Proof of a conjecture of S. Chowla

AbstractLet θ(k, p) be the least s such that the congruence x1k + … + xsk ≡ 0(mod p) has a nontrivial solution. Let θ(k) = {max θ(k, p)| p > 1 + 2k}. The purpose of this note is to prove the following conjecture of S. Chowla: θ(k) = O(k12+ϵ)

Growing season precipitation in Finland under recent and projected climate

v2010okKA

ABO and Rhesus Blood Groups in Acute Puumala Hantavirus Infection

Puumala hantavirus (PUUV) causes hemorrhagic fever with renal syndrome. We aimed to evaluate whether ABO and rhesus blood groups associate with the susceptibility or the severity of PUUV infection. We analyzed blood groups in 289 adult patients treated in Tampere University hospital due to PUUV infection during the years 1982–2017. Patients’ blood group distribution was compared to that of healthy, voluntary blood donors living in the Tampere University Hospital responsibility area (n = 21,833). The severity of PUUV infection, as judged by the severity of acute kidney injury (AKI), thrombocytopenia, inflammation, capillary leakage, and the length of hospital care, was analyzed across the groups. The ABO and rhesus blood group distributions did not differ between the patients and blood donors. Patients with non-O blood groups had lower systolic blood pressure compared to patients with blood group O, but there was no difference in other markers of capillary leakage or in the severity of AKI. Minor deviations in the number of platelets and leukocytes were detected between the O and non-O blood groups. To conclude, patients with blood group O may be less susceptible to hypotension, but otherwise blood groups have no major influences on disease susceptibility or severity during acute PUUV infection

The Clinical Presentation of Puumala Hantavirus Induced Hemorrhagic Fever with Renal Syndrome Is Related to Plasma Glucose Concentration

Puumala hantavirus (PUUV) causes a hemorrhagic fever with renal syndrome characterized by thrombocytopenia, increased capillary leakage, and acute kidney injury (AKI). As glucosuria at hospital admission predicts the severity of PUUV infection, we explored how plasma glucose concentration associates with disease severity. Plasma glucose values were measured during hospital care in 185 patients with PUUV infection. They were divided into two groups according to maximum plasma glucose concentration: P-Gluc < 7.8 mmol/L (n = 134) and P-Gluc ≥ 7.8 mmol/L (n = 51). The determinants of disease severity were analyzed across groups. Patients with P-Gluc ≥7.8 mmol/L had higher hematocrit (0.46 vs. 0.43; p < 0.001) and lower plasma albumin concentration (24 vs. 29 g/L; p < 0.001) than patients with P-Gluc < 7.8 mmol/L. They presented with higher prevalence of pulmonary infiltrations and pleural effusion in chest radiograph, higher prevalence of shock and greater weight change during hospitalization. Patients with P-Gluc ≥ 7.8 mmol/L were characterized by lower platelet count (50 vs. 66 × 109/L; p = 0.001), more severe AKI (plasma creatinine 272 vs. 151 µmol/L; p = 0.001), and longer hospital treatment (8 vs. 6 days; p < 0.001) than patients with P-Gluc < 7.8 mmol/L. Plasma glucose level is associated with the severity of capillary leakage, thrombocytopenia, inflammation, and AKI in patients with acute PUUV infection

Rakennusten energialaskennan testivuosi 2012 ja arviot ilmastonmuutoksen vaikutuksista

Tiivistelmä Ilmaston lämpeneminen vaikuttaa rakennusten lämmitys- ja jäähdytysenergian tarpeeseen. Tässä tutkimuksessa muodostettiin rakennusten energialaskennassa Suomessa käytettävät uudet sääaineistot, tuotettiin ilmastoskenaarioiden avulla rakennusten energialaskelmiin soveltuvat tulevaisuuden sääaineistot ja arvioitiin rakennusten energiankulutusta vuoden 2030 muuttuneessa ilmastossa Rakennusten energialaskentaa varten kehitetty uusi testivuosi (TRY2012) korvaa aiemmin käytetyn testivuoden 1979. Uuden testivuoden tunnittaiset sääaineistot energialaskennan vyöhykkeillä I–II, III ja IV muodostettiin Vantaalla, Jyväskylässä ja Sodankylässä vuosina 1980–2009 tehtyjen säähavaintojen perusteella. Testivuoden kunkin kalenterikuukauden sääaineistot valittiin sellaiselta vuodelta, jonka aikana kyseisen kuukauden sääolot olivat mahdollisimman lähellä ilmastollista keskimääräistilaa. Käytännössä kalenterikuukausien valinta tehtiin tilastollisella menetelmällä tarkastellen lämpötilaa, kosteutta, auringon säteilyä ja tuulen nopeutta. Näitä neljää säämuuttujaa painotettiin sen mukaan, kuinka paljon ne vaikuttavat Suomessa rakennusten lämmitys- ja jäähdytystarpeeseen. Tyypilliselle uudispientalolle ja toimistorakennukselle tehdyt simuloinnit osoittivat, että lämmitys- ja jäähdytystarpeen kannalta tärkein säämuuttuja on ulkoilman lämpötila, mutta kesällä auringon säteilyn vaikutus on suunnilleen yhtä suuri. Tutkimuksessa arvioitiin myös ilmastonmuutoksen vaikutuksia. Ilmastomallien tulosten pohjalta laadittiin tilastollisilta ominaisuuksiltaan vuosien 2030, 2050 ja 2100 arvioitua ilmastoa vastaavat tulevaisuuden testivuosien sääaineistot. Vuoden 2030 tienoilla vuoden keskilämpötilan arvioidaan olevan paikkakunnasta riippuen 1,2–1,5 astetta korkeampi kuin TRY2012:n perusteella. Talvella keskilämpötila nousee noin kaksi astetta ja kesällä vajaan asteen. Lämpötilan vaihtelevuus pienenee talvipuolella vuotta noin 10 %. Auringon säteilyn väheneminen talvella ja keväällä, tuulen vähäinen voimistuminen marrashelmikuussa ja ilman suhteellisen kosteuden pieni kasvu loka–huhtikuussa otettiin myös huomioon tulevaisuuden testivuosia laadittaessa. Lopuksi arvioitiin ilmastonmuutoksen vaikutuksia rakennusten energiantarpeeseen nykyisiä rakentamismääräyksiä noudatettaessa. Laskelmissa esimerkkinä käytetyn pientalon tilojen ja ilmanvaihdon lämmitystarve vähenee vuoteen 2030 mennessä noin 10 % ja jäähdytystarve kasvaa 17–19%. Toimistotalon lämmitystarve on vastaavasti 13% pienempi ja jäähdytystarve 13-15 % suurempi kuin nykyisessä ilmastossa. Kaikkiaan rakennusten kokonaisostoenergiankulutus vähenee vuoteen 2030 mennessä 4–7 % ilmaston muuttumisen takia.Abstract: The ongoing climate change is expected to affect the energy demand for heating and cooling of buildings. Building energy consumption is often assessed by simulation algorithms that require hourly meteorological data. For this purpose, weather observations from the year 1979 have previously been used in Finland as a reference. Here, we describe a new test reference year, TRY2012, that was constructed by using weather observations at three measurement stations (Vantaa, Jyväskylä and Sodankylä) during 1980–2009. TRY2012 consists of weather data for twelve months that originate from different calendar years, each month having weather conditions close to the long-term climatological average. The months for TRY2012 were selected using Finkelstein-Schafer parameters for four climatic variables (air temperature, humidity, solar radiation and wind speed); these parameters were weighted depending on how important individual climatic variables are for the building energy consumption in Finland. Calculations for two example buildings, a detached house and an office building, indicate that the most influential climatic variable for annual energy demand is air temperature. In summer, solar radiation and air temperature are of broadly equal influence. We also assessed the influence of human-induced climate change on typical weather conditions for the years 2030, 2050 and 2100. Multi-model mean estimates from 7 to 19 global climate models, together with the TRY2012 weather data, were used to construct artificial meteorological data for the future. The projected reference year TRY2030 is 1.2–1.5ºC warmer than TRY2012, with the lower end of the range corresponding to Vantaa in southern Finland and the higher value to Sodankylä in the north. Seasonal mean temperature is projected to increase by about two degrees in winter and by slightly less than one degree in summer. The variability in temperature will diminish in the winter half of the year by about 10 %. In addition, the projections include decreases in solar radiation in winter and spring, slight increases in wind speed in November-February, and small rises in relative air humidity in all seasons except summer. Utilizing the reference years TRY2012 and TRY2030, we calculated the mean monthly and annual energy consumption for the two example buildings in the current and projected future climate. Based on the simulations, the heat energy consumption of spaces and ventilation will decrease by 10% for the detached house and by 10–13% for the office building, whereas space cooling electricity will increase by 17–19% for the detached house and by 13–15% for the office building. Because electricity for cooling relative to the total delivered energy is minor, the total energy consumption of the example buildings is projected to decrease by 4–7% by 2030

Flash-Like Albuminuria in Acute Kidney Injury Caused by Puumala Hantavirus Infection

Transient proteinuria and acute kidney injury (AKI) are characteristics of Puumala virus (PUUV) infection. Albuminuria peaks around the fifth day and associates with AKI severity. To evaluate albuminuria disappearance rate, we quantified albumin excretion at different time points after the fever onset. The study included 141 consecutive patients hospitalized due to acute PUUV infection in Tampere University Hospital, Finland. Timed overnight albumin excretion (cU-Alb) was measured during the acute phase in 133 patients, once or twice during the convalescent phase within three months in 94 patients, and at six months in 36 patients. During hospitalization, 30% of the patients had moderately increased albuminuria (cU-Alb 20–200 μg/min), while 57% presented with severely increased albuminuria (cU-Alb >200 μg/min). Median cU-Alb was 311 μg/min (range 2.2–6460) ≤7 days after fever onset, 235 μg/min (range 6.8–5479) at 8–13 days and 2.8 μg/min (range 0.5–18.2) at 14–20 days. After that, only one of the measurements showed albuminuria (35.4 μg/min at day 44). At six months, the median cU-Alb was 2.0 μg/min (range 0.6–14.5). Albuminuria makes a flash-like appearance in PUUV infection and returns rapidly to normal levels within 2–3 weeks after fever onset. In the case of AKI, this is a unique phenomenon