Microrheology simulations, where microspheres of identical size were placed at the center of homogeneous 3D entangled networks with varying actin density. Microspheres were "sticky'', i.e. interactions between microsphere and polymers were repulsive on overlap, and attractive at short ranges just outside overlap. Counterpart with cross-linked rather than entangled networks can be found in experiment E37.

FunCoup network information for gene PCMP-E37 in Arabidopsis thaliana. PP403_ARATH Putative pentatricopeptide repeat-containing protein At5g37570

Full factorial ANOVA for E37 condition.

The 1991 Census Expanded Community Profiles present 44 tables comprising more detailed information than that of the basic community profiles which provide characteristics of persons and/or dwellings for Local Government Areas (LGA) in Australia. This table contains data relating to nature of occupancy by landlord type by annual household income by household type. Counts are of family, group and lone person households living in occupied private dwellings (excludes caravans etc in caravan pasts), based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by LGA 1991 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2101.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1991 geographic boundaries are available from data.gov.au. For more information please refer to the 1991 Census Dictionary.

This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MSVAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. The results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.

How combinations of gene-environment interactions collectively give rise to genotype-environment interactions is not fully understood. To shed light on this problem, we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains. This phenotype is detectable when certain segregants are grown on ethanol at 37°C (‘E37’), a condition that differs from the standard culturing environment in both its carbon source (ethanol as opposed to glucose) and temperature (37°C as opposed to 30°C). Using recurrent backcrossing with phenotypic selection, we identified 16 contributing loci. To examine how these loci interact with each other and the environment, we focused on a subset of four loci that together can lead to poor growth in E37. We measured the growth of all 16 haploid combinations of alleles at these loci in all four possible combinations of carbon source (ethanol or glucose) and temperature (30 or 37°C) in a nearly isogenic population. This revealed that the four loci act in an almost entirely additive manner in E37. However, we also found that these loci have weaker effects when only carbon source or temperature is altered, suggesting that their effect magnitudes depend on the severity of environmental perturbation. Consistent with such a possibility, cloning of three causal genes identified factors that have unrelated functions in stress response. Thus, our results indicate that polymorphisms in stress response can show effects that are intensified by environmental stress, thereby resulting in major genotype-environment interactions when multiple of these variants co-occur.

The 1991 Census Expanded Community Profiles present 44 tables comprising more detailed information than that of the basic community profiles which provide characteristics of persons and/or dwellings …Show full descriptionThe 1991 Census Expanded Community Profiles present 44 tables comprising more detailed information than that of the basic community profiles which provide characteristics of persons and/or dwellings for Statistical Local Areas (SLA) in Australia. This table contains data relating to nature of occupancy by landlord type by annual household income by household type. Counts are of family, group and lone person households living in occupied private dwellings (excludes caravans etc in caravan pasts), based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by SLA 1991 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2101.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1991 geographic boundaries are available from data.gov.au. For more information please refer to the 1991 Census Dictionary. Please note: (a) Comprises households where at least one, but not all, member(s) aged 15 years or more did not state an income and/or at least one spouse, offspring, or co-tenant was temporarily absent. (b) Comprises households where no members present stated an income. (c) Comprises 'other/Inadequately described' and 'not stated'. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (1991): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

E37Tip Uluslararası E yolu ağıBaşlangıç BremenBitiş KölnUzunluk 336 km 209 mil Geçtiği Yerler AlmanyaHarita Avrupa E yol

We investigate the channels through which news influences the subjective beliefs of economic agents, with a particular focus on their subjective uncertainty. The main insight of the paper is that news that is more at odds with agents’ prior beliefs generates an increase in uncertainty; news that is more consistent with their prior beliefs generates a decrease in uncertainty. We illustrate this insight theoretically and then estimate the model empirically using data on US output and professional forecasts to provide novel measures of news shocks and uncertainty. We then estimate impulse responses from the identified shocks to show that news shocks can affect macroeconomic variables in ways that resemble the effects of uncertainty shocks. Our results suggest that controlling for news can potentially diminish the estimated effects of uncertainty shocks on real variables, particularly at longer horizons.

Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. First, HNN conducts the supervised estimation of nonlinearities that arise when translating a high-dimensional set of observed regressors into latent states. Second, forecasts are economically interpretable. Among other findings, the contribution of real activity to inflation appears understated in traditional PCs. In contrast, HNN captures the 2021 upswing in inflation and attributes it to a large positive output gap starting from late 2020. The unique path of HNN’s gap comes from dispensing with unemployment and GDP in favor of an amalgam of nonlinearly processed alternative tightness indicators.

ග න ව ග න ඉත හ සය ග න භ ව තය ග ල ය ග න භ ව තය ප රදත තම ම ප රදස න හ ව ශ ලත වය 800 600 ප ක සල අන ක ත ව භ දනයන 320 240 ප ක

The wastewater disposal industry has developed negatively over the last five years. Between 2019 and 2024, industry turnover fell by an average of 0.7% per year, which is partly due to the reduced water consumption of machines as a result of technological progress and the expansion of water-saving measures as a result of increased environmental awareness. The production volume of the manufacturing industry, which was reduced in 2020 as a result of the coronavirus pandemic and later due to global supply bottlenecks, also had a negative impact on industry turnover. In the current year, industry turnover is likely to fall by 0.1% year-on-year to €10.8 billion.The decline in industry growth is partly due to the fact that production volumes in Germany are likely to fall in the current year due to the difficult economic environment. The declining number of households in Germany and the resulting drop in waste disposal requirements are also likely to have a negative impact on the industry. Increased expenditure on the maintenance of sewer and drainage networks is likely to prevent an increase in the profitability of wastewater disposal companies in the current year.Due to the expected long-term increase in the frequency and severity of extreme weather events, the need for waste disposal is likely to increase over the next five years, which should result in industry turnover growing by an average of 2.9% per year to EUR 12.5 billion by 2029. Rising production volumes should also contribute to this. IBISWorld expects investment spending by industry players to grow over the next five years due to the expected increase in requirements for wastewater purity based on growing environmental awareness. This additional expenditure is likely to keep the profit margin lower than in previous years. The need to maintain and renovate the sewer network is also putting pressure on the profitability of industry players.

This table presents 3 indexes showing the development of labour input in the sector of industry (excluding construction): Number of persons employed, the hours worked and the wages and salaries. The number of person employed shows the development of employment in Industry. It can be defined as the total number of persons who work in the observation unit as well as persons who work outside the unit who belong to it and are paid by it. The hours worked show the development in the volume of work. The total number of hours worked represents the aggregate number of hours actually worked for the output of the observation unit during the reference period. The wages and salaries index approximate the development of the wage and salaries bill. Wages and salaries are defined as the total remuneration, in cash or in kind, payable to all persons counted on the payroll (including home workers), in return for work done during the accounting period, regardless of whether it is paid on the basis of working time, output or piecework and whether it is paid regularly. These three indexes are presented for the industrial sector (excluding construction) section B to E of NACE Rev.2 (E37, E38 and E39 not included). The indexes are presented in calendar and seasonally adjusted form.

This is the AmeriFlux version of the carbon flux data for the site US-A37 ARM-SGP-Waukomis. Site Description - The E37 site is located in the middle of a grassy field with a cultivated field to the South, near Waukomis, Oklahoma.

This is a working unpublished document based on the NZMS260 Map Series, and is a precursor to the publication of QMAP geological map 14 Haast. Map, pencil and ink on transparency, rich in detail, good condition. - Observation measure: Interpretation only. - Map size: 900 x 800 mm. Notes: Annotation in margin, no separate legend. Keywords: HAAST; GEOLOGIC MAPS; QMAP; QUATERNARY; LANDSLIDES; FAULTS; FAULTING; FOLDS; FOLDING; CASCADE; JACKSONS BAY

The Predictions of AcousticS with Smart Experimental Networks of GlidERS (PASSENGERS) project is a partnership between universities and the Naval Research Laboratory to improve the capability of data-assimilative ocean models to predict acoustic propagation in dynamic oceanography environments. It includes the deployment of teams of gliders in small (10 km) spatial footprints with integrated passive acoustics focused on mapping the physical and acoustic environment in the dynamic region of the Atlantis II Seamounts. Delayed mode dataset. _NCProperties=version=2,netcdf=4.8.1,hdf5=1.12.1 acknowledgement=This deployment is supported by the Office of Naval Research (ONR) cdm_data_type=TrajectoryProfile cdm_profile_variables=profile_id cdm_trajectory_variables=trajectory comment=Deployed by glider engineer Jack Slater with the PASSENGERS Science Team and Atlantic Explorer Crew aboard R/V Atlantic Explorer near the Atlantis II Seamount. contributor_name=Donglai Gong, Travis Miles, Catherine Edwards, Jeff Book, Jack Slater, John Kerfoot, Laura Nazzaro, Lori Garzio contributor_role=Principal Investigator and Pilot, Principal Investigator, Principal Investigator, Principal Investigator, Technician and Pilot, Data Manager, Data Manager, Data Manager Conventions=CF-1.6, COARDS, ACDD-1.3 defaultGraphQuery=temperature,depth,time&time>=max(time)-1days&temperature!=NaN&.draw=markers&.marker=6%7C3&.color=0xFFFFFF&.colorBar=Rainbow2%7C%7C%7C%7C%7C&.bgColor=0xffccccff&.yRange=%7C%7Cfalse deployment_name=electa-20230523T1947 Easternmost_Easting=-61.87324673491929 featureType=TrajectoryProfile geospatial_bounds=POLYGON ((37.6265157559269 -61.87405264020573, 37.6265157559269 -61.87324673491929, 37.62517344701432 -61.87324673491929, 37.62517344701432 -61.87405264020573, 37.6265157559269 -61.87405264020573)) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=38.092198098524285 geospatial_lat_min=37.62517344701432 geospatial_lat_resolution=0.00001 degree geospatial_lat_units=degrees_north geospatial_lon_max=-61.87324673491929 geospatial_lon_min=-62.934735 geospatial_lon_resolution=0.00001 degree geospatial_lon_units=degrees_east geospatial_vertical_max=6.099373999999999E37 geospatial_vertical_min=-0.8368166 geospatial_vertical_positive=down geospatial_vertical_resolution=0 geospatial_vertical_units=m gts_ingest=True history=2023-08-11T19:09:09Z: /tmp/tmp3t3xhay8/electa_20230612T233235Z_dbdlvorr_xy.nc created 2023-08-11T19:09:09Z: /home/kerfoot/code/glider-proc/scripts/proc_deployment_profiles_to_nc.py /home/coolgroup/slocum/deployments/2023/electa-20230523T1947/data/in/ascii/dbd/electa_2023_161_0_34_dbd.dat

id=electa-20230523T1947 infoUrl=https://rucool.marine.rutgers.edu institution=Virginia Institute of Marine Science - The College of William & Mary instrument=In Situ/Laboratory Instruments > Profilers/Sounders > CTD instrument_vocabulary=NASA/GCMD Instrument Keywords Version 8.5 keywords_vocabulary=NASA/GCMD Earth Sciences Keywords Version 8.5 naming_authority=edu.rutgers.rucool ncei_template_version=NCEI_NetCDF_Trajectory_Template_v2.0 Northernmost_Northing=38.092198098524285 platform=In Situ Ocean-based Platforms > AUVS > Autonomous Underwater Vehicles platform_type=Slocum Glider platform_vocabulary=NASA/GCMD Platforms Keywords Version 8.5 processing_level=Raw Slocum glider time-series dataset from the native data file format. Additional quality control variables provided where applicable. Thresholds used for quality control flags are under development. Delayed mode dataset. program=Office of Naval Research Task Force Ocean project=PASSENGERS references=https://rucool.marine.rutgers.edu sea_name=North Atlantic source=Observational Slocum glider data from source dba file electa-2023-161-0-34-dbd(01520034) sourceUrl=(local files) Southernmost_Northing=37.62517344701432 standard_name_vocabulary=CF Standard Name Table v27 subsetVariables=source_file, profile_id, profile_lat, profile_lon, profile_time time_coverage_duration=PT10M14S time_coverage_end=2023-06-12T23:37:42Z time_coverage_resolution=PTS time_coverage_start=2023-05-23T20:24:33Z uuid=02fab619-482b-4a14-812d-f13d85c481c9 Westernmost_Easting=-62.934735 wmo_id=4803929 wmo_platform_code=4803929

Fayl Faylın tarixçəsi Faylın istifadəsi Faylın qlobal istifadəsi MetaməlumatlarSınaq göstərişi ölçüsü 800 436 piksel Dig

There are strict targets for European countries to meet when it comes to collecting and treating wastewater and managing sewage plants. The 2024 updated EU Urban Wastewater Directive means the threat of court action or fines looms over the heads of governments that don’t have adequate sewage networks and infrastructure. This has encouraged demand for sewerage companies and has supported a steady workflow in recent years. Revenue in the Sewerage industry is anticipated to tumble at a compound annual rate of 4.1% to €31.9 billion over the five years through 2025. Revenue in 2025 is expected to grow by 0.5%. Since the COVID-19 outbreak, people have spent more time at home thanks to the adoption of remote working, which has boosted household wastewater production and increased demand for sewage companies. However, challenging operating conditions have hindered growth prospects, typified by soaring inflation and government budget cuts, with inflation outpacing industry revenue between 2021 and 2024. Moreover, industrial activity has struggled somewhat in recent years, with industrial production decreasing 1.2% in 2023 compared to 2022, according to Eurostat. Profit margins have also shrunk in the past five years due to increased investment in upgrading ageing assets. However, substantial price hikes, driven by regulatory-approved investment plans like the EU Urban Waste Water Treatment Directive, are expected to boost sewerage revenue in 2025. Revenue in the industry is expected to expand by a compound annual rate of 4.6% to reach €39.8 billion over the five years through 2030. Populations across European countries continue to multiply, which is increasing demand for housebuilding. As the number of households increases across Europe, the number linked to sewage networks will grow, raising demand for sewerage services. Assistance from funds like the EU’s Cohesion Fund will continue supporting the expansion of sewage networks in European countries that lack infrastructure and network lines. Romania will be one of the biggest winners as funding will support the building of treatment plants, water pumps and pipe laying that will help it meet sewage standards.

There are strict targets for European countries to meet when it comes to collecting and treating wastewater and managing sewage plants. The 2024 updated EU Urban Wastewater Directive means the threat of court action or fines looms over the heads of governments that don’t have adequate sewage networks and infrastructure. This has encouraged demand for sewerage companies and has supported a steady workflow in recent years. Revenue in the Sewerage industry is anticipated to tumble at a compound annual rate of 4.1% to €31.9 billion over the five years through 2025. Revenue in 2025 is expected to grow by 0.5%. Since the COVID-19 outbreak, people have spent more time at home thanks to the adoption of remote working, which has boosted household wastewater production and increased demand for sewage companies. However, challenging operating conditions have hindered growth prospects, typified by soaring inflation and government budget cuts, with inflation outpacing industry revenue between 2021 and 2024. Moreover, industrial activity has struggled somewhat in recent years, with industrial production decreasing 1.2% in 2023 compared to 2022, according to Eurostat. Profit margins have also shrunk in the past five years due to increased investment in upgrading ageing assets. However, substantial price hikes, driven by regulatory-approved investment plans like the EU Urban Waste Water Treatment Directive, are expected to boost sewerage revenue in 2025. Revenue in the industry is expected to expand by a compound annual rate of 4.6% to reach €39.8 billion over the five years through 2030. Populations across European countries continue to multiply, which is increasing demand for housebuilding. As the number of households increases across Europe, the number linked to sewage networks will grow, raising demand for sewerage services. Assistance from funds like the EU’s Cohesion Fund will continue supporting the expansion of sewage networks in European countries that lack infrastructure and network lines. Romania will be one of the biggest winners as funding will support the building of treatment plants, water pumps and pipe laying that will help it meet sewage standards.

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthlymore » prior was computed from operational radiosonde launches at ARM SGP, OK.« less