Social mating systems (e.g., monogamy, polygamy, or polyandry) are relatively stable behavioral strategies developed by environmental in animals, but the genetic imprint of a particular mating system is often incongruent with the social mating system due to extrapair matings. However, the genetics of mating systems remain little understood in rodents. In this study, we investigated the genetic signature of the mating system of 141 (63 females, 78 males) field-captured Midday Gerbils (Meriones meridianus) -a rodent species commonly found in the Minqin Desert in China-through microsatellite site analyses of genetic structure and relatedness. Seven pairs of highly polymorphic microsatellite loci were selected and were highly polymorphic, the combined exclusion probability was greater than 0.99. The parent pair paternity test by Cervus 3.0 software show that, eleven mother-offspring and nine father-offspring relationships were identified in 2018, involving 26 individuals from 10 families. Similarly, 19 mother-offspring and 19 father-offspring relationships were identified in 2019, involving 48 individuals from 18 families. All three types of genetic mating structure were identified: monogamy (19 families), polyandry (4 families), and polygyny (5 families), providing evidence that the genetics underlying mating systems in this species are variable, can be incongruent with behavioral evidence for social mating systems, and could vary based on environmental cues, including degree of perceived or actual predation.

Background AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. Materials and methods In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. Results On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). Conclusions AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. Clinical relevance statement This study involves the process of data collection, randomization and crossover reading procedure. Graphical Abstract

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