(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer’s diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

Severe traumatic brain injury (TBI) induces seizures or status epilepticus (SE) in 20-30% of patients during the acute phase. We hypothesized that severe TBI induced with lateral fluid-percussion injury (FPI) triggers post-impact SE. Adult Sprague-Dawley male rats were anesthetized with isoflurane and randomized into the sham-operated experimental control or lateral FPI-induced severe TBI groups. Electrodes were implanted right after impact or sham-operation, then videoelectroencephalogram (EEG) monitoring was started. In addition, video-EEG was recorded from naïve rats. During the first 72 h post-TBI, injured rats had seizures that were intermingled with other epileptiform EEG patterns typical to nonconvulsive SE, including occipital intermittent rhythmic delta activity, lateralized or generalized periodic discharges, spike-and-wave complexes, poly-spikes, poly-spike-and-wave complexes, generalized continuous spiking, burst suppression, or suppression. Almost all (98%) of the electrographic seizures were recorded during 0-72 h post-TBI (23.2-17.4 seizures/rat). Mean latency from the impact to the first electrographic seizure was 18.4-15.1 h. Mean seizure duration was 86-57 sec. Analysis of high-resolution videos indicated that only 41% of electrographic seizures associated with behavioral abnormalities, which were typically subtle (Racine scale 1-2). Fifty-nine percent of electrographic seizures did not show any behavioral manifestations. In most of the rats, epileptiform EEG patterns began to decay spontaneously on Days 5-6 after TBI. Interestingly, also a few sham-operated and naïve rats had post-operation seizures, which were not associated with EEG background patterns typical to non-convulsive SE seen in TBI rats. To summarize, our data show that lateral FPI-induced TBI results in non-convulsive SE with subtle behavioral manifestations; this explains why it has remained undiagnosed until now. The lateral FPI model provides a novel platform for assessing the mechanisms of acute symptomatic non-convulsive SE and for testing treatments to prevent post-injury SE in a clinically relevant context.

We investigated the common wasp, Vespula vulgaris as a bioindicator and biomonitor of metals in the industrial area. Using traps, we collected 257 yellowjackets along a pollution gradient in the Harjavalta Cu-Ni smelter in Southwest Finland. Our method detected metal elements such as arsenic (As), cobalt (Co), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), zinc (Zn), and mercury (Hg) in wasps. The data analyses revealed V. vulgaris can be a proper indicator for As, Cd, Co, Cu, Ni, and Pb, rather than for Fe and Zn contamination. Body burdens of As, Cd, Co, Cu, Ni, and Pb decreased with an increase in distance from smelter. Enrichment factor (EF) followed the pattern Pb ˃ Cd ˃ As ˃ Co ˃ Cu ˃ Ni. The highest bioaccumulation (BAF) values were revealed for Cd (5.9) and the lowest for Pb (0.1). Specially designed software (WaspFacer) allowed revealing body burdens of As, Cd, Co, Cu, Ni, and Pb to be associated with rather smaller than more asymmetric facial colour markings in yellowjackets. These results add to the body of literature on how heavy metal contaminants can have tangible phenotypic effects on insects and open future opportunities for using wasps as indicators of metal pollution.

Noninvasive, affordable circulating biomarkers for difficult-to-diagnose mild traumatic brain injury (mTBI) are an unmet medical need. Although blood microRNA (miRNA) levels are reportedly altered after traumatic brain injury (TBI), their diagnostic potential for mTBI remains inconclusive. We hypothesized that acutely altered plasma miRNAs could serve as diagnostic biomarkers both in the lateral fluid percussion injury (FPI) model and clinical mTBI. We performed plasma small RNA-sequencing from adult male Sprague–Dawley rats (n = 31) at 2 days post-TBI, followed by polymerase chain reaction (PCR)-based validation of selected candidates. miR-9a-3p, miR-136-3p, and miR-434-3p were identified as the most promising candidates at 2 days after lateral FPI. Digital droplet PCR (ddPCR) revealed 4.2-, 2.8-, and 4.6-fold elevations in miR-9a-3p, miR-136-3p, and miR-434-3p levels (p < 0.01 for all), respectively, distinguishing rats with mTBI from naïve rats with 100% sensitivity and specificity. DdPCR further identified a subpopulation of mTBI patients with plasma miR-9-3p (n = 7/15) and miR-136-3p (n = 5/15) levels higher than one standard deviation above the control mean at <2 days postinjury. In sTBI patients, plasma miR-9-3p levels were 6.5- and 9.2-fold in comparison to the mTBI and control groups, respectively. Thus, plasma miR-9-3p and miR-136-3p were identified as promising biomarker candidates for mTBI requiring further evaluation in a larger patient population.

Abstract. Physico-chemical and microbiological water quality in the drinking water distribution systems (DWDSs) of five waterworks in Finland with different raw water sources and treatment processes was explored. Water quality was monitored during four seasons with on-line equipment and bulk water samples were analysed in laboratory. Seasonal changes in the water quality were more evident in DWDSs of surface waterworks compared to the ground waterworks and artificially recharging ground waterworks (AGR). Between seasons, temperature changed significantly in every system but pH and EC changed only in one AGR system. Seasonal change was seen also in the absorbance values of all systems. The concentration of microbially available phosphorus (MAP, μg PO₄-P/l) was the highest in drinking water originating from the waterworks supplying groundwater. Total assimilable organic carbon (AOC, μg AOC-C/l) concentrations were significantly different between the DWDSs other than between the two AGR systems. This study reports differences in the water quality between surface and ground waterworks using a wide set of parameters commonly used for monitoring. The results confirm that every distribution system is unique, and the water quality is affected by environmental factors, raw water source, treatment methods and disinfection.