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ObjectiveCancer burden can be reduced when the population's knowledge of cancer prevention and control measures is increased. However, current epidemiological research investigating cancer prevention and control knowledge in China is limited. This study aimed to examine the core knowledge levels of cancer prevention and control measures as well as its influencing factors among adults in Fujian, China.Study designA cross-sectional study.MethodsFrom September to December 2021, a total of 2,440 Chinese urban and rural adults from Fujian Province, located in Southeastern China, were randomly selected for this cross-sectional study. The probability proportionate approach to sampling was used. A 38-item questionnaire that covered demographics and basic knowledge of cancer, including concepts, screening, therapy, and rehabilitation-related key points was used to measure knowledge levels of cancer prevention and control measures among 2,074 participants. The level of each participants' core knowledge of cancer prevention and control measures was defined as a rate calculated by the number of correct answers divided by the total number of questions. The binary logistic regression model was used to determine if influencing factors were associated with core knowledge awareness.ResultsIn total, 1,290 participants (62.2%) were in the low knowledge group and 784 (37.8%) were in the high knowledge group. The average knowledge rate of cancer prevention and control measures among all participants was 56.01%. Participants from urban areas, who held white-collar jobs, were married, had a bachelor's degree or above, had a family history of cancer, or self-rated their health level as good or average were associated with higher rates of cancer prevention and control core knowledge (overall p < 0.05).ConclusionThese findings may assist healthcare providers and/or researchers in designing effective primary preventive interventions to enhance the general population's cancer prevention and control knowledge, and subsequently decrease the cancer burden in China.

Most coexisting insect species exhibit stunted growth compared to individual species on plants. This phenomenon reflects an interspecific antagonism drawing extensive attention, while the underlying mechanisms remain largely uncharacterized. Mirids (Apolygus lucorum) and cotton bollworms (Helicoverpa armigera) are two common cotton pests. We identified a secretory protein, ASP1, from the oral secretion of mirids, found in the nucleus of mirid-infested cotton leaves. ASP1 specifically targets the transcriptional co-repressor TOPLESS (TPL) and inhibits NINJA-mediated recruitment of TPL, promoting plant defense response and gossypol accumulation in cotton glands. ASP1-enhanced defense inhibits the growth of cotton bollworms on cotton plants, while having limited impact on mirids. The mesophyll-feeding characteristic allows mirids to avoid most cotton glands, invalidating cotton defense. Our investigation reveals the molecular mechanism by which mirids employ cotton defense to selectively inhibit the feeding of cotton bollworms. The study revealed the underlying mechanism by which mirids manipulate cotton defense to confront the cotton bollworms co-existing on cotton plants. The mirid-secreted protein ASP1 enhances cotton defense by specifically targeting the transcriptional co-repressor TPL, thereby inhibiting cotton bollworm feeding. [ABSTRACT FROM AUTHOR]

Argumentation is one of the central practices in science learning and helps deepen students' conceptual understanding. Students should learn how to communicate ideas including procedure tests, data interpretations, and investigation outcomes in verbal and written forms through argument structure. This article presents a negotiation model to show how argument can be a vehicle to drive students to learn core ideas of density. The negotiation model consists of five phases: (1) creating a testable question, (2) constructing an argument in groups, (3) critiquing arguments publicly, (4) advancing students' arguments, and (5) writing and reflecting individually.

Although treatment outcome in prize-based contingency management has been shown to depend on reinforcement schedule, the optimal schedule is still unknown. Therefore, we conducted a retrospective analysis of data from a randomized clinical trial (Ghitza et al., 2007) to determine the effects of the probability of winning a prize (low vs. high) and the size of the prize won (small, large, or jumbo) on likelihood of abstinence until the next urine-collection day for heroin and cocaine users (N = 116) in methadone maintenance. Higher probability of winning, but not the size of individual prizes, was associated with a greater percentage of cocaine-negative, but not opiate-negative, urines. DESCRIPTORS: computer automation, contingency management, methadone, operant conditioning, opiate, substance abuse

To examine the effect of reinforcer density in prize-based abstinence reinforcement, heroin/cocaine users (N = 116) in methadone maintenance (100 mg/day) were randomly assigned to a noncontingent control group (NonC) or to 1 of 3 groups that earned prize draws for abstinence: manual drawing with standard prize density (MS) or computerized drawing with standard (CS) or high (CH) density. Probabilities (prizes/draw) were standard (50%) and high (78%); prize density was double blind. Mean prize values were CH, $286; CS, $167; MS, $139; and NonC, $171. Outcomes were % opioid/cocaine-negative urines during the 12-week intervention and then 8 weeks postintervention as well as diagnosis of dependence up to 6 months poststudy. CH had significantly more negative specimens than did NonC during intervention and had more than all groups during postintervention treatment: Mean % negative (95% confidence interval) during postintervention treatment adjusted for baseline drug use and dropout were CH, 55% (14%-90%); CS, 7% (1%-27%); MS, 4% (1%-12%); and NonC, 3% (1%-10%). Current cocaine dependence diagnoses after treatment were significantly lower in contingent compared with noncontingent groups. Computerized drawing with higher-density prizes enhanced reduction of cocaine use; abstinence reinforcement had long-term therapeutic benefits. Keywords: contingency management, opiate, cocaine dependence, opiate dependence, methadone maintenance

An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a user-specified skeleton, called an event structure, which consists of a number of variables representing events and temporal constraints among these variables; the goal of the discovery is to find temporal patterns, i.e., instantiations of the variables in the structure that appear frequently in the time sequence. This paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern-discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set. Index Terms - Data mining, knowledge discovery, time sequences, temporal databases, time granularity, temporal constraints, temporal patterns.