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This article presents a cost-effective camera network system that employs neural network-based object detection and stereo vision to assist a pan–tilt–zoom camera in imaging fast, erratically moving small aerial targets. Compared to traditional radar systems, this approach offers advantages in supporting real-time target differentiation and ease of deployment. Based on the principle of knowledge distillation, a novel data augmentation method is proposed to coordinate the latest open-source pre-trained large models in semantic segmentation, text generation, and image generation tasks to train a BicycleGAN for image enhancement. The resulting dataset is tested on various model structures and backbone sizes of two mainstream object detection frameworks, Ultralytics’ YOLO and MMDetection. Additionally, the algorithm implements and compares two popular object trackers, Bot-SORT and ByteTrack. The experimental proof-of-concept deploys the YOLOv8n model, which achieves an average precision of 82.2% and an inference time of 0.6 ms. Alternatively, the YOLO11x model maximises average precision at 86.7% while maintaining an inference time of 9.3 ms without bottlenecking subsequent processes. Stereo vision achieves accuracy within a median error of 90 mm following a drone flying over 1 m/s in an 8 m × 4 m area of interest. Stable single-object tracking with the PTZ camera is successful at 15 fps with an accuracy of 92.58%.

Abstract Background Geriatric rehabilitation is a multidisciplinary intervention that promotes functional recovery in older adults. Our objective was to assess the efficacy of geriatric rehabilitation in inpatient and geriatric day hospital settings. Methods We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, PsycINFO, PEDro and AgeLine from inception to September 30, 2022 for randomized controlled trials (RCTs) including older adults (age ≥ 65 years) undergoing geriatric rehabilitation (inpatient or day hospital) with a usual care comparator group. Primary outcome measures included mortality, long-term care home (LTCH) admission, and functional status. Secondary outcomes included discharge/remaining at home, functional improvement, length of stay, cognition, mood, and quality of life. Records were screened, abstracted and assessed for risk of bias (Cochrane Risk of Bias [RoB] 2) by two reviewers independently. We conducted a random effects meta-analysis to summarize risk ratios (RR, dichotomous outcomes) and standardized mean differences (SMD, continuous outcomes). Results Of the 5304 records screened, 29 studies (7999 patients) met eligibility criteria. There were 23 RCTs of inpatient geriatric rehabilitation (6428 patients) and six of geriatric day hospital (1571 patients) reporting outcomes of mortality (26 studies), LTCH admission (22 studies), functional status (19 studies), length of stay (18 studies), cognition (5 studies), mood (5 studies) and quality of life (6 studies). The primary outcome of mortality at longest follow up was lower in the rehabilitation group (RR 0.84, 95% confidence interval [CI] 0.76 to 0.93, I2 = 0%). LTCH admission was lower in the rehabilitation group at longest follow up (RR 0.86, 95% CI 0.75 to 0.98, I2 = 8%). Functional status was better in the rehabilitation group at longest follow up (SMD 0.09, 95% CI 0.02 to 0.16, I2 = 24%). Cognition was improved in the rehabilitation group (mean difference of mini-mental status exam score 0.97, 95% CI 0.35 to 1.60, I2 = 0%). No difference was found for patient length of stay, mood, or quality of life. Conclusions Geriatric rehabilitation in inpatient and day hospital settings reduced mortality, LTCH admission, and functional impairment. Future studies should explore implementation of this intervention for older adults. Review registration PROSPERO: CRD42022345078.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

Abstract In machine learning (ML), association patterns in the data, paths in decision trees, and weights between layers of the neural network are often entangled due to multiple underlying causes, thus masking the pattern-to-source relation, weakening prediction, and defying explanation. This paper presents a revolutionary ML paradigm: pattern discovery and disentanglement (PDD) that disentangles associations and provides an all-in-one knowledge system capable of (a) disentangling patterns to associate with distinct primary sources; (b) discovering rare/imbalanced groups, detecting anomalies and rectifying discrepancies to improve class association, pattern and entity clustering; and (c) organizing knowledge for statistically supported interpretability for causal exploration. Results from case studies have validated such capabilities. The explainable knowledge reveals pattern-source relations on entities, and underlying factors for causal inference, and clinical study and practice; thus, addressing the major concern of interpretability, trust, and reliability when applying ML to healthcare, which is a step towards closing the AI chasm.

The stabilisation capabilities of unmanned aerial vehicles (UAVs) with bicopter underactuated swashplateless rotors are highly sensitive to motor-induced vibration. Due to the requirement of the active control of underactuated swashplateless rotors, conventional designs are limited in reducing vibration through control optimisation. A solution with customized passive spring-damping structures on a unique underactuated swashplateless rotor of a tiltrotor bicopter platform is presented. The implementation of this structure effectively reduces the self-coherent vibration in flights. As a result, a higher level of control authority has been achieved without setting excessive low-pass filtering for vibration. Experimentally obtained inertial measurement unit (IMU) data, rotor speed, rotor tilt angle, and the cyclic stator response are presented for comparison with Simulink model predictions.

Hyperspectral imagers, or imaging spectrometers, are used in many remote sensing environmental studies in fields such as agriculture, forestry, geology, and hydrology. In recent years, compact hyperspectral imagers were developed using commercial-off-the-shelf components, but there are not yet any off-the-shelf data acquisition systems on the market to deploy them. The lack of a self-contained data acquisition system with navigation sensors is a challenge that needs to be overcome to successfully deploy these sensors on remote platforms such as drones and aircraft. Our work is the first successful attempt to deploy an entirely open-source system that is able to collect hyperspectral and navigation data concurrently for direct georeferencing. In this paper, we describe a low-cost, lightweight, and deployable data acquisition device for the open-source hyperspectral imager (OpenHSI). We utilised commercial-off-the-shelf hardware and open-source software to create a compact data acquisition device that can be easily transported and deployed. The device includes a microcontroller and a custom-designed PCB board to interface with ancillary sensors and a Raspberry Pi 4B/NVIDIA Jetson. We demonstrated our data acquisition system on a Matrice M600 drone at a beach in Sydney, Australia, collecting timestamped hyperspectral, navigation, and orientation data in parallel. Using the navigation and orientation data, the hyperspectral data were georeferenced. While the entire system including the pushbroom hyperspectral imager and housing weighed 735 g, it was designed to be easy to assemble and modify. This low-cost, customisable, deployable data acquisition system provides a cost-effective solution for the remote sensing of hyperspectral data for everyone.

Abstract Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

Background The global pandemic has caused breast cancer (BC) patients who are receiving chemotherapy to face more challenges in taking care of themselves than usual. A novel nurse-led mHealth program (mChemotherapy) is designed to foster self-management for this population. The aim of the pilot study is to determine the feasibility, usability, and acceptability of an mChemotherapy program for breast cancer patients undergoing chemotherapy. The objective also is to evaluate the preliminary effects of this program on adherence to app usage, self-efficacy, quality of life, symptom burden, and healthcare utilization among this group of patients. Methods This is a single-blinded randomized controlled pilot study that includes one intervention group (mChemotherapy group) and one control group (routine care group). Ninety-four breast cancer patients who commence chemotherapy in a university-affiliated hospital will be recruited. Based on the Individual and Family Self-management Theory, this 6-week mChemotherapy program, which includes a combination of self-regulation activities and nurse-led support, will be provided. Data collection will be conducted at baseline, week 3 (T1), and week 6 (T2). A general linear model will be utilized for identifying the between-group, within-group, and interaction effects. Qualitative content analysis will be adopted to analyze, extract, and categorize the interview transcripts. Discussions Breast cancer patients receiving chemotherapy are a population that often experiences a heavy symptom burden. During the pandemic, they have had difficulties in self-managing the side effects of chemotherapy due to the lack of face-to-face professional support. An mChemotherapy program will be adopted through a self-regulation process and with the provision of nurse-led real-time professional support for these patients. If proven effective, BC patients who engage in this program will be more likely to take an active role in managing their symptoms, take responsibility for their own health, and subsequently improve their self-efficacy and adherence to the use of the app.

Abstract Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it needs methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare. Results Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover a smaller set of succinct significant patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.

Abstract Background Our previous studies showed that topical application of mesenchymal stem cells (MSCs) improved functional recovery in rat traumatic brain injury (TBI) model, and hypoxic precondition further enhanced the therapeutic effects of MSCs. There was no previous study on the attenuation of cerebral edema by MSCs. We investigated whether topical application of normoxic and hypoxic MSCs could reduce cerebral edema in an experimental TBI model. Methods Two million normoxic (N = 24) and hypoxic (N = 24) MSCs were applied topically to exposed the cerebral cortex in a controlled cortical impact (CCI) model. The MSCs were fixed in position with fibrin glue. No treatment was given to control animals (TBI only: n = 24). After surgery, four animals in each group were sacrificed daily (day 1 to day 6) for edema evaluation. Normal animals without TBI were used as reference (n = 4). The expressions of GFAP, AQP4, and MMP9 were also investigated by immunofluorescence staining and RT-PCR at day 3. Results The edema peaked within 3 days after TBI. Compared with the control, hypoxic MSCs reduced brain water content significantly (p < 0.05). Both hypoxic and normoxic MSCs downregulated the expression of MMP9 and normalized AQP4 distribution to astrocyte end feet. Conclusion Our preliminary study showed that topical application of hypoxic MSCs suppressed both vasogenic and cytotoxic edema formation.

Pest birds are a salient problem in agriculture all around the world due to the damage they can cause to commercial or high-value crops. Recent advancements in Unmanned Aerial Vehicles (UAVs) have motivated the use of drones in pest bird deterrence, with promising success already being demonstrated over traditional bird control techniques. This paper presents a novel bird deterrence solution in the form of tethered UAVs, which are attached and arranged in a grid-like fashion across a vineyard property. This strategy aims to bypass the power and endurance limitations of untethered drones while still utilising their dynamism and scaring potential. A simulation model has been designed and developed to assess the feasibility of different UAV arrangements, configurations, and strategies against expected behavioural responses of incoming bird flocks, despite operational and spatial constraints imposed by a tether. Attempts at quantifying bird persistence and relative effort following UAV-induced deterrence are also introduced through a novel bird energy expenditure model. This aims to serve as a proxy for selecting control techniques that reduce future foraging missions. The simulation model successfully isolated candidate configurations, which were able to deter both single and multiple incoming bird flocks using a centralised multi-UAV control strategy. Overall, this study indicates that a grid of autonomous ground-tethered UAV platforms is viable as a bird deterrence solution in agriculture, a novel solution not seen nor dealt with elsewhere to the authors’ knowledge.

Background: Multiple long-term care (LTC) reports have issued similar recommendations for improvement across Canadian LTC homes. Our primary objective was to identify the most common recommendations made over the past 10 years. Our secondary objective was to estimate the total cost of studying LTC issues repeatedly from 1998 to 2020. Methods: The qualitative and cost analyses were conducted in Canada from July to October 2020. Using a list of reports, inquiries and commissions from The Royal Society of Canada Working Group on Long-Term Care, we coded recurrent recommendations in LTC reports. We contacted the sponsoring organizations for a cost estimate, including direct and indirect costs. All costs were adjusted to 2020 Canadian dollar values. Results: Of the 80 Canadian LTC reports spanning the years of 1998 to 2020, 24 (30%) were based on a national level and 56 (70%) were focused on provinces or municipalities. Report length ranged from 4 to 1491 pages and the median number of contributors was 14 (interquartile range, IQR, 5–26) per report. The most common recommendation was to increase funding to LTC to improve staffing, direct care and capacity (67% of reports). A median of 8 (IQR 3.25–18) recommendations were made per report. The total cost for all 80 reports was estimated to be $23,626,442.78. Conclusions: Problems in Canadian LTC homes and their solutions have been known for decades. Despite this, governments and non-governmental agencies continue to produce more reports at a monetary and societal cost to Canadians.

Background: Multiple long-term care (LTC) reports have issued similar recommendations for improvement across Canadian LTC homes. Our primary objective was to identify the most common recommendations made over the past 10 years. Our secondary objective was to estimate the total cost of studying LTC issues repeatedly from 1998 to 2020. Methods: The qualitative and cost analyses were conducted in Canada from July to October 2020. Using a list of reports, inquiries and commissions from The Royal Society of Canada Working Group on Long-Term Care, we coded recurrent recommendations in LTC reports. We contacted the sponsoring organizations for a cost estimate, including direct and indirect costs. All costs were adjusted to 2020 Canadian dollar values. Results: Of the 80 Canadian LTC reports spanning the years of 1998 to 2020, 24 (30%) were based on a national level and 56 (70%) were focused on provinces or municipalities. Report length ranged from 4 to 1491 pages and the median number of contributors was 14 (interquartile range, IQR, 5–26) per report. The most common recommendation was to increase funding to LTC to improve staffing, direct care and capacity (67% of reports). A median of 8 (IQR 3.25–18) recommendations were made per report. The total cost for all 80 reports was estimated to be $23,626,442.78. Conclusions: Problems in Canadian LTC homes and their solutions have been known for decades. Despite this, governments and non-governmental agencies continue to produce more reports at a monetary and societal cost to Canadians.

Background: School-based green space activities have been found to be beneficial to the physical activity level and lifestyle habits of adolescent students. However, their effects on green space use and satisfaction, mental health, and dietary behaviors required further investigation. This study aimed to investigate the effects of school-based hydroponic planting integrated with health promotion activities in improving green space use, competence and satisfaction, healthy lifestyle, mental health, and health-related quality of life (QoL) among early adolescent students in secondary schools.Methods: This study adopted a three-group comparison design (one control and two intervention groups). Secondary school students (N = 553) of grades 7–9 participated in either (1) hydroponic planting (two times per week for 8 months) integrated with health promotion activities; (2) only health promotion activities (one time per week for 6 weeks); or (3) control group. Outcomes assessed by questionnaire included green space use and satisfaction, life happiness, lifestyle, depressive symptoms, and health-related QoL.Results: After adjusting for sex and school grade, the scores in “green space distance and use” and “green space activity and competence” were significantly better in the intervention groups than in the control group. Hydroponic planting integrated with health promotion activities was also associated with better scores in dietary habits and resistance to substance use. Intervention groups had a higher score in “Green space sense and satisfaction” and life happiness when compared with the control group.Conclusions: Our study shows that the school-based hydroponic planting integrated with health promotion activities were feasible and, to a certain extent, useful to improve green space use and competence, dietary habits, and resistance to substance use among early adolescent students in secondary schools in urban areas. Future studies should address the limitations identified, for example, designing a randomized controlled trial that could fit school schedules to generate new evidence for physical and mental health in adolescent communities.

Abstract Background Decapods are an order of crustaceans which includes shrimps, crabs, lobsters and crayfish. They occur worldwide and are of great scientific interest as well as being of ecological and economic importance in fisheries and aquaculture. However, our knowledge of their biology mainly comes from the group which is most closely related to crustaceans – insects. Here we produce a de novo transcriptome database, crustacean annotated transcriptome (CAT) database, spanning multiple tissues and the life stages of seven crustaceans. Description A total of 71 transcriptome assemblies from six decapod species and a stomatopod species, including the coral shrimp Stenopus hispidus, the cherry shrimp Neocaridina davidi, the redclaw crayfish Cherax quadricarinatus, the spiny lobster Panulirus ornatus, the red king crab Paralithodes camtschaticus, the coconut crab Birgus latro, and the zebra mantis shrimp Lysiosquillina maculata, were generated. Differential gene expression analyses within species were generated as a reference and included in a graphical user interface database at http://cat.sls.cuhk.edu.hk/. Users can carry out gene name searches and also access gene sequences based on a sequence query using the BLAST search function. Conclusions The data generated and deposited in this database offers a valuable resource for the further study of these crustaceans, as well as being of use in aquaculture development.

Background: Multiple long-term care (LTC) reports have issued similar recommendations for improvement across Canadian LTC homes. Our primary objective was to identify the most common recommendations made over the past 10 years. Our secondary objective was to estimate the total cost of studying LTC issues repeatedly from 1998 to 2020. Methods: The qualitative and cost analyses were conducted in Canada from July to October 2020. Using a list of reports, inquiries and commissions from The Royal Society of Canada Working Group on Long-Term Care, we coded recurrent recommendations in LTC reports. We contacted the sponsoring organizations for a cost estimate, including direct and indirect costs. All costs were adjusted to 2020 Canadian dollar values. Results: Of the 80 Canadian LTC reports spanning the years of 1998 to 2020, 24 (30%) were based on a national level and 56 (70%) were focused on provinces or municipalities. Report length ranged from 4 to 1491 pages and the median number of contributors was 14 (interquartile range, IQR, 5–26) per report. The most common recommendation was to increase funding to LTC to improve staffing, direct care and capacity (67% of reports). A median of 8 (IQR 3.25–18) recommendations were made per report. The total cost for all 80 reports was estimated to be $23,626,442.78. Conclusions: Problems in Canadian LTC homes and their solutions have been known for decades. Despite this, governments and non-governmental agencies continue to produce more reports at a monetary and societal cost to Canadians.

Objectives Prevention of falls and fall-related injuries is a priority due to the substantial health and financial burden of falls on patients and healthcare systems. Deprescribing medications known as ‘fall-risk increasing drugs’ (FRIDs) is a common strategy to prevent falls. We conducted a systematic review to determine its efficacy for the prevention of falls and fall-related complications.Design Systematic review and meta-analysis.Data sources MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, CINAHL and grey literature from inception to 1 August 2020.Eligibility criteria for selecting studies Randomised controlled trials of FRID withdrawal compared with usual care evaluating the rate of falls, incidence of falls, fall-related injuries, fall-related fractures, fall-related hospitalisations or adverse effects related to the intervention in adults aged ≥65 years.Data extraction and synthesis Two reviewers independently performed citation screening, data abstraction, risk of bias assessment and certainty of evidence grading. Random-effects models were used for meta-analyses.Results Five trials involving 1305 participants met eligibility criteria. Deprescribing FRIDs did not change the rate of falls (rate ratio (RaR) 0.98, 95% CI 0.63 to 1.51), the incidence of falls (risk difference 0.01, 95% CI −0.06 to 0.09; relative risk 1.04, 95% CI 0.86 to 1.26) or rate of fall-related injuries (RaR 0.89, 95% CI 0.57 to 1.39) over a follow-up period of 6–12 months. No trials evaluated the impact of deprescribing FRIDs on fall-related fractures or hospitalisations.Conclusion There is a paucity of robust high-quality evidence to support or refute that a FRID deprescribing strategy alone is effective at preventing falls or fall-related injury in older adults. Although there may be other reasons to deprescribe FRIDs, our systematic review found that it may result in little to no difference in the rate or risk of falls as a sole falls reduction strategy.PROSPERO registration number CRD42016040203.

Temozolomide (TMZ), an oral alkylating agent, is a cornerstone of standard-of-care multimodality therapy for glioblastoma. In spite of significant efforts to treat this disease, the tumor carries a poor prognosis and is considered incurable largely due to the development of chemoresistance. One of the main mechanisms for this phenomenon is the activation of tumor DNA repair systems, such as O-6-methylguanine-DNA methyltransferase, that removes TMZ-induced DNA adducts and restores genomic integrity. Recent advances in the understanding of TMZ resistance oncobiology have introduced several novel independent molecular mechanisms involving epigenetic, transcriptomic, proteomic aberrations as well as alterations in apoptosis-autophagy processes, receptor tyrosine kinase activity, the tumor microenvironment, and the emergence of glioma stem cells. This article describes a multifaceted summary of the latest proposed causes for TMZ resistance and their complex interactions. It is believed that only by comprehending this growing network of interdependent mechanisms can effective combinatorial oncologic therapeutic strategies be developed to improve patient overall survival.

Abstract Background A protein family has similar and diverse functions locally conserved. An aligned pattern cluster (APC) can reflect the conserved functionality. Discovering aligned residue associations (ARAs) in APCs can reveal subtle inner working characteristics of conserved regions of protein families. However, ARAs corresponding to different functionalities/subgroups/classes could be entangled because of subtle multiple entwined factors. Methods To discover and disentangle patterns from mixed-mode datasets, such as APCs when the residues are replaced by their fundamental biochemical properties list, this paper presents a novel method, Extended Aligned Residual Association Discovery and Disentanglement (E-ARADD). E-ARADD discretizes the numerical dataset to transform the mixed-mode dataset into an event-value dataset, constructs an ARA Frequency Matrix and then converts it into an adjusted Statistical Residual (SR) Vector Space (SRV) capturing statistical deviation from randomness. By applying Principal Component (PC) Decomposition on SRV, PCs ranked by their variance are obtained. Finally, the disentangled ARAs are discovered when the projections on a PC is re-projected to a vector space with the same basis vectors of SRV. Results Experiments on synthetic, cytochrome c and class A scavenger data have shown that E-ARADD can a) disentangle the entwined ARAs in APCs (with residues or biochemical properties), b) reveal subtle AR clusters relating to classes, subtle subgroups or specific functionalities. Conclusions E-ARADD can discover and disentangle ARs and ARAs entangled in functionality and location of protein families to reveal functional subgroups and subgroup characteristics of biological conserved regions. Experimental results on synthetic data provides the proof-of-concept validation on the successful disentanglement that reveals class-associated ARAs with or without class labels as input. Experiments on cytochrome c data proved the efficacy of E-ARADD in handing both types of residue data. Our novel methodology is not only able to discover and disentangle ARs and ARAs in specific statistical/functional (PCs and RSRVs) spaces, but also their locations in the protein family functional domains. The success of E-ARADD shows its great potential to proteomic research, drug discovery and precision and personalized genetic medicine.

Emerging evidence has shown that cell-cell interactions between testicular cells, in particular at the Sertoli cell-cell and Sertoli-germ cell interface, are crucial to support spermatogenesis. The unique ultrastructures that support cell-cell interactions in the testis are the basal ES (ectoplasmic specialization) and the apical ES. The basal ES is found between adjacent Sertoli cells near the basement membrane that also constitute the blood-testis barrier (BTB). The apical ES is restrictively expressed at the Sertoli-spermatid contact site in the apical (adluminal) compartment of the seminiferous epithelium. These ultrastructures are present in both rodent and human testes, but the majority of studies found in the literature were done in rodent testes. As such, our discussion herein, unless otherwise specified, is focused on studies in testes of adult rats. Studies have shown that the testicular cell-cell interactions crucial to support spermatogenesis are mediated through distinctive signaling proteins and pathways, most notably involving FAK, Akt1/2 and Cdc42 GTPase. Thus, manipulation of some of these signaling proteins, such as FAK, through the use of phosphomimetic mutants for overexpression in Sertoli cell epithelium in vitro or in the testis in vivo, making FAK either constitutively active or inactive, we can modify the outcome of spermatogenesis. For instance, using the toxicant-induced Sertoli cell or testis injury in rats as study models, we can either block or rescue toxicant-induced infertility through overexpression of p-FAK-Y397 or p-FAK-Y407 (and their mutants), including the use of specific activator(s) of the involved signaling proteins against pAkt1/2. These findings thus illustrate that a potential therapeutic approach can be developed to manage toxicant-induced male reproductive dysfunction. In this review, we critically evaluate these recent findings, highlighting the direction for future investigations by bringing the laboratory-based research through a translation path to clinical investigations.

Bisphenol A (BPA) is a well-known endocrine disruptor, widely used in various consumer products and ubiquitously found in air, water, food, dust, and sewage leachates. Recently, several countries have restricted the use of BPA and replaced them with bisphenol S (BPS) and bisphenol F (BPF), which have a similar chemical structure to BPA. Compared to BPA, both BPS and BPF have weaker estrogenic effects, but their effects on human reproductive function including endometrial receptivity and embryo implantation still remain largely unknown. We used an in vitro spheroid (blastocyst surrogate) co-culture assay to investigate the effects of BPA, BPS, and BPF on spheroid attachment on human endometrial epithelial cells, and further delineated their role on steroid hormone receptor expression. We also used transcriptomics to investigate the effects of BPA, BPS, and BPF on the transcriptome of human endometrial cells. We found that bisphenol treatment in human endometrial Ishikawa cells altered estrogen receptor alpha (ERα) signaling and upregulated progesterone receptors (PR). Bisphenols suppressed spheroid attachment onto Ishikawa cells, which was reversed by the downregulation of PR through PR siRNA. Overall, we found that bisphenol compounds can affect human endometrial epithelial cell receptivity through the modulation of steroid hormone receptor function leading to impaired embryo implantation.

Abstract In this study, GFP-MSCs were topically applied to the surface of cerebral cortex within 1 hour of experimental TBI. No treatment was given to the control group. Three days after topical application, the MSCs homed to the injured parenchyma and improved the neurological function. Topical MSCs triggered earlier astrocytosis and reactive microglia. TBI penumbra and hippocampus had higher cellular proliferation. Apoptosis was suppressed at hippocampus at 1 week and reduced neuronal damaged was found in the penumbral at day 14 apoptosis. Proteolytic neuronal injury biomarkers (alphaII-spectrin breakdown products, SBDPs) and glial cell injury biomarker, glial fibrillary acidic protein (GFAP)-breakdown product (GBDPs) in injured cortex were also attenuated by MSCs. In the penumbra, six genes related to axongenesis (Erbb2); growth factors (Artn, Ptn); cytokine (IL3); cell cycle (Hdac4); and notch signaling (Hes1) were up-regulated three days after MSC transplant. Transcriptome analysis demonstrated that 7,943 genes were differentially expressed and 94 signaling pathways were activated in the topical MSCs transplanted onto the cortex of brain injured rats with TBI. In conclusion, topical application offers a direct and efficient delivery of MSCs to the brain.

Human stanniocalcin-1 (STC1) is a glycoprotein known to participate in inflammation and tumor progression. However, its role in cancer-macrophage interaction at the tumor environment is not known. In this study, the co-culture of the human metastatic hepatocellular carcinoma cell line (MHCC97L) stably transfected with a control vector (MHCC97L/P), or STC1-overexpressing vector (MHCC97L/S1) with human leukemia monocytic cell line (THP-1) was conducted. We reported that MHCC97L/S1 suppressed the migratory activity of THP-1. Real-time PCR analysis revealed the downregulation of the pro-migratory factors, monocyte-chemoattractant protein receptors, CCR2 and CCR4, and macrophage-migratory cytokine receptor, CSF-1R. Transcriptomic analysis of the THP-1 cells co-cultured with either MHCC97L/P or MHCC97L/S1, detected 1784 differentially expressed genes. The Ingenuity Canonical Pathway analysis predicted that RhoA signaling was associated with the inhibition of the cell migration. Western blot analysis revealed a significant reduction of Ser19-phosphorylation on MLC2, a Rho-A downstream target, in the THP-1 cells. Xenograft tumors derived from MHCC97/S1 in mice showed a remarkable decrease in infiltrating macrophages. Collectively, this is the first report to demonstrate the inhibitory effect of STC1-overexpressing cancer cells on macrophage migration/infiltration. Our data support further investigations on the relationship between tumor STC1 level and macrophage infiltration.