Leaf phonological characters of main tree species in urban forest of Shenyang (2005–2009 year).

a<p>The timing for each leaf phenology is indicated by the days since March 1 every year from 2005 to 2009.</p

Correlation analysis of leaf phenological characters of main tree species in urban forest of Shenyang.

a<p>The timing for each leaf phenology is indicated by the days since March 1 every year from 2005 to 2009.</p><p>**p<0.01.</p

DataSheet_1_High efficacy of azacitidine combined with homoharringtonine, idarubicin, and cytarabine in newly diagnosed patients with AML: A single arm, phase 2 trial.pdf

IntroductionThis study aims to evaluate the efficacy and safety of the novel combination of Aza and HIA as the frontline induction therapy in newly diagnosed AML patients eligible for intensive chemotherapy (IC) (registered on ClinicalTrials.gov, number NCT04248595).MethodsAza (75mg/m2/d on days1-5 subcutaneous) is administered in combination with HIA [HHT 2mg/m2/d on days 4-8 intravenous over 3 hours, idarubicin 6mg/m2/d on days 4-6 intravenous, and cytarabine 100mg/m2/d on days 4-10 intravenous]. The primary endpoint was complete remission (CR) or CR with incomplete blood count recovery (CRi). Secondary endpoints were overall survival (OS), relapse-free survival (RFS), and adverse events (AEs).ResultsA total of 20 AML patients (aged 18-70 years) were enrolled between Jan 2020 and Sep 2022. 95% (19/20) of patients achieved CR/CRi, and 89.5% (17/19) had undetectable MRD, in which 94.7% (18/19) reached CR/CRi, and 88.9% (16/18) obtained MRD negative after the 1st cycle of induction therapy. Median OS and RFS were both not reached during the follow-up. The estimated 2-year OS and RFS were 87.5% (95%CI, 58.6% to 96.7%) and 87.1% (95%CI, 57.3% to 96.6%), respectively. No patient discontinued the treatment for AEs.DiscussionThis study provides preliminary evidence for this novel combination therapy as the first-line induction therapy for young or older AML patients fit for IC.</p

The relationships between leaf abscission phenology and mean air temperature in every ten days from September to November for main tree species in urban forest of Shenyang.

<p>The full line-numbers of tree species with leaf abscission (%), the dotted line-the mean air temperature in every ten day (°C).</p

The relationships between beginning and ending time of leaf emergence.

<p>The relationships between beginning and ending time of leaf emergence.</p

The relationships between beginning and ending time of leaf abscission.

<p>The relationships between beginning and ending time of leaf abscission.</p

Detailed description of the strains used in S2 Fig.

Detailed description of the strains used in S2 Fig.</p

Model summary.

a, Summary of the growth law model and parameters. ϕR is the ribosomal fraction of the proteome. ϕ0 is a constant, and κt is a parameter denoting the translational capacity. ϕP is called the P-sector, the proteome fraction that includes ‘catabolic’ enzymes catalyzing this nutrient flux. κn is the ‘nutritional capacity’ or ‘nutrient quality’. The sum of proteome sectors cannot surpass a maximum fraction, denoted by . λmax resembles a maximum growth rate that is related to the growth-rate invariant fraction of the proteome ϕfixed via λmax = (1−ϕfixed)κt. b, Illustration how different nutrient quality results in different growth rates. For a low nutrient quality (left), a high expression level of the P-sector is required to achieve sufficient nutrient flux. This leaves fewer resources available for the ribosomal sector ϕR and overall results in slower growth. Conversely, for high nutrient quality κn (right), a higher nutrient flux is catalyzed by a smaller P-sector, freeing up proteomic resources for higher expression of the ribosomal sector ϕR and resulting in faster growth. c, Illustration of nutrient flux and growth rate as a function of cAMP-mediated C-sector expression (left). The C-sector is one of the major components of the P-sector in the growth theory, as illustrated in the pie chart (right). Another large part being the ppGpp-activated protein sector that we denote as the S-sector, where S stands for stress. Most transporters and substrate-specific metabolic genes are part of the C-sector and increasing the C-sector increases nutrient flux (dashed lines, left top panel). Higher nutrient flux leads to an increase in growth rate, but only up to an optimum level, at which flux for biosynthesis balances nutrient flux (left bottom panel). At even higher C-sector expression, growth rate drops because there are insufficient proteomic resources for the R-sector to process nutrient flux generated by the disproportionate C-sector. Nutrient quality κn is determined by how much nutrient flux is achieved per C-sector induction. A “good” substrate results in a steep increase in nutrient flux (blue dashed line, left top panel), whereas a “poor” substrate results in a much flatter induction of nutrient flux (orange dashed line, left bottom panel). Therefore, “poor” substrates result in higher cAMP levels but slower growth rates than “good” substrates (left bottom panel). The steepness of nutrient flux induction, defining nutrient quality, is determined by the catalytic rates of substrate-specific enzymes, but also by the expression level of substrate-specific transporters and enzymes. We denote the core proteome fraction of substrate-specific transporters and enzymes by C*-sector, which is a fraction f in the much larger P-sector, f = ϕC*/ϕP. We then denote the core nutrient quality based on fundamental biochemical enzymatic properties by . The effective nutrient quality that emerges in the growth laws can be modulated by changing the expression fraction f, . We hypothesize that because the core enzyme proteome fraction ϕC* is a small fraction of the P-sector ϕP, even for the costliest substrates in terms of protein cost, nutrient quality can be dialed up or down in response to ecological needs by changing the expression fraction f. We denote the P-sector fraction that is not part of substrate-specific metabolism as the “adaptability” sector ϕAD: ϕAD = ϕP−ϕC*. Components of this sector are not important for supporting growth in the current growth conditions, but instead constitute a preparatory response. d, Illustration how the core catabolic fraction determines nutrient quality and growth rate. Within the co-regulated C-sector, low expression and a weak induction of substrate-specific enzymes will result in a lower effective nutrient quality and a higher expression level of the adaptability sector ϕAD (left). Conversely, a large core catabolic fraction results in fast growth and relatively low expression of the adaptability sector ϕAD (right). By dialing the core catabolic fraction, bacteria can convert information about their environment conveyed by the nutrient present, into resource allocation decisions determining their adaptability and preparedness for changing environments or the onset of stress. (illustrations created with Biorender).</p

Growth rate effect of individual genetic modifications and combinations.

Growth rates on glucose and mannose minimal medium of different strains. Data points are biological repeats. Unpaired t-test with welch correction was performed. (please see S3 Table for detailed description of strains). (DOCX)</p

Protein copy number calculation.

Protein copy number calculation of the main carbon transporting enzyme (or the first enzyme in the primary carbon degradation pathway) from Li at. al. [12]. (DOCX)</p