Criminal Poisoning: A Hospital-Based Survey in an Academic Center of Iran

Background: One of the most common medical emergencies worldwide is deliberate or accidental poisoning. The ever-increasing consumption of toxins and fatal drugs is getting more critical by the time. The purpose of intentional poisoning is to harm self and others. It includes objectives like robbery, sexual abusing (rape). The perpetrators of sedative and hypnotic drugs could sedate patients and make them sleepy. Methods: This descriptive, cross-sectional study investigated the suspected Drug-Facilitated Crime (DFC) admitted patients and Outpatient Department (OPD) in the emergency room and toxicology ward. A researcher-made questionnaire was completed for each patient. The biological samples (urine, blood) were provided to a clinical toxicology lab.Results: The information of 40 suspected DFC patients was analyzed. 70% of intoxicated patients were men, and 30% were women. The patients’ mean age was 31-43 years; 42% were low in education, and 37% were self-employed. Furthermore, 75% of total intoxicated patients had a sedentary level of consciousness. Approximately 92.5% had one positive substance test, and 7.5% had negative lab test results. The most used drug was diazepam, by 70%. The motivation for poisoning was 80% robbery, 12.5% for rape, and 7.5% were no diagnostic.Conclusion: The prevalence of poisoning, especially deliberate poisoning, is dramatically growing in this part of the world. Accordingly, this study reveals the flexibility of criminals in Tehran Province, Iran. Thus, the Ministry of Health should seriously control legal and illegal drugs purchase

Boosting Theory-of-Mind Performance in Large Language Models via Prompting

Large language models (LLMs) excel in many tasks in 2023, but they still face challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require understanding agents' beliefs, goals, and mental states, are essential for common-sense reasoning involving humans, making it crucial to enhance LLM performance in this area. This study measures the ToM performance of GPT-4 and three GPT-3.5 variants (Davinci-2, Davinci-3, GPT-3.5-Turbo), and investigates the effectiveness of in-context learning in improving their ToM comprehension. We evaluated prompts featuring two-shot chain of thought reasoning and step-by-step thinking instructions. We found that LLMs trained with Reinforcement Learning from Human Feedback (RLHF) (all models excluding Davinci-2) improved their ToM accuracy via in-context learning. GPT-4 performed best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell short of the 87% human accuracy on the test set. However, when supplied with prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM accuracy, with GPT-4 reaching 100%. These results demonstrate that appropriate prompting enhances LLM ToM reasoning, and they underscore the context-dependent nature of LLM cognitive capacities.Comment: 27 pages, 4 main figures, 2 supplementary figure

Learning Representations from Temporally Smooth Data

Events in the real world are correlated across nearby points in time, and we must learn from this temporally smooth data. However, when neural networks are trained to categorize or reconstruct single items, the common practice is to randomize the order of training items. What are the effects of temporally smooth training data on the efficiency of learning? We first tested the effects of smoothness in training data on incremental learning in feedforward nets and found that smoother data slowed learning. Moreover, sampling so as to minimize temporal smoothness produced more efficient learning than sampling randomly. If smoothness generally impairs incremental learning, then how can networks be modified to benefit from smoothness in the training data? We hypothesized that two simple brain-inspired mechanisms, leaky memory in activation units and memory-gating, could enable networks to rapidly extract useful representations from smooth data. Across all levels of data smoothness, these brain-inspired architectures achieved more efficient category learning than feedforward networks. This advantage persisted, even when leaky memory networks with gating were trained on smooth data and tested on randomly-ordered data. Finally, we investigated how these brain-inspired mechanisms altered the internal representations learned by the networks. We found that networks with multi-scale leaky memory and memory-gating could learn internal representations that un-mixed data sources which vary on fast and slow timescales across training samples. Altogether, we identified simple mechanisms enabling neural networks to learn more quickly from temporally smooth data, and to generate internal representations that separate timescales in the training signal

Investigation of Knowledge Management (KM): The Case of Iranian Agricultural Experts

Nowadays efficient and productive knowledge management is a key competency for organizations and require proper arrangement of factors such as people, processes and organizational infrastructures. The purpose of this study was to investigate attitude and skill in applying knowledge management (KM) of agricultural experts. The paper was conducted using survey research. The sample was consisted of 120 experts in Jihad-e-Keshavarzi Organization of Mazandaran province. Data was gathered using questionnaire. The most important findings of the study showed that monthly income, organizational characteristics of experts, group-human factors, infrastructural factors, strategic and management factors, structural and process factors, access to information resources and technologies and attitude to development of knowledge management had positive and significant correlations with skills in applying knowledge management. Results of stepwise regression analysis also showed that independent variables of access to information resources and technologies, structural and process factors, organizational characteristics of experts, infrastructural factors, group-human factors and monthly income explained 50 percent of the variability in the skill in applying knowledge management. Keywords: Knowledge management, Experts, Jihad-e-Keshavarzi, Mazandaran, Ira

COSTS AND BENEFITS OF INTEGRATING INFORMATION SEQUENCES

Information from the world unfolds over time, and to navigate the everyday world and make future predictions, our brain needs to integrate information over time. For instance, when having a conversation with someone, our brain needs to accumulate information about words and sentences to comprehend the ongoing discussion and respond appropriately. However, ubiquitous accumulation of information can cause interference, especially if we end up combining unrelated information. For instance, the topic of conversation may change from one sentence to the next, in which case combining information from consecutive sentences could cause interference and confusion. These examples demonstrate that integrating information over time is sometimes necessary for successful comprehension and prediction, but it should not be performed indiscriminately. How then should temporal integration mechanisms be implemented, especially in constrained brain-like learning architectures? What kinds of temporal integration and separation mechanisms are employed by contemporary machine learning models? And how do these integration and separation processes compare against what we observe in human behavior? In this thesis, we examined the costs and benefits of integrating and separating information sequences in humans and machines. In the first two projects we focused on learning and tested the performance of biologically-plausible temporal integration mechanisms in neural networks; we characterized the efficacy of these systems in learning categories from a sequence of examples, and investigated how their internal representations are altered by how they integrate information over time. In two further projects we focused on online comprehension and prediction, in the setting of humans reading natural language sequences, and we contrasted our findings with neural network models that predict and generate natural language sequences. We tested how online comprehension and subsequent memory are affected by interruptions in the text that humans are reading. Finally, we tested how neural language models respond to the insertion of incongruent information into a broader coherent text, and we compared these findings against our observations of how humans handle interruptions while reading. Altogether, these studies identify mechanisms by which humans and machines can exploit temporal continuity in the environment, in the service of learning about, understanding and predicting our dynamic world

Accuracy of Visual inspection with acetic acid (VIA) for early detection of cervical dysplasia in Tehran, Iran

Objective: To evaluate the accuracy of visual inspection with 5 acetic acid (VIA) when used to detect cervical cancer and its precursors. Methods: The study population included women attended Family Planning and Gynecological Clinic in Bagher Abad Health Center and Mirza Koochak Khan Hospital for regular cervical screening tests. After obtaining informed consent from each woman, VIA was performed. One hundred with a positive VIA test and 100 women with a negative VIA test were randomly selected for this study. Cytology and colposcopy examination were performed for all 200 cases and cervical biopsies were conducted for those individuals showing abnormal colposcopic findings. Results: Nine cases in VIA-positive group and two cases in VIA-negative group had an abnormal cytology. Ninety five women in the VIA-positive group and 25 in the VIA-negative group had abnormal colposcopic findings. From biopsy examination, 67 (71) of cases in the VIA-positive group and 3 (12) cases in the VIA-negative group had a final diagnosis of dysplasia. Among biopsied samples, only 7 cases of VIA-positive group showed abnormal result and the remaining were normal. Based on these results, VIA test sensitivity and specificity were 95.7 and 44.0 respectively, while they were 10 and 92 for cytology tests. Conclusions: The results of this study indicate that although VIA is a sensitive screening test for detection of cervical dysplasia, it can not be used by itself. Applying VIA along with Pap smears helps to detect a higher number of cases with cancer precursor lesions

Malignant mixed mullerian tumor of the uterus associated with tamoxifen therapy in a patient with a history of breast cancer

Tamoxifen is the drug of choice in the treatment of breast cancer. Recent reports show an increased incidence of endometrial carcinoma in patients taking tamoxifen. In this article, we report a case of malignant mixed mullerian tumor after tamoxifen use. Copyright © 2006 by Razi Institute for Drug Research (RIDR)