UNASSIGNED: In this PRISMA-compliant systematic review, we identify and synthesize the findings of research in which neuroimaging and assessments of achievement have been used to examine the relationships among aspects of developmental programming, neurodevelopment, and achievement in reading and mathematics.
UNASSIGNED: Forty-seven studies met inclusion criteria. The majority examined the impact of prematurity (n = 32) and prenatal alcohol exposure (n = 13). Several prematurity studies reported a positive correlation between white-matter integrity of callosal fibers and executive functioning and/or achievement, and white matter properties were consistently associated with cognitive and academic performance in preterm and full-term children. Volumetric studies reported positive associations between academic and cognitive abilities and white and gray matter volume in regions such as the insula, putamen, and prefrontal lobes. Functional MRI studies demonstrated increased right-hemispheric language processing among preterm children. Altered activation of the frontoparietal network related to numerical abilities was also reported. Prenatal alcohol exposure studies reported alterations in white matter microstructure linked to deficits in cognitive functioning and academic achievement, including mathematics, reading, and vocabulary skills. Volumetric studies reported reductions in cerebral, cerebellar, and subcortical gray matter volumes associated with decreased scores on measures of executive functioning, attention, working memory, and academic performance. Functional MRI studies demonstrated broad, diffuse activation, reduced activation in canonical regions, and increased activation in non-canonical regions during numeric tasks.
UNASSIGNED: A preponderance of studies linked prematurity and prenatal alcohol exposure to altered neurodevelopmental processes and suboptimal academic achievement. Limitations and recommendations for future research are discussed.
UNASSIGNED: Identifier: DOI 10.17605/OSF.IO/ZAN67.

BACKGROUND: Mentorship is critical to success in postgraduate science, technology, engineering, math, and medicine (STEMM) settings. As such, the purpose of this study is to comprehensively explore the state of mentorship interventions in postgraduate STEMM settings to identify novel practices and future research directions. The selection criteria for reviewed articles included: 1) published between 2002 and 2022, 2) peer-reviewed, 3) in English, 4) postgraduate mentees, 5) a program where mentorship is a significant, explicit focus, and 6) a description of mentee outcomes related to the mentorship intervention. Overall, 2583 articles were screened, and 109 articles were reviewed.
RESULTS: Most postgraduate STEMM mentorship intervention studies lack strong evidence to evaluate the effectiveness of the intervention, with only 5.5% of articles designed as randomized controlled trials. Most mentorship interventions (45.6%) were created for faculty, and few (4%) were for postdoctoral researchers. Also, only 18.8% of interventions focused on underrepresented groups in STEMM. Most interventions (53.7%) prescribed a dyadic structure, and there was more mentorship training for mentors than mentees.
CONCLUSIONS: Overall, these findings identify gaps in mentorship interventions and provide step-by-step guidance for future interventions, including a consideration for underrepresented groups and postdoctoral scholars, robust mentorship training, and more randomized controlled trials.

It is well-understood that the science, technology, engineering, and mathematics (STEM) fields have unique challenges that discourage recruiting and retaining underrepresented minorities. Research programs aimed at undergraduates have arisen as a critical mechanism for fostering innovation and addressing the challenges faced by underrepresented minorities. Here, we review various undergraduate research programs designed to provide exposure to undergraduates, with a focus on underrepresented minorities in STEM disciplines. We provide insight into selected programs\' objectives, key features, potential limitations, and outcomes. We also offer recommendations for future improvements of each research program, particularly in the context of mentorship. These programs range from broad-reaching initiatives (e.g., Leadership Alliance) to more specific programs targeting underrepresented students. By offering a nuanced understanding of each program\'s structure, we seek to provide a brief overview of the landscape of diversity-focused STEM initiatives and a guide on how to run a research program effectively.

This systematic review presented a comprehensive survey of studies that applied transcranial magnetic stimulation and transcranial electrical stimulation to parietal and nonparietal areas to examine the neural basis of symbolic arithmetic processing. All findings were compiled with regard to the three assumptions of the triple-code model (TCM) of number processing. Thirty-seven eligible manuscripts were identified for review (33 with healthy participants and 4 with patients). Their results are broadly consistent with the first assumption of the TCM that intraparietal sulcus both hold a magnitude code and engage in operations requiring numerical manipulations such as subtraction. However, largely heterogeneous results conflicted with the second assumption of the TCM that the left angular gyrus subserves arithmetic fact retrieval, such as the retrieval of rote-learned multiplication results. Support is also limited for the third assumption of the TCM, namely, that the posterior superior parietal lobule engages in spatial operations on the mental number line. Furthermore, results from the stimulation of brain areas outside of those postulated by the TCM show that the bilateral supramarginal gyrus is involved in online calculation and retrieval, the left temporal cortex in retrieval, and the bilateral dorsolateral prefrontal cortex and cerebellum in online calculation of cognitively demanding arithmetic problems. The overall results indicate that multiple cortical areas subserve arithmetic skills.

The prevalence of mathematical difficulties (MD) among primary school children in China and its related factors remain unclear. To provide a suitable foundation for formulating appropriate screening and intervention strategies, this study aimed to estimate the prevalence of MD and its possible factors among primary school children in China. Articles were identified from seven databases and included if they reported on the prevalence of MD among primary school children in China. A total of 30,881 articles were retrieved, 975 of which were subjected to full evaluation. In total, 54 studies involving 34,815 participants (mean age 9.86 ± 3.13 years) were included. Analyses were conducted using the Meta and Metafor packages in the statistical software environment R. We used meta-regression and subgroup analyses by variable type to evaluate the potential prevalence factors. The pooled prevalence of MD among primary school children in China was 8.97%, whereas the prevalence rates of MD in lower, middle, and upper primary schools were 11.57%, 10.07%, and 4.76%, respectively. The results showed that the prevalence of MD among primary school children in China was significantly related to grade level and the cut-off points selected for screening MD and that the prevalence of MD in middle primary school was higher in girls than in boys. The prevalence of MD in primary schools in China was slightly higher than that in countries such as the United Kingdom and the United States. The prevalence of MD was higher in the lower and middle grades of primary school. The grade level and cut-off points selected during MD screening were closely related to the prevalence of MD. More attention should be paid to girls with MD in middle primary school.
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023410311, identifier: CRD42023410311.

We conducted a systematic review and meta-analysis to examine if individuals with reading difficulties (RD), mathematics difficulties (MD), or unspecified learning difficulties (ULD) experience internalizing problems (i.e., anxiety, depression, somatic complaints, and social withdrawal) to the same extent, and if the effect sizes are influenced by moderators (age, internalizing problems type, anxiety type, rater type, selection criteria, and attention control). We reviewed 2,806 studies published in English between January 2000 and April 2023. Our final sample consisted of 96 studies that reported effect sizes or data to calculate them (a total of 120 unique samples, 83,260 participants, age range = 7.3 - 34.8 years). Risk of bias and sensitivity were assessed. A random-effects model analysis revealed a significant and moderate overall effect size (Hedge\'s g = -.54), indicating that individuals with RD, MD or ULD experience more internalizing problems than their chronological-age (CA) controls. Follow-up analyses showed that neither learning difficulties type nor age of participants were significant moderators, but selection criteria (diagnosis versus screening) and internalizing problems type were. These findings suggest that individuals with RD do not differ from those with MD or ULD in internalizing problems, and all fare poorly compared to CA controls.

Mathematics is often treated as different from other disciplines, since arguments in the field rely on deductive proof rather than empirical evidence as in the natural sciences. A mathematical paper can therefore, at least in principle, be replicated simply by reading it. While this distinction is sometimes taken as the basis to claim that the results in mathematics are therefore certain, mathematicians themselves know that the published literature contains many mistakes. Reading a proof is not easy, and checking whether an argument constitutes a proof is surprisingly difficult. This article uses peer review of submissions to mathematics journals as a site where referees are explicitly concerned with checking whether a paper is correct and therefore could be published. Drawing on 95 qualitative interviews with mathematics journal editors, as well as a collection of more than 100 referee reports and other correspondence from peer review processes, this article establishes that while mathematicians acknowledge that peer review does not guarantee correctness, they still value it. For mathematicians, peer review \'adds a bit of certainty\', especially in contrast to papers only submitted to preprint servers such as arXiv. Furthermore, during peer review there can be disagreements not just regarding the importance of a result, but also whether a particular argument constitutes a proof or not (in particular, whether there are substantial gaps in the proof). Finally, the mathematical community is seen as important when it comes to accepting arguments as proofs and assigning certainty to results. Publishing an argument in a peer-reviewed journal is often only the first step in having a result accepted. Results get accepted if they stand the test of time and are used by other mathematicians.

BACKGROUND: A study to reveal existing pedagogical content knowledge or technological pedagogical content knowledge frameworks and their effectiveness in teaching mathematics is crucial to inform the reader, teacher, and researcher. This review study intended to explore the trends of the pedagogical content knowledge (PCK) framework, how it has changed over time until the most recent version of technological and pedagogical content knowledge (TPACK) was developed, and their effectiveness in teaching mathematics.
METHODS: We initially downloaded 273 articles from the first 30 Google Scholar pages and analyzed 229 journal articles. We got 24 frameworks from 64 journal articles since Shulman\'s first model in 1986. About 52 out of 229 were mathematics studies. Among these studies, we found that 18 studies have extensively investigated the use of identified frameworks.
RESULTS: The frameworks were presented and descriptively discussed in chronological order. The empirical studies that compared the role of pedagogical content knowledge and technological pedagogical content knowledge models among classrooms with teachers who possess and do not possess such skills were demonstrated.
CONCLUSIONS: The gap in empirical studies was identified, and further studies about the intervention of PCK and TPACK models were suggested to gain more insight into the mathematics classroom.

Mathematics incorporates a broad range of skills, which includes basic early numeracy skills, such as subitizing and basic counting to more advanced secondary skills including mathematics calculation and reasoning. The aim of this review was to undertake a detailed investigation of the severity and pattern of early numeracy and secondary mathematics skills in people with epilepsy. Searches were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Twenty adult studies and 67 child studies were included in this review. Overall, meta-analyses revealed significant moderate impairments across all mathematics outcomes in both adults (g= -0.676), and children (g= -0.593) with epilepsy. Deficits were also observed for specific mathematics outcomes. For adults, impairments were found for mathematics reasoning (g= -0.736). However, two studies found that mathematics calculation was not significantly impaired, and an insufficient number of studies examined early numeracy skills in adults. In children with epilepsy, significant impairments were observed for each mathematics outcome: early numeracy (g= -0.383), calculation (g= -0.762), and reasoning (g= -0.572). The gravity of impairments also differed according to the site of seizure focus for children and adults, suggesting that mathematics outcomes were differentially vulnerable to the location of seizure focus.

Numerous mathematical nutrition models have been developed in the last sixty years to predict the dietary supply and requirement of farm animals\' energy and protein. Although these models, usually developed by different groups, share similar concepts and data, their calculation routines (i.e., submodels) have rarely been combined into generalized models. This lack of mixing submodels is partly because different models have different attributes, including paradigms, structural decisions, inputs/outputs, and parameterization processes that could render them incompatible for merging. Another reason is that predictability might increase due to offsetting errors that cannot be thoroughly studied. Alternatively, combining concepts might be more accessible and safer than combining models\' calculation routines because concepts can be incorporated into existing models without changing the modeling structure and calculation logic, though additional inputs might be needed. Instead of developing new models, improving the merging of extant models\' concepts might curtail the time and effort needed to develop models capable of evaluating aspects of sustainability. Two areas of beef production research that are needed to ensure adequate diet formulation include accurate energy requirements of grazing animals (decrease methane emissions) and efficiency of energy use (reduce carcass waste and resource use) by growing cattle. A revised model for energy expenditure of grazing animals was proposed to incorporate the energy needed for physical activity, as the British feeding system recommended, and eating and rumination (HjEer) into the total energy requirement. Unfortunately, the proposed equation can only be solved iteratively through optimization because HjEer requires metabolizable energy (ME) intake. The other revised model expanded an existing model to estimate the partial efficiency of using ME for growth (kg) from protein proportion in the retained energy by including an animal degree of maturity and average daily gain (ADG) as used in the Australian feeding system. The revised kg model uses carcass composition, and it is less dependent on dietary ME content, but still requires an accurate assessment of the degree of maturity and ADG, which in turn depends on the kg. Therefore, it needs to be solved iteratively or using one-step delayed continuous calculation (i.e., use the previous day\'s ADG to compute the current day\'s kg). We believe that generalized models developed by merging different models\' concepts might improve our understanding of the relationships of existing variables that were known for their importance but not included in extant models because of the lack of proper information or confidence at that time.