 (Control group; without ALE), T1 (1% ALE), T2 (2% ALE), and T3 (3% ALE). Fish fry was reared in concrete tanks (7.0 m, 1.6 m, 1.0: L, W, H; water volume 11.2 m3/tank), with an average initial weight 4.04 ± 0.03 g/ fry, and each treatment was triplicated. Fry was randomly distributed at a stocking rate of 450 individuals/ tanks. The water quality parameters revealed that temperature, pH, salinity, dissolved oxygen (DO) and nitrates were found in a promising range as given by FAO/WHO limits. However, the record values obtained for Electric Conductivity (EC), Total dissolved solids (TDS), and alkalinities were not found in all tanks' suitable range according to FAO/WHO limits. The results revealed a significant impact of different percentages of dietary ALE supplementation on fry's body composition and haematological parameters. Moreover, the final body weight, final body length, average daily weight gain (g), net weight gain (g) and specific growth rate (%) were significantly higher (p < 0.05) in T1 and T2 compared with T0 and T3 treatments. The poorest feed conversion ratio was recorded in the T2 group compared with other treatments. Thus, the current study provides information about the nutritional quality of Nile tilapia culturing in Pakistan.

A virtual sub-internship for otolaryngology-head and neck surgery.

OBJECTIVE: During the COVID-19 pandemic, away rotations were suspended for safety purposes. This led to the development of online interactive learning modules for students, now known as virtual sub-internships (VSIs). To date, VSIs within otolaryngology-head and neck surgery (OHNS) have been limited in their description and design. STUDY DESIGN: Cross-sectional survey. SETTING: ACGME-accredited OHNS residency program. METHODS: Our curriculum for an OHNS VSI is presented. Based on the model used by our OHNS residency program, the VSI consisted of a 2-week block of activities and interactive small-group discussions. A post-VSI feedback survey was distributed to participants; results are reported. RESULTS: Six 2-week VSI sessions were administered. Twenty-one individuals participated from all US regions, median 4 individuals per session. Fifteen participants (71.4%) completed the feedback survey. Of survey respondents, 20% lacked a home OHNS residency program, and no respondents' home-programs featured a VSI. All respondents were satisfied with the schedule and organization of the VSI, and 73.3% (11 of 15) felt it was of appropriate duration. All respondents reported a high degree of familiarity with the program, with a greater (86.7%) or equal (13.3%) level of interest in applying to the residency program as a result of participating in the VSI. CONCLUSION: This VSI curriculum offers a well-received virtual learning experience for medical students applying to OHNS residency. It provides an opportunity for programs to expand their appeal to potential applicants who may otherwise be restricted in their ability to travel.

Use of the mixed reality tool "VSI Patient Education" for more comprehensible and imaginable patient educations before epilepsy surgery and stereotactic implantation of DBS or stereo-EEG electrodes.

PURPOSE: It is unknown which patient education strategy before epilepsy surgery or stereotactic electrode implantation is best for patients. This prospective and randomized clinical study investigates whether the use of the mixed reality tool "VSI Patient Education" (VSI PE) running on HoloLens® glasses is superior to the use of a rubber brain model as a 3-dimensional tool for patient education before epilepsy surgery and stereotactic electrode implantation. MATERIAL AND METHODS: 17 patients with indication for epilepsy surgery or stereotactic electrode implantation were included in the study and randomized into two groups. All patients were informed with both comparative tools VSI PE (apoQlar®) and a rubber brain model (3B Scientific®) in a chronological order depending on group assignment. Afterwards, the patient and, if present, a relative (12) each filled out a questionnaire. For statistical analysis, Wilcoxon rank-sum tests were performed. RESULTS: Patients found their patient education highly significantly more comprehensible (p = 0.001**, r = 0.84) and almost significantly more imaginable (p=0.020, r = 0.57), when their doctor used VSI PE compared to the rubber brain model. The patients felt significantly less anxious as a result of VSI PE (p = 0.008*, r = 0.64). Highly significantly more patients chose VSI PE as the preferred patient education tool (p < 0.001**, r = 0.91), and almost significantly more patients decided VSI PE to be the future standard tool (p = 0.020, r = 0.56). Significantly more relatives chose VSI PE as the preferred patient education tool (p = 0.004*, r = 0.83), and significantly more relatives decided VSI PE to be the future standard tool (p = 0.002*, r = 0.91). CONCLUSION: VSI Patient Education is a promising new mixed reality tool for informing patients before epileptic surgery or stereotactic electrode implantation in order to enhance comprehension and imagination and reduce fear and worries. It might strengthen patient commitment and have a positive influence on patients' decisions in favor of medically indicated surgical operations.

The forgotten numbers: A closer look at COVID-19 non-fatal valuations.

Our research estimates COVID-19 non-fatal economic losses in the U.S. using detailed data on cumulative cases and hospitalizations from January 22, 2020 to July 27, 2020, from the Centers for Disease Control and Prevention (CDC). As of July 27, 2020, the cumulative confirmed number of cases was about 4.2 million with almost 300,000 of them entailing hospitalizations. Due to data collection limitations the confirmed totals reported by the CDC undercount the actual number of cases and hospitalizations in the U.S. Using standard assumptions provided by the CDC, we estimate that as of July 27, 2020, the actual number of cumulative COVID-19 cases in the U.S. is about 47 million with almost 1 million involving hospitalizations. Applying value per statistical life (VSL) and relative severity/injury estimates from the Department of Transportation (DOT), we estimate an overall non-fatal unadjusted valuation of $2.2 trillion for the U.S. with a weighted average value of about $46,000 per case. This is almost 40% higher than the total valuation of $1.6 trillion (using about $11 million VSL from the DOT) for all approximately 147,000 COVID-19 fatalities. We also show a variety of estimates that adjust the non-fatal valuations by the dreaded and uncertainty aspect of COVID-19, age, income, and a factor related to fatality categorization. The adjustments show current overall non-fatal valuations ranging from about $1.5 trillion to about $9.6 trillion. Finally, we use CDC forecast data to estimate non-fatal valuations through November 2020, and find that the overall cumulative valuation increases from about $2.2 trillion to about $5.7 trillion or to about 30% of GDP. Because of the larger numbers of cases involved our calculations imply that non-fatal infections are as economically serious in the aggregate as ultimately fatal infections.