Miracle-dz/UniFAR-FTUs huggingface dataset:
MEMS device for applying shear and tension to an epithelium combined with fluorescent live cell imaging

Mechanical forces play important roles in the biological function of cells and tissues. While numerous studies have probed the force response of cells and measured cell-generated forces, they have primarily focused on tensile, but not shear forces. Here, we describe the design, fabrication, and application of a silicon micromachined device that is capable of independently applying and sensing both tensile and shear forces in an epithelial cell monolayer. We integrated the device with an upright microscope to enable live cell brightfield and fluorescent imaging of cells over many hours following mechanical perturbation. Using devices of increasing stiffness and the same displacement input, we demonstrate that epithelia exhibit concomitant higher maximum resistive tensile forces and quicker force relaxation. In addition, we characterized the force response of the epithelium to cyclic shear loading. While the maximum resistive forces of epithelia under cyclic shear perturbation remained unchanged between cycles, cyclic loading led to faster relaxation of the resistive forces. The device presented here can be applied to studying the force response of other monolayer-forming cell types and is compatible with pharmacological perturbation of cell structures and functions.
Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm−2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).
Assessment of the availability of essential medicines and inventory control practice at university of Gondar comprehensive specialized hospital, Amhara regional state of Ethiopia: institutional based cross-sectional study design

Background: Assessment of availability of essential medicines is a critical component of universal health coverage and important factor to address patients’ satisfaction and increase their health seeking behavior. Proper inventory management of essential drugs in the health facilities is critical in ensuring availability of essential drugs. The objectives of the study were to assess availability of essential medicines and inventory control practice in university of Gondar comprehensive specialized hospital. Methods: The study was conducted using institution based cross sectional study design from March 10 to April 10, 2019. The data was collected by data abstraction formats and structured observation checklist and data was analyzed by Microsoft office excel for the assessment of the essential medicine’s availability and inventory control practices in Gondar university comprehensive specialized hospital. Results: The overall average availability of essential medicines during survey period in Gondar university hospital was 79.17%. The average stock out in the last six months period was 41.67%. The mean duration of stock out of essential medicines of the hospital in the last six months period was 31.7 days and average frequency of stock out was 0.7. The discrepancy between physical count and stock record count of essential medicines ranged from 0% to about 95%. Conclusions: The average availability of essential medicines during data collection period was fairly high. However, the facility was stock out for significant percent of essential medicines over the last six months period and stock out to lesser extent of percent at the time of survey period.
R-star in Transition Economies: Evidence from Slovakia

The aim of this paper is to estimate the equilibrium real interest rate in Slovakia by means of a semi-structural unobserved components model. The equilibrium real interest rate is understood here as a short-term, risk-free real interest rate consistent with output at its potential level, and inflation at its target level after the effect of all cyclical shocks have disappeared. Contribution to the literature is in two ways: i) development of a modelling framework for small, open, and converging economies which can be used for other transition economies, and (ii) assessment of the adoption of the euro and its effect on the equilibrium real interest rate. Based on the estimates, the equilibrium real interest rate fell from the positive pre-euro (also pre-crisis) level into to the negative territory.
Characterisation of the endemic Sulawesi Lenomys meyeri (Muridae, Murinae) and the description of a new species of Lenomys

In 1969, DJ Mulvaney sent me a batch of subfossils excavated by him and his colleagues from caves and rock-shelters in the Makassar region of Sulawesi (Mulvaney and Soejono, 1970). Among them are samples from a cave (Leang Burung 1) containing examples of Lenomys meyeri, a large-bodied rat endemic to Sulawesi, and a single right dentary with an intact molar row that is from a smaller-bodied Lenomys, which represents a new species. The small Lenomys along with specimens of L. meyeri were excavated from a level with a radiocarbon date of 2820 ± 210 BP, and other examples of L. meyeri were found in an underlying stratum dated at 3420 ± 400 BP (Mulvaney and Soejono, 1970: 171).
LENGTH-WEIGHT RELATIONSHIPS AND CONDITION FACTOR OF OREOCHROMIS MOSSAMBICUS (PETERS, 1852) FROM MANCHAR LAKE DISTT. JAMSHORO, SINDH, PAKISTAN

Oreochromis mossambicus are among the most widely distributed exotic fishes in the world and mostly used for food. The present study was undertaken to unveil information on the length-weight relationships (LWRs) and condition factor (Kn) of freshwater fish Oreochromis mossambicus (Peters, 1852) from Manchar Lake District Jamshoro, after post super flood in Sindh. Fish were studied by examining 364 specimens (Male 186 and female 78) collected from August 2011 to July 2012. These were 10-26 cm in total length (TL) and 19.8- 295 g in weight. Length-weight relationships calculated were, Log W = -1.8007+3.0556 × Log L (Combined population), Log W = -1.7802+3.0406 × Log L (male population) and Log W = -1.8062 + 3.0577× Log L (female population). The relative condition factor (Kn) for Combined population was 0.87 – 1.07 (Mean 1.0023±0.0671), while for male 0.86 – 1.09 (Mean 1.0027 ± 0.0733) and females 0.87 – 1.07 (Mean 1.0020 ±0.0631). The Length-weight relationships and condition factor indicate isometric and satisfactory growth of Oreochromis mossambicus in Manchar Lake.
Study of System of Crop Intensification (SCI) and Phosphorus Management on Growth, Yield and Economics of Greengram (Vigna radiata L.)

The mung bean (Vigna radiata L.) is under cultivation since pre-historic time in India. It is also known as green gram and serve are a major source of dietary protein for the vast majority of people. Pulses are considered as lifeblood of Agriculture. Pulses occupy a unique position in every farming system viz., main, catch, cover, green manure, intercrop and mix crop. Their inclusion in rotation kept the soil alive and productive. Pulse crops enrich the soil fertility by means of addition of organic matter and fixation of atmospheric nitrogen mediated by root nodule of Rhizobium bacteria. They are the cheapest International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 8 (2020) Journal homepage: http://www.ijcmas. com
Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.
Is There a Promising Market for the A2 Milk? Analysis of Italian Consumer Preferences

Milk consumption has been on the decline for decades. Part of the cause for this is due to changes in consumer behavior and interest in healthy eating. The aim of this study was to estimate the premium price that consumers are willing to pay for A2 milk. A2 milk is a newcomer type of product containing only A2 beta-casein that is spreading in many countries of the world as a functional food. In addition, it is produced by traditional breeds of cattle that are adapted to the marginal areas and could be a virtuous model of the sustainable management system to which the consumer lately seems to turn in an increasingly conscious way. To accomplish this aim, contingent valuation has been used, which permits using a survey and a direct estimation of the premium price that consumers are willing to pay (WTP). The question format used is a dichotomous choice valuation based on a double-bound model. Statistical analysis was carried out using descriptive statistical analysis. The empirical results reveal that consumers are willing to pay a premium for A2 milk. In addition, individuals interested in product quality and already buying functional products are attracted to this type of product.
An alternative nonparametric tail risk measure

The proposition of tail risk as a new asset pricing factor has gained traction in recent years. Recent work by Almeida, Ardison, Garcia, and Vicente (Nonparametric tail risk, stock returns, and the macroeconomy. J. Financ. Economet., 2017, 15(3), 333–376) proxies the cross-sectional variation in returns by Fama-French portfolios, which are further summarized into a few basis assets via principal component analysis. The number of states of nature is set higher than that of the basis assets to estimate the stochastic discount factors, which in turn risk-neutralize the excess expected shortfall as a tail risk measure. As an alternative approach to this means of dimension reduction, we propose tackling the problem directly by forming portfolios that minimize the excess expected shortfall. Our direct measure exhibits greater explanatory power when applied to more liquid, nonlottery-style stock returns. More importantly, our proposed approach reveals direct exposure to downside systematic risk without the need for an additional risk-neutralization step.
Enhanced Wet Deposition of Water‐Soluble Organic Nitrogen During the Harvest Season: Influence of Biomass Burning and In‐Cloud Scavenging

Globally water‐soluble organic nitrogen (WSON) accounts for about one third of water‐soluble total nitrogen in atmospheric deposition, yet the key factors influencing WSON deposition remain uncertain. In the present study, precipitation and aerosol samples were collected during 2016–2018 at a forest site in the Pearl River Delta, south China. Rainwater WSON concentrations were found to elevate significantly during fall, and wet deposition of WSON during fall could account for about 50% of its annual total wet deposition. The wet deposition and rainwater concentrations of K+ and levoglucosan, which are tracers for biomass burning emissions, also increased substantially during fall. The average WSON/water‐soluble total nitrogen ratio in precipitations (0.48) was over two times higher than that in aerosols (0.20) during fall, while during other seasons, the ratios in precipitations (0.17) and in aerosols (0.19) were comparable. Moreover, concentrations of WSON in rainwater, unlike that of inorganic N, did not decrease significantly with the increase of precipitation amount during fall. These results suggested that enhanced biomass burning during fall (harvest season) might be an important source of in‐cloud WSON, and in‐cloud scavenging, instead of below‐cloud washout, likely contributed remarkably to the wet deposition of WSON.
Restoring Fire-Dependent Ponderosa Pine Forests in Western Montana

| suppression. M any foresters and ecologists recognize that disruption of the historic pattern of frequent fires in ponderosa pine forests has resulted in major ecological changes, including increasingly severe wildfires and insect and disease epidemics (Weaver, 1943; Covington and Moore, 1992; Mutch and others, 1993; Everett, 1994). In response to this realization, there is increasing interest among natural resource managers, biologists, and the public in restoring ponderosa pine forests to more natural and sustainable conditions (American Forests, 1995). The Intermountain Research Station and the University of Montana’s School of Forestry, in cooperation with the Bitterroot and Lolo National Forests have been testing the effectiveness of different silvicultural and prescribed-fire treatments for restoring ponderosa pine forests, and we will report some observations and initial findings here. But first we will summarize ecological changes that have occurred and describe our restoration treatments. For thousands of years fire shaped the composition and structure of North American forests, favoring species such as ponderosa pine (Pinus ponderosa) that are fireresistant and require fire to regenerate and compete successfully with other species (Pyne, 1982, Agee, 1993). In the inland West, pure ponderosa pine and mixed ponderosa pine-fir types are the most extensive of the fire-dependent forests. Non-fire-dependent species associated with ponderosa pine are interior Douglas-fir (Pseudotsuga menziesii var. glauca), grand fir (Abies grandis), white fir (A. concolor), and incensecedar (Calocedrus decurrens). Pure and mixed ponderosa pine types cover about 40 million acres (16 million hectares) in the western United States, an area equal to that of the state of Washington (van Hooser and Keegan, 1988). Prior to the early 1900s these forests were characterized by frequent lowto moderate-intensity fires, mostly underbums that killed few overstory pines. Historically, fires at intervals averaging five to 30 years in most areas thinned small trees and helped produce open, park-like, fire-resistant stands (Amo, 1988). Today many of these forests have changed dramatically and are experiencing critical health problems as a result of 60-80 years of fire exclusion and logging of overstory pines. For example, more than a million acres in eastern Oregon’s Blue Mountains now consist mostly of dead or dying trees, primarily fir thickets impacted by insect and disease epidemics (Mutch and others, 1993). Also, large stand-destroying wildfires, formerly rare in the open ponderosa pine forests, have become common in the dense stands that have developed as a result of fire exclusion. Dense stands also provide fuel ladders that cause fires to increase in intensity and climb into tree crowns. Severe fires in ponderosa pine made up a large portion of the three million acres that burned in the inland West during 1994. Past logging, which selectively removed large pines and left understory trees has allowed rapid development of conifer thickets (Habeck, 1990). Extensive coni-
Modular assembly of the nucleolar large subunit processome

Early co-transcriptional events of eukaryotic ribosome assembly result in the formation of the small and large subunit processomes. We have determined cryo-EM reconstructions of the nucleolar large subunit processome in different conformational states at resolutions up to 3.4 Ångstroms. These structures reveal how steric hindrance and molecular mimicry are used to prevent premature folding states and binding of later factors. This is accomplished by the concerted activity of 21 ribosome assembly factors that stabilize and remodel pre-ribosomal RNA and ribosomal proteins. Mutually exclusive conformations of these particles suggest that the formation of the polypeptide exit tunnel is achieved through different folding pathways during subsequent stages of ribosome assembly.
Algorithm design for optimization of service requests for repair

Statement of the problem of service request for repair is formalized and algorithms of solution are presented. An algorithm is designed for the service of the greatest number of requests for repair within the established deadlines. An algorithm is proposed for constructing the optimal sequence of servicing repair requests. The optimal sequence is determined basing on the least total time of delays while completion of the requests service within the established deadlines. Usage of the optimization algorithms for the service of requests for repair in certain examples.
Machine learning in cybersecurity: a comprehensive survey

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user’s privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. In recent years, machine learning (ML) has been widely employed in cybersecurity, for example, intrusion or malware detection and biometric-based user authentication. However, ML algorithms are vulnerable to attacks both in the training and testing phases, which usually leads to remarkable performance decreases and security breaches. Comparatively, limited studies have been conducted to understand the essence and degree of the vulnerabilities of ML techniques against security threats and their defensive mechanisms. It is imperative to systematize recent works related to cybersecurity using ML to seek the attention of researchers, scientists, and engineers. Therefore, in this paper, we provide a comprehensive survey of the works that have been carried out most recently (from 2013 to 2018) on ML in cybersecurity, describing the basics of cyber-attacks and corresponding defenses, the basics of the most commonly used ML algorithms, and proposed ML and data mining schemes for cybersecurity in terms of features, dimensionality reduction, and classification/detection techniques. In this context, this article also provides an overview of adversarial ML, including the security characteristics of deep learning methods. Finally, open issues and challenges in cybersecurity are highlighted and potential future research directions are discussed.
Serial Eviction Filing: Civil Courts, Property Management, and the Threat of Displacement

Abstract:Drawing on over 8 million eviction court records from twenty-eight states, this study shows the role that eviction filings play in extracting monetary sanctions from tenants. In so doing, it documents an unanticipated feature of housing insecurity: serial eviction filings. Serial eviction filings occur when a property manager files to evict the same household repeatedly from the same address. Almost half of all eviction filings in our sample are associated with serial filings. Combining multivariate analysis with in-depth interviews conducted with thirty-three property managers and ten attorneys and court officials, we document the dynamics and consequences of serial eviction filings. When legal environments expedite the eviction process, property managers use the housing court to collect rent and late fees, passing costs on to tenants. Serial eviction filings exacerbate tenants’ housing cost burden and compromise their ability to find future housing. Using tract-level rent and filing fees, we estimate that each eviction filing translates into approximately $180 in fines and fees for the typical renter household, raising their monthly housing cost by 20%. The study challenges existing views of eviction as a discrete event concentrated among poor renters. Rather, it may be better conceived of as a routinized, drawn-out process affecting a broader segment of the rental market and entailing consequences beyond displacement.