Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug ...combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease. In recent years, a ...large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs' roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA-disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA-disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA-disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA-disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.
A weak-dynamic coloring of a graph is a vertex coloring (not necessarily proper) in such a way that each vertex of degree at least two sees at least two colors in its neighborhood. It is proved that ...the weak-dynamic chromatic number of the class of
k
-planar graphs (resp. IC-planar graphs) is equal to (resp. at most) the chromatic number of the class of 2
k
-planar graphs (resp. 1-planar graphs), and therefore every IC-planar graph has a weak-dynamic 6-coloring (being sharp) and every 1-planar graph has a weak-dynamic 9-coloring. Moreover, we conclude that the well-known Four Color Theorem is equivalent to the proposition that every planar graph has a weak-dynamic 4-coloring, or even that every
C
4
-free bipartite planar graph has a weak-dynamic 4-coloring. It is also showed that deciding if a given graph has a weak-dynamic
k
-coloring is NP-complete for every integer
k
≥
3
.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA-disease ...associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA-disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and
, we developed an improved diffusion model to predict potential lncRNA-disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.
The study of arc-pancyclicity of tournaments has a long history. Let
T
be a regular
c
-partite tournament with partite sets
V
1
,
V
2
,
…
,
V
c
. Alspach proved that every arc of a regular tournament ...is in a
k
-cycle for each
k
∈
{
3
,
4
,
…
,
n
}
. In this paper, we extend the concept of arc-pancyclicity of regular tournaments to regular multipartite tournaments. We prove that for any regular
c
-partite (
c
≥
3
) tournament
T
, if
V
i
,
V
j
≠
∅
, then there is a
(
V
j
,
V
i
)
-path in
T
that transverses exactly
k
partite sets for each
k
∈
{
4
,
…
,
c
}
. This theorem is best possible in some sense and it confirms a conjecture proposed by Guo.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - ...one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Let G=（V,E）be a graph andφbe a total coloring of G by using the color set{1,2,...,k}.Let f（v）denote the sum of the color of the vertex v and the colors of all incident edges of v.We say thatφis ...neighbor sum distinguishing if for each edge uv∈E（G）,f（u）=f（v）.The smallest number k is called the neighbor sum distinguishing total chromatic number,denoted byχ′′nsd（G）.Pil′sniak and Wo′zniak conjectured that for any graph G with at least two vertices,χ′′nsd（G）（G）＋3.In this paper,by using the famous Combinatorial Nullstellensatz,we show thatχ′′nsd（G）2（G）＋col（G）-1,where col（G）is the coloring number of G.Moreover,we prove this assertion in its list version.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Caring for people with Alzheimer's disease (AD) is burdensome, especially when family members act as caregivers. This multicenter survey first aimed to investigate caregivers' mental states as well ...as its influencing factors in caring for people with different severities of AD in China.
People with AD and their caregivers from 30 provincial regions in mainland China were enrolled from October 2020 to December 2020 to be surveyed for caregivers' mental states and living conditions, as well as caregivers' attitudes toward treatment and caring. Logistic regression was used to explore the factors that influence the positive and negative states of caregivers who care for people with different stages of AD.
A total of 1,966 valid questionnaires were analyzed (mild AD: 795, moderate AD: 521, severe AD: 650). A total of 73.6% of caregivers maintained normal states (mild group: 71.9%, moderate group: 73.9%, severe group: 75.2%;
= 2.023,
= 0.364), and the proportions of caregivers with positive and negative states were 26.3% (mild group: 38.4%, moderate group: 24.6%, severe group: 13.1%;
= 119.000,
< 0.001) and 36.5% (mild group: 25.2%, moderate group: 36.9%, severe group: 50.2%;
= 96.417,
< 0.001), respectively. The major factors that both influenced caregivers' positive and negative states were the severity of AD, perceived efficacy of treatment, safety issues after AD dementia diagnosis and perceived social support (
< 0.005), while neuropsychiatric symptoms causing stress in caregivers (
< 0.001) only affected the negative states of caregivers. The results of further analysis according to disease severity showed that safety issues after AD dementia diagnosis (
< 0.005) only made significant differences in the mild-to-moderate group.
To reduce negative states and promote positive states among caregivers, flexible and sensitive caregiving support could be built on caregivers' demands in caring for people with different stages of AD. The support of emotion, social functioning and nursing skills is one of the significant ways for health workers to enhance caregivers' competency.
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