WiPFIM: A digital platform for interlinking biocollections of wild plants, fruits, associated insects, and their molecular barcodes.

The current knowledge on insects feeding on fruits is limited, and some of the scarce existing data on the fruit-associated insects are secluded within the host institutions. Consequently, their value is not fully realized. Moreover, in countries like Kenya, the integration of biocollections data within a digital framework has not been fully exploited. To address these gaps, this article presents a description of the development of a web-based platform for data sharing and integrating biodiversity historical data of wild plants, fruits, associated insects, and their molecular barcodes (WiPFIM) while leveraging data science technologies. The barcodes corresponding to the biocollections data were retrieved from BOLD database. The platform is an online resource about fruit-insect interactions that can be of interest to a worldwide community of users and can be useful in building innovative tools. The platform is accessible online at https://test-dmmg.icipe.org/wpfhi.

Protocol for producing hyperpolarized 13C-bicarbonate for clinical MRI of extracellular pH in aggressive tumors.

Tumor acidosis is one of the hallmarks indicating the initiation and progression of various cancers. Here, we present a protocol for preparing a hyperpolarized (HP) 13C-bicarbonate tissue pH MRI imaging contrast agent to detect aggressive tumors. We describe the steps for the formulation and polarization of a precursor molecule 13C-glycerol carbonate (13C-GLC), the post-dissolution reaction, and converting HP 13C-GLC to an injectable HP 13C-bicarbonate solution. We then detail procedures for MRI data acquisition to generate tumor pH maps for assessing tumor aggressiveness. For complete details on the use and execution of this protocol, please refer to Mu et al.1.

Correlation of Stature with Great Toe Length in 5-10 Years Aged Bangladeshi Children.

The cross sectional, analytic and descriptive type study was conducted among 5-10 years aged Bangladeshi children at different areas of Mymensingh District (Fulbaria, Trisal, Haluaghat, Fulpur and Muktagacha), Bangladesh on 109 Bangladeshi children from January 2016 to December 2016. Nonrandom purposive sampling technique was taken for sample collection. Any kind of foot deformity resulting either from physical injury or congenital anomaly was excluded to construct standard data. The present anthropometric study was designed to construct data of 5 to 10 years aged Bangladeshi children regarding great toe length, to measure correlation of stature with great toe length and comparison of great toe length between male and female children. This study has been made out to grow interest among the researchers for future study of different country. Stature of the subject was measured with the stadiometer and great toe length was measured using slide caliper. The children were requested to stand with weight distributed equally on both feet. The legs were perpendicular to the feet. The mean great toe length of both sides of 5, 6, 7, 8, 9 and 10 years aged male were 2.90±0.51 cm, 3.00±0.38 cm, 3.18±0.42 cm, 3.41±0.26 cm, 3.34±0.32cm and 3.57±0.45 cm respectively and those of female were 2.93±0.70 cm, 2.70±0.43 cm, 3.05±0.37 cm, 3.02±0.25 cm, 3.42±0.55 cm and 3.62±0.44 cm respectively. Great toe length showed non-significant positive correlation with stature in 5 years old female, 7 years old male and female, 8 years old male, 9 and 10 years old male and female children. In 5 years old male, 6 years old male and female and 8 years old female children, great toe length showed non-significant negative correlation with stature. Comparison of great toe length between male and female children was done by Unpaired Students 't' test which was statistically non-significant.

Combining temperature-dependent life table data into Insect Life Cycle Model to forecast fall armyworm Spodoptera frugiperda (JE Smith) distribution in maize agro-ecological zones in Africa.

The fall armyworm (FAW), Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), an invasive agricultural pest, has significantly impacted crop yields across Africa. This study investigated the relationship between temperature and FAW life history traits, employing life cycle modeling at temperatures of 20, 25, 28, 30, and 32°C. The development time for eggs, larvae, and pupae varied from 0-3 days, 10-18 days, and 7-16 days, respectively. The optimal temperature range for immature stage survival and female fecundity was identified as 21-25°C, with the intrinsic rate of increase (rm) and gross reproductive rate (GRR) peaking at 25-28°C. Model validation confirmed the accuracy of these findings. The research further projected the Establishment Risk Index (ERI), Activity Index (AI), and Generation Index (GI) for FAW under current and future climates (2050 and 2070) using RCP 2.6 and RCP 8.5 scenarios. Results indicate that RCP 2.6 leads to a reduction in high-risk FAW areas, particularly in central Africa. Conversely, RCP 8.5 suggests an increase in areas conducive to FAW activity. These findings highlight the impact of climate policy on pest dynamics and the importance of incorporating climatic factors into pest management strategies. The study predicts a potential decrease in FAW prevalence in West Africa by 2070 under aggressive climate mitigation, providing a basis for future FAW management approaches.

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food-A decision support system.

Crickets (Gryllus bimaculatus) produce sounds as a natural means to communicate and convey various behaviors and activities, including mating, feeding, aggression, distress, and more. These vocalizations are intricately linked to prevailing environmental conditions such as temperature and humidity. By accurately monitoring, identifying, and appropriately addressing these behaviors and activities, the farming and production of crickets can be enhanced. This research implemented a decision support system that leverages machine learning (ML) algorithms to decode and classify cricket songs, along with their associated key weather variables (temperature and humidity). Videos capturing cricket behavior and weather variables were recorded. From these videos, sound signals were extracted and classified such as calling, aggression, and courtship. Numerical and image features were extracted from the sound signals and combined with the weather variables. The extracted numerical features, i.e., Mel-Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral Coefficients, and chroma, were used to train shallow (support vector machine, k-nearest neighbors, and random forest (RF)) ML algorithms. While image features, i.e., spectrograms, were used to train different state-of-the-art deep ML models, i,e., convolutional neural network architectures (ResNet152V2, VGG16, and EfficientNetB4). In the deep ML category, ResNet152V2 had the best accuracy of 99.42%. The RF algorithm had the best accuracy of 95.63% in the shallow ML category when trained with a combination of MFCC+chroma and after feature selection. In descending order of importance, the top 6 ranked features in the RF algorithm were, namely humidity, temperature, C#, mfcc11, mfcc10, and D. From the selected features, it is notable that temperature and humidity are necessary for growth and metabolic activities in insects. Moreover, the songs produced by certain cricket species naturally align to musical tones such as C# and D as ranked by the algorithm. Using this knowledge, a decision support system was built to guide farmers about the optimal temperature and humidity ranges and interpret the songs (calling, aggression, and courtship) in relation to weather variables. With this information, farmers can put in place suitable measures such as temperature regulation, humidity control, addressing aggressors, and other relevant interventions to minimize or eliminate losses and enhance cricket production.

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI.

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique.

A pharmacokinetic model for hyperpolarized 13C-pyruvate MRI when using metabolite-specific bSSFP sequences.

PURPOSE: Metabolite-specific balanced SSFP (MS-bSSFP) sequences are increasingly used in hyperpolarized [1-13C]Pyruvate (HP 13C) MRI studies as they improve SNR by refocusing the magnetization each TR. Currently, pharmacokinetic models used to fit conversion rate constants, kPL and kPB, and rate constant maps do not account for differences in the signal evolution of MS-bSSFP acquisitions. METHODS: In this work, a flexible MS-bSSFP model was built that can be used to fit conversion rate constants for these experiments. The model was validated in vivo using paired animal (healthy rat kidneys n = 8, transgenic adenocarcinoma of the mouse prostate n = 3) and human renal cell carcinoma (n = 3) datasets. Gradient echo (GRE) acquisitions were used with a previous GRE model to compare to the results of the proposed GRE-bSSFP model. RESULTS: Within simulations, the proposed GRE-bSSFP model fits the simulated data well, whereas a GRE model shows bias because of model mismatch. For the in vivo datasets, the estimated conversion rate constants using the proposed GRE-bSSFP model are consistent with a previous GRE model. Jointly fitting the lactate T2 with kPL resulted in less precise kPL estimates. CONCLUSION: The proposed GRE-bSSFP model provides a method to estimate conversion rate constants, kPL and kPB, for MS-bSSFP HP 13C experiments. This model may also be modified and used for other applications, for example, estimating rate constants with other hyperpolarized reagents or multi-echo bSSFP.

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI.

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM Attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies and technical developments of this imaging technique.

Spatio-temporal characterization of phenotypic resistance in malaria vector species.

BACKGROUND: Malaria, a deadly disease caused by Plasmodium protozoa parasite and transmitted through bites of infected female Anopheles mosquitoes, remains a significant public health challenge in sub-Saharan Africa. Efforts to eliminate malaria have increasingly focused on vector control using insecticides. However, the emergence of insecticide resistance (IR) in malaria vectors pose a formidable obstacle, and the current IR mapping models remain static, relying on fixed coefficients. This study introduces a dynamic spatio-temporal approach to characterize phenotypic resistance in Anopheles gambiae complex and Anopheles arabiensis. We developed a cellular automata (CA) model and applied it to data collected from Ethiopia, Nigeria, Cameroon, Chad, and Burkina Faso. The data encompasses georeferenced records detailing IR levels in mosquito vector populations across various classes of insecticides. In characterizing the dynamic patterns of confirmed resistance, we identified key driving factors through correlation analysis, chi-square tests, and extensive literature review. RESULTS: The CA model demonstrated robustness in capturing the spatio-temporal dynamics of confirmed IR states in the vector populations. In our model, the key driving factors included insecticide usage, agricultural activities, human population density, Land Use and Land Cover (LULC) characteristics, and environmental variables. CONCLUSIONS: The CA model developed offers a robust tool for countries that have limited data on confirmed IR in malaria vectors. The embrace of a dynamical modeling approach and accounting for evolving conditions and influences, contribute to deeper understanding of IR dynamics, and can inform effective strategies for malaria vector control, and prevention in regions facing this critical health challenge.

Anthropometric Study of Foot Breadth of 5-10 Years Aged Bangladeshi Children.

The present anthropometric study was designed to construct data of 5-10 years aged Bangladeshi children regarding foot breadth and an attempt has been made out to grow interest among the researchers for future study and also to compare the data with the data of the people of other races. This cross-sectional, descriptive and analytic type study was conducted among 5-10 years aged 109 Bangladeshi children (70 male and 39 female) at different areas of Mymensingh district (Fulpur, Muktagacha, Fulbaria, Trisal and Haluaghat), Bangladesh from January 2016 to December 2016. Non-random purposive sampling technique was taken for sample collection. Any kind of foot deformity due to either from congenital or physical injury was excluded to construct standard measurement. Foot breadth was measured using slide caliper. The children were asked to stand with weight distributed equally on both feet. The legs were perpendicular to the feet. The mean foot breadth of right side of 5, 6, 7, 8, 9 and 10 years aged male children were 7.08±.61 cm, 7.11±.30 cm, 7.61±.45 cm, 7.44±.43 cm, 8.12±.38 cm and 8.17±.44 cm respectively and those of female children were 6.55±.64 cm, 7.17±.27 cm, 7.04±.70 cm, 7.62±.54 cm, 7.48±.88 cm and 7.85±.72 cm respectively. The mean foot breadth of left side of 5, 6, 7, 8, 9 and 10 years aged male children were 7.04±.60 cm, 7.03±.30 cm, 7.52±.47 cm, 7.36±.45 cm, 8.03±.38 cm and 8.08±.43 cm respectively and those of female children were 6.46±.63 cm, 7.10±.31 cm, 6.95±.71 cm, 7.54±.52 cm, 7.37±.89 cm and 7.77±.71 cm respectively. Comparison of foot breadth between male and female children was done by Unpaired Students 't' test and the differences of means were statistically non-significant. Data were tabulated and statistically analyzed using Microsoft excel and SPSS software. The results of present study would be useful in anatomy, orthopedics, forensic science, plastic surgery, radiology, podiatry, archeology, anthropology and nutrition science.

A mathematical model for mapping the insecticide resistance trend in the Anopheles gambiae mosquito population under climate variability in Africa.

The control of arthropod disease vectors using chemical insecticides is vital in combating malaria, however the increasing insecticide resistance (IR) poses a challenge. Furthermore, climate variability affects mosquito population dynamics and subsequently IR propagation. We present a mathematical model to decipher the relationship between IR in Anopheles gambiae populations and climate variability. By adapting the susceptible-infected-resistant (SIR) framework and integrating temperature and rainfall data, our model examines the connection between mosquito dynamics, IR, and climate. Model validation using field data achieved 92% accuracy, and the sensitivity of model parameters on the transmission potential of IR was elucidated (e.g. µPRCC = 0.85958, p-value < 0.001). In this study, the integration of high-resolution covariates with the SIR model had a significant impact on the spatial and temporal variation of IR among mosquito populations across Africa. Importantly, we demonstrated a clear association between climatic variability and increased IR (width = [0-3.78], α = 0.05). Regions with high IR variability, such as western Africa, also had high malaria incidences thereby corroborating the World Health Organization Malaria Report 2021. More importantly, this study seeks to bolster global malaria combat strategies by highlighting potential IR 'hotspots' for targeted intervention by National malria control programmes.

Shared Activities as a Protective Factor Against Behavioral and Psychological Symptoms of Dementia and Caregiver Stress.

Background and Objectives: Most persons with dementia experience behavioral and psychological symptoms (BPSD). While there is evidence that structured activity programs can be beneficial for persons with dementia and their caregivers, it is not well understood how joint engagement in shared activities affects BPSD and caregiver stress. The purpose of this study was to examine the moderating effect of doing a shared activity on the BPSD and caregiver stress. Research Design and Methods: This study used an intensive longitudinal observational design in which caregivers completed baseline and once-a-day diary surveys for 21 days. Caregivers were asked whether they did a pleasant noncare activity with their relative, the presence of 8 BPSD, and their stress level. A moderation model in a structural equation model examined the relationship between these variables. Results: Our sample consisted of 453 caregivers (87.4% female, 51.4% non-Hispanic White, mean age 53 years [standard deviation {SD}: 14]) and person living with dementia whose mean age was 79 years (SD: 9). On days when the caregivers engaged in a shared activity together with person living with dementia, there was a significant decrease in the BPSD (estimate -0.038, standard error [SE] = 0.016, 95% confidence interval [CI]: -0.069, -0.007, p value = 0.018). The effects of engaging in a shared activity decreased the impact of caregiver stress by 0.052 (estimate -0.052, SE = 0.018, 95% CI: -0.087, -0.017, p value = 0.004). At the between-person level, no differences were found in BPSD across caregivers who engaged or did not engage in shared activities. Discussion and Implications: The results of our study indicate that doing a shared activity is associated with reduced BPSD among persons with dementia and may buffer the impact of caregiver stress on BPSD. Shared activities should be considered a key intervention component for dementia caregivers.

3D balanced SSFP UTE MRI for multiple contrasts whole brain imaging.

PURPOSE: This study aimed to develop a new high-resolution MRI sequence for the imaging of the ultra-short transverse relaxation time (uT2) components in the brain, while simultaneously providing proton density (PD) contrast for reference and quantification. THEORY: The sequence combines low flip angle balanced SSFP (bSSFP) and UTE techniques, together with a 3D dual-echo rosette k-space trajectory for readout. METHODS: The expected image contrast was evaluated by simulations. A study cohort of six healthy volunteers and eight multiple sclerosis (MS) patients was recruited to test the proposed sequence. Subtraction between two TEs was performed to extract uT2 signals. In addition, conventional longitudinal relaxation time (T1) weighted, T2-weighted, and PD-weighted MRI sequences were also acquired for comparison. RESULTS: Typical PD-contrast was found in the second TE images, while uT2 signals were selectively captured in the first TE images. The subtraction images presented signals primarily originating from uT2 components, but only if the first TE is short enough. Lesions in the MS subjects showed hyperintense signals in the second TE images but were hypointense signals in the subtraction images. The lesions had significantly lower signal intensity in subtraction images than normal white matter (WM), which indicated a reduction of uT2 components likely associated with myelin. CONCLUSION: 3D isotropic sub-millimeter (0.94 mm) spatial resolution images were acquired with the novel bSSFP UTE sequence within 3 min. It provided easy extraction of uT2 signals and PD-contrast for reference within a single acquisition.

Mixed Supervision of Histopathology Improves Prostate Cancer Classification From MRI.

Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=198 ) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).

Current methods for hyperpolarized [1-13C]pyruvate MRI human studies.

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of HP agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate-by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation; (2) MRI system setup and calibrations; (3) data acquisition and image reconstruction; and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the "HP 13C MRI Consensus Group" as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods and Equipment study groups. It further aims to provide a comprehensive reference for future consensus, building as the field continues to advance human studies with this metabolic imaging modality.

Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.

Tracheostomy decannulation readiness: A cross sectional study comparing standardised evaluation for tacheostomy decannulation to flexible endoscopic evaluation of swallowing examination.

INTRODUCTION: Tracheostomy is a procedure commonly performed in neurocritical and mechanically ventilated patients in the intensive care unit. Dysphagia and impaired airway protection are the main causes for a delay in tracheostomy decannulation in patients with neurological disorders. Endoscopic evaluation is an objective examination of readiness for tracheostomy decannulation with flexible endoscopic evaluation of swallowing (FEES) as the most commonly used method, yet it requires special expertise and is heavily dependent on its operator in assessing the parameters. A relatively new method for assessing decannulation readiness in neurologic disorder, the Standardized Endoscopic Swallowing Evaluation for Tracheostomy Decannulation (SESETD) was introduced in 2013 by Warnecke, et al. This method includes stepwise evaluation of secretion management, spontaneous swallowing and laryngeal sensitivity. This study aims to find conformity between the SESETD and FEES in assessing readiness for tracheostomy decannulation in patients with neurologic disorders. MATERIALS AND METHODS: This study is a cross-sectional study conducted on 36 neurologic patients at Cipto Mangunkusumo General Hospital which was aimed to find the agreement between two modalities for tracheostomy decannulation readiness, FEES and SESETD based on parameters, standing secretion, spontaneous swallowing and laryngeal sensitivity. RESULT: A total of 36 subjects were examined and 22 of them underwent successful tracheostomy decannulation. The agreement between FEES and SESETD showed significant results with p-value <0.0001 and Kappa value = 0.47. CONCLUSION: There was conformity between FEES and SESETD in evaluating tracheostomy decannulation readiness based on three parameters: standing secretion, spontaneous swallowing and laryngeal sensitivity.

Potato Solanum tuberosum L. Phytoene Synthase Genes (StPSY1, StPSY2, and StPSY3) Are Involved in the Plant Response to Cold Stress.

The structure and phylogeny of the Solanum tuberosum L. phytoene synthase genes StPSY1, StPSY2, and StPSY3 were characterized. Their expression was studied in potato seedlings exposed to cold stress in the dark phase of the diurnal cycle to simulate night cooling. All of the three genes were activated as the temperature decreased, and the greatest response was observed for StPSY1. StPSY3 was for the first time shown to respond to cold stress and photoperiod. A search for cis-regulatory elements was carried out in the promoter regions and 5'-UTRs of the StPSY genes, and the regulation of all three genes proved associated with the response to light. A high level of cold-induced activation of StPSY1 was tentatively attributed to the presence of cis elements associated with sensitivity to cold and ABA.