Background: Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood. Objective: To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas. Methods: ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level. Results: A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells. Conclusion: We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.
Citation: Xiaowei Zhang, Jiayu Tan, Xinyu Zhang, Kritika Pandey, Yuqing Zhong, Guitao Wu, Kejun He. Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2407-2431. doi: 10.3934/mbe.2024106
[1] | Victor Zhenyu Guo . Almost primes in Piatetski-Shapiro sequences. AIMS Mathematics, 2021, 6(9): 9536-9546. doi: 10.3934/math.2021554 |
[2] | Yukai Shen . kth powers in a generalization of Piatetski-Shapiro sequences. AIMS Mathematics, 2023, 8(9): 22411-22418. doi: 10.3934/math.20231143 |
[3] | Jinyun Qi, Zhefeng Xu . Almost primes in generalized Piatetski-Shapiro sequences. AIMS Mathematics, 2022, 7(8): 14154-14162. doi: 10.3934/math.2022780 |
[4] | Yanbo Song . On two sums related to the Lehmer problem over short intervals. AIMS Mathematics, 2021, 6(11): 11723-11732. doi: 10.3934/math.2021681 |
[5] | Xiaoqing Zhao, Yuan Yi . High-dimensional Lehmer problem on Beatty sequences. AIMS Mathematics, 2023, 8(6): 13492-13502. doi: 10.3934/math.2023684 |
[6] | Mingxuan Zhong, Tianping Zhang . Partitions into three generalized D. H. Lehmer numbers. AIMS Mathematics, 2024, 9(2): 4021-4031. doi: 10.3934/math.2024196 |
[7] | Bingzhou Chen, Jiagui Luo . On the Diophantine equations x2−Dy2=−1 and x2−Dy2=4. AIMS Mathematics, 2019, 4(4): 1170-1180. doi: 10.3934/math.2019.4.1170 |
[8] | Rui Wang, Jiangtao Peng . On the inverse problems associated with subsequence sums of zero-sum free sequences over finite abelian groups Ⅱ. AIMS Mathematics, 2021, 6(2): 1706-1714. doi: 10.3934/math.2021101 |
[9] | Jinyan He, Jiagui Luo, Shuanglin Fei . On the exponential Diophantine equation (a(a−l)m2+1)x+(alm2−1)y=(am)z. AIMS Mathematics, 2022, 7(4): 7187-7198. doi: 10.3934/math.2022401 |
[10] | Baria A. Helmy, Amal S. Hassan, Ahmed K. El-Kholy, Rashad A. R. Bantan, Mohammed Elgarhy . Analysis of information measures using generalized type-Ⅰ hybrid censored data. AIMS Mathematics, 2023, 8(9): 20283-20304. doi: 10.3934/math.20231034 |
Background: Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood. Objective: To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas. Methods: ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level. Results: A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells. Conclusion: We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.
Let q be an integer. For each integer a with
1⩽a<q, (a,q)=1, |
we know that [1] there exists one and only one ˉa with
1⩽ˉa<q |
such that
aˉa≡1(q). |
Define
R(q):={a:1⩽a⩽q,(a,q)=1,2∤a+ˉa}, |
r(q):=#R(q). |
The work [2] posed the problem of investigating a nontrivial estimation for r(q) when q is an odd prime. Zhang [3,4] gave several asymptotic formulas for r(q), one of which is:
r(q)=12ϕ(q)+O(q12d2(q)log2q), |
where ϕ(q) is the Euler function and d(q) is the divisor function. Lu and Yi [5] studied a generalization of the Lehmer problem over short intervals. Let n⩾2 be a fixed positive integer, q⩾3 and c be integers with
(nc,q)=1. |
They defined
rn(θ1,θ2,c;q)=#{(a,b)∈[1,θ1q]×[1,θ2q]∣ab≡c( mod q),n∤a+b}, |
where 0<θ1,θ2⩽1, and obtained
rn(θ1,θ2,c;q)=(1−1n)θ1θ2φ(q)+O(q1/2τ6(q)log2q), |
where the O constant depends only on n. In addition, Xi and Yi [6] considered generalized Lehmer problem over short intervals. Han and Liu [7] gave an upper bound estimation for another generalization of the Lehmer problem over incomplete interval.
Guo and Yi [8] also found the Lehmer problem has good distribution properties on Beatty sequences. For fixed real numbers α and β, defined by
Bα,β:=(⌊αn+β⌋)∞n=1. |
Beatty sequences are linear sequences. Based on the results obtained, we conjecture the Lehmer problem also has good distribution properties in some non-linear sequences.
The Piatetski-Shapiro sequence is a non-linear sequence, defined by
Nc={⌊nc⌋:n∈N}, |
where c∈R is non-integer with c>1 and z∈R. This sequence was first introduced by Piatetski-Shapiro [9] to study prime numbers in sequences of the form ⌊f(n)⌋, where f(n) is a polynomial. A positive integer is called square-free if it is a product of distinct primes. The distribution of square-free numbers in the Piatetski-Shapiro sequence has been studied extensively. Stux [10] found that, as x tends to infinity,
∑n≤x⌊nc⌋ is square-free 1=(6π2+o(1))x, for 1<c<43. | (1.1) |
In 1978, Rieger [11] improved the range to 1<c<3/2 and obtained
6xπ2+O(x(2c+1)/4+ε), for 1<c<32. |
Considering the results obtained, we develop this problem by investigating
R(c;q):=∑n∈Nc∩R(q)n is square-free 1 |
and range of c when q tends to infinity. By methods of exponential sum and Kloosterman sums and fairly detailed calculations, we get the following result, which is significant for understanding the distribution properties of the Lehmer problem.
Theorem 1.1. Let q be an odd integer and large enough,
γ:=1/candc∈(1,43), |
we obtain
R(c;q)=3π2∏p∣q(1+p−1)−1qγ+O(∑p∣q(1−p−12)−1qγ−12)+O(q713γ+413∏p∣q(1−p−12)−1logq)+O(q34d3(q)logq)+O(qγ−16d2(q)log3q), |
where the O constant only depends on c.
This paper consists of three main sections. Introduction covers the origins and developments of the Lehmer problem, along with several interesting results. It also presents relevant findings related to the Piatetski-Shapiro sequences. The second section includes some definitions and lemmas throughout the paper. The third section outlines the calculation process, where we use additive characteristics to convert the congruence equations into exponential sum problems. We then employ the Kloosterman sums and exponential sums methods to derive an interesting asymptotic formula.
To complete the proof of the theorem, we need the following several definitions and lemmas.
In this paper, we denote by ⌊t⌋ and {t} the integral part and the fractional part of t, respectively. As is customary, we put
e(t):=e2πitand{t}:=t−⌊t⌋. |
The notation ‖t‖ is used to denote the distance from the real number t to the nearest integer; that is,
‖t‖:=minn∈Z|t−n|. |
And ∑′ indicates that the variable summed over takes values coprime to the number q. Throughout the paper, ε always denotes an arbitrarily small positive constant, which may not be the same at different occurrences; the implied constants in symbols O,≪, and ≫ may depend (where obvious) on the parameters c and ε, but are absolute otherwise. For given functions F and G, the notations
F≪G, G≫F andF=O(G) |
are all equivalent to the statement that the inequality
|F|⩽C|G| |
holds with some constant C>0.
Lemma 2.1. Let 1c(m) denote the characteristic function of numbers in a Piatetski-Shapiro sequence, then
1c(m)=γmγ−1+O(mγ−2)+ψ(−(m+1)γ)−ψ(−mγ), |
where
ψ(t)=t−⌊t⌋−12 andγ=1/c. |
Proof. Note that an integer m has the form
m=⌊nc⌋ |
for some integer n if and only if
m⩽nc<m+1,−(m+1)γ<−n⩽−mγ. |
So
1c(m)=⌊−mγ⌋−⌊−(m+1)γ⌋=−mγ−ψ(−mγ)+(m+1)γ+ψ(−(m+1)γ)=γmγ−1+O(mγ−2)+ψ(−(m+1)γ)−ψ(−mγ). |
Thie completes the proof.
Lemma 2.2. Let H⩾1 be an integer, ah,bh be real numbers, we have
|ψ(t)−∑0<|h|⩽Hahe(th)|⩽∑|h|⩽Hbhe(th),ah≪1|h|,bh≪1H. |
Proof. In 1985, Vaaler showed how Beurling's function could be used to construct a trigonometric polynomial approximation to ψ(x). For each positive integer N, Vaaler's construction yields a trigonometric polynomial ψ∗ of degree N which satisfies
|ψ∗(x)−ψ(x)|≤12N+2∑|n|≤N(1−|n|N+1)e(nx), |
where
ψ∗(x)=−∑1≤|n|≤N(2πin)−1ˆJN+1(n)e(nx),H(z)=sin2πzπ2{∞∑n=−∞sgn(n)(z−n)2+2z},J(z)=12H′(z),HN(z)=sin2πzπ2{∑|n|≤Nsgn(n)(z−n)2+2z},JN(z)=12H′N(z), |
and sgn(n) is the sign of n. The Fourier transform ˆJ(t) satisfies
ˆJ(t)={1,t=0;πt(1−|t|)cotπt+|t|,0<|t|<1;0,t≥1. |
To be short, we denote
ah=−(2πih)−1ˆJH+1(h)≪1|h|,bh=12H+2(1−|h|H+1)≪1H. |
There are more details in Appendix Theorem A.6. of [12].
Lemma 2.3. Denote
Kl(m,n;q)=q∑a=1q∑b=1ab≡1(modq)e(ma+nbq), |
then
Kl(m,n;q)≪(m,n,q)12q12d(q), |
where (m,n,q) is the greatest common divisor of m,n and q and d(q) is the number of positive divisors of q.
Proof. The proof is given in [13].
Lemma 2.4. (Korobov [14]) Let α be a real number, Q be an integer, and P be a positive integer, then
|Q+P∑x=Q+1e(αx)|⩽min(P,12‖α‖). |
Lemma 2.5. (Karatsuba [15]) For any number b, U<0, K⩾1, let
a=sr+θr2,(r,s)=1, r⩾1, |θ|⩽1, |
then
∑k⩽Kmin(U,1‖ak+b‖)≪(Kr+1)(U+rlogr). |
Lemma 2.6. Suppose f is continuously differentiable, f′(n) is monotonic, and
‖f′(n)‖⩾λ1>0 |
on I, then
∑n∈Ie(f(n))≪λ−11. |
Proof. See [12, Theorem 2.1].
Lemma 2.7. Let k be a positive integer, k⩾2. Suppose that f(n) is a real-valued function with k continuous derivatives on [N,2N], Further suppose that
0<F⩽f(k)(n)⩽hF. |
Then
|∑N<x⩽2Ne(f(n))|≪FκNλ+F−1, |
where the implied constant is absolute.
Proof. See [12, Chapter 3].
By the definition of Mobius function
μ(n)={(−1)ω(n),∀p|n,p2∤n,0,∃p2|n, |
it is clear that n is square-free if and only if
μ2(n)=1, |
where ω(n) is the number of prime divisor of n. So
R(c;q)=12q∑′n=1(1−(−1)n+ˉn)μ2(n)1c(n)=12(R1−R2), | (3.1) |
where
R1=q∑′n=1μ2(n)1c(n) |
and
R2=q∑′n=1(−1)n+ˉnμ2(n)1c(n). |
From Lemma 2.1, we have
R1=q∑′n=1μ2(n)1c(n)=q∑′n=1μ2(n)(γnγ−1+O(nγ−2)+ψ(−(n+1)γ)−ψ(−nγ))=R11+R12, | (3.2) |
where
R11:=q∑′n=1μ2(n)(γnγ−1+O(nγ−2))=q∑′n=1μ2(n)γnγ−1+O(q∑′n=1μ2(n)nγ−2). |
Let
D={d:p|d⇒p|q} |
and λ(n) is Liouville function. When n∈R(q),
μ2(n)={∑dm=n,d∈Dλ(d)μ2(m),(n,q)=1,0,(n,q)>1. | (3.3) |
We just consider the first term of R11. Applying Euler summuation [1],
q∑′n=1μ2(n)γnγ−1=q∑n=1∑dm=nd∈Dλ(d)μ2(m)γ(dm)γ−1=∑d∈Dλ(d)dγ−1∑m⩽qdμ2(m)γmγ−1=∑d∈Dλ(d)dγ−1∑m⩽qd(∑l2∣mμ(l))γmγ−1=∑d∈Dλ(d)dγ−1∑l⩽(qd)12μ(l)l2γ−2∑m⩽qdl2γmγ−1=∑d∈Dλ(d)dγ−1∑l⩽(qd)12μ(l)l2γ−2((qdl2)γ+O((qdl2)γ−1))=qγ∑d∈Dλ(d)d−1∑l⩽(qd)12μ(l)l−2+O(∑d∈D∑l⩽(qd)12qγ−1)=qγ∑d∈Dλ(d)d−1(∑lμ(l)l−2+O((qd)−12))+O(∏p∣q(1−p−12)−1qγ−12)=6π2∏p∣q(1+p−1)−1qγ+O(∏p∣q(1−p−12)−1qγ−12), |
thus
R11=6π2∏p∣q(1+p−1)−1qγ+O(∏p∣q(1−p−12)−1qγ−12). | (3.4) |
For R12, by Lemma 2.2, we have
R12:=q∑′n=1μ2(n)(ψ(−(n+1)γ)−ψ(−nγ))=R121+O(R122), | (3.5) |
where
R121:=q∑′n=1μ2(n)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh))) |
and
R122:=q∑′n=1μ2(n)(∑|h|⩽Hbh(e(−(n+1)γh)+e(−nγh))). |
Define
f(t)=e(((dt)γ−(dt+1)γ)h)−1, |
then
f(t)≪ |h|(dt)γ−1,∂f(t)∂t≪|h|dγ−1tγ−2. |
By Lemma 2.2 and Eq (3.3),
R121=∑0<|h|⩽Hah∑d∈Dλ(d)(∑1<m⩽qdμ2(m)e(−(dm)γh)f(m))≪∑0<|h|⩽H|h|−1∑d∈D|∫qd0f(t)d(∑1<m⩽tμ2(m)e(−(dm)γh))|≪∑0<|h|⩽H|h|−1∑d∈D|f(qd)(∑1<m⩽qdμ2(m)e(−(dm)γh))|+∑0<|h|⩽H|h|−1∑d∈D|∫qd0∂f(t)∂t∑1<m⩽tμ2(m)e(−(dm)γh)dt|. |
Let
(κ,λ)=(16,23) |
be an exponential pair. Applying Lemma 2.7, it's easy to see
∑1<m⩽tμ2(m)e(−(dm)γh)=∑m⩽t(∑l2∣mμ(l))e(−(dm)γh)≪∑l⩽t12|∑m⩽tl2e(−(dl2m)γh)|≪∑l⩽t12logq(((dl2)γ|h|(tl2)γ−1)16(tl2)23+((dl2)γ|h|(tl2)γ−1)−1)≪logq∑l⩽t12((dγ|h|)16t16γ+12l−1+(dγ|h|)−1t1−γl−2)≪(dγ|h|)16t16γ+12log2q+(dγ|h|)−1t1−γlogq≪(dγ|h|)16t16γ+12log2q, |
thus
R121≪∑0<|h|⩽H|h|−1∑d∈D|h|qγ−1(dγ|h|)16(qd)16γ+12log2q+∑0<|h|⩽H|h|−1∑d∈D∫qd0|h|dγ−1tγ−2(dγ|h|)16t16γ+12log2qdt≪∑0<|h|⩽H|h|16∑d∈Dd−12q76γ−12log2q+∑0<|h|⩽H|h|16∑d∈Dd76γ−1log2q∫qd0t76γ−32dt≪H76∏p∣q(1−p−12)−1q76γ−12log2q. | (3.6) |
For R122, the contribution from h≠0 can be bounded by similar methods of Eq (3.6). Taking
H=q913−713γ⩾1, |
we obtain
R122=b0q∑′n=1μ2(n)+∑0<|h|⩽Hbhq∑′n=1μ2(n)(e(−(n+1)γh)+e(−nγh))≪H−1q+H76∏p∣q(1−p−12)−1q76γ−12logq≪q713γ+413∏p∣q(1−p−12)−1log2q. | (3.7) |
It follows from Eqs (3.5)–(3.7),
R12≪q713γ+413∏p∣q(1−p−12)−1log2q. |
Hence
R1=6π2∏p∣q(1+p−1)−1qγ+O(∑p∣q(1−p−12)−1qγ−12)+O(q713γ+413∏p∣q(1−p−12)−1log2q). | (3.8) |
Similarly,
R2=q∑′n=1(−1)n+ˉnμ2(n)1c(n)=RP21+RP22, | (3.9) |
where
R21=q∑′n=1(−1)n+ˉnμ2(n)(γnγ−1+O(nγ−2)) |
and
R22=q∑′n=1(−1)n+ˉnμ2(n)(ψ(−(n+1)γ)−ψ(−nγ)). |
We also just consider the first term of R21.
q∑′n=1(−1)n+ˉnμ2(n)γnγ−1=q∑′n=1(−1)n+ˉn(∑d2∣nμ(d))γnγ−1=q∑′n=1(−1)n+ˉn∑d2∣nd⩽q14μ(d)γnγ−1+q∑′n=1(−1)n+ˉn∑d2∣nq14<d⩽q12μ(d)γnγ−1. | (3.10) |
It is easy to see
q∑′n=1(−1)n+ˉn∑d2∣nq14<d⩽q12μ(d)γnγ−1≪q∑′n=1∑d2∣nq14<d⩽q12γnγ−1≪qγ−14. | (3.11) |
Since for integers m and a, one has
1qq∑s=1e(s(m−a)q)={1,m≡a( mod q);0,m≢a( mod q). |
This gives
q∑′n=1(−1)n+ˉn∑d2∣nd⩽q14μ(d)γnγ−1=q∑n=1q∑m=1nm≡1(modq)(−1)n+m∑d2∣nd⩽q14μ(d)γnγ−1q−1∑a=1a=mq−1∑b=1b=n1=q∑n=1q∑m=1nm≡1(modq)q−1∑a=1a=mq−1∑b=1b=n(−1)a+b∑d2∣bd⩽q14μ(d)γbγ−1=q∑n=1q∑m=1nm≡1(modq)q−1∑a=1a≡m(modq)q−1∑b=1b≡n(modq)(−1)a+b∑d2∣bd⩽q14μ(d)γbγ−1=q∑n=1q∑m=1nm≡1(modq)q−1∑a=1q−1∑b=1(−1)a+b∑d2∣bd⩽q14μ(d)γbγ−1×(1qq∑s=1e(s(m−a)q))(1qq∑t=1e(t(n−b)q))=1q2q∑s=1q∑t=1(∑nm≡1(modq)e(sm+tnq))×(q−1∑a=1(−1)ae(−saq))(q−1∑b=1(−1)be(−tbq)∑d2∣bd⩽q14μ(d)γbγ−1). | (3.12) |
From Lemma 2.3,
∑nm≡1(modq)e(sm+tnq)=Kl(s,t;q)≪(s,t,q)12q12d(q). | (3.13) |
Note the estimate
|q−1∑a=1(−1)ae(−saq)|≪1|e(12−sq)−1|≪1|cossqπ| | (3.14) |
holds. By Abel summation and Lemma 2.4, we have
q−1∑b=1(−1)be(−tbq)∑d2∣bd⩽q14μ(d)γbγ−1=∑d⩽q14μ(d)⌊q−1d2⌋∑b=1(−1)d2be(−td2bq)γ(d2b)γ−1≪∑d⩽q14d2(γ−1)|⌊q−1d2⌋∑b=1(−1)d2be(−td2bq)γbγ−1|≪∑d⩽q14d2(γ−1)γ(qd2)γ−1max1⩽β⩽⌊q−1d2⌋|β∑b=1(−1)d2be(−td2bq)|≪∑d⩽q142∣dγqγ−1max1⩽β⩽⌊q−1d2⌋|β∑b=1e(−td2bq)|+∑d⩽q142∤dγqγ−1max1⩽β⩽⌊q−1d2⌋|β∑b=1(−1)be(−td2bq)|≪∑d⩽q142∣dqγ−1min(⌊q−1d2⌋,12‖d2qt‖)+∑d⩽q142∤dqγ−1min(⌊q−1d2⌋,12‖12−d2qt‖). | (3.15) |
To be short, combining Eqs (3.13)–(3.15), we denote
R211:=q−2q∑s=1q∑t=1(s,t,q)12d(q)q121|cossqπ|∑d⩽q142∣dqγ−1min(⌊q−1d2⌋,12‖d2qt‖)≪qγ−3q∑s=1q∑t=1(s,t,q)12d(q)q121|cossqπ|∑d⩽q14min(q−1d2,12‖d2qt‖)≪qγ−3∑u∣qu12d(q)q12qu∑s=11|cossuqπ|∑d⩽q14qu∑t=1min(q−1d2,12‖d2qut‖) |
and
R212:=q−2q∑s=1q∑t=1(s,t,q)12d(q)q121|cossqπ|∑d⩽q142∤dqγ−1min(⌊q−1d2⌋,12‖12−d2qt‖). |
Let
(d2,qu)=r, (d2r,qur)=1, |
making use of Lemma 2.5, we have
qu∑t=1min(q−1d2,12‖d2qut‖)≪(ququr+1)(q−1d2+qurlogq)≪qrd2+qulogq. |
Insert it to R211, then
R211≪qγ−3∑u∣qu12d(q)q12qu∑s=11|1−2suq|∑d⩽q14∑(d2,qu)=r(qrd2+qulogq)≪qγ−3∑u∣qu12d(q)q12qulogq∑d⩽q14∑r|d2r|qu(qrd2+qulogq)≪qγ−3∑u∣qu12d(q)q12qulogq∑r|qu∑d⩽q14r12(qd2+qulogq)≪qγ−3∑u∣qu12d(q)q12qulogq(qd(q)+q54ud(q)logq)≪qγ−14d3(q)log3q. |
By the same method of R211,
R212≪qγ−14d3(q)log3q. |
Following from Eqs (3.10) and (3.11), estimations of R211 and R212,
R21≪qγ−14+RP211+RP212≪qγ−14d3(q)log3q. | (3.16) |
By the similar method of R12 and R21,
R22=R221+O(R222), | (3.17) |
where
R221:=q∑′n=1(−1)n+ˉnμ2(n)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh))) |
and
R222:=q∑′n=1(−1)n+ˉnμ2(n)(∑|h|⩽Hbh(e(−(n+1)γh)+e(−nγh))). |
It is obvious that
R221=q∑′n=1(−1)n+ˉn(∑d2∣nμ(d))(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh)))=q∑′n=1(−1)n+ˉn∑d2∣nd⩽q16μ(d)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh)))+q∑′n=1(−1)n+ˉn∑d2∣nq16<d⩽q12μ(d)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh))). | (3.18) |
From the estimate
e(nγh−(n+1)γh)−1≪(nγ−(n+1)γ)h≪γnγ−1h, |
by partial summation,
q∑′n=1(−1)n+ˉn∑d2∣nq16<d⩽q12μ(d)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh)))≪q∑′n=1∑d2∣nq16<d⩽q12|∑0<|h|⩽Hahe(−nγh)(e(nγh−(n+1)γh)−1)|≪q∑′n=1∑d2∣nq16<d⩽q12γnγ−1HlogH≪qγ−16HlogH. | (3.19) |
For another term of R221,
q∑′n=1(−1)n+ˉn∑d2∣nd⩽q16μ(d)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh)))=q∑′n=1(−1)n+ˉn∑d2∣nd⩽q16μ(d)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh)))×(1qq∑a=1q∑s=1e(s(m−a)q))(1qq∑b=1q∑t=1e(t(n−b)q))=1q2q∑s=1q∑t=1(∑nm≡1(modq)e(sm+tnq))(q−1∑a=1(−1)ae(−saq))×(q−1∑b=1(−1)be(−tbq)∑d2∣bd⩽q16μ(d)∑0<|h|⩽Hah(e(−(b+1)γh)−e(−bγh))). | (3.20) |
We just need to give an estimation of the last part in (3.20). Similarly, let
g(x)=e(((d2x)γ−(d2x+1)γ)h)−1, |
then
g(x)≪ |h|(d2x)γ−1,∂g(x)∂x≪|h|d2γ−2xγ−2. |
By partial summation,
q−1∑b=1(−1)be(−tbq)∑d2∣bd⩽q16μ(d)∑0<|h|⩽Hah(e(−(b+1)γh)−e(−bγh))=∑0<|h|⩽Hah∑d⩽q16μ(d)∑1⩽b⩽⌊q−1d2⌋e((d22−td2q)b−(d2b)γh)g(b),≪∑0<|h|⩽H|h|−1∑d⩽q16|∫⌊q−1d2⌋1g(x)d(∑1<b⩽xe((d22−td2q)b−(d2b)γh))|≪∑0<|h|⩽H|h|−1∑d⩽q16|g(⌊q−1d2⌋)∑1⩽b⩽⌊q−1d2⌋e((d22−td2q)b−(d2b)γh)|+∑0<|h|⩽H|h|−1∑d⩽q16|∫⌊q−1d2⌋1∂g(x)∂x∑1⩽b⩽xe((d22−td2q)b−(d2b)γh)dx|, |
where
g(⌊q−1d2⌋)≪|h|qγ−1 |
and
∑1⩽b⩽⌊q−1d2⌋e((d22−td2q)b−(d2b)γh)=∑1⩽b⩽q16e((d22−td2q)b−(d2b)γh)+∑q16<b⩽⌊q−1d2⌋e((d22−td2q)b−(d2b)γh). |
It is obvious that
∑1⩽b⩽q16e((d22−td2q)b−(d2b)γh)≪q16. |
Suppose q be large enough and for b>q16, when 2∤d or q∤td2,
‖(d22−td2q)−γd2γbγ−1h‖−1⩾12‖(12−tq)d2‖−1>0, |
and applying Lemma 2.6, we have
∑q16<b⩽⌊q−1d2⌋e((d22−td2q)b−(d2b)γh)≪‖(d22−td2q)−γd2γbγ−1h‖−1≪‖(12−tq)d2‖−1. |
So
∑1⩽b⩽⌊q−1d2⌋e((d22−td2q)b−(d2b)γh)≪{q16+‖(12−tq)d2‖−1,2∤d or q∤td2;qd2,2∣d and q∣td2; |
which means
q−1∑b=1(−1)be(−tbq)∑d2∣bd⩽q16μ(d)∑0<|h|⩽Hah(e(−(b+1)γh)−e(−bγh))≪∑0<|h|⩽H|h|−1∑d⩽q162∤d or q∤td2|h|qγ−1(q16+‖(12−tq)d2‖−1)+∑0<|h|⩽H|h|−1∑d⩽q162∣d and q∣td2|h|qγd−2≪Hqγ−1(q13+∑d⩽q162∤d or q∤td2‖(12−tq)d2‖−1)+Hqγ∑d⩽q162∣d and q∣td2d−2. |
We denote
T(c):=∑d⩽q16#{(qu−2t)d2≡c(mod2qu),t⩽qu}≪∑d⩽q16(qu,d2)≪q13d(q), |
thus,
q∑′n=1(−1)n+ˉn∑d2∣nd⩽q16μ(d)(∑0<|h|⩽Hah(e(−(n+1)γh)−e(−nγh)))≪q−2q∑s=1q∑t=1(s,t,q)12d(q)q12Hqγ−1|cossqπ|(q13+∑d⩽q162∤d or q∤td2‖(12−tq)d2‖−1)+q−2q∑s=1q∑t=1(s,t,q)12d(q)q12Hqγ|cossqπ|∑d⩽q162∣d and q∣td2d−2≪Hqγ−52∑u∣qu12d(q)qu∑s=11|1−2suq|qu∑t=1(q13+∑d⩽q16‖(12−utq)d2‖−1)+Hqγ−32∑u∣qu12d(q)qu∑s=11|1−2suq|∑d⩽q16qu∑t=1q∣td2d−2≪Hqγ−16d2(q)logq+Hqγ−13d2(q)logq+Hqγ−32∑u∣qu−12d(q)log2qmaxC∑C<c⩽2C‖C2qu‖−1T(c)≪Hqγ−16d2(q)log2q. | (3.21) |
With Eqs (3.18) and (3.19), we have
R221≪Hqγ−16d2(q)log2q+Hqγ−16logH. | (3.22) |
For R222, the contribution from h=0 can be bounded by similar methods of R21, and the contribution from h≠0 can be bounded by similar methods of R221. Taking
H=logq, |
we obtain
R222=b0q∑′n=1(−1)n+ˉnμ2(n)+q∑′n=1(−1)n+ˉnμ2(n)(∑0<|h|⩽Hbh(e(−(n+1)γh)+e(−nγh)))≪H−1q34d3(q)log2q+Hqγ−16d2(q)log2q≪q34d3(q)logq+qγ−16d2(q)log3q. | (3.23) |
Following from Eqs (3.16), (3.22), and (3.23),
R2≪qγ−16d2(q)log3q+q34d3(q)logq. | (3.24) |
Hence, from Eqs (3.1), (3.8), and (3.24), we derive that
R(c;q)=3π2∏p∣q(1+p−1)−1qγ+O(∑p∣q(1−p−12)−1qγ−12)+O(q713γ+413∏p∣q(1−p−12)−1logq)+O(q34d3(q)logq)+O(qγ−16d2(q)log3q). |
We need the error terms to be smaller than the main term, so
{713γ+413<γ,34<γ, |
which means the range of c is (1,43). The reason why the range of c is changed is that R(c;q) requires q large enough.
In this paper, we generalize the Lehmer problem by considering the count of square-free numbers in the intersection of the Lehmer set and Piatetski-Shapiro sequence when q is an odd integer and large enough. By methods of exponential sum and Kloosterman sum, we study its asymptotic properties and give a sharp asymptotic formula as q tends to infinity.
Based on this result, we will consider some distribution problems similar to the Lehmer problem with more special sequences, which is significant for understanding the distribution properties of those problems.
Xiaoqing Zhao: calculations, writing and editing; Yuan Yi: methodology and reviewing. All authors have read and agreed to the published version of the manuscript.
In preparing this manuscript, we employed the language model ChatGPT-4 for the purpose of grammatical corrections. It did not influence the calculations and conclusion in this paper.
This work is supported by Natural Science Foundation No.12271422 of China. The authors would like to express their gratitude to the referee for very helpful and detailed comments.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
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